Overview

To facilitate local memory allocation on FPGA devices, the Vitis Vision library functions are provided in templates with compile-time parameters. Data is explicitly copied from cv::Mat to xf::cv::Mat and is stored in physically contiguous memory to achieve the best possible performance. After processing, the output in xf::cv::Mat is copied back to cv::Mat to write it into the memory.

xf::cv::Mat Image Container Class

xf::cv::Mat is a template class that serves as a container for storing image data and its attributes.

Note

The xf::cv::Mat image container class is similar to the cv::Mat class of the OpenCV library.

Class Definition

template <int T, int ROWS, int COLS, int NPC, int XFCVDEPTH = _XFCVDEPTH_DEFAULT>
class Mat {
   public:
        unsigned char allocatedFlag; // flag to mark memory allocation in this class
        int rows, cols, size;        // actual image size

        typedef XF_TNAME(T, NPC) DATATYPE;
        using _DATATTYPE = typename std::conditional<
                (XFCVDEPTH < 0),
                DATATYPE*,                 // Case of Memory Mapped pointer
                typename std::conditional< // Case of Stream
                        (XFCVDEPTH == 0),
                        hls::stream<DATATYPE>,           // Case of default Dtream depth or user can override outside
                        hls::stream<DATATYPE, XFCVDEPTH> // Case of Stream depth specified
                        >::type>::type;
        _DATATTYPE data;

        Mat(); // default constructor
        Mat(Size _sz);
        Mat(int _rows, int _cols);
        Mat(int _size, int _rows, int _cols);
        Mat(int _rows, int _cols, void* _data);
        Mat(const Mat&); // copy constructor

        ~Mat();

        Mat& operator=(const Mat&); // Assignment operator

        template <int D = XFCVDEPTH, typename std::enable_if<(D < 0)>::type* = nullptr>
        void alloc_data() {
#ifndef __SYNTHESIS__
                data = (DATATYPE*)malloc(size * sizeof(DATATYPE));

                if (data == NULL) {
                        fprintf(stderr, "\nFailed to allocate memory\n");
                } else {
                        allocatedFlag = 1;
                }
#endif
        }

        template <int D = XFCVDEPTH, typename std::enable_if<(D >= 0)>::type* = nullptr>
        void alloc_data() {
                // This is a stream
        }

        template <int D = XFCVDEPTH, typename std::enable_if<(D < 0)>::type* = nullptr>
        void free_data() {
                if (data != NULL) {
#ifndef __SYNTHESIS__
                        free(data);
#endif
                }
        }

        template <int D = XFCVDEPTH, typename std::enable_if<(D >= 0)>::type* = nullptr>
        void free_data() {}

        template <int D = XFCVDEPTH, typename std::enable_if<(D < 0)>::type* = nullptr>
        void copyData(const Mat& src) {
                for (int i = 0; i < (rows * ((cols + NPC - 1) >> XF_BITSHIFT(NPC))); ++i) {
                        data[i] = src.data[i];
                }
        }

        template <int D = XFCVDEPTH, typename std::enable_if<(D >= 0)>::type* = nullptr>
        void copyData(const Mat& src) {
                // This is a stream
                assert(0);
        }

        template <int D = XFCVDEPTH, typename std::enable_if<(D < 0)>::type* = nullptr>
        void assignDataPtr(void* _data) {
                data = (DATATYPE*)_data;
        }

        template <int D = XFCVDEPTH, typename std::enable_if<(D >= 0)>::type* = nullptr>
        void assignDataPtr(void* _data) {
                // This is a stream
                assert(0);
        }

        template <int D = XFCVDEPTH, typename std::enable_if<(D < 0)>::type* = nullptr>
        XF_TNAME(T, NPC)
        read(int index) {
                return data[index];
        }

        template <int D = XFCVDEPTH, typename std::enable_if<(D >= 0)>::type* = nullptr>
        XF_TNAME(T, NPC)
        read(int index) {
                return data.read();
        }
        float read_float(int index);

        template <int D = XFCVDEPTH, typename std::enable_if<(D < 0)>::type* = nullptr>
        void write(int index, XF_TNAME(T, NPC) val) {
                data[index] = val;
        }

        template <int D = XFCVDEPTH, typename std::enable_if<(D >= 0)>::type* = nullptr>
        void write(int index, XF_TNAME(T, NPC) val) {
                data.write(val);
        }
        void write_float(int index, float val);

        template <int D = XFCVDEPTH, typename std::enable_if<(D >= 0)>::type* = nullptr>
        void init(int _rows, int _cols, void* _data) {
                init(_rows, _cols);
                copyTo(_data);
        }

        template <int D = XFCVDEPTH, typename std::enable_if<(D < 0)>::type* = nullptr>
        void init(int _rows, int _cols, void* _data) {
                init(_rows, _cols, false);
                assignDataPtr(_data);
        }

        void init(int _rows, int _cols, bool allocate = true);
        void copyTo(void* fromData);
        unsigned char* copyFrom();

        const int type() const;
        const int depth() const;
        const int channels() const;

        template <int DST_T>
        void convertTo(Mat<DST_T, ROWS, COLS, NPC, XFCVDEPTH>& dst, int otype, double alpha = 1, double beta = 0);
};

Parameter Descriptions

The following table lists the xf::cv::Mat class parameters and their descriptions:

Table xf::cv::Mat Class Parameter Descriptions

Parameter	Description
rows	The number of rows in the image or height of the image.
cols	The number of columns in the image or width of the image.
size	The number of words stored in the data member. The value is calculated using rows*cols/(number of pixels packed per  word).
allocatedFlag	Flag for memory allocation status
*data	class parameters and the pointer to the words that store the pixels of the image.

The following table lists the member functions and their descriptions:

Table xf::cv::Mat Member Function Descriptions

Member Functions	Description
Mat()	This default constructor initializes the Mat object sizes, using the template parameters ROWS and COLS.
Mat(int _rows, int _cols)	This constructor initializes the Mat object using arguments _rows and _cols.
Mat(const xf::cv::Mat &_src)	This constructor helps clone a Mat object to another. New memory will be allocated for the newly created constructor.
Mat(int _rows, int _cols, void *_data)	This constructor initializes the Mat object using arguments _rows, _cols, and _data. The *data member of the Mat object points to the memory allocated for _data argument, when this constructor is used. No new memory is allocated for the *data member.
convertTo(Mat <DST_T,ROWS, COLS, NPC> &dst, int otype, double alpha=1, double beta=0)	Refer to xf::cv::convertTo
copyTo(* fromData)	Copies the data from Data pointer into physically contiguous memory allocated inside the constructor.
copyFrom()	Returns the pointer to the first location of the *data member.
read(int index)	Readout a value from a given location and return it as a packed (for multi-pixel/clock) value.
read_float(in t index)	Readout a value from a given location and return it as a float value
write(int index, XF_TNAME(T,NP C) val)	Writes a packed (for multi-pixel/clock) value into the given location.
write_float(i nt index, float val)	Writes a float value into the given location.
type()	Returns the type of the image.
depth()	Returns the depth of the image
channels()	Returns number of channels of the image
~Mat()	This is a default destructor of the Mat object.

Template parameters of the xf::cv::Mat class are used to set the depth of the pixel, number of channels in the image, number of pixels packed per word, maximum number of rows and columns of the image. The following table lists the template parameters and their descriptions:

Table xf::cv::Mat Template Parameter Descriptions

Parameters	Description
TYPE	Type of the pixel data. For example, XF_8UC1 stands for 8-bit unsigned and one channel pixel. More types can be found in include/common/xf_params.h.
HEIGHT	Maximum height of an image.
WIDTH	Maximum width of an image.
NPC	The number of pixels to be packed per word. For instance, XF_NPPC1 for 1 pixel per word; and XF_NPPC8 for 8 pixels per word.

Pixel-Level Parallelism

The amount of parallelism to be implemented in a function from Vitis Vision is kept as a configurable parameter. In most functions, there are two options for processing data.

• Single-pixel processing

• Processing eight pixels in parallel

The following table describes the options available for specifying the level of parallelism required in a particular function:

Table Options Available for Specifying the Level of Parallelism

Option	Description
XF_NPPC1	Process 1 pixel per clock cycle
XF_NPPC2	Process 2 pixels per clock cycle
XF_NPPC4	Process 4 pixels per clock cycle
XF_NPPC8	Process 8 pixels per clock cycle

Macros to Work With Parallelism

There are two macros that are defined to work with parallelism.

• The XF_NPIXPERCYCLE(flags) macro resolves to the number of pixels processed per cycle.

  • XF_NPIXPERCYCLE(XF_NPPC1) resolves to 1

  • XF_NPIXPERCYCLE(XF_NPPC2) resolves to 2

  • XF_NPIXPERCYCLE(XF_NPPC4) resolves to 4

  • XF_NPIXPERCYCLE(XF_NPPC8) resolves to 8

• The XF_BITSHIFT(flags) macro resolves to the number of times to shift the image size to right to arrive at the final data transfer size for parallel processing.

  • XF_BITSHIFT(XF_NPPC1) resolves to 0

  • XF_BITSHIFT(XF_NPPC2) resolves to 1

  • XF_BITSHIFT(XF_NPPC4) resolves to 2

  • XF_BITSHIFT(XF_NPPC8) resolves to 3

Data Types

Data types will differ, depending on the combination of the depth of pixels and the number of channels in the image. The generic nomenclature of the parameter is listed below.

XF_<Number of bits per pixel><signed (S) or unsigned (U) or float (F)>C<number of channels>

For example, for an 8-bit pixel - unsigned - 1 channel the data type is XF_8UC1.

The following table lists the available data types for the xf::cv::Mat class:

Table xf::cv::Mat Class - Available Data Types

Option	Number of bits per Pixel	Unsigned/ Signed/ Float Type	Number of Channels
XF_2UC1	2	Unsigned	1
XF_8UC1	8	Unsigned	1
XF_8UC2	8	Unsigned	2
XF_8UC3	8	Unsigned	3
XF_8UC4	8	Unsigned	4
XF_10UC1	10	Unsigned	1
XF_10UC2	10	Unsigned	2
XF_10UC3	10	Unsigned	3
XF_10UC4	10	Unsigned	4
XF_12UC1	12	Unsigned	1
XF_12UC2	12	Unsigned	2
XF_12UC3	12	Unsigned	3
XF_12UC4	12	Unsigned	4
XF_16UC1	16	Unsigned	1
XF_16SC1	16	Signed	1
XF_32UC1	32	Unsigned	1
XF_32FC1	32	Float	1
XF_32FC3	32	Float	3
XF_32SC1	32	Signed	1

Manipulating Data Type

Based on the number of pixels to process per clock cycle and the type parameter, there are different possible data types. The Vitis Vision library uses these datatypes for internal processing and inside the xf::cv::Mat class. The following are a few supported types:

• XF_TNAME(TYPE,NPPC) resolves to the data type of the data member of the xf::cv::Mat object. For instance, XF_TNAME(XF_8UC1,XF_NPPC8) resolves to ap_uint<64>.

• Word width = pixel depth * number of channels * number of pixels to process per             cycle (NPPC).

• XF_DTUNAME(TYPE,NPPC) resolves to the data type of the pixel. For instance, XF_DTUNAME(XF_32FC1,XF_NPPC1) resolves to float.

• XF_PTSNAME(TYPE,NPPC) resolves to the ‘C’ data type of the pixel. For instance, XF_PTSNAME             (XF_16UC1,XF_NPPC2) resolves to unsigned             short.

Note

ap_uint<>, ap_int<>, ap_fixed<>, and ap_ufixed<> types belong to the high-level synthesis (HLS) library. For more information, see the Vivado Design Suite User Guide: High-Level Synthesis (UG902).

xf::cv::imread

The function xf::cv::imread loads an image from the specified file path, copies into xf::cv::Mat and returns it. If the image cannot be read (because of missing file, improper permissions, unsupported or invalid format), the function exits with a non-zero return code and an error statement.

Note

In an HLS standalone mode like Cosim, use cv::imread followed by copyTo function, instead of xf::cv::imread.

API Syntax

template<int PTYPE, int ROWS, int COLS, int NPC>
xf::cv::Mat<PTYPE, ROWS, COLS, NPC> imread (char *filename, int type)

Parameter Descriptions

The table below describes the template and the function parameters.

Table xf::cv::imread Parameter Description

Parameter	Description
PTYPE	Input pixel type. Value should be in accordance with the ‘type’ argument’s value.
ROWS	Maximum height of the image to be read
COLS	Maximum width of the image to be read
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
filename	Name of the file to be loaded
type	Flag that depicts the type of image. The values are: ‘0’ for gray scale ‘1’ for color image

xf::cv::imwrite

The function xf::cv::imwrite saves the image to the specified file from the given xf::cv::Mat. The image format is chosen based on the file name extension. This function internally uses cv::imwrite for the processing. Therefore, all the limitations of cv::imwrite are also applicable to xf::cv::imwrite.

API Syntax

template <int PTYPE, int ROWS, int COLS, int NPC>
void imwrite(const char *img_name, xf::cv::Mat<PTYPE, ROWS, COLS, NPC> &img)

Parameter Descriptions

The table below describes the template and the function parameters.

Table xf::cv::imwrite Parameter Description

Parameter	Description
PTYPE	Input pixel type. Supported types are: XF_8UC1, XF_16UC1, XF_8UC4, and XF_16UC4
ROWS	Maximum height of the image to be read
COLS	Maximum width of the image to be read
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
img_name	Name of the file with the extension
img	xf::cv::Mat array to be saved

xf::cv::absDiff

The function xf::cv::absDiff computes the absolute difference between each individual pixels of an xf::cv::Mat and a cv::Mat, and returns the difference values in a cv::Mat.

API Syntax

template <int PTYPE, int ROWS, int COLS, int NPC>
void absDiff(cv::Mat &cv_img, xf::cv::Mat<PTYPE, ROWS, COLS, NPC>& xf_img, cv::Mat &diff_img )

Parameter Descriptions

The table below describes the template and the function parameters.

Table xf::cv::absDiff Parameter Description

Parameter	Description
PTYPE	Input pixel type
ROWS	Maximum height of the image to be read
COLS	Maximum width of the image to be read
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1, XF_NPPC4, and XF_NPPC8 for 1-pixel, 4-pixel, and 8-pixel parallel operations respectively.
cv_img	cv::Mat array to be compared
xf_img	xf::cv::Mat array to be compared
diff_img	Output difference image(cv::Mat)

xf::cv::convertTo

The xf::cv::convertTo function performs bit depth conversion on each individual pixel of the given input image. This method converts the source pixel values to the target data type with appropriate casting.

dst(x,y)= cast<target-data-type>(α(src(x,y)+β))

Note: The output and input Mat cannot be the same. That is, the converted image cannot be stored in the Mat of the input image.

API Syntax

template<int DST_T> void convertTo(xf::cv::Mat<DST_T,ROWS, COLS, NPC> &dst, int ctype, double alpha=1, double beta=0)

Parameter Descriptions

The table below describes the template and function parameters.

Table xf::cv::convertTo Parameter Description

Parameter	Description
DST_T	Output pixel type. Possible values are XF_8UC1, XF_16UC1, XF_16SC1, and XF_32SC1.
ROWS	Maximum height of image to be read
COLS	Maximum width of image to be read
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1, XF_NPPC4, and XF_NPPC8 for 1-pixel, 4-pixel, and 8-pixel parallel operations respectively. XF_32SC1 and XF_NPPC8 combination is not supported.
dst	Converted xf Mat
ctype	Conversion type : Possible values are listed here. //Down-convert: XF_CONVERT_16U_TO_8U XF_CONVERT_16S_TO_8U XF_CONVERT_32S_TO_8U XF_CONVERT_32S_TO_16U XF_CONVERT_32S_TO_16S //Up-convert: XF_CONVERT_8U_TO_16U XF_CONVERT_8U_TO_16S XF_CONVERT_8U_TO_32S XF_CONVERT_16U_TO_32S XF_CONVERT_16S_TO_32S
alpha	Optional scale factor
beta	Optional delta added to the scaled values

Vitis Vision Library Functions

The Vitis Vision library is a set of select OpenCV functions optimized for Zynq-7000, Zynq UltraScale+ MPSoC, Alveo U200 and U50 devices. The maximum resolution supported for all the functions is 4K, except Houghlines and HOG (RB mode).

Note

Resolution Conversion (Resize) in 8 pixel per cycle mode, Dense Pyramidal LK Optical Flow, and Dense Non-Pyramidal LK Optical Flow functions are not supported on the Zynq-7000 SoC ZC702 devices, due to the higher resource utilization.

Note

Number of pixel per clock depends on the maximum bus width a device can support. For example: Zynq-7000 SoC has 64-bit interface and so for a pixel type 16UC1, maximum of four pixel per clock (XF_NPPC4) is possible.

Absolute Difference

API Syntax

The absdiff function finds the pixel wise absolute difference between two input images and returns an output image. The input and the output images must be the XF_8UC1 type.

Where,

• I_out(x, y) is the intensity of output image at (x,y) position.

• I_in1(x, y) is the intensity of first input image at (x,y) position.

• I_in2(x, y) is the intensity of second input image at (x,y) position.

template<int SRC_T, int ROWS, int COLS, int NPC=1>
void absdiff(
xf::cv::Mat<int SRC_T, int ROWS, int COLS, int NPC> src1,
xf::cv::Mat<int SRC_T, int ROWS, int COLS, int NPC> src2,
xf::cv::Mat<int SRC_T, int ROWS, int COLS, int NPC> dst )

Parameter Descriptions

The following table describes the template and the function parameters.

Table absdiff Parameter Description

Parameter	Description
SRC_T	Input and Output pixel type. Only 8-bit, unsigned, 1 and 3 channels are supported (XF_8UC1 and XF_8UC3)
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. Must be multiple of 8, for 8-pixel operation.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
src1	Input image
src2	Input image
dst	Output image

Resource Utilization

The following table summarizes the resource utilization in different configurations, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a grayscale HD (1080x1920) image.

Table absdiff Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	0	62	67	17
8 Pixel	150	0	0	67	234	39

Performance Estimate

The following table summarizes the performance in different configurations, as generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table absdiff Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9
8 pixel operation (150 MHz)	1.69

Deviation from OpenCV

There is no deviation from OpenCV, except that the absdiff function supports 8-bit pixels.

Accumulate

The accumulate function adds an image (src1) to the accumulator image (src2), and generates the accumulated result image (dst).

API Syntax

template<int SRC_T, int DST_T, int ROWS, int COLS, int NPC=1>
void accumulate (
xf::cv::Mat<int SRC_T, int ROWS, int COLS, int NPC> src1,
xf::cv::Mat<int SRC_T, int ROWS, int COLS, int NPC> src2,
xf::cv::Mat<int DST_T, int ROWS, int COLS, int NPC> dst )

Parameter Descriptions

The following table describes the template and the function parameters.

Table accumulate Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 1 and 3 channels are supported (XF_8UC1 and XF_8UC3)
DST_T	Output pixel type. Only 16-bit, unsigned, 1 and 3 channels are supported (XF_16UC1 and XF_16UC3)
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. Recommend using a multiple of 8, for an 8-pixel operation.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
src1	Input image
src2	Input image
dst	Output image

Resource Utilization

The following table summarizes the resource utilization in different configurations, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table accumulate Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	0	62	55	12
8 Pixel	150	0	0	389	285	61

The following table summarizes the resource utilization in different configurations, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1, to process 4K 3 Channel image.

Table 16. accumulate Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	1	207	72	32

Performance Estimate

The following table summarizes the performance in different configurations, as generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table 17. accumulate Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9
8 pixel operation (150 MHz)	1.7

Deviation from OpenCV

In OpenCV the accumulated image is stored in the second input image. The src2 image acts as both input and output, as shown below:

Whereas, in the Vitis Vision implementation, the accumulated image is stored separately, as shown below:

Accumulate Squared

The accumulateSquare function adds the square of an image (src1) to the accumulator image (src2) and generates the accumulated result (dst).

The accumulated result is a separate argument in the function, instead of having src2 as the accumulated result. In this implementation, having a bi-directional accumulator is not possible as the function makes use of streams.

API Syntax

template<int SRC_T, int DST_T, int ROWS, int COLS, int NPC=1>
void accumulateSquare (
xf::cv::Mat<int SRC_T, int ROWS, int COLS, int NPC> src1,
xf::cv::Mat<int SRC_T, int ROWS, int COLS, int NPC> src2,
xf::cv::Mat<int DST_T, int ROWS, int COLS, int NPC> dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table accumulateSquare Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 1 and 3 channels are supported (XF_8UC1 and XF_8UC3)
DST_T	Output pixel type. Only 16-bit, unsigned, 1 and 3 channels are supported (XF_16UC1 and XF_16UC3)
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image (must be multiple of 8, for 8-pixel operation)
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
src1	Input image
src2	Input image
dst	Output image

Resource Utilization

The following table summarizes the resource utilization in different configurations, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a grayscale HD (1080x1920) image.

Table accumulateSquare Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	1	71	52	14
8 Pixel	150	0	8	401	247	48

The following table summarizes the resource utilization in different configurations, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process 4K 3 Channel image.

Table accumulateSquare Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	3	227	86	37

Performance Estimate

The following table summarizes the performance in different configurations, as generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table accumulateSquare Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9
8 pixel operation (150 MHz)	1.6

Deviation from OpenCV

In OpenCV the accumulated squared image is stored in the second input image. The src2 image acts as input as well as output.

Whereas, in the Vitis Vision implementation, the accumulated squared image is stored separately.

Accumulate Weighted

The accumulateWeighted function computes the weighted sum of the input image (src1) and the accumulator image (src2) and generates the result in dst.

The accumulated result is a separate argument in the function, instead of having src2 as the accumulated result. In this implementation, having a bi-directional accumulator is not possible, as the function uses streams.

API Syntax

template<int SRC_T, int DST_T, int ROWS, int COLS, int NPC=1>
void accumulateWeighted (
xf::cv::Mat<int SRC_T, int ROWS, int COLS, int NPC> src1,
xf::cv::Mat<int SRC_T, int ROWS, int COLS, int NPC> src2,
xf::cv::Mat<int DST_T, int ROWS, int COLS, int NPC> dst,
float alpha )

Parameter Descriptions

The following table describes the template and the function parameters.

Table accumulateWeighted Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 1 and 3 channels are supported (XF_8UC1 and XF_8UC3)
DST_T	Output pixel type. Only 16-bit, unsigned, 1 and 3 channels are supported (XF_16UC1 and XF_16UC3)
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. Recommend multiples of 8, for an 8-pixel operation.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
src1	Input image
src2	Input image
dst	Output image
alpha	Weight applied to input image

Resource Utilization

The following table summarizes the resource utilization in different configurations, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a grayscale HD (1080x1920) image.

Table accumulateWeighted Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	5	295	255	52
8 Pixel	150	0	19	556	476	88

The following table summarizes the resource utilization in different configurations, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a 4K 3 Channel image.

Table accumulateWeighted Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	9	457	387	95

Performance Estimate

The following table summarizes the performance in different configurations, as generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table accumulateWeighted Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9
8 pixel operation (150 MHz)	1.7

Deviation from OpenCV

The resultant image in OpenCV is stored in the second input image. The src2 image acts as input as well as output, as shown below:

Whereas, in Vitis Vision implementation, the accumulated weighted image is stored separately.

AddS

The AddS function performs the addition operation between pixels of input image src and given scalar value scl and stores the result in dst.

dst(x,y)= src(x,y) + scl

Where (x,y) is the spatial coordinate of the pixel.

API Syntax

template<int POLICY_TYPE, int SRC_T, int ROWS, int COLS, int NPC =1>
void addS(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src1, unsigned char _scl[XF_CHANNELS(SRC_T,NPC)],xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table 26. AddS Parameter Description

Parameter	Description
SRC_T	Input pixel type. 8-bit, unsigned, 1 channel is supported (XF_8UC1).
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. In case of N-pixel parallelism, width should be multiple of N.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
_src1	First input image
_scl	Input scalar value, the size should be number of channels.
_dst	Output image

Resource Utilization

The following table summarizes the resource utilization of the AddS function in both the resource optimized (8 pixel) mode and normal mode, as generated using Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA.

Table 27. AddS Function Resource Utilization Summary

Name	Resource Utilization
	1 pixel per clock operation	8 pixel per clock operation
	300 MHz	150 MHz
BRAM_18K	0	0
DSP48E	0	0
FF	100	101
LUT	52	185
CLB	20	45

Performance Estimate

The following table summarizes a performance estimate of the kernel in different configurations, generated using Vivado HLS 2019.1 tool for Xczu9eg-ffvb1156-1-i-es1 FPGA to process a grayscale HD (1080x1920) image.

Table 28. AddS Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9
8 pixel operation (150 MHz)	1.7

Add Weighted

The addweighted function calculates a weighted sum of two input images src1, src2 and generates the result in dst.

dst(x,y)= src1(x,y)*alpha+src2(x,y)*beta+ gamma

API Syntax

template< int SRC_T , int DST_T,   int ROWS, int COLS, int NPC=1>
void addWeighted(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src1, float alpha, xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src2, float beta, float gamma, xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table 29. Addweighted Parameter Description

Parameter	Description
SRC_T	Input Pixel Type. 8-bit, unsigned,1 channel is supported (XF_8UC1)
DST_T	Output Pixel Type. 8-bit, unsigned,1 channel is supported (XF_8UC1)
ROWS	Maximum height of input and output image
COLS	Maximum width of input and output image. In case of N-pixel parallelism, width should be multiple of N
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
_src1	First Input image
Alpha	Weight applied on first image
_src2	Second Input image
Beta	Weight applied on second image
gamma	Scalar added to each sum
_dst	Output image

Resource Utilization

The following table summarizes the resource utilization of the Addweighted function in Resource optimized (8 pixel) mode and normal mode, as generated in Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA.

Table 30. Addweighted Function Resource Utilization Summary

Name	Resource Utilization
	1 pixel per clock operation	8 pixel per clock operation
	300 MHz	150 MHz
BRAM_18K	0	0
DSP48E	11	25
FF	903	680
LUT	851	1077
CLB	187	229

Performance Estimate

The following table summarizes a performance estimate of the kernel in different configurations, generated using Vivado HLS 2019.1 tool for Xczu9eg-ffvb1156-1-i-es1 FPGA to process a grayscale HD (1080x1920) image.

Table 31. Addweighted Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9
8 pixel operation (150 MHz)	1.7

Auto Exposure Correction

Auto exposure correction improves contrast and brightness of the image and also corrects the exposure of the input frame. The algorithm uses luminence histogram equalization to improve overall exposure and contrast of the image. Luminence (V) is extracted after converting input image to HSV color space. Once the algorthm is applied the image is converted back to RGB color space.

API Syntax

template <int SRC_T, int DST_T, int SIN_CHANNEL_TYPE, int ROWS, int COLS, int NPC = 1>
void autoexposurecorrection(xf::cv::Mat<SRC_T, ROWS, COLS, NPC>& src,
                            xf::cv::Mat<DST_T, ROWS, COLS, NPC>& dst,
                            unsigned int hist_array1[1][256],
                            unsigned int hist_array2[1][256])

Parameter Descriptions

The following table describes template parameters and arguments of the function.

Table AEC Parameter Description

Parameter	Description
SRC_T	Input pixel type. 8-bit unsigned 3 channel is supported (XF_8UC3).
DST_T	Output pixel type. 8-bit unsigned 3 channel is supported (XF_8UC3).
ROWS	Maximum height of input and output image
COLS	Maximum width of input and output image. In case of N-pixel parallelism, width should be multiple of N.
SIN_CHANNEL_TYPE	Single channel type. should be XF_8UC1
NPC	Number of pixels to be processed per cycle; possible options is XF_NPPC1, XF_NPPC2 AND so on
src	Input image
dst	Output image
hist_array1	Histogram array
hist_array2	Histogram array

Resource Utilization

The following table summarizes the resource utilization of kernel in different configurations, generated using Vitis HLS 2020.2 tool, to process a FULL HD image.

Table AEC Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 pixel	300	4	18	6713	2996	1103
2 pixel	300	4	27	7618	3705	1257

Performance Estimate

The following table summarizes a performance estimate of the kernel in different configurations, as generated using Vitis HLS 2020.2 tool, to process a FULL HD image.

Table AEC Function Performance Estimate Summary

Operating Mode	Operating Frequency (MHz)	Latency Estimate Max (ms)
1 pixel	300	7
2 pixel	300	3.7

Auto White Balance

Grayworld whitebalancing algorithm:

This algorithm scales the values of pixels based on a gray-world assumption which states that the average of all channels should result in a gray image. It adds a modification which thresholds pixels based on their saturation value and only uses pixels below the provided threshold in finding average pixel values. Saturation is calculated using the following for a 3-channel RGB image per pixel I and is in the range [0, 1]:

A threshold of 1 means that all pixels are used to white-balance, while a threshold of 0 means no pixels are used. Lower thresholds are useful in white-balancing saturated images.

Simple whitebalancing algorithm:

A simple white balance algorithm that works by independently stretching each of the input image channels to the specified range(maximum and minimum). Computes channel wise intensity histogram and ignores p% maximum and minimum values and finally normalize each channel with min and max. For increased robustness it ignores the top and bottom \(p\%\ \ (4\%\ is\ fixed)\) of pixel values.

API Syntax for Simple white balance

template <int SRC_T, int DST_T, int ROWS, int COLS, int NPC = 1, int WB_TYPE, int HIST_SIZE>
void AWBhistogram(xf::cv::Mat<SRC_T, ROWS, COLS, NPC>& src1,
              xf::cv::Mat<SRC_T, ROWS, COLS, NPC>& src2,
              uint32_t histogram[3][HIST_SIZE],
              float thresh,
              float inputMin,
              float inputMax,
              float outputMin,
              float outputMax)

template <int SRC_T, int DST_T, int ROWS, int COLS, int NPC = 1, int WB_TYPE, int HIST_SIZE, int S_DEPTH = 2>
void AWBNormalization(xf::cv::Mat<SRC_T, ROWS, COLS, NPC>& src,
                  xf::cv::Mat<DST_T, ROWS, COLS, NPC, S_DEPTH>& dst,
                  uint32_t histogram[3][HIST_SIZE],
                  float thresh,
                  float inputMin,
                  float inputMax,
                  float outputMin,
                  float outputMax)

API Syntax for Grayworld white balance

template <int SRC_T, int DST_T, int ROWS, int COLS, int NPC = 1, int WB_TYPE>
void AWBChannelGain(xf::cv::Mat<SRC_T, ROWS, COLS, NPC>& src,
                xf::cv::Mat<DST_T, ROWS, COLS, NPC>& dst,
                float thresh,
                int i_gain[3])

template <int SRC_T, int DST_T, int ROWS, int COLS, int NPC = 1, int WB_TYPE, int S_DEPTH = 2>
void AWBGainUpdate(xf::cv::Mat<SRC_T, ROWS, COLS, NPC>& src1,
               xf::cv::Mat<DST_T, ROWS, COLS, NPC, S_DEPTH>& src2,
               float thresh,
               int i_gain[3])

Parameter Descriptions

The following table describes the template and the function parameters.

Table Autowhitebalance Parameter Description

Parameter	Description
SRC_T	Input Pixel Type.
DST_T	Output Pixel Type.
ROWS	Maximum height of input and output image (Must be multiple of NPC)
COLS	Maximum width of input and output image (Must be multiple of NPC)
NPC	Number of Pixels to be processed per cycle.
WB_TYPE	White balance type. Supported types are Gray world and simple.
HIST_SIZE	Histogram size.
Src1	Input image.
Src2	Input image.
histogram	Histogram array for the Simple AWB.
i_gain	Gain values for gray-world AWB.
dst	Output image.
thresh	Threshold value, which is used in gray world white balance method to compute average pixel values below the threshold value.
inputMin	Input image range minimum value.
inputMax	Input image range maximum value.
outputMin	Output image range minimum value.
outputMax	Output image range maximum value.

Resource Utilization

The following table summarizes the resource utilization of the kernel in different configurations, generated using Vitis HLS 2020.1 tool for the Xilinx xc7vx485t-ffg1157-1 FPGA, to process a 4K image.

Table Autowhitebalance Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 pixel	300	14	10	4798	4953	1757
2 pixel	300	14	10	8335	8535	2901

Performance Estimate

The following table summarizes a performance estimate of the kernel in different configurations, as generated using Vitis HLS 2020.1 tool for the Xilinx xc7vx485t-ffg1157-1 FPGA, to process a 4K image.

Table Autowhitebalance Function Performance Estimate Summary

Operating Mode	Operating Frequency (MHz)	Latency Estimate
		Max (ms)
1 pixel	300	55.2 for still image(27.9 for video stream)
2 pixel	300	28 for still image(14.2 for video stream)

Bad Pixel Correction

An image sensor may have a certain number of defective/bad pixels that may be the result of manufacturing faults or variations in pixel voltage levels based on temperature or exposure. The Badpixelcorrection module removes the defective pixels in the image using below operation.

If the middle pixel value is lesser than minimum neighborhood value, will consider minimum neighborhood value as mid pixel, otherwise mid pixel value is greater than maximum neighborhood value, will consider maximum neighborhood as mid pixel.

API Syntax

template<int TYPE, int ROWS, int COLS, int NPPC=1, int BORDER_T=XF_BORDER_CONSTANT, int USE_URAM=0>void badpixelcorrection(xf::cv::Mat<TYPE, ROWS, COLS, NPPC> &_src,xf::cv::Mat<TYPE, ROWS, COLS, NPPC> &_dst)

The following table describes the template and the function parameters.

Table badpixelcorrection Parameter Description

Parameter	Description
TYPE	Input and Output Pixel Type.
ROWS	Maximum height of input and output image (Must be multiple of NPPC)
COLS	Maximum width of input and output image (Must be multiple of NPPC)
NPPC	Number of Pixels to be processed per cycle.
BORDER_T	Border Type supported is XF_BORDER_CONSTANT
USE_URAM	Enable to map storage structures to UltraRAM.
_src	Input Bayer image
_dst	Output Bayer image

Resource Utilization

The following table summarizes the resource utilization of the kernel in different configurations, generated using Vivado HLS 2019.2 tool for the Xilinx xc7vx485t-ffg1157-1 FPGA, to process a 4K image.

Table badpixelcorrection Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	SLICE
1 pixel	300	10	0	979	744	355
2 pixel	300	10	0	1148	1177	458

Performance Estimate

The following table summarizes a performance estimate of the kernel in different configurations, as generated using Vivado HLS 2019.2 tool for the Xilinx xc7vx485t-ffg1157-1, to process 4K image.

Table badpixelcorrection Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Latency Estimate
		Max (ms)
1 pixel	300	27.8
2 pixel	300	14.2

Brute-force (Bf) Feature Matcher

Bf matcher takes the descriptor of one feature in first set and is matched with all other features in second set and the closest one is returned.

API Syntax

template <int PU = 1, int MAX_KEYPOINTS = 10000>
void bfMatcher(ap_uint<256> desc_list_q[MAX_KEYPOINTS],
               ap_uint<256> desc_list_t[MAX_KEYPOINTS],
               ap_int<16> match_list[MAX_KEYPOINTS],
               ap_uint<32> num_keypoints_q,
               ap_uint<32> num_keypoints_t,
               float ratio_thresh)

Parameter Descriptions

The following table describes template paramters and arguments of the function.

Table brute-force matcher Parameter Description

Parameter	Description
PU	Parallel units / compute units. Number of parallel matches computed. Default is ‘1’. Increasing this parameter results in lesser compute time, but also consumes more hardware resources.
MAX_KEYPOINTS	Maximum keypoints in the query and training feature sets.
desc_list_q	Feature descriptor query list of 256-bit type.
desc_list_t	Feature descriptor training list of 256-bit type.
match_list	Index of corresponding matches for query list in training set.
num_keypoints_q	Total number keypoints in the query set. This must not exceed MAX_KEYPOINTS.
num_keypoints_t	Total number keypoints in the training set. This must not exceed MAX_KEYPOINTS.
ratio_thresh	Ratio threshold for lowe’s test, for strong matches.

Resource Utilization

The following table summarizes the resource utilization of the kernel in different configurations, generated using Vitis 2020.2 tool, for MAX_KEYPOINTS of 10000.

Table brute-force matcher Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT
PU = 1	300	162	0	5152	8453
PU = 2	300	176	0	9471	16320
PU = 10	300	176	0	17708	48839

Performance Estimate

The following table summarizes a performance estimate of the kernel in different configurations, as generated using Vitis 2020.2 tool, for MAX_KEYPOINTS of 10000.

Table brute-force matcher Function Performance Estimate Summary

Operating Mode	Operating Frequency (MHz)	Latency Estimate Max (ms)
PU = 1	300	333.4
PU = 2	300	168.6
PU = 10	300	34.285

Bilateral Filter

In general, any smoothing filter smoothens the image which will affect the edges of the image. To preserve the edges while smoothing, a bilateral filter can be used. In an analogous way as the Gaussian filter, the bilateral filter also considers the neighboring pixels with weights assigned to each of them. These weights have two components, the first of which is the same weighing used by the Gaussian filter. The second component takes into account the difference in the intensity between the neighboring pixels and the evaluated one.

The bilateral filter applied on an image is:

Where

and is a gaussian filter with variance .

The gaussian filter is given by:

API Syntax

template<int FILTER_SIZE, int BORDER_TYPE, int TYPE, int ROWS, int COLS, int NPC=1>
void bilateralFilter (
xf::cv::Mat<int TYPE, int ROWS, int COLS, int NPC> src,
xf::cv::Mat<int TYPE, int ROWS, int COLS, int NPC> dst,
float sigma_space, float sigma_color )

Parameter Descriptions

The following table describes the template and the function parameters.

Table bilateralFilter Parameter Description

Parameter	Description
FILTER_SIZE	Filter size. Filter size of 3 (XF_FILTER_3X3), 5 (XF_FILTER_5X5) and 7 (XF_FILTER_7X7) are supported
BORDER_TYPE	Border type supported is XF_BORDER_CONSTANT
TYPE	Input and output pixel type. Only 8-bit, unsigned, 1 channel, and 3 channels are supported (XF_8UC1 and XF_8UC3)
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image (must be multiple of 8, for 8-pixel operation)
NPC	Number of pixels to be processed per cycle; this function supports XF_NPPC1 and XF_NPPC8.
src	Input image
dst	Output image
sigma_space	Standard deviation of filter in spatial domain
sigma_color	Standard deviation of filter used in color space

Resource Utilization

The following table summarizes the resource utilization of the kernel in different configurations, generated using Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to progress a grayscale HD (1080x1920) image.

Table bilateralFilter Resource Utilization Summary

Operating Mode	Filter Size	Operating Frequency (MHz)	Utilization Estimate
			BRAM_18K	DSP_48Es	FF	LUT
1 Pixel	3x3	300	6	22	4934	4293
	5x5	300	12	30	5481	4943
	7x7	300	37	48	7084	6195

The following table summarizes the resource utilization of the kernel in different configurations, generated using Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to progress a 4K 3 channel image.

Table bilateralFilter Resource Utilization Summary

Operating Mode	Filter Size	Operating Frequency (MHz)	Utilization Estimate
			BRAM_18K	DSP_48Es	FF	LUT
1 Pixel	3x3	300	12	32	8342	7442
	5x5	300	27	57	10663	8857
	7x7	300	49	107	12870	12181

Performance Estimate

The following table summarizes a performance estimate of the kernel in different configurations, as generated using Vivado HLS 2019.1 tool for Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a grayscale HD (1080x1920) image.

Table 35. bilateralFilter Function Performance Estimate Summary

Operating Mode	Filter Size	Latency Estimate
		300 MHz
		Max Latency (ms)
1 pixel	3x3	7.18
	5x5	7.20
	7x7	7.22

Deviation from OpenCV

Unlike OpenCV, Vitis Vision only supports filter sizes of 3, 5 and 7.

Bit Depth Conversion

The convertTo function converts the input image bit depth to the required bit depth in the output image.

API Syntax

template <int SRC_T, int DST_T, int ROWS, int COLS, int NPC=1>
void convertTo(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> &_src_mat, xf::cv::Mat<DST_T, ROWS, COLS, NPC> &_dst_mat, ap_uint<4> _convert_type, int _shift)

Parameter Descriptions

The following table describes the template and the function parameters.

Table 36. convertTo Parameter Description

Parameter	Description
SRC_T	Input pixel type. 8-bit, unsigned, 1 channel (XF_8UC1), 16-bit, unsigned, 1 channel (XF_16UC1), 16-bit, signed, 1 channel (XF_16SC1), 32-bit, signed, 1 channel (XF_32SC1) are supported.
DST_T	Output pixel type. 8-bit, unsigned, 1 channel (XF_8UC1), 16-bit, unsigned, 1 channel (XF_16UC1), 16-bit, signed, 1 channel (XF_16SC1), 32-bit, signed, 1 channel (XF_32SC1) are supported.
ROWS	Height of input and output images
COLS	Width of input and output images
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively. XF_NPPC8 is not supported with the 32-bit input and output pixel type.
_src_mat	Input image
_dst_mat	Output image
_convert_ty pe	This parameter specifies the type of conversion required. (See XF_convert_bit_depth_e enumerated type in file xf_params.h for possible values.)
_shift	Optional scale factor

Possible Conversions

The following table summarizes supported conversions. The rows are possible input image bit depths and the columns are corresponding possible output image bit depths (U=unsigned, S=signed).

Table 37. convertTo Function Supported Conversions

INPUT/OUTPUT	U8	U16	S16	U32	S32
U8	NA	yes	yes	NA	yes
U16	yes	NA	NA	NA	yes
S16	yes	NA	NA	NA	yes
U32	NA	NA	NA	NA	NA
S32	yes	yes	yes	NA	NA

Resource Utilization

The following table summarizes the resource utilization of the convertTo function, generated using Vivado HLS 2019.1 tool for the Xilinx® Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a grayscale HD (1080x1920) image.

Table 38. convertTo Function Resource Utilization Summary For XF_CONVERT_8U_TO_16S Conversion

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	8	581	523	119
8 Pixel	150	0	8	963	1446	290

Table 39. convertTo Function Resource Utilization Summary For XF_CONVERT_16U_TO_8U Conversion

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	8	591	541	124
8 Pixel	150	0	8	915	1500	308

Performance Estimate

The following table summarizes the performance in different configurations, as generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table 40. convertTo Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9
8 pixel operation (150 MHz)	1.69

Bitwise AND

The bitwise_and function performs the bitwise AND operation for each pixel between two input images, and returns an output image.

Where,

• is the intensity of output image at (x, y) position

• is the intensity of first input image at (x, y) position

• is the intensity of second input image at (x, y) position

API Syntax

template<int SRC_T, int ROWS, int COLS, int NPC=1>
void bitwise_and (
xf::cv::Mat<int SRC_T, int ROWS, int COLS, int NPC> src1,
xf::cv::Mat<int SRC_T, int ROWS, int COLS, int NPC> src2,
xf::cv::Mat<int SRC_T, int ROWS, int COLS, int NPC> dst )

Parameter Descriptions

The following table describes the template and the function parameters.

Table 41. bitwise_and Parameter Description

Parameter	Description
SRC_T	Input and output pixel type. Supports 1 channel and 3 channels (XF_8UC1 and XF_8UC3)
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image (must be a multiple of 8, for 8 pixel mode)
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations, respectively.
src1	Input image
src2	Input image
dst	Output image

Resource Utilization

The following table summarizes the resource utilization in different configurations, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a grayscale HD (1080x1920) image.

Table 42. bitwise_and Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	0	62	44	10
8 Pixel	150	0	0	59	72	13

The following table summarizes the resource utilization in different configurations, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a 4K 3Channel image.

Table 43. bitwise_and Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	1	155	61	22

Performance Estimate

The following table summarizes the performance in different configurations, as generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table 44. bitwise_and Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9
8 pixel operation (150 MHz)	1.7

Bitwise NOT

The bitwise_not function performs the pixel wise bitwise NOT operation for the pixels in the input image, and returns an output image.

Where,

• is the intensity of output image at (x, y) position

• is the intensity of input image at (x, y) position

API Syntax

template<int SRC_T, int ROWS, int COLS, int NPC=1>
void bitwise_not (
xf::cv::Mat<int SRC_T, int ROWS, int COLS, int NPC> src,
xf::cv::Mat<int SRC_T, int ROWS, int COLS, int NPC> dst )

Parameter Descriptions

The following table describes the template and the function parameters.

Table 45. bitwise_not Parameter Description

Parameter	Description
SRC_T	Input and output pixel type. Supports 1 channel and 3 channels (XF_8UC1 and XF_8UC3).
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. Must be a multiple of 8 for 8 pixel mode.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations, respectively.
src	Input image
dst	Output image

Resource Utilization

The following table summarizes the resource utilization in different configurations, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a grayscale HD (1080x1920) image.

Table 46. bitwise_not Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	0	97	78	20
8 Pixel	150	0	0	88	97	21

The following table summarizes the resource utilization in different configurations, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a 4K 3Channel image.

Table 47. bitwise_not Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	1	155	61	22

… rubric:: Performance Estimate

The following table summarizes the performance in different configurations, as generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table 48. bitwise_not Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9
8 pixel operation (150 MHz)	1.7

Bitwise OR

The bitwise_or function performs the pixel wise bitwise OR operation between two input images, and returns an output image.

Where,

• is the intensity of output image at (x, y) position

• is the intensity of first input image at (x, y) position

• is the intensity of second input image at (x, y) position

API Syntax

template<int SRC_T, int ROWS, int COLS, int NPC=1>
void bitwise_or (
xf::cv::Mat<int SRC_T, int ROWS, int COLS, int NPC> src1,
xf::cv::Mat<int SRC_T, int ROWS, int COLS, int NPC> src2,
xf::cv::Mat<int SRC_T, int ROWS, int COLS, int NPC> dst )

Parameter Descriptions

The following table describes the template and the function parameters.

Table 49. bitwise_or Parameter Description

Parameter	Description
SRC_T	Input and output pixel type. Supports 1 channel and 3 channels (XF_8UC1 and XF_8UC3).
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. Must be multiple of 8, for 8 pixel mode.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
src1	Input image
src2	Input image
dst	Output image

Resource Utilization

The following table summarizes the resource utilization in different configurations, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a grayscale HD (1080x1920) image.

Table 50. bitwise_or Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	0	62	44	10
8 Pixel	150	0	0	59	72	13

The following table summarizes the resource utilization in different configurations, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a 4K 3Channel image

Table 51. bitwise_or Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	1	155	61	22

Performance Estimate

The following table summarizes the performance in different configurations, as generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table 52. bitwise_or Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9
8 pixel operation (150 MHz)	1.7

Bitwise XOR

The bitwise_xor function performs the pixel wise bitwise XOR operation between two input images, and returns an output image, as shown below:

Where,

• is the intensity of output image at (x, y) position

• is the intensity of first input image at (x, y) position

• is the intensity of second input image at (x, y) position

API Syntax

template<int SRC_T, int ROWS, int COLS, int NPC=1>
void bitwise_xor(
xf::cv::Mat<int SRC_T, int ROWS, int COLS, int NPC> src1,
xf::cv::Mat<int SRC_T, int ROWS, int COLS, int NPC> src2,
xf::cv::Mat<int SRC_T, int ROWS, int COLS, int NPC> dst )

Parameter Descriptions

The following table describes the template and the function parameters.

Table 53. bitwise_xor Parameter Description

Parameter	Description
SRC_T	Input and output pixel type. Supports 1 channel and 3 channels (XF_8UC1 and XF_8UC3).
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. Must be multiple of 8, for 8 pixel mode.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
src1	Input image
src2	Input image
dst	Output image

Resource Utilization

The following table summarizes the resource utilization in different configurations, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a grayscale HD (1080x1920) image:

Table 54. bitwise_xor Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	0	62	44	10
8 Pixel	150	0	0	59	72	13

Performance Estimate

The following table summarizes the resource utilization in different configurations, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a 4k Channel image

Table 55. bitwise_xor Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	1	155	61	22

The following table summarizes the performance in different configurations, as generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image:

Table 56. bitwise_xor Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9
8 pixel operation (150 MHz)	1.7

Blacklevelcorrection

Black level leads to the whitening of image in dark region and perceived loss of overall contrast. The Blacklevelcorrection algorithm corrects the black and white levels of the overall image.

API Syntax

template <int SRC_T,int MAX_ROWS,int MAX_COLS,int NPPC = XF_NPPC1,int MUL_VALUE_WIDTH = 16,int FL_POS = 15,int USE_DSP = 1>
void blackLevelCorrection(xf::cv::Mat<SRC_T, MAX_ROWS, MAX_COLS, NPPC>& _Src,
                          xf::cv::Mat<SRC_T, MAX_ROWS, MAX_COLS, NPPC>& _Dst,
                          XF_CTUNAME(SRC_T, NPPC) black_level,
                          float mul_value)

Parameter Descriptions

The following table describes the template and the function parameters.

Table blacklevelcorrection correction Parameter Description

Parameter	Description
MUL_VALUE_WIDTH	Width of multiplication factor.
FL_POS	Number of fractional bits in multiplication factor.
USE_DSP	Enables usage of DSP for multiplication.
SRC_T	Input pixel type. 8/10/12/16-bit unsigned 1 channel are supported (XF_8UC1, XF_10UC1, XF_12UC1, XF_16UC1).
DST_T	Output pixel type. 8/10/12/16-bit unsigned 1 channel are supported (XF_8UC1, XF_10UC1, XF_12UC1, XF_16UC1).
MAX_ROWS	Maximum height of input and output image.
MAX_COLS	Maximum width of input and output image. In case of N-pixel parallelism, width should be multiple of N.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1, XF_NPPC2 AND so on
_Src	Input image
_Dst	Output image
black_level	Black level value
mul_value	Multiplication factor for blacklevel correction; which is computed as maxlevel/(maxlevel-blacklevel)

Resource Utilization

The following table summarizes the resource utilization of the kernel in different configurations, generated using Vitis HLS 2020.2 tool, to process a FULL HD image.

Table blacklevelcorrection Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 pixel-8U	300	0	0	279	271	70

Performance Estimate

The following table summarizes a performance estimate of the kernel in different configurations, as generated using Vitis HLS 2020.2 tool, to process a FULL HD image.

Table blacklevelcorrection Function Performance Estimate Summary

Operating Mode	Operating Frequency (MHz)	Latency Estimate Max (ms)
1 pixel	300	7
2 pixel	300	3.6

Box Filter

The boxFilter function performs box filtering on the input image. Box filter acts as a low-pass filter and performs blurring over the image. The boxFilter function or the box blur is a spatial domain linear filter in which each pixel in the resulting image has a value equal to the average value of the neighboring pixels in the image.

API Syntax

template<int BORDER_TYPE,int FILTER_TYPE, int SRC_T, int ROWS, int COLS,int NPC=1,bool USE_URAM=false>
void boxFilter(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src_mat,xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _dst_mat)

Parameter Descriptions

The following table describes the template and the function parameters.

Table 57. boxFilter Parameter Description

Parameter	Description
FILTER_SIZE	Filter size. Filter size of 3(XF_FILTER_3X3), 5(XF_FILTER_5X5) and 7(XF_FILTER_7X7) are supported
BORDER_TYPE	Border Type supported is XF_BORDER_CONSTANT
SRC_T	Input and output pixel type. 8-bit, unsigned, 16-bit unsigned and 16-bit signed, 1 channel is supported (XF_8UC1)
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image (must be multiple of 8, for 8-pixel operation)
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
USE_URAM	Enable to map storage structures to UltraRAM
_src_mat	Input image
_dst_mat	Output image

Resource Utilization

The following table summarizes the resource utilization of the kernel in different configurations, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a grayscale HD (1080x1920) image.

Table 58. boxFilter Function Resource Utilization Summary

Operating Mode	Filter Size	Operating Frequency (MHz)	Utilization Estimate
			BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	3x3	300	3	1	545	519	104
	5x5	300	5	1	876	870	189
	7x7	300	7	1	1539	1506	300
8 Pixel	3x3	150	6	8	1002	1368	264
	5x5	150	10	8	1576	3183	611
	7x7	150	14	8	2414	5018	942

The following table summarizes the resource utilization of the kernel in different configurations, generated using the Vivado HLS™ 2019.1 tool for the xczu7ev-ffvc1156-2-e FPGA, to process a grayscale 4K (3840x2160) image with UltraRAM enable.

Table 59. boxFilter Function Resource Utilization Summary with UltraRAM enabled

Operating Mode	Filter Size	Operating Frequency (MHz)	Utilization Estimate
			BRAM_18K	URAM	DSP_48Es	FF	LUT
1 Pixel	3x3	300	0	1	1	821	521
	5x5	300	0	1	1	1204	855
	7x7	300	0	1	1	2083	1431
8 Pixel	3x3	150	0	3	8	1263	1480
	5x5	150	0	5	8	1771	3154
	7x7	150	0	7	8	2700	5411

Performance Estimate

The following table summarizes the performance of the kernel in different configurations, as generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table 60. boxFilter Function Performance Estimate Summary

Operating Mode	Operating Frequency (MHz)	Filter Size	Latency Estimate
			Max Latency (ms)
1 pixel	300	3x3	7.2
	300	5x5	7.21
	300	7x7	7.22
8 pixel	150	3x3	1.7
	150	5x5	1.7
	150	7x7	1.7

BoundingBox

The boundingbox function highlights the region of interest (ROI) from the input image using below equations.

P(X,Y) ≤ P(xi, yi) ≤ P(X,Y’)

P(X’,Y) ≤ P(xi, yi) ≤ P(X’,Y’)

Where,

• P(xi, yi) - Current pixel location

• P(X,Y) - Top left corner of ROI

• P(X,Y’) - Top right corner of ROI

• P(X’,Y) - Bottom left corner of ROI

• P(X’,Y’) - Bottom Right of ROI

API Syntax

template<int SRC_T, int ROWS, int COLS, int MAX_BOXES=1, int NPC=1>
void boundingbox(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src_mat, xf::cv::Rect_<int> *roi , xf::cv::Scalar<4,unsigned char > *color, int num_box)

Parameter Descriptions

The following table describes the template and the function parameters.

Table 61. boundingbox Parameter Description

Parameter	Description
SRC_T	Input pixel Type. Only 8-bit, unsigned, 1 channel and 3 channel is supported (XF_8UC1,XF_8UC3).
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. Must be multiple of NPC.
MAX_BOXES	Maximum number of boxes, fixed to 5.
NPC	Number of pixels to be processed per cycle, possible options are XF_NPPC1 only.
_src_mat	Input image
roi	ROI is a xf::cv::Rect object that consists of the top left corner of the rectangle along with the height and width of the rectangle.
color	The xf::cv::Scalar object consists of color information for each box (ROI).
num_box	Number of boxes to be detected. It should be equal or less than MAX_BOXES.

Resource Utilization

The following table summarizes the resource utilization in different configurations, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA.

Table 62. boundingbox Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	5	4	2521	1649	409

Performance Estimate

The following table summarizes the performance of the kernel in 1-pixel mode as generated using Vivado HLS 2019.1 tool for the Xilinx xczu9eg-ffvb1156-2-i-es2 FPGA to process a grayscale 4K (2160x3840) image for highlighting 3 different boundaries (480x640, 100x200, 300x300).

Table 63. boundingbox Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	0.15

Vitis Vision Reference

The xf::cv::boundingbox is complaint with below Vitis Vision function:

void rectangle(Mat& img, Rect rec, const Scalar& color, int thickness=1, int lineType=8, int shift=0 )

Canny Edge Detection

The Canny edge detector finds the edges in an image or video frame. It is one of the most popular algorithms for edge detection. Canny algorithm aims to satisfy three main criteria:

1. Low error rate: A good detection of only existent edges.

2. Good localization: The distance between edge pixels detected and real edge pixels have to be minimized.

3. Minimal response: Only one detector response per edge.

In this algorithm, the noise in the image is reduced first by applying a Gaussian mask. The Gaussian mask used here is the average mask of size 3x3. Thereafter, gradients along x and y directions are computed using the Sobel gradient function. The gradients are used to compute the magnitude and phase of the pixels. The phase is quantized and the pixels are binned accordingly. Non-maximal suppression is applied on the pixels to remove the weaker edges.

Edge tracing is applied on the remaining pixels to draw the edges on the image. In this algorithm, the canny up to non-maximal suppression is in one kernel and the edge linking module is in another kernel. After non-maxima suppression, the output is represented as 2-bit per pixel, Where:

• 00 - represents the background

• 01 - represents the weaker edge

• 11 - represents the strong edge

The output is packed as 8-bit (four 2-bit pixels) in 1 pixel per cycle operation and packed as 16-bit (eight 2-bit pixels) in 8 pixel per cycle operation. For the edge linking module, the input is 64-bit, such 32 pixels of 2-bit are packed into a 64-bit. The edge tracing is applied on the pixels and returns the edges in the image.

API Syntax

The .. rubric:: API Syntax for Canny is:

template<int FILTER_TYPE,int NORM_TYPE,int SRC_T,int DST_T, int ROWS, int COLS,int NPC,int NPC1,bool USE_URAM=false>
void Canny(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src_mat,xf::cv::Mat<DST_T, ROWS, COLS, NPC1> & _dst_mat,unsigned char _lowthreshold,unsigned char _highthreshold)

The .. rubric:: API Syntax for EdgeTracing is:

template<int SRC_T, int DST_T, int ROWS, int COLS,int NPC_SRC,int NPC_DST,bool USE_URAM=false>
voidEdgeTracing(xf::cv::Mat<SRC_T, ROWS, COLS, NPC_SRC> & _src,xf::cv::Mat<DST_T, ROWS, COLS, NPC_DST> & _dst)

Parameter Descriptions

The following table describes the xf::cv::Canny template and function parameters:

Table 64. xf::cv::Canny Parameter Description

Parameter	Description
FILTER_TYPE	The filter window dimensions. The options are 3 and 5.
NORM_TYPE	The type of norm used. The options for norm type are L1NORM and L2NORM.
SRC_T	Input pixel type. Only 8-bit, unsigned, 1 channel is supported (XF_8UC1)
DST_T	Output pixel type. Only XF_2UC1 is supported. The output in case of NPC=XF_NPPC1 is 8-bit and packing four 2-bit pixel values into 8-bit. The output in case of NPC=XF_NPPC8 is 16-bit, 8-bit, 2-bit pixel values are packing into 16-bit.
ROWS	Maximum height of input and output image
COLS	Maximum width of input and output image (must be a multiple of 8, in case of 8 pixel mode)
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively. In XF_NPPC, the output image pixels are packed and precision is XF_NPPC4. In XF_NPPC8, output pixels precision is XF_NPPC8.
NPC1	The output NPC is 32.Packing 2bit, 32 pixels into 64 bit pointer
USE_URAM	Enable to map some storage structures to URAM
_src_mat	Input image
_dst_mat	Output image
_lowthreshold	The lower value of threshold for binary thresholding.
_highthreshold	The higher value of threshold for binary thresholding.

The following table describes the EdgeTracing template and function parameters:

Table 65. EdgeTracing Parameter Description

Parameter	Description
SRC_T	Input pixel type
DST_T	Output pixel type
ROWS	Maximum height of input and output image
COLS	Maximum width of input and output image (must be a multiple of 32)
NPC_SRC	Number of pixels to be processed per cycle. Fixed to XF_NPPC32.
NPC_DST	Number of pixels to be written to destination. Fixed to XF_NPPC8.
USE_URAM	Enable to map storage structures to URAM.
_src	Input image
_dst	Output image

Resource Utilization

The following table summarizes the resource utilization of xf::cv::Canny and EdgeTracing in different configurations, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a grayscale HD (1080x1920) image for Filter size is 3.

Table 66. xf::cv::Canny and EdgeTracing Function Resource Utilization Summary

Name	Resource Utilization
	1 pixel	1 pixel	8 pixel	8 pixel	Edge Linking	Edge Linking
	L1NORM,FS:3	L2NORM,FS:3	L1NORM,FS:3	L2NORM,FS:3
	300 MHz	300 MHz	150 MHz	150 MHz	300 MHz	150 MHz
BRAM_18K	22	18	36	32	84	84
DSP48E	2	4	16	32	3	3
FF	3027	3507	4899	6208	17600	14356
LUT	2626	3170	6518	9560	15764	14274
CLB	606	708	1264	1871	2955	3241

The following table summarizes the resource utilization of xf::cv::Canny and EdgeTracing in different configurations, generated using Vivado HLS 2019.1 tool for the xczu7ev-ffvc1156-2-e FPGA, to process a grayscale 4K image for Filter size is 3.

Table 67. xf::cv::Canny and EdgeTracing Function Resource Utilization Summary with UltraRAM Enable

Name	Resource Utilization
	1 pixel	1 pixel	8 pixel	8 pixel	Edge Linking	Edge Linking
	L1NORM,FS:3	L2NORM,FS:3	L1NORM,FS:3	L2NORM,FS:3
	300 MHz	300 MHz	150 MHz	150 MHz	300 MHz	150 MHz
BRAM_18K	10	8	3	3	4	4
URAM	1	1	15	13	8	8
DSP48E	2	4	16	32	8	8
FF	3184	3749	5006	7174	5581	7054
LUT	2511	2950	6695	9906	4092	6380

Performance Estimate

The following table summarizes the performance of the kernel in different configurations, as generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image for L1NORM, filter size is 3 and including the edge linking module.

Table 68. xf::cv::Canny and EdgeTracing Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	10.8
8 pixel operation (150 MHz)	8.5

Deviation from OpenCV

In OpenCV Canny function, the Gaussian blur is not applied as a pre-processing step.

Channel Combine

The merge function, merges single channel images into a multi-channel image. The number of channels to be merged should be two, three or four.

API Syntax

template<int SRC_T, int DST_T, int ROWS, int COLS, int NPC=1>
void merge(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> &_src1, xf::cv::Mat<SRC_T, ROWS, COLS, NPC> &_src2, xf::cv::Mat<SRC_T, ROWS, COLS, NPC> &_src3, xf::cv::Mat<SRC_T, ROWS, COLS, NPC> &_src4, xf::cv::Mat<DST_T, ROWS, COLS, NPC> &_dst)

template<int SRC_T, int DST_T, int ROWS, int COLS, int NPC=1>
void merge(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> &_src1, xf::cv::Mat<SRC_T, ROWS, COLS, NPC> &_src2, xf::cv::Mat<SRC_T, ROWS, COLS, NPC> &_src3, xf::cv::Mat<DST_T, ROWS, COLS, NPC> &_dst)

template<int SRC_T, int DST_T, int ROWS, int COLS, int NPC=1>
void merge(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> &_src1, xf::cv::Mat<SRC_T, ROWS, COLS, NPC> &_src2, xf::cv::Mat<DST_T, ROWS, COLS, NPC> &_dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table 69. merge Parameter Description

Paramete r	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 1, channel is supported (XF_8UC1)
DST_T	Output pixel type. 8-bit, unsigned,2,3 and 4 channels are supported (XF_8UC2, XF_8UC3 and XF_8UC4)
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. Must be multiple of 8 for 8 pixel mode.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 for 1 pixel operation.
_src1	Input single-channel image
_src2	Input single-channel image
_src3	Input single-channel image (only for 3 and 4 input config)
_src4	Input single-channel image (only for 4 input config)
_dst	Output multi-channel image

Resource Utilization

The following table summarizes the resource utilization of the merge function, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process 4 single-channel HD (1080x1920) images.

Table 70. merge Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	8	494	386	85

Performance Estimate

The following table summarizes the performance in different configurations, as generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1, to process 4 single channel HD (1080x1920) images.

Table 71. merge Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.92

Channel Extract

The extractChannel function splits a multi-channel array (32-bit pixel-interleaved data) into several single-channel arrays and returns a single channel. The channel to be extracted is specified by using the channel argument.

The value of the channel argument is specified by macros defined in the xf_channel_extract_e enumerated data type. The following table summarizes the possible values for the xf_channel_extract_e enumerated data type:

Table 72. xf_channel_extract_e Enumerated Data Type Values

Channel	Enumerated Type
Unknown	XF_EXTRACT_CH_0
Unknown	XF_EXTRACT_CH_1
Unknown	XF_EXTRACT_CH_2
Unknown	XF_EXTRACT_CH_3
RED	XF_EXTRACT_CH_R
GREEN	XF_EXTRACT_CH_G
BLUE	XF_EXTRACT_CH_B
ALPHA	XF_EXTRACT_CH_A
LUMA	XF_EXTRACT_CH_Y
Cb/U	XF_EXTRACT_CH_U
Cr/V/Value	XF_EXTRACT_CH_V

API Syntax

template<int SRC_T, int DST_T, int ROWS, int COLS, int NPC=1>
void extractChannel(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src_mat, xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst_mat, uint16_t _channel)

Parameter Descriptions

The following table describes the template and the function parameters.

Table 73. extractChannel Parameter Description

Paramete r	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 4channel is supported (XF_8UC4)
DST_T	Output pixel type. Only 8-bit, unsigned, 1 channel is supported (XF_8UC1)
ROWS	Maximum height of input and output image
COLS	Maximum width of input and output image. Must be multiple of 8 for 8 pixel mode
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 for 1 pixel operation.
_src_ma t	Input multi-channel image
_dst_ma t	Output single channel image
_channe l	Channel to be extracted (See xf_channel_extract_e enumerated type in file xf_params.h for possible values.)

Resource Utilization

The following table summarizes the resource utilization of the extractChannel function, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a 4 channel HD (1080x1920) image.

Table 74. extractChannel Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	8	508	354	96

Performance Estimate

The following table summarizes the performance in different configurations, as generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1, to process a 4 channel HD (1080x1920) image.

Table 75. extractChannel Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.92

Color Conversion

The color conversion functions convert one image format to another image format, for the combinations listed in the following table. The rows represent the input formats and the columns represent the output formats. Supported conversions are discussed in the following sections.

Table 76. Supported Color Conversions

I/O Formats	RGBA	NV12	NV21	IYUV	UYVY	YUYV	YUV4	RGB	BGR
RGBA	N/A	For details, see the RGBA to NV12	For details, see the RGBA to NV21	For details, see the RGBA/RGB to IYUV			For details, see the RGBA/RGB to YUV4
NV12	For details, see the NV12 to RGBA	N/A	For details, see the NV12 to NV21/NV21 to NV12	For details, see the NV12 to IYUV	For details, see the NV12/NV21 to UYVY/YUYV	For details, see the NV12/NV21 to UYVY/YUYV	For details, see the NV12 to YUV4	For details, see the NV12/NV21 to RGB/ BGR	For details, see the NV12/NV21 to RGB/ BGR
NV21	For details, see the NV21 to RGBA	For details, see the NV12 to NV21/NV21 to NV12	N/A	For details, see the NV21 to IYUV	For details, see the NV12/NV21 to UYVY/YUYV	For details, see the NV12/NV21 to UYVY/YUYV	For details, see the NV21 to YUV4	For details, see the NV12/NV21 to RGB/ BGR	For details, see the NV12/NV21 to RGB/ BGR
IYUV	For details, see the IYUV to RGBA/RGB	For details, see the IYUV to NV12		N/A			For details, see the IYUV to YUV4	For details, see the IYUV to RGBA/RGB
UYVY	For details, see the UYVY to RGBA	For details, see the UYVY to NV12		For details, see the UYVY to IYUV	N/A
YUYV	For details, see the YUYV to RGBA	For details, see the YUYV to NV12		For details, see the YUYV to IYUV		N/A
YUV4							N/A
RGB		For details see the RGB/ BGR to NV12/NV21	For details see theRGB/ BGR to NV12/NV21	For details see the RGBA/RGB to IYUV	For details see the RGB/BGR to UYVY/YUYV	For details see the RGB/BGR to UYVY/YUYV	For details see the RGBA/RGB to YUV4		For details see the BGR to RGB / RGB to BGR
BGR		For details see the RGB/ BGR to NV12/NV21	For details see theRGB/ BGR to NV12/NV21		For details see the RGB/BGR to UYVY/YUYV	For details see the RGB/BGR to UYVY/YUYV		For details see the BGR to RGB / RGB to BGR

Other conversions

Few other conversions are also added. BGR/RGB<->HSV,BGR/RGB<->HLS,BGR/RGB<->YCrCb,BGR/RGB<->XYZ and RGB<->BGR conversions are added.

RGB to YUV Conversion Matrix

Following is the formula to convert RGB data to YUV data:

YUV to RGB Conversion Matrix

Following is the formula to convert YUV data to RGB data:

Source: http://www.fourcc.org/fccyvrgb.php

RGBA/RGB to YUV4

The rgba2yuv4 function converts a 4-channel RGBA image to YUV444 format and the rgb2yuv4 function converts a 3-channel RGB image to YUV444 format. The function outputs Y, U, and V streams separately.

API Syntax

template <int SRC_T, int DST_T, int ROWS, int COLS, int NPC=1>
void rgba2yuv4(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src, xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _y_image, xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _u_image, xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _v_image)

template <int SRC_T, int DST_T, int ROWS, int COLS, int NPC=1>
void rgb2yuv4(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src, xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _y_image, xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _u_image, xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _v_image)

Parameter Descriptions

The following table describes the template and the function parameters.

Table (rgba/rgb)2yuv4 Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 4(RGBA) and 3(RGB)-channel are supported (XF_8UC4 and XF_8UC3).
DST_T	Output pixel type. Only 8-bit, unsigned, 1-channel is supported (XF_8UC1).
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. Must be a multiple of 8 for 8 pixel mode.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
_src	Input Y plane of size (ROWS, COLS).
_y_image	Output Y image of size (ROWS, COLS).
_u_image	Output U image of size (ROWS, COLS).
_v_image	Output V image of size (ROWS, COLS).

Resource Utilization

The following table summarizes the resource utilization of RGBA/RGB to YUV4 for different configurations, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a HD (1080x1920) image.

Table (rgba/rgb)2yuv4 Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	9	589	328	96

Performance Estimate

The following table summarizes the performance of RGBA/RGB to YUV4 for different configurations, as generated using the Vivado HLS 2019.1 version for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table (rgba/rgb)2yuv4 Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	1.89

RGBA/RGB to IYUV

The rgba2iyuv function converts a 4-channel RGBA image to IYUV (4:2:0) format and the rgb2iyuv function converts a 3-channel RGB image to IYUV (4:2:0) format. The function outputs Y, U, and V planes separately. IYUV holds subsampled data, Y is sampled for every RGBA/RGB pixel and U,V are sampled once for 2row and 2column(2x2) pixels. U and V planes are of (rows/2)*(columns/2) size, by cascading the consecutive rows into a single row the planes size becomes (rows/4)*columns.

API Syntax

template <int SRC_T, int DST_T, int ROWS, int COLS, int NPC=1>
void rgba2iyuv(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src, xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _y_image, xf::cv::Mat<DST_T, ROWS/4, COLS, NPC> & _u_image, xf::cv::Mat<DST_T, ROWS/4, COLS, NPC> & _v_image)

template <int SRC_T, int DST_T, int ROWS, int COLS, int NPC=1>
void rgb2iyuv(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src, xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _y_image, xf::cv::Mat<DST_T, ROWS/4, COLS, NPC> & _u_image, xf::cv::Mat<DST_T, ROWS/4, COLS, NPC> & _v_image)

Parameter Descriptions

The following table describes the template and the function parameters.

Table (rgba/rgb)2iyuv Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 8-bit,unsigned, 4(RGBA) and 3(RGB)-channel are supported (XF_8UC4 and XF_8UC3).
DST_T	Output pixel type. Only 8-bit,unsigned, 1-channel is supported (XF_8UC1).
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. Must be a multiple of 8 for 8 pixel mode.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
_src	Input Y plane of size (ROWS, COLS).
_y_image	Output Y image of size (ROWS, COLS).
_u_image	Output U image of size (ROWS/4, COLS).
_v_image	Output V image of size (ROWS/4, COLS).

Resource Utilization

The following table summarizes the resource utilization of RGBA/RGB to IYUV for different configurations, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a HD (1080x1920) image.

Table (rgba/rgb)2iyuv Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	9	816	472	149

Performance Estimate

The following table summarizes the performance of RGBA/RGB to IYUV for different configurations, as generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table (rgba/rgb)2iyuv Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	1.8

RGBA to NV12

The rgba2nv12 function converts a 4-channel RGBA image to NV12 (4:2:0) format. The function outputs Y plane and interleaved UV plane separately. NV12 holds the subsampled data, Y is sampled for every RGBA pixel and U, V are sampled once for 2row and 2columns (2x2) pixels. UV plane is of (rows/2)*(columns/2) size as U and V values are interleaved.

API Syntax

template <int SRC_T, int Y_T, int UV_T, int ROWS, int COLS, int NPC=1>
void rgba2nv12(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src, xf::cv::Mat<Y_T, ROWS, COLS, NPC> & _y, xf::cv::Mat<UV_T, ROWS/2, COLS/2, NPC> & _uv)

Parameter Descriptions

The following table describes the template and the function parameters.

Table rgba2nv12 Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 8-bit,unsigned, 4-channel is supported (XF_8UC4).
Y_T	Output pixel type. Only 8-bit,unsigned, 1-channel is supported (XF_8UC1).
UV_T	Output pixel type. Only 8-bit,unsigned, 2-channel is supported (XF_8UC2).
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. Must be a multiple of 8 for 8 pixel mode.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
_src	Input RGBA image of size (ROWS, COLS).
_y	Output Y image of size (ROWS, COLS).
_uv	Output UV image of size (ROWS/2, COLS/2).

Resource Utilization

The following table summarizes the resource utilization of RGBA to NV12 for different configurations, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a HD (1080x1920) image.

Table rgba2nv12 Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	9	802	452	128

Performance Estimate

The following table summarizes the performance of RGBA to NV12 for different configurations, as generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table rgba2nv12 Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	1.8

RGBA to NV21

The rgba2nv21 function converts a 4-channel RGBA image to NV21 (4:2:0) format. The function outputs Y plane and interleaved VU plane separately. NV21 holds subsampled data, Y is sampled for every RGBA pixel and U, V are sampled once for 2 row and 2 columns (2x2) RGBA pixels. UV plane is of (rows/2)*(columns/2) size as V and U values are interleaved.

API Syntax

template <int SRC_T, int Y_T, int UV_T, int ROWS, int COLS, int NPC=1>
void rgba2nv21(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src, xf::cv::Mat<Y_T, ROWS, COLS, NPC> & _y, xf::cv::Mat<UV_T, ROWS/2, COLS/2, NPC> & _uv)

Parameter Descriptions

The following table describes the template and the function parameters.

Table rgba2nv21 Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 4-channel is supported (XF_8UC4).
Y_T	Output pixel type. Only 8-bit, unsigned, 1-channel is supported (XF_8UC1).
UV_T	Output pixel type. Only 8-bit, unsigned, 1-channel is supported (XF_8UC2).
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. Must be a multiple of 8 for 8 pixel mode.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
_src	Input RGBA image of size (ROWS, COLS).
_y	Output Y image of size (ROWS, COLS).
_uv	Output UV image of size (ROWS/2, COLS/2).

Resource Utilization

The following table summarizes the resource utilization of RGBA to NV21 for different configurations, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a HD (1080x1920) image.

Table rgba2nv21 Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	9	802	453	131

Performance Estimate

The following table summarizes the performance of RGBA to NV21 for different configurations, as generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table rgba2nv21 Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	1.89

YUYV to RGBA

The yuyv2rgba function converts a single-channel YUYV (YUV 4:2:2) image format to a 4-channel RGBA image. YUYV is a sub-sampled format, a set of YUYV value gives 2 RGBA pixel values. YUYV is represented in 16-bit values where as, RGBA is represented in 32-bit values.

API Syntax

template<int SRC_T, int DST_T, int ROWS, int COLS, int NPC=1>
void yuyv2rgba(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src, xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table yuyv2rgba Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 16-bit, unsigned, 1-channel is supported (XF_16UC1).
DST_T	Output pixel type. Only 8-bit, unsigned, 4-channel is supported (XF_8UC4).
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. Must be a multiple of 8 incase of 8 pixel mode.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
_src	Input image of size (ROWS, COLS).
_dst	Output image of size (ROWS, COLS).

Resource Utilization

The following table summarizes the resource utilization of YUYV to RGBA for different configurations, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a HD (1080x1920) image.

Table yuyv2rgba Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	6	765	705	165

Performance Estimate

The following table summarizes the performance of UYVY to RGBA for different configurations, as generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table yuyv2rgba Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9

YUYV to NV12

The yuyv2nv12 function converts a single-channel YUYV (YUV 4:2:2) image format to NV12 (YUV 4:2:0) format. YUYV is a sub-sampled format, 1 set of YUYV value gives 2 Y values and 1 U and V value each.

API Syntax

template<int SRC_T,int Y_T,int UV_T,int ROWS,int COLS,int NPC=1,int NPC_UV=1>
void yuyv2nv12(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src,xf::cv::Mat<Y_T, ROWS, COLS, NPC> & _y_image,xf::cv::Mat<UV_T, ROWS/2, COLS/2, NPC_UV> & _uv_image)

Parameter Descriptions

The following table describes the template and the function parameters.

Table yuyv2nv12 Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 16-bit, unsigned, 1-channel is supported (XF_16UC1).
Y_T	Output pixel type. Only 8-bit, unsigned, 1-channel is supported (XF_8UC1).
UV_T	Output UV image pixel type. Only 8-bit, unsigned, 2-channel is supported (XF_8UC2).
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. Must be a multiple of 8 for 8 pixel mode.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
NPC_UV	Number of UV image Pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
_src	Input image of size (ROWS, COLS).
_y_image	Output Y plane of size (ROWS, COLS).
_uv_image	Output U plane of size (ROWS/2, COLS/2).

Resource Utilization

The following table summarizes the resource utilization of YUYV to NV12 for different configurations, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a HD (1080x1920) image.

Table yuyv2nv12 Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	0	831	491	149
8 Pixel	150	0	0	1196	632	161

Performance Estimate

The following table summarizes the performance of YUYV to NV12 for different configurations, as generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table yuyv2nv12 Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9
8 pixel operation (150 MHz)	1.7

YUYV to IYUV

The yuyv2iyuv function converts a single-channel YUYV (YUV 4:2:2) image format to IYUV(4:2:0) format. Outputs of the function are separate Y, U, and V planes. YUYV is a sub-sampled format, 1 set of YUYV value gives 2 Y values and 1 U and V value each. U, V values of the odd rows are dropped as U, V values are sampled once for 2 rows and 2 columns in the IYUV(4:2:0) format.

API Syntax

template<int SRC_T, int DST_T, int ROWS, int COLS, int NPC=1>
void yuyv2iyuv(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src, xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _y_image, xf::cv::Mat<DST_T, ROWS/4, COLS, NPC> & _u_image, xf::cv::Mat<DST_T, ROWS/4, COLS, NPC> & _v_image)

Parameter Descriptions

The following table describes the template and the function parameters.

Table yuyv2iyuv Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 16-bit, unsigned,1 channel is supported (XF_16UC1).
DST_T	Output pixel type. Only 8-bit, unsigned, 1 channel is supported (XF_8UC1).
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. Must be a multiple of 8 for 8 pixel modes.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
_src	Input image of size (ROWS, COLS).
_y_image	Output Y plane of size (ROWS, COLS).
_u_image	Output U plane of size (ROWS/4, COLS).
_v_image	Output V plane of size (ROWS/4, COLS).

Resource Utilization

The following table summarizes the resource utilization of YUYV to IYUV for different configurations, generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a HD (1080x1920) image.

Table yuyv2iyuv Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	0	835	497	149
8 Pixel	150	0	0	1428	735	210

Performance Estimate

The following table summarizes the performance of YUYV to IYUV for different configurations, as generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table yuyv2iyuv Function Performance Estimate

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9
8 pixel operation (150 MHz)	1.7

UYVY to IYUV

The uyvy2iyuv function converts a UYVY (YUV 4:2:2) single-channel image to the IYUV format. The outputs of the functions are separate Y, U, and V planes. UYVY is sub sampled format. One set of UYVY value gives two Y values and one U and V value each.

API Syntax

template<int SRC_T, int DST_T, int ROWS, int COLS, int NPC=1>
void uyvy2iyuv(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src, xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _y_image,xf::cv::Mat<DST_T, ROWS/4, COLS, NPC> & _u_image, xf::cv::Mat<DST_T, ROWS/4, COLS, NPC> & _v_image)

Parameter Descriptions

The following table describes the template and the function parameters.

Table uyvy2iyuv Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 16-bit, unsigned, 1-channel is supported (XF_16UC1).
DST_T	Output pixel type. Only 8-bit, unsigned, 1-channel is supported (XF_8UC1).
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. Must be a multiple of 8 for 8 pixel mode.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
_src	Input image of size (ROWS, COLS).
_y_image	Output Y plane of size (ROWS, COLS).
_u_image	Output U plane of size (ROWS/4, COLS).
_v_image	Output V plane of size (ROWS/4, COLS).

Resource Utilization

The following table summarizes the resource utilization of UYVY to IYUV for different configurations, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a HD (1080x1920) image.

Table uyvy2iyuv Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	0	835	494	139
8 Pixel	150	0	0	1428	740	209

Performance Estimate

The following table summarizes the performance of UYVY to IYUV for different configurations, as generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table uyvy2iyuv Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9
8 pixel operation (150 MHz)	1.7

UYVY to RGBA

The uyvy2rgba function converts a UYVY (YUV 4:2:2) single-channel image to a 4-channel RGBA image. UYVY is sub sampled format, 1set of UYVY value gives 2 RGBA pixel values. UYVY is represented in 16-bit values where as RGBA is represented in 32-bit values.

API Syntax

template<int SRC_T, int DST_T, int ROWS, int COLS, int NPC=1>
void uyvy2rgba(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src, xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table uyvy2rgba Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 16-bit, unsigned, 1-channel is supported (XF_16UC1).
DST_T	Output pixel type. Only 8-bit, unsigned, 1-channel is supported (XF_8UC1).
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. Must be a multiple of 8 for 8 pixel mode.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
_src	Input image of size (ROWS, COLS).
_dst	Output image of size (ROWS, COLS).

Resource Utilization

The following table summarizes the resource utilization of UYVY to RGBA for different configurations, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a HD (1080x1920) image.

Table uyvy2rgba Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	6	773	704	160

Performance Estimate

The following table summarizes the performance of UYVY to RGBA for different configurations, as generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table uyvy2rgba Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.8

UYVY to NV12

The uyvy2nv12 function converts a UYVY (YUV 4:2:2) single-channel image to NV12 format. The outputs are separate Y and UV planes. UYVY is sub sampled format, 1 set of UYVY value gives 2 Y values and 1 U and V value each.

API Syntax

template<int SRC_T, int Y_T, int UV_T, int ROWS, int COLS, int NPC=1, int NPC_UV=1>
void uyvy2nv12(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src,xf::cv::Mat<Y_T, ROWS, COLS, NPC> & _y_image,xf::cv::Mat<UV_T, ROWS/2, COLS/2, NPC_UV> & _uv_image)

Parameter Descriptions

The following table describes the template and the function parameters.

Table uyvy2nv12 Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 16-bit, unsigned, 1-channel is supported (XF_16UC1).
Y_T	Output pixel type. Only 8-bit, unsigned, 1-channel is supported (XF_8UC1).
UV_T	Output UV image pixel type. Only 8-bit, unsigned, 2-channel is supported (XF_8UC2).
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. Must be a multiple of 8 for 8 pixel mode.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
NPC_UV	Number of UV image Pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC4 for 1 pixel and 8 pixel operations respectively.
_src	Input image of size (ROWS, COLS).
_y_image	Output Y plane of size (ROWS, COLS).
_uv_image	Output U plane of size (ROWS/2, COLS/2).

Resource Utilization

The following table summarizes the resource utilization of UYVY to NV12 for different configurations, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a HD (1080x1920) image.

Table uyvy2nv12 Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	0	831	488	131
8 Pixel	150	0	0	1235	677	168

Performance Estimate

The following table summarizes the performance of UYVY to NV12 for different configurations, as generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table uyvy2nv12 Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9
8 pixel operation (150 MHz)	1.7

IYUV to RGBA/RGB

The iyuv2rgba function converts single channel IYUV (YUV 4:2:0) image to a 4-channel RGBA image and iyuv2rgb function converts single channel IYUV (YUV 4:2:0) image to a 3-channel RGB image . The inputs to the function are separate Y, U, and V planes. IYUV is sub sampled format, U and V values are sampled once for 2 rows and 2 columns of the RGBA/RGB pixels. The data of the consecutive rows of size (columns/2) is combined to form a single row of size (columns).

API Syntax

template<int SRC_T, int DST_T, int ROWS, int COLS, int NPC=1>
void iyuv2rgba(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & src_y, xf::cv::Mat<SRC_T, ROWS/4, COLS, NPC> & src_u,xf::cv::Mat<SRC_T, ROWS/4, COLS, NPC> & src_v, xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst0)

template<int SRC_T, int DST_T, int ROWS, int COLS, int NPC=1>
void iyuv2rgb(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & src_y, xf::cv::Mat<SRC_T, ROWS/4, COLS, NPC> & src_u,xf::cv::Mat<SRC_T, ROWS/4, COLS, NPC> & src_v, xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst0)

Parameter Descriptions

The following table describes the template and the function parameters.

Table iyuv2(rgba/rgb) Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 1-channel is supported (XF_8UC1).
DST_T	Output pixel type. Only 8-bit, unsigned, 4(RGBA) and 3(RGB)-channel are supported (XF_8UC4 and XF_8UC3).
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. Must be a multiple of 8 for 8 pixel mode.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
src_y	Input Y plane of size (ROWS, COLS).
src_u	Input U plane of size (ROWS/4, COLS).
src_v	Input V plane of size (ROWS/4, COLS).
_dst0	Output RGBA image of size (ROWS, COLS).

Resource Utilization

The following table summarizes the resource utilization of IYUV to RGBA/RGB for different configurations, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a HD (1080x1920) image.

Table iyuv2(rgba/rgb) Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	2	5	1208	728	196

Performance Estimate

The following table summarizes the performance of IYUV to RGBA/RGB for different configurations, as generated using the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table iyuv2(rgba/rgb) Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9

IYUV to NV12

The iyuv2nv12 function converts single channel IYUV image to NV12 format. The inputs are separate U and V planes. There is no need of processing Y plane as both the formats have a same Y plane. U and V values are rearranged from plane interleaved to pixel interleaved.

API Syntax

template<int SRC_T, int UV_T, int ROWS, int COLS, int NPC =1, int NPC_UV=1>
void iyuv2nv12(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & src_y, xf::cv::Mat<SRC_T, ROWS/4, COLS, NPC> & src_u,xf::cv::Mat<SRC_T, ROWS/4, COLS, NPC> & src_v,xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _y_image, xf::cv::Mat<UV_T, ROWS/2, COLS/2, NPC_UV> & _uv_image)

Parameter Descriptions

The following table describes the template and the function parameters.

Table iyuv2nv12 Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 1-channel is supported (XF_8UC1).
UV_T	Output pixel type. Only 8-bit, unsigned, 2-channel is supported (XF_8UC2).
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. Must be a multiple of 8 for 8 pixel mode.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
NPC_UV	Number of UV Pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC4 for 1 pixel and 4-pixel operations respectively.
src_y	Input Y plane of size (ROWS, COLS).
src_u	Input U plane of size (ROWS/4, COLS).
src_v	Input V plane of size (ROWS/4, COLS).
_y_image	Output V plane of size (ROWS, COLS).
_uv_image	Output UV plane of size (ROWS/2, COLS/2).

Resource Utilization

The following table summarizes the resource utilization of IYUV to NV12 for different configurations, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a HD (1080x1920) image..

Table iyuv2nv12 Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	12	907	677	158
8 Pixel	150	0	12	1591	1022	235

Performance Estimate

The following table summarizes the performance of IYUV to NV12 for different configurations, as generated using the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table iyuv2nv12 Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9
8 pixel operation (150 MHz)	1.7

IYUV to YUV4

The iyuv2yuv4 function converts a single channel IYUV image to a YUV444 format. Y plane is same for both the formats. The inputs are separate U and V planes of IYUV image and the outputs are separate U and V planes of YUV4 image. IYUV stores subsampled U,V values. YUV format stores U and V values for every pixel. The same U, V values are duplicated for 2 rows and 2 columns (2x2) pixels in order to get the required data in the YUV444 format.

API Syntax

template<int SRC_T, int ROWS, int COLS, int NPC=1>
void iyuv2yuv4(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & src_y, xf::cv::Mat<SRC_T, ROWS/4, COLS, NPC> & src_u,xf::cv::Mat<SRC_T, ROWS/4, COLS, NPC> & src_v,xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _y_image, xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _u_image, xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _v_image)

Parameter Descriptions

The following table describes the template and the function parameters.

Table iyuv2yuv4 Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 1-channel is supported (XF_8UC1).
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. Must be a multiple of 8, for 8 pixel mode.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
src_y	Input Y plane of size (ROWS, COLS).
src_u	Input U plane of size (ROWS/4, COLS).
src_v	Input V plane of size (ROWS/4, COLS).
_y_image	Output Y image of size (ROWS, COLS).
_u_image	Output U image of size (ROWS, COLS).
_v_image	Output V image of size (ROWS, COLS).

Resource Utilization

The following table summarizes the resource utilization of IYUV to YUV4 for different configurations, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a HD (1080x1920) image.

Table iyuv2yuv4 Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	0	1398	870	232
8 Pixel	150	0	0	2134	1214	304

Performance Estimate

The following table summarizes the performance of IYUV to YUV4 for different configurations, as generated using the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table iyuv2yuv4 Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	13.8
8 pixel operation (150 MHz)	3.4

NV12 to IYUV

The nv122iyuv function converts NV12 format to IYUV format. The function inputs the interleaved UV plane and the outputs are separate U and V planes. There is no need of processing the Y plane as both the formats have a same Y plane. U and V values are rearranged from pixel interleaved to plane interleaved.

API Syntax

template<int SRC_T, int UV_T, int ROWS, int COLS, int NPC=1, int NPC_UV=1>
void nv122iyuv(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & src_y, xf::cv::Mat<UV_T, ROWS/2, COLS/2, NPC_UV> & src_uv,xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _y_image,xf::cv::Mat<SRC_T, ROWS/4, COLS, NPC> & _u_image,xf::cv::Mat<SRC_T, ROWS/4, COLS, NPC> & _v_image)

Parameter Descriptions

The following table describes the template and the function parameters.

Table nv122iyuv Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 1-channel is supported (XF_8UC1).
UV_T	Input pixel type. Only 8-bit, unsigned, 2-channel is supported (XF_8UC2).
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image (must be a multiple of 8, for 8 pixel mode).
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
NPC_UV	Number of UV image Pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC4 for 1 pixel and 4-pixel operations respectively.
src_y	Input Y plane of size (ROWS, COLS).
src_uv	Input UV plane of size (ROWS/2, COLS/2).
_y_image	Output Y plane of size (ROWS, COLS).
_u_image	Output U plane of size (ROWS/4, COLS).
_v_image	Output V plane of size (ROWS/4, COLS).

Resource Utilization

The following table summarizes the resource utilization of NV12 to IYUV for different configurations, as generated in the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a HD (1080x1920) image.

Table nv122iyuv Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	1	1344	717	208
8 Pixel	150	0	1	1961	1000	263

Performance Estimate

The following table summarizes the performance of NV12 to IYUV for different configurations, as generated using the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table nv122iyuv Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9
8 pixel operation (150 MHz)	1.7

NV12 to RGBA

The nv122rgba function converts NV12 image format to a 4-channel RGBA image. The inputs to the function are separate Y and UV planes. NV12 holds sub sampled data, Y plane is sampled at unit rate and 1 U and 1 V value each for every 2x2 Y values. To generate the RGBA data, each U and V value is duplicated (2x2) times.

API Syntax

template<int SRC_T, int UV_T, int DST_T, int ROWS, int COLS, int NPC=1>
void nv122rgba(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & src_y,xf::cv::Mat<UV_T, ROWS/2, COLS/2, NPC> & src_uv,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst0)

Parameter Descriptions

The following table describes the template and the function parameters.

Table nv122rgba Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 1-channel is supported (XF_8UC1).
UV_T	Input pixel type. Only 8-bit, unsigned, 2-channel is supported (XF_8UC2).
DST_T	Output pixel type. Only 8-bit,unsigned,4channel is supported (XF_8UC4).
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. Must be a multiple of 8, for 8 pixel mode.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
src_y	Input Y plane of size (ROWS, COLS).
src_uv	Input UV plane of size (ROWS/2, COLS/2).
_dst0	Output RGBA image of size (ROWS, COLS).

Resource Utilization

The following table summarizes the resource utilization of NV12 to RGBA for different configurations, as generated in the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a HD (1080x1920) image.

Table nv122rgba Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	2	5	1191	708	195

Performance Estimate

The following table summarizes the performance of NV12 to RGBA for different configurations, as generated using the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table nv122rgba Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9

NV12 to YUV4

The nv122yuv4 function converts a NV12 image format to a YUV444 format. The function outputs separate U and V planes. Y plane is same for both the image formats. The UV planes are duplicated 2x2 times to represent one U plane and V plane of the YUV444 image format.

API Syntax

template<int SRC_T,int UV_T, int ROWS, int COLS, int NPC=1, int NPC_UV=1>
void nv122yuv4(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & src_y, xf::cv::Mat<UV_T, ROWS/2, COLS/2, NPC_UV> & src_uv,xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _y_image, xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _u_image,xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _v_image)

Parameter Descriptions

The following table describes the template and the function parameters.

Table nv122yuv4 Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 1-channel is supported (XF_8UC1).
UV_T	Input pixel type. Only 8-bit, unsigned, 2-channel is supported (XF_8UC2).
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image (must be a multiple of 8, for 8 pixel mode).
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
NPC_UV	Number of UV image Pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC4 for 1 pixel and 4-pixel operations respectively.
src_y	Input Y plane of size (ROWS, COLS).
src_uv	Input UV plane of size (ROWS/2, COLS/2).
_y_image	Output Y plane of size (ROWS, COLS).
_u_image	Output U plane of size (ROWS, COLS).
_v_image	Output V plane of size (ROWS, COLS).

Resource Utilization

The following table summarizes the resource utilization of NV12 to YUV4 for different configurations, as generated in the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a HD (1080x1920) image.

Table nv122yuv4 Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	0	1383	832	230
8 Pixel	150	0	0	1772	1034	259

Performance Estimate

The following table summarizes the performance of NV12 to YUV4 for different configurations, as generated using the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table nv122yuv4 Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	13.8
8 pixel operation (150 MHz)	3.4

NV21 to IYUV

The nv212iyuv function converts a NV21 image format to an IYUV image format. The input to the function is the interleaved VU plane only and the outputs are separate U and V planes. There is no need of processing Y plane as both the formats have same the Y plane. U and V values are rearranged from pixel interleaved to plane interleaved.

API Syntax

template<int SRC_T, int UV_T, int ROWS, int COLS, int NPC=1,int NPC_UV=1>
void nv212iyuv(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & src_y, xf::cv::Mat<UV_T, ROWS/2, COLS/2, NPC_UV> & src_uv,xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _y_image, xf::cv::Mat<SRC_T, ROWS/4, COLS, NPC> & _u_image,xf::cv::Mat<SRC_T, ROWS/4, COLS, NPC> & _v_image)

Parameter Descriptions

The following table describes the template and the function parameters.

Table nv212iyuv Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 1-channel is supported (XF_8UC1).
UV_T	Input pixel type. Only 8-bit, unsigned, 2-channel is supported (XF_8UC2).
ROWS	Maximum height of input and output image .
COLS	Maximum width of input and output image. Must be a multiple of 8, for 8 pixel mode.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
NPC_UV	Number of UV image Pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC4 for 1 pixel and 4-pixel operations respectively.
src_y	Input Y plane of size (ROWS, COLS).
src_uv	Input UV plane of size (ROWS/2, COLS/2).
_y_image	Output Y plane of size (ROWS, COLS).
_u_image	Output U plane of size (ROWS/4, COLS).
_v_image	Output V plane of size (ROWS/4, COLS).

Resource Utilization

The following table summarizes the resource utilization of NV21 to IYUV for different configurations, as generated in the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a HD (1080x1920) image.

Table nv212iyuv Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	1	1377	730	219
8 Pixel	150	0	1	1975	1012	279

Performance Estimate

The following table summarizes the performance of NV21 to IYUV for different configurations, as generated using the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table nv212iyuv Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9
8 pixel operation (150 MHz)	1.7

NV21 to RGBA

The nv212rgba function converts a NV21 image format to a 4-channel RGBA image. The inputs to the function are separate Y and VU planes. NV21 holds sub sampled data, Y plane is sampled at unit rate and one U and one V value each for every 2x2 Yvalues. To generate the RGBA data, each U and V value is duplicated (2x2) times.

API Syntax

template<int SRC_T, int UV_T, int DST_T, int ROWS, int COLS, int NPC=1>
void nv212rgba(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & src_y, xf::cv::Mat<UV_T, ROWS/2, COLS/2, NPC> & src_uv,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst0)

Parameter Descriptions

The following table describes the template and the function parameters.

Table nv212rgba Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 1-channel is supported (XF_8UC1).
UV_T	Input pixel type. Only 8-bit, unsigned, 2-channel is supported (XF_8UC2).
DST_T	Output pixel type. Only 8-bit, unsigned, 4-channel is supported (XF_8UC4).
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. Must be a multiple of 8, incase of 8 pixel mode.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
src_y	Input Y plane of size (ROWS, COLS).
src_uv	Input UV plane of size (ROWS/2, COLS/2).
_dst0	Output RGBA image of size (ROWS, COLS).

Resource Utilization

The following table summarizes the resource utilization of NV21 to RGBA for different configurations, as generated in the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a HD (1080x1920) image.

Table nv212rgba Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	2	5	1170	673	183

Performance Estimate

The following table summarizes the performance of NV12 to RGBA for different configurations, as generated using the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table nv212rgba Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9

NV21 to YUV4

The nv212yuv4 function converts an image in the NV21 format to a YUV444 format. The function outputs separate U and V planes. Y plane is same for both formats. The UV planes are duplicated 2x2 times to represent one U plane and V plane of YUV444 format.

API Syntax

template<int SRC_T, int UV_T, int ROWS, int COLS, int NPC=1,int NPC_UV=1>
void nv212yuv4(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & src_y, xf::cv::Mat<UV_T, ROWS/2, COLS/2, NPC_UV> & src_uv, xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _y_image, xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _u_image, xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _v_image)

Parameter Descriptions

The following table describes the template and the function parameters.

Table nv212yuv4 Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 1-channel is supported (XF_8UC1).
UV_T	Input pixel type. Only 8-bit, unsigned, 2-channel is supported (XF_8UC2).
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image (must be a multiple of 8, for 8 pixel mode).
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
NPC_UV	Number of UV image Pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC4 for 1 pixel and 4-pixel operations respectively.
src_y	Input Y plane of size (ROWS, COLS).
src_uv	Input UV plane of size (ROWS/2, COLS/2).
_y_image	Output Y plane of size (ROWS, COLS).
_u_image	Output U plane of size (ROWS, COLS).
_v_image	Output V plane of size (ROWS, COLS).

Resource Utilization

The following table summarizes the resource utilization of NV21 to YUV4 for different configurations, as generated in the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a HD (1080x1920) image.

Table nv212yuv4 Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	0	1383	817	233
8 Pixel	150	0	0	1887	1087	287

Performance Estimate

The following table summarizes the performance of NV21 to YUV4 for different configurations, as generated using the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table nv212yuv4 Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	13.8
8 pixel operation (150 MHz)	3.5

RGB to GRAY

The rgb2gray function converts a 3-channel RGB image to GRAY format.

Y= 0.299*R+0.587*G+0.114*B

Where,

• Y = Gray pixel

• R= Red channel

• G= Green channel

• B= Blue channel

API Syntax

template<int SRC_T, int DST_T, int ROWS, int COLS, int NPC=1>
void rgb2gray(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src, xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table RGB2GRAY Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 3-channel is supported (XF_8UC3).
DST_T	Output pixel type. Only 8-bit, unsigned, 1-channel is supported (XF_8UC1)
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image.
NPC	Number of pixels to be processed per cycle.
_src	RGB input image
_dst	GRAY output image

Resource Utilization

The following table summarizes the resource utilization of RGB to GRAY for different configurations, as generated in the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a HD (1080x1920) image.

Table RGB2GRAY Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT
1 Pixel	300	0	3	439	280

Performance Estimate

The following table summarizes the performance of RGB to GRAY for different configurations, as generated using the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1, to process a HD (1080x1920) image.

Table RGB2GRAY Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9

BGR to GRAY

The bgr2gray function converts a 3-channel BGR image to GRAY format.

Y= 0.299*R+0.587*G+0.114*B

Where,

• Y = Gray pixel

• R= Red channel

• G= Green channel

• B= Blue channel

API Syntax

template<int SRC_T, int DST_T, int ROWS, int COLS, int NPC=1>
void bgr2gray(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src, xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table bgr2gray Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 3-channel is supported (XF_8UC3).
DST_T	Output pixel type. Only 8-bit, unsigned,1-channel is supported (XF_8UC1).
ROWS	Maximum height of input and output image. Must be multiple of 8.
COLS	Maximum width of input and output image. Must be multiple of 8.
NPC	Number of pixels to be processed per cycle.
_src	BGR input image
_dst	GRAY output image

Resource Utilization

The following table summarizes the resource utilization of BGR to GRAY for different configurations, as generated in the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a HD (1080x1920) image.

Table bgr2gray Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT
1 Pixel	300	0	3	439	280

Performance Estimate

The following table summarizes the performance of BGR to GRAY for different configurations, as generated using the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table bgr2gray Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9

GRAY to RGB

The gray2rgb function converts a gray intensity image to RGB color format.

R<-Y, G<-Y, B<-Y

• Y = Gray pixel

• R= Red channel

• G= Green channel

• B= Blue channel

API Syntax

template<int SRC_T,int DST_T,int ROWS,int COLS,int NPC=1>void gray2rgb(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table gray2rgb Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 1-channel is supported (XF_8UC1).
DST_T	Output pixel type. Only 8-bit, unsigned, 3-channel is supported (XF_8UC3).
ROWS	Maximum height of input and output image. Must be multiple of 8.
COLS	Maximum width of input and output image. Must be multiple of 8.
NPC	Number of pixels to be processed per cycle.
_src	GRAY input image.
_dst	RGB output image.

Resource Utilization

The following table summarizes the resource utilization of gray2rgb for different configurations, as generated in the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a HD (1080x1920) image.

Table gray2rgb Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT
1 Pixel	300	0	0	156	184

Performance Estimate

The following table summarizes the performance of gray2rgb for different configurations, as generated using the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table gray2rgb Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9

GRAY to BGR

The gray2bgr function converts a gray intensity image to RGB color format.

R<-Y, G<-Y, B<-Y

Where,

• Y = Gray pixel

• R= Red channel

• G= Green channel

• B= Blue channel

API Syntax

template<int SRC_T,int DST_T,int ROWS,int COLS,int NPC=1>
void gray2bgr(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table gray2bgr Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 1-channel is supported (XF_8UC1).
DST_T	Output pixel type. Only 8-bit, unsigned, 3-channel is supported (XF_8UC3).
ROWS	Maximum height of input and output image. Must be multiple of 8.
COLS	Maximum width of input and output image. Must be multiple of 8.
NPC	Number of pixels to be processed per cycle;
_src	GRAY input image.
_dst	BGR output image.

Resource Utilization

The following table summarizes the resource utilization of gray2bgr for different configurations, as generated in the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a HD (1080x1920) image.

Table gray2bgr Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT
1 Pixel	300	0	0	156	184

Performance Estimate

The following table summarizes the performance of gray2bgr for different configurations, as generated using the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1, to process a HD (1080x1920) image.

Table gray2bgr Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9

HLS to RGB/BGR

The hls2(rgb/bgr) function converts HLS color space to 3-channel RGB/BGR image.

API Syntax

template<int SRC_T,int DST_T,int ROWS,int COLS,int NPC=1>void hls2rgb(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst)template<int SRC_T,int DST_T,int ROWS,int COLS,int NPC=1>void hls2bgr(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table HLS2RGB/BGR Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 3-channel is supported (XF_8UC3).
DST_T	Output pixel type. Only 8-bit, unsigned, 3-channel is supported (XF_8UC3).
ROWS	Maximum height of input and output image. Must be multiple of 8.
COLS	Maximum width of input and output image. Must be multiple of 8.
NPC	Number of pixels to be processed per cycle.
_src	HLS input image.
_dst	RGB/BGR output image.

Resource Utilization

The following table summarizes the resource utilization of HLS2RGB/BGRR for different configurations, as generated in the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a HD (1080x1920) image.

Table HLS2RGB/BGR Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT
1 Pixel	300	0	3	4366	3096

Performance Estimate

The following table summarizes the performance of HLS2RGB/BGR for different configurations, as generated using the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1, to process a HD (1080x1920) image.

Table HLS2RGB/BGR Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9

RGB to XYZ

The rgb2xyz function converts a 3-channel RGB image to XYZ color space.

• R= Red channel

• G= Green channel

• B= Blue channel

API Syntax

template<int SRC_T,int DST_T,int ROWS,int COLS,int NPC=1>void rgb2xyz(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table RGB2XYZ Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 3-channel is supported (XF_8UC3).
DST_T	Output pixel type. Only 8-bit, unsigned, 3-channel is supported. (XF_8UC3).
ROWS	Maximum height of input and output image. Must be multiple of 8.
COLS	Maximum width of input and output image. Must be multiple of 8.
NPC	Number of pixels to be processed per cycle.
_src	RGB input image.
_dst	XYZ output image.

Resource Utilization

The following table summarizes the resource utilization of RGB to XYZ for different configurations, as generated in the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a HD (1080x1920) image.

Table RGB2XYZ Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT
1 Pixel	300	0	8	644	380

Performance Estimate

The following table summarizes the performance of RGB to XYZ for different configurations, as generated using the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1, to process a HD (1080x1920) image.

Table RGB2XYZ Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9

BGR to XYZ

The bgr2xyz function converts a 3-channel BGR image to XYZ color space.

• R= Red channel

• G= Green channel

• B= Blue channel

API Syntax

template<int SRC_T,int DST_T,int ROWS,int COLS,int NPC=1>void bgr2xyz(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table RGB2XYZ Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 3-channel is supported (XF_8UC3).
DST_T	Output pixel type. Only 8-bit, unsigned, 3-channel is supported (XF_8UC3).
ROWS	Maximum height of input and output image. Must be a multiple of 8.
COLS	Maximum width of input and output image. Must be a multiple of 8.
NPC	Number of pixels to be processed per cycle.
_src	BGR input image.
_dst	XYZ output image.

Resource Utilization

The following table summarizes the resource utilization of BGR to XYZ for different configurations, as generated in the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a HD (1080x1920) image.

Table BGR2XYZ Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT
1 Pixel	300	0	8	644	380

Performance Estimate

The following table summarizes the performance of BGR to XYZ for different configurations, as generated using the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1, to process a HD (1080x1920) image.

Table BGR2XYZ Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9

RGB/BGR to YCrCb

The (rgb/bgr)2ycrcb function converts a 3-channel RGB image to YCrCb color space.

• Y = 0.299*R + 0.587*G + 0.114*B

• Cr= (R-Y)*0.713+delta

• Cb= (B-Y)*0.564+delta

API Syntax

template<int SRC_T,int DST_T,int ROWS,int COLS,int NPC=1>void rgb2ycrcb(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst)

template<int SRC_T,int DST_T,int ROWS,int COLS,int NPC=1>void bgr2ycrcb(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table RGB/BGR2YCrCb Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 3-channel is supported (XF_8UC3)
DST_T	Output pixel type. Only 8-bit, unsigned, 3-channel is supported (XF_8UC3)
ROWS	Maximum height of input and output image. Must be multiple of 8.
COLS	Maximum width of input and output image. Must be multiple of 8.
NPC	Number of pixels to be processed per cycle
_src	RGB/BGR input image
_dst	YCrCb output image

Resource Utilization

The following table summarizes the resource utilization of RGB/BGR2YCrCb for different configurations, as generated in the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a HD (1080x1920) image.

Table RGB/BGR2YCrCb Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT
1 Pixel	300	0	5	660	500

Performance Estimate

The following table summarizes the performance of RGB/BGR2YCrCb for different configurations, as generated using the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1, to process a HD (1080x1920) image.

Table RGB/BGR2YCrCb Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9

RGB/BGR to HSV

The (rgb/bgr)2hsv function converts a 3-channel RGB image to HSV color space.

API Syntax

template<int SRC_T,int DST_T,int ROWS,int COLS,int NPC=1>void rgb2hsv(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst)

template<int SRC_T,int DST_T,int ROWS,int COLS,int NPC=1> void bgr2hsv(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table RGB/BGR2HSV Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 3-channel is supported (XF_8UC3).
DST_T	Output pixel type. Only 8-bit, unsigned, 3-channel is supported (XF_8UC3).
ROWS	Maximum height of input and output image. Must be multiple of 8.
COLS	Maximum width of input and output image. Must be multiple of 8.
NPC	Number of pixels to be processed per cycle
_src	RGB/BGR input image
_dst	HSV output image

Resource Utilization

The following table summarizes the resource utilization of RGB/BGR2HSV for different configurations, as generated in the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a HD (1080x1920) image.

Table RGB/BGR2HSV Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT
1 Pixel	300	6	8	1582	1274

Performance Estimate

The following table summarizes the performance of RGB/BGR2HSV for different configurations, as generated using the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1, to process a HD (1080x1920) image.

Table RGB/BGR2HSV Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9

RGB/BGR to HLS

The (rgb/bgr)2hls function converts a 3-channel RGB image to HLS color space.

API Syntax

template<int SRC_T,int DST_T,int ROWS,int COLS,int NPC=1>void rgb2hls(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst)template<int SRC_T,int DST_T,int ROWS,int COLS,int NPC=1>void bgr2hls(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table RGB/BGR2HLS Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 3-channel is supported (XF_8UC3).
DST_T	Output pixel type. Only 8-bit, unsigned, 3-channel is supported (XF_8UC3).
ROWS	Maximum height of input and output image. Must be multiple of 8.
COLS	Maximum width of input and output image. Must be multiple of 8.
NPC	Number of pixels to be processed per cycle.
_src	RGB/BGR input image.
_dst	HLS output image.

Resource Utilization

The following table summarizes the resource utilization of RGB/BGR2HLS for different configurations, as generated in the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a HD (1080x1920) image.

Table RGB/BGR2HLS Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT
1 Pixel	300	0	3	4366	3096

Performance Estimate

The following table summarizes the performance of RGB/BGR2HLS for different configurations, as generated using the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1, to process a HD (1080x1920) image.

Table RGB/BGR2HLS Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9

YCrCb to RGB/BGR

The ycrcb2(rgb/bgr) function converts YCrCb color space to 3-channel RGB/BGR image.

Where,

• R= Y+1.403*(Cr-delta)

• G= Y-0.714*(Cr-delta)-0.344*(cb-delta)

• B= Y+1.773+(Cb-delta)

API Syntax

template<int SRC_T,int DST_T,int ROWS,int COLS,int NPC=1>void ycrcb2rgb(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst)

template<int SRC_T,int DST_T,int ROWS,int COLS,int NPC=1>void ycrcb2bgr(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table YCrCb2RGB/BGR Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 3-channel is supported (XF_8UC3).
DST_T	Output pixel type. Only 8-bit, unsigned, 3-channel is supported (XF_8UC3).
ROWS	Maximum height of input and output image. Must be a multiple of 8.
COLS	Maximum width of input and output image. Must be a multiple of 8.
NPC	Number of pixels to be processed per cycle.
_src	YCrCb input image.
_dst	RGB/BGR output image.

Resource Utilization

The following table summarizes the resource utilization of YCrCb2RGB/BGR for different configurations, as generated in the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a HD (1080x1920) image.

Table YCrCb2RGB/BGR Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT
1 Pixel	300	0	4	538	575

Performance Estimate

The following table summarizes the performance of YCrCb2RGB/BGR for different configurations, as generated using the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1, to process a HD (1080x1920) image.

Table YCrCb2RGB/BGR Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9

HSV to RGB/BGR

The hsv2(rgb/bgr) function converts HSV color space to 3-channel RGB/BGR image.

API Syntax

template<int SRC_T,int DST_T,int ROWS,int COLS,int NPC=1>void hsv2rgb(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst)

template<int SRC_T,int DST_T,int ROWS,int COLS,int NPC=1>void hsv2bgr(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table HSV2RGB/BGR Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 3-channel is supported (XF_8UC3)
DST_T	Output pixel type. Only 8-bit, unsigned, 3-channel is supported (XF_8UC3)
ROWS	Maximum height of input and output image. Must be multiple of 8.
COLS	Maximum width of input and output image. Must be multiple of 8.
NPC	Number of pixels to be processed per cycle
_src	HSV input image
_dst	RGB/BGR output image

Resource Utilization

The following table summarizes the resource utilization of HSV2RGB/BGRR for different configurations, as generated in the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a HD (1080x1920) image.

Table HSV2RGB/BGR Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT
1 Pixel	300	0	8	1543	1006

Performance Estimate

The following table summarizes the performance of HSV2RGB/BGR for different configurations, as generated using the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1, to process a HD (1080x1920) image.

Table HSV2RGB/BGR Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9

NV12/NV21 to RGB/ BGR

The nv122rgb/nv122bgr/nv212rgb/nv212bgr converts NV12 image format to a 3-channel RGB/BGR image. The inputs to the function are separate Y and UV planes. NV12 holds sub sampled data, Y plane is sampled at unit rate, and 1 U and 1 V value each for every 2x2 Y values. To generate the RGB data, each U and V value is duplicated (2x2) times.

API Syntax

NV122RGB:

template<int SRC_T,int UV_T,int DST_T,int ROWS,int COLS,int NPC=1,int NPC_UV=1>void nv122rgb(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & src_y,xf::cv::Mat<UV_T, ROWS/2, COLS/2, NPC_UV> & src_uv,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst0)

NV122BGR:

template<int SRC_T,int UV_T,int DST_T,int ROWS,int COLS,int NPC=1,int NPC_UV=1>void nv122bgr(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & src_y,xf::cv::Mat<UV_T, ROWS/2, COLS/2, NPC_UV> & src_uv,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst0)

NV212RGB:

template<int SRC_T,int UV_T,int DST_T,int ROWS,int COLS,int NPC=1,int NPC_UV=1>void nv212rgb(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & src_y,xf::cv::Mat<UV_T, ROWS/2, COLS/2, NPC_UV> & src_uv,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst0)

NV212BGR:

template<int SRC_T,int UV_T,int DST_T,int ROWS,int COLS,int NPC=1,int NPC_UV=1>void nv212bgr(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & src_y, xf::cv::Mat<UV_T, ROWS/2, COLS/2, NPC_UV> & src_uv, xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst0)

Parameter Descriptions

The following table describes the template and the function parameters.

Table Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 8-bit,unsigned, 1-channel is supported (XF_8UC1).
UV_T	Input pixel type. Only 8-bit, unsigned, 2-channel is supported (XF_8UC2).
ROWS	Maximum height of input and output image
COLS	Maximum width of input and output image. Must be a multiple of NPC for N pixel mode.
NPC	Number of Y Pixels to be processed per cycle. Possible options are XF_NPPC1,XF_NPPC2,XF_NPPC4 and XF_NPPC8.
NPC_UV	Number of UV Pixels to be processed per cycle. Possible options are XF_NPPC1,XF_NPPC2 and XF_NPPC4.
src_y	Y input image of size(ROWS, COLS)
src_uv	UV output image of size (ROWS/2, COLS/2).
_dst0	Output UV image of size (ROWS, COLS).

Resource Utilization

The following table summarizes the resource utilization of NV12/NV21 to RGB/ BGR function in Normal mode (1 pixel), as generated in the Vivado HLS 2019.1 tool for the Xilinx xczu9eg-ffvb1156-2-i-es2 FPGA to process a HD (1080x1920) image.

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	2	5	339	289	76

Performance Estimate

The following table summarizes the performance of the kernel in single pixel configuration as generated using Vivado HLS 2018.3 tool for the Xilinx xczu9eg-ffvb1156-2-i-es2 FPGA to process a HD (1080x1920) image.

Table Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9

NV12 to NV21/NV21 to NV12

The nv122nv21/nv212nv12 function converts a NV12 (YUV4:2:0) to NV21 (YUV4:2:0) or vice versa, where 8-bit Y plane followed by an interleaved U/V plane with 2x2 sub-sampling.

API Syntax

NV122NV21:

template<int SRC_Y,int SRC_UV,int ROWS,int COLS,int NPC=1,int NPC_UV=1>
void nv122nv21(xf::cv::Mat<SRC_Y, ROWS, COLS, NPC> & _y,xf::cv::Mat<SRC_UV, ROWS/2, COLS/2, NPC_UV> & _uv,xf::cv::Mat<SRC_Y, ROWS, COLS, NPC> & out_y,xf::cv::Mat<SRC_UV, ROWS/2, COLS/2, NPC_UV> & out_uv)

NV212NV12:

template<int SRC_Y, int SRC_UV, int ROWS, int COLS, int NPC=1,int NPC_UV=1>void nv212nv12(xf::cv::Mat<SRC_Y, ROWS, COLS, NPC> & _y, xf::cv::Mat<SRC_UV, ROWS/2, COLS/2, NPC_UV> & _uv, xf::cv::Mat<SRC_Y, ROWS, COLS, NPC> & out_y, xf::cv::Mat<SRC_UV, ROWS/2, COLS/2, NPC_UV> & out_uv)

Parameter Descriptions

The following table describes the template and the function parameters.

Table Parameter Description

Parameter	Description
SRC_Y	Input Y pixel type. Only 8-bit, unsigned, 1-channel is supported (XF_8UC1)
SRC_UV	Input UV pixel type. Only 8-bit, unsigned, 2-channel is supported (XF_8UC2)
ROWS	Maximum height of input and output image
COLS	Maximum width of input and output image. Must be multiple of N.
NPC_Y	Number of Y pixels to be processed per cycle. Possible options are XF_NPPC1,XF_NPPC2,XF_NPPC4 and XF_NPPC8.
NPC_UV	Number of UV Pixels to be processed per cycle. Possible options are XF_NPPC1,XF_NPPC2 and XF_NPPC4.
_y	Y input image
_uv	UV input image
out_y	Y output image
out_uv	UV output image

Resource Utilization

The following table summarizes the resource utilization of NV122NV21/NV212NV12 function in Normal mode (1-Pixel), as generated in the Vivado HLS 2019.1 tool for the Xilinx xczu9eg-ffvb1156-2-i-es2 FPGA to process a HD (1080x1920) image.

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	0	258	161	61

Performance Estimate

The following table summarizes the performance of the kernel in single pixel configuration as generated using Vivado HLS 2019.1 tool for the Xilinx xczu9eg-ffvb1156-2-i-es2 FPGA to process a HD (1080x1920) image.

Table Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9

NV12/NV21 to UYVY/YUYV

The NV12/NV21 to UYVY/YUYV function converts a NV12/NV21 (YUV4:2:0) image to a single-channel YUYV/UYVY (YUV 4:2:2) image format. YUYV is a sub-sampled format. YUYV/UYVY is represented in 16-bit values whereas, RGB is represented in 24-bit values.

API Syntax

NV122UYVY:

template<int SRC_Y, int SRC_UV, int DST_T, int ROWS, int COLS, int NPC=1,int NPC_UV=1>void nv122uyvy(xf::cv::Mat<SRC_Y, ROWS, COLS, NPC> & _y,xf::cv::Mat<SRC_UV, ROWS/2, COLS/2, NPC_UV> & _uv,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst)

NV122YUYV:

template<int SRC_Y, int SRC_UV, int DST_T, int ROWS, int COLS, int NPC=1,int NPC_UV=1>void nv122yuyv(xf::cv::Mat<SRC_Y, ROWS, COLS, NPC> & _y, xf::cv::Mat<SRC_UV, ROWS/2, COLS/2, NPC_UV> & _uv, xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst)

NV212UYVY:

template<int SRC_Y, int SRC_UV, int DST_T, int ROWS, int COLS, int NPC=1,int NPC_UV=1>void nv212uyvy(xf::cv::Mat<SRC_Y, ROWS, COLS, NPC> & _y, xf::cv::Mat<SRC_UV, ROWS/2, COLS/2, NPC_UV> & _uv,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst)

NV212YUYV:

template<int SRC_Y, int SRC_UV, int DST_T,int ROWS, int COLS, int NPC=1,int NPC_UV=1>void nv212yuyv(xf::cv::Mat<SRC_Y, ROWS, COLS, NPC> & _y, xf::cv::Mat<SRC_UV, ROWS/2, COLS/2, NPC_UV> & _uv, xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table Parameter Description

Parameter	Description
SRC_Y	Input Y image pixel type. Only 8-bit, unsigned, 1-channel is supported (XF_8UC1).
SRC_UV	Input UV image pixel type. Only 8-bit, unsigned, 2-channel is supported (XF_8UC2).
DST_T	Output pixel type. Only 16-bit, unsigned, 1-channel is supported (XF_16UC1).
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. Must be multiple of NPC.
NPC	Number of pixels to be processed per cycle. Possible options are XF_NPPC1,XF_NPPC2,XF_NPPC4 and XF_NPPC8.
NPC_UV	Number of pixels to be processed per cycle. Possible options are XF_NPPC1,XF_NPPC2 and XF_NPPC4.
_y	Y input image
_uv	UV input image
_dst	UYVY/YUYV output image

Resource Utilization

The following table summarizes the resource utilization of NV12/NV21 to UYVY/YUYV function in Normal mode(1-Pixel), as generated in the Vivado HLS 2019.1 tool for the Xilinx xczu9eg-ffvb1156-2-i-es2 FPGA to process a HD (1080x1920) image.

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	1	0	337	201	64

Performance Estimate

The following table summarizes the performance of the kernel in single pixel configuration as generated using Vivado HLS 2019.1 tool for the Xilinx xczu9eg-ffvb1156-2-i-es2 FPGA to process a HD (1080x1920) image.

Table Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9

UYVY/YUYV to RGB/BGR

The yuyv2rgb/yuyv2bgr/uyvy2rgb/uyvy2bgr function converts a single-channel YUYV/UYVY (YUV 4:2:2) image format to a 3- channel RGB/BGR image. YUYV/UYVY is a sub-sampled format, a set of YUYV/UYVY values gives 2 RGB pixel values. YUYV/UYVY is represented in 16-bit values whereas, RGB/BGR is represented in 24-bit values.

API Syntax

YUYV2RGB:

template<int SRC_T,int DST_T,int ROWS,int COLS,int NPC=1>void yuyv2rgb(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst)

YUYV2BGR:

template<int SRC_T,int DST_T,int ROWS,int COLS,int NPC=1>void yuyv2bgr(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst)

UYVY2RGB

template<int SRC_T,int DST_T,int ROWS,int COLS,int NPC=1>void uyvy2rgb(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst)

UYVY2BGR:

template<int SRC_T,int DST_T,int ROWS,int COLS,int NPC=1>void uyvy2bgr(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 16-bit, unsigned,1-channel is supported (XF_16UC1).
DST_T	Output pixel type. Only 8-bit, unsigned, 3-channel is supported (XF_8UC3).
ROWS	Maximum height of input and output image
COLS	Maximum width of input and output image. Must be a multiple of NPC for N pixel mode.
NPC	Number of Y pixels to be processed per cycle. Possible options are XF_NPPC1,XF_NPPC2,XF_NPPC4 and XF_NPPC8.
_src	Input image of size(ROWS, COLS)
_dst	Output image of size (ROWS, COLS).

Resource Utilization

The following table summarizes the resource utilization of UYVY/YUYV to RGB/BGR function in Normal mode(1-Pixel), as generated in the Vivado HLS 2019.1 tool for the Xilinx xczu9eg-ffvb1156-2-i-es2 FPGA to process a HD (1080x1920) image.

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	6	444	486	109

Performance Estimate

The following table summarizes the performance of the kernel in single pixel configuration as generated using Vivado HLS 2019.1 tool for the Xilinx xczu9eg-ffvb1156-2-i-es2 FPGA to process a HD (1080x1920) image.

Table Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9

UYVY to YUYV/ YUYV to UYVY

The yuyv2uyvy/uyvy2yuyv function converts a YUYV (YUV4:2:2) to UYVY (YUV4:2:2) or vice versa, where 8-bit Y plane followed by an interleaved U/V plane with 2x2 sub sampling.

API Syntax

UYVY2YUYV:

template<int SRC_T,int DST_T,int ROWS,int COLS,int NPC=1>void uyvy2yuyv(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & uyvy,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & yuyv)

YUYV2UYVY:

template<int SRC_T,int DST_T,int ROWS,int COLS,int NPC=1>void yuyv2uyvy(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & yuyv,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & uyvy)

Parameter Descriptions

The following table describes the template and the function parameters.

Table Parameter Description

Parameter	Description
SRC_T	Input Y pixel type. Only 16-bit, unsigned, 1-channel is supported (XF_16UC1).
ROWS	Maximum height of input and output image
COLS	Maximum width of input and output image. Must be a multiple of N.
NPC	Number of pixels to be processed per cycle. Possible options are XF_NPPC1,XF_NPPC2,XF_NPPC4 and XF_NPPC8.
yuyv	Input image
uyvy	Output image

Resource Utilization

The following table summarizes the resource utilization of UYVY to YUYV/ YUYV to UYVY function in Normal mode (1 pixel), as generated in the Vivado HLS 2019.1 tool for the Xilinx xczu9eg-ffvb1156-2-i-es2 FPGA.

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	1	368	176	109

Performance Estimate

The following table summarizes the performance of the kernel in single pixel configuration as generated using Vivado HLS 2019.1 tool for the Xilinx xczu9eg-ffvb1156-2-i-es2 FPGA to process a grayscale HD (1080x1920) image.

Table Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9

UYVY/YUYV to NV21

The UYVY/YUYV2NV21 function converts a single-channel YUYV/UYVY (YUV 4:2:2) image format to NV21 (YUV 4:2:0) format. YUYV/UYVY is a sub-sampled format, 1 set of YUYV/UYVY value gives 2 Y values and 1 U and V value each.

API Syntax

UYVY2NV21:

template<int SRC_T,int Y_T,int UV_T,int ROWS,int COLS,int NPC=1,int NPC_UV=1>void uyvy2nv21(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src,xf::cv::Mat<Y_T, ROWS, COLS, NPC> & _y_image,xf::cv::Mat<UV_T, ROWS/2, COLS/2, NPC_UV> & _uv_image)

YUYV2NV21:

template<int SRC_T,int Y_T,int UV_T,int ROWS,int COLS,int NPC=1,int NPC_UV=1>void yuyv2nv21(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src,xf::cv::Mat<Y_T, ROWS, COLS, NPC> & _y_image,xf::cv::Mat<UV_T, ROWS/2, COLS/2, NPC_UV> & _uv_image)

Parameter Descriptions

The following table describes the template and the function parameters.

Table Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 16-bit, unsigned,1-channel is supported (XF_16UC1).
Y_T	Output Y image pixel type. Only 8-bit, unsigned, 1-channel is supported (XF_8UC1).
UV_T	Output UV image pixel type. Only 8-bit, unsigned, 2-channel is supported (XF_8UC2).
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. Must be multiple of NPC.
NPC	Number of pixels to be processed per cycle; Possible options are XF_NPPC1,XF_NPPC2,XF_NPPC4 and XF_NPPC8.
NPC_UV	Number of U, V Pixels to be processed per cycle; Possible options are XF_NPPC1,XF_NPPC2 and XF_NPPC4.
_src	Input image
_y_image	Y Output image
_uv_image	UV Output image

Resource Utilization

The following table summarizes the resource utilization of UYVY/YUYV to NV21 function in Normal mode (1 pixel), as generated in the Vivado HLS 2019.1 tool for the Xilinx xczu9eg-ffvb1156-2-i-es2 FPGA to process a HD (1080x1920) image.

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	0	215	73	42

Performance Estimate

The following table summarizes the performance of the kernel in single pixel configuration as generated using Vivado HLS 2019.1 tool for the Xilinx xczu9eg-ffvb1156-2-i-es2 FPGA to process a HD (1080x1920) image.

Table Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9

RGB/ BGR to NV12/NV21

The rgb2nv12/bgr2nv12/rgb2nv21/bgr2nv21 converts a 3-channel RGB/BGR image to NV12/NV21 (4:2:0) format. The function outputs Y plane and interleaved UV/VU plane separately. NV12/NV21 holds the subsampled data, Y is sampled for every RGB/BGR pixel and U, V are sampled once for 2 rows and 2 columns (2x2) pixels. UV/VU plane is of (rows/2)*(columns/2) size as U and V values are interleaved.

API Syntax

RGB2NV12

template <int SRC_T, int Y_T, int UV_T, int ROWS, int COLS, int NPC=1,int NPC_UV=1>void rgb2nv12(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src, xf::cv::Mat<Y_T, ROWS, COLS, NPC> & _y, xf::cv::Mat<UV_T, ROWS/2, COLS/2, NPC_UV> & _uv)

BGR2NV12

template <int SRC_T, int Y_T, int UV_T, int ROWS, int COLS, int NPC=1,int NPC_UV=1>void bgr2nv12(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src, xf::cv::Mat<Y_T, ROWS, COLS, NPC> & _y, xf::cv::Mat<UV_T, ROWS/2, COLS/2, NPC_UV> & _uv)

RGB2NV21

template <int SRC_T, int Y_T, int UV_T, int ROWS, int COLS, int NPC=1,int NPC_UV=1>void rgb2nv21(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src, xf::cv::Mat<Y_T, ROWS, COLS, NPC> & _y, xf::cv::Mat<UV_T, ROWS/2, COLS/2, NPC_UV> & _uv)

BGR2NV21

template <int SRC_T, int Y_T, int UV_T, int ROWS, int COLS, int NPC=1,int NPC_UV=1>void bgr2nv21(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src, xf::cv::Mat<Y_T, ROWS, COLS, NPC> & _y, xf::cv::Mat<UV_T, ROWS/2, COLS/2, NPC_UV> & _uv)

Parameter Descriptions

The following table describes the template and the function parameters.

Table Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 3-channel is supported (XF_8UC3).
Y_T	Output pixel type. Only 8-bit, unsigned, 1-channel is supported (XF_8UC1).
UV_T	Output pixel type. Only 8-bit, unsigned, 2-channel is supported (XF_8UC2).
ROWS	Maximum height of input and output image
COLS	Maximum width of input and output image. Must be a multiple of NPC for N pixel mode.
NPC	Number of Pixels to be processed per cycle. Possible options are XF_NPPC1,XF_NPPC2,XF_NPPC4 and XF_NPPC8.
NPC_UV	Number of Pixels to be processed per cycle. Possible options are XF_NPPC1,XF_NPPC2 and XF_NPPC4
_src	RGB input image of size(ROWS,COLS)
_y	Output Y image of size (ROWS, COLS).
_uv	Output UV image of size (ROWS/2, COLS/2).

Resource Utilization

The following table summarizes the resource utilization of RGB/BGR to NV12/NV21 function in Normal mode (1-Pixel), as generated in the Vivado HLS 2019.1 tool for the Xilinx xczu9eg-ffvb1156-2-i-es2 FPGA to process a HD (1080x1920) image.

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	9	413	279	66

Performance Estimate

The following table summarizes the performance of the kernel in single pixel configuration as generated using Vivado HLS 2019.1 tool for the Xilinx xczu9eg-ffvb1156-2-i-es2 FPGA to process a HD (1080x1920) image.

Table Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9

BGR to RGB / RGB to BGR

The bgr2rgb/rgb2bgr function converts a 3-channel BGR to RGB format or RGB to BGR format.

API Syntax

template<int SRC_T,int DST_T,int ROWS,int COLS,int NPC=1>void bgr2rgb(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst)

template<int SRC_T,int DST_T,int ROWS,int COLS,int NPC=1>void rgb2bgr(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 3-channel is supported (XF_8UC3).
DST_T	Output pixel type. Only 8-bit, unsigned, 3-channel is supported (XF_8UC3).
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. Must be multiple of N.
NPC	Number of Pixels to be processed per cycle. Possible options are XF_NPPC1,XF_NPPC2,XF_NPPC4 and XF_NPPC8.
_src	BGR/RGB input image
_dst	RGB/BGR output image

Resource Utilization

The following table summarizes the resource utilization of RGB to BGR/ BGR to RGB function in Normal mode (1-Pixel), as generated in the Vivado HLS 2019.1 tool for the Xilinx xczu9eg-ffvb1156-2-i-es2 FPGA.

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	0	317	118	98

Performance Estimate

The following table summarizes the performance of the kernel in single pixel configuration as generated using Vivado HLS 2019.1 tool for the Xilinx xczu9eg-ffvb1156-2-i-es2 FPGA to process a HD (1080x1920) image.

Table Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9

RGB/BGR to UYVY/YUYV

The RGB/BGR to UYVY/YUYV function converts a 3- channel RGB/BGR image to a single-channel YUYV/UYVY (YUV 4:2:2) image format. YUYV is a sub-sampled format, 2 RGBA pixel gives set of YUYV/UYVY values. YUYV/UYVY is represented in 16-bit values whereas, RGB is represented in 24-bit values

API Syntax

RGB to UYVY:

template<int SRC_T,int DST_T,int ROWS,int COLS,int NPC=1>void rgb2uyvy(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst)

RGB to YUYV:

template<int SRC_T,int DST_T,int ROWS,int COLS,int NPC=1>void rgb2yuyv(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst)

BGR to UYVY:

template<int SRC_T,int DST_T,int ROWS,int COLS,int NPC=1>void bgr2uyvy(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst)

BGR to YUYV:

template<int SRC_T,int DST_T,int ROWS,int COLS,int NPC=1>void bgr2yuyv(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 3-channel is supported (XF_8UC3)
DST_T	Output pixel type. Only 16-bit, unsigned, 1-channel is supported (XF_16UC1)
ROWS	Maximum height of input and output image
COLS	Maximum width of input and output image. Must be multiple of NPC.
NPC	Number of pixels to be processed per cycle. Possible options are XF_NPPC1,XF_NPPC2,XF_NPPC4 and XF_NPPC8..
_src	RGB/BGR input image
_dst	UYVY/YUYV output image

Resource Utilization

The following table summarizes the resource utilization of RGB/BGR to UYVY/YUYV function in normal mode(1-Pixel), as generated in the Vivado HLS 2019.1 tool for the Xilinx xczu9eg-ffvb1156-2-i-es2 FPGA.

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	9	249	203	55

Performance Estimate

The following table summarizes the performance of the kernel in single pixel configuration as generated using Vivado HLS 2019.1 tool for the Xilinx xczu9eg-ffvb1156-2-i-es2 FPGA to process a HD (1080x1920) image.

Table Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9

XYZ to RGB/BGR

The xyz2rgb function converts XYZ color space to 3-channel RGB image.

API Syntax

template<int SRC_T,int DST_T,int ROWS,int COLS,int NPC=1>void xyz2rgb(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst)template<int SRC_T,int DST_T,int ROWS,int COLS,int NPC=1>void xyz2bgr(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table XYZ2RGB/BGR Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 3-channel is supported (XF_8UC3).
DST_T	Output pixel type. Only 8-bit, unsigned, 3-channel is supported (XF_8UC3).
ROWS	Maximum height of input and output image. Must be multiple of 8.
COLS	Maximum width of input and output image. Must be multiple of 8.
NPC	Number of pixels to be processed per cycle.
_src	XYZ input image.
_dst	RGB/BGR output image.

Resource Utilization

The following table summarizes the resource utilization of XYZ2RGB/BGR for different configurations, as generated in the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a HD (1080x1920) image.

Table XYZ2RGB/BGR Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT
1 Pixel	300	0	8	639	401

Performance Estimate

The following table summarizes the performance of XYZ2RGB/BGR for different configurations, as generated using the Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1, to process a HD (1080x1920) image.

Table XYZ2RGB/BGR Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9

Color correction matrix

Color correction matrix algorithm converts the input image color format to output image color format using the colorcorrection matrix provided by the user (CCM_TYPE).

API Syntax

template <int CCM_TYPE, int SRC_T, int DST_T, int ROWS, int COLS, int NPC = 1>
void colorcorrectionmatrix(xf::cv::Mat<SRC_T, ROWS, COLS, NPC>& _src_mat,
                           xf::cv::Mat<SRC_T, ROWS, COLS, NPC>& _dst_mat)

Parameter Descriptions

The following table describes template parameters and arguments of the function.

Table colorcorrectionmatrix correction Parameter Description

Parameter	Description
CCM_TYPE	colorcorrection matrix.
SRC_T	Input pixel type. 8/10/12/16-bit unsigned, 3 channel are supported (XF_8UC3, XF_10UC3, XF_12UC3, XF_16UC3).
DST_T	Output pixel type. 8/10/12/16-bit unsigned, 3 channel are supported (XF_8UC3, XF_10UC3, XF_12UC3, XF_16UC3).
ROWS	Maximum height of input and output image
COLS	Maximum width of input and output image. In case of N-pixel parallelism, width should be multiple of N.
NPC	Number of pixels to be processed per cycle; possible options is XF_NPPC1, XF_NPPC2 AND so on
_src_mat	Input image
_dst_mat	Output image

Resource Utilization

The following table summarizes the resource utilization of the kernel in different configurations, generated using Vitis HLS 2020.2 tool, to process a FULL HD image.

Table colorcorrectionmatrix correction Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 pixel-8U	300	0	9	283	254	73
1 pixel-16U	300	0	9	353	239	75

Performance Estimate

The following table summarizes a performance estimate of the kernel in different configurations, as generated using Vitis HLS 2020.2 tool, to process a FULL HD image.

Table colorcorrectionmatrix correction Function Performance Estimate Summary

Operating Mode	Operating Frequency (MHz)	Latency Estimate Max (ms)
1 pixel	300	7
2 pixel	300	3.6

Color Thresholding

The colorthresholding function compares the color space values of the source image with low and high threshold values, and returns either 255 or 0 as the output.

API Syntax

template<int SRC_T,int DST_T,int MAXCOLORS, int ROWS, int COLS,int NPC>
          void colorthresholding(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src_mat,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst_mat,unsigned char low_thresh[MAXCOLORS*3], unsigned char high_thresh[MAXCOLORS*3])

Parameter Descriptions

The table below describes the template and the function parameters.

Parameter	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 3 channel is supported (XF_8UC3).
DST_T	Output pixel type. Only 8-bit, unsigned, 1 channel is supported (XF_8UC1).
MAXCOLORS	Maximum number of color values
ROWS	Maximum height of input and output image
COLS	Maximum width of input and output image. Must be a multiple of 8, for 8 pixel mode.
NPC	Number of pixels to be processed per cycle. Only XF_NPPC1 supported.
_src_mat	Input image
_dst_mat	Thresholded image
low_thresh	Lowest threshold values for the colors
high_thresh	Highest threshold values for the colors

Compare

The Compare function performs the per element comparison of pixels in two corresponding images src1, src2 and stores the result in dst.

dst(x,y)=src1(x,y) CMP_OP src2(x,y)

CMP_OP – a flag specifies correspondence between the pixels.

• XF_CMP_EQ : src1 is equal to src2

• XF_CMP_GT : src1 is greater than src2

• XF_CMP_GE : src1 is greater than or equal to src2

• XF_CMP_LT : src1 is less than src2

• XF_CMP_LE : src1 is less than or equal to src2

• XF_CMP_NE : src1 is unequal to src2

If the comparison result is true, then the corresponding element of dst is set to 255; else it is set to 0.

API Syntax

template<int CMP_OP,  int SRC_T , int ROWS, int COLS, int NPC=1>
void compare(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src1, xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src2, xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table Compare Parameter Description

Parameter	Description
CMP_OP	The flag that specify the relation between the elements needs to be checked
SRC_T	Input Pixel Type. 8-bit, unsigned, 1 channel is supported (XF_8UC1)
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. In case of N-pixel parallelism, width should be multiple of N
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
_src1	First input image
_src2	Second input image
_dst	Output image

Resource Utilization

The following table summarizes the resource utilization of the Compare XF_CMP_NE configuration in Resource optimized (8 pixels) mode and normal mode as generated using Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA.

Table Compare Function Resource Utilization Summary

Name	Resource Utilization
	1 pixel per clock operation	8 pixel per clock operation
	300 MHz	150 MHz
BRAM_18K	0	0
DSP48E	0	0
FF	87	60
LUT	38	84
CLB	16	20

Performance Estimate

The following table summarizes a performance estimate of the kernel in different configurations, generated using Vivado HLS 2019.1 tool for Xczu9eg-ffvb1156-1-i-es1 FPGA to process a grayscale HD (1080x1920) image.

Table Compare Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9
8 pixel operation (150 MHz)	1.7

CompareS

The CompareS function performs the comparison of a pixel in the input image (src1) and the given scalar value scl, and stores the result in dst.

dst(x,y)=src1(x,y) CMP_OP scalar

CMP_OP – a flag specifies correspondence between the pixel and the scalar.

• XF_CMP_EQ : src1 is equal to scl

• XF_CMP_GT : src1 is greater than scl

• XF_CMP_GE : src1 is greater than or equal to scl

• XF_CMP_LT : src1 is less than scl

• XF_CMP_LE : src1 is less than or equal to scl

• XF_CMP_NE : src1 is unequal to scl

If the comparison result is true, then the corresponding element of dst is set to 255, else it is set to 0.

API Syntax

template<int CMP_OP,  int SRC_T , int ROWS, int COLS, int NPC=1>
void compareS(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src1, unsigned char _scl[XF_CHANNELS(SRC_T,NPC)], xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table CompareS Parameter Description

Parameter	Description
CMP_OP	The flag that specifying the relation between the elements to be checked
SRC_T	Input pixel type. 8-bit, unsigned, 1 channel is supported (XF_8UC1).
ROWS	Maximum height of input and output image
COLS	Maximum width of input and output image. In case of N-pixel parallelism, the width should be a multiple of N
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixels operations respectively.
_src1	First input image
_scl	Input scalar value, the size should be number of channels
_dst	Output image

Resource Utilization

The following table summarizes the resource utilization of the CompareS function with XF_CMP_NE configuration in Resource optimized (8 pixels) mode and normal mode as generated using Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA

Table CompareS Function Resource Utilization Summary

Name	Resource Utilization
	1 pixel per clock operation	8 pixel per clock operation
	300 MHz	150 MHz
BRAM_18K	0	0
DSP48E	0	0
FF	93	93
LUT	39	68
CLB	21	28

Performance Estimate

The following table summarizes a performance estimate of the kernel in different configurations, generated using Vivado HLS 2019.1 tool for Xczu9eg-ffvb1156-1-i-es1 FPGA to process a grayscale HD (1080x1920) image.

Table CompareS Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9
8 pixel operation (150 MHz)	1.7

convertScaleAbs

The convertScaleAbs function converts an input image src with optional linear transformation, save the result as image dst.

dst(x,y)= src1(x,y)*scale+shift

API Syntax

template< int SRC_T,int DST_T, int ROWS, int COLS, int NPC = 1>
void convertScaleAbs(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & src1, xf::cv::Mat<DST_T, ROWS, COLS, NPC> & dst,float scale, float shift)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . convertScaleAbs Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 1 channel is supported (XF_8UC1).
DST_T	Output pixel type. Only 8-bit, unsigned, 1 channel is supported (XF_8UC1).
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. In case of N-pixel parallelism, width should be multiple of N.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
src1	Input image
scale	Scale factor
shift	Delta/shift added to scaled value.
dst	Output image

Resource Utilization

The following table summarizes the resource utilization of the convertScaleAbs function in Resource optimized (8 pixel) mode and normal mode as generated using Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA.

Table . convertScaleAbs Function Resource Utilization Summary

Name	Resource Utilization
	1 pixel per clock operation	8 pixel per clock operation
	300 MHz	150 MHz
BRAM_18K	0	0
DSP48E	10	38
FF	949	1971
LUT	1052	1522
CLB	218	382

Performance Estimate

The following table summarizes a performance estimate of the kernel in different configurations, generated using Vivado HLS 2019.1 tool for Xczu9eg-ffvb1156-1-i-es1 FPGA to process a grayscale HD (1080x1920) image…

Table . convertScaleAbs Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9
8 pixel operation (150 MHz)	1.7

Crop

The Crop function extracts the region of interest (ROI) from the input image.

P(X,Y) ≤ P(xi, yi) ≤ P(X’,Y’)

• P(X,Y) - Top left corner of ROI

• P(X’,Y’) - Bottom Right of ROI

API Syntax

template<int SRC_T, int ROWS, int COLS,int ARCH_TYPE=0,int NPC=1>
void crop(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src_mat,xf::cv::Mat<SRC_T, ROWS, COLS, NPC>  &_dst_mat,xf::cv::Rect_<unsigned int> &roi)

Parameter Descriptions

The following table describes the template and the function parameters.

Table Crop Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 1 and 3 channels are supported (XF_8UC1 and XF_8UC3).
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. Must be multiple of 8 for 8-pixel operation.
ARCH_TYPE	Architecture type. 0 resolves to stream implementation and 1 resolves to memory mapped implementation.
NPC	Number of pixels to be processed per cycle. NPC should be power of 2.
_src_mat	Input image
_dst_mat	Output ROI image
roi	ROI is a xf::cv::Rect object that consists of the top left corner of the rectangle along with the height and width of the rectangle.

Resource Utilization

The following table summarizes the resource utilization of crop function in normal mode (NPC=1) for 3 ROIs (480x640, 100x200, 300x300) as generated in the Vivado HLS 2019.1 tool for the Xilinx xczu9eg-ffvb1156-2-i-es2 FPGA.

Table Crop Function Resource Utilization Summary

Name	Resource Utilization
	1 pixel per clock operation	8 pixel per clock operation
	300 MHz	150 MHz
BRAM_18K	6	8
DSP48E	10	10
FF	17482	16995
LUT	16831	15305

Performance Estimate

The following table summarizes a performance estimate of the kernel in different configurations, generated using Vivado HLS 2019.1 tool for Xczu9eg-ffvb1156-1-i-es1 FPGA to process a grayscale HD (1080x1920) image for 3 ROIs (480x640, 100x200, 300x300).

Table Crop Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	1.7
8 pixel operation (150 MHz)	0.6

Multiple ROI Extraction

You can call the xf::cv::crop function multiple times in accel.cpp.

Multiple ROI Extraction Example

void crop_accel(xf::cv::Mat<TYPE, HEIGHT, WIDTH, NPIX> &_src,xf::cv::Mat<TYPE,HEIGHT, WIDTH, NPIX> _dst[NUM_ROI],xf::cv::Rect_<unsigned int> roi[NUM_ROI])

{xf::cv::crop<TYPE, TYPE, HEIGHT, WIDTH, NPIX>(_src, _dst[0],roi[0]); xf::cv::crop<TYPE, TYPE, HEIGHT, WIDTH, NPIX>(_src, _dst[1],roi[1]); xf::cv::crop<TYPE, TYPE, HEIGHT, WIDTH, NPIX>(_src, _dst[2],roi[2]);}

Custom CCA

The custom CCA function takes a binary image as input which contains a fruit on a conveyer belt (black background) and returns the total fruit pixels minus defect, total defect pixels and defect image which has the defects marked as ‘255’. This function is a custom made solution for defect detection in fruit, which ideally works with other pre-processing functions.

The custom CCA algorthm works in two-passes. The first pass includes labelling the background, foreground and defect in forward and reverse raster-scan. The second pass to perform an ‘&’ operation over the forward and reverse partial output data.

API Syntax

template <int HEIGHT, int WIDTH>
void ccaCustom(
uint8_t* in_ptr1,
uint8_t* in_ptr2,
uint8_t* tmp_out_ptr1,
uint8_t* tmp_out_ptr2,
uint8_t* out_ptr,
int& obj_pix,
int& def_pix,
int height,
int width)

Parameter Descriptions

The following table describes the template and the function parameters.

Table accumulate Parameter Description

Parameter	Description
HEIGHT	Maximum height of input and output image.
WIDTH	Maximum width of input and output image.
in_ptr1	Input 8-bit image pointer for forward pass, binary 8-bit image (‘0’ and ‘255’)
in_ptr1	Input 8-bit image pointer for the parallel computation of reverse pass, binary 8-bit image (‘0’ and ‘255’)
tmp_out_ptr1	8-bit pointer to store and read from the temporary buffer in DDR for the forward pass. This memory must be allocated before the kernel call.
tmp_out_ptr2	8-bit pointer to store and read from the temporary buffer in DDR for the reverse pass. This memory must be allocated before the kernel call.
out_ptr	Output 8-bit image pointer for the which contains the defects image. Defect pixels are marked as ‘255’.
obj_pix	output - no. of object/foreground pixels without the count of defect pixels.
def_pix	output - no. of defect pixels in the object/foreground.
height	Height of the input image
Width	Width of the input image

Resource Utilization

The following table summarizes the resource utilization for custom CCA, generated using Vivado HLS 2021.1 tool for the xczu9eg-ffvb1156-1-i-es1, to process a FHD (1080x1920) image.

Table customCCA Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	10	10	11166	7556	1757

The following table summarizes the resource utilization for custom CCA, generated using Vivado HLS 2021.1 tool for the xczu9eg-ffvb1156-1-i-es1, to process a 4K image.

Table 16. customCCA Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	12	10	11199	7804	1748

Performance Estimate

The following table summarizes the performance for custom CCA, as generated using Vivado HLS 2019.1 tool for the xczu9eg-ffvb1156-1-i-es1, to process a FHD (1080x1920) image.

Table 17. customCCA Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	14

Custom Convolution

The filter2D function performs convolution over an image using a user-defined kernel.

Convolution is a mathematical operation on two functions f and g, producing a third function, The third function is typically viewed as a modified version of one of the original functions, that gives the area overlap between the two functions to an extent that one of the original functions is translated.

The filter can be unity gain filter or a non-unity gain filter. The filter must be of type XF_16SP. If the co-efficients are floating point, it must be converted into the Qm.n and provided as the input as well as the shift parameter has to be set with the ‘n’ value. Else, if the input is not of floating point, the filter is provided directly and the shift parameter is set to zero.

API Syntax

template<int BORDER_TYPE,int FILTER_WIDTH,int FILTER_HEIGHT, int SRC_T,int DST_T, int ROWS, int COLS,int NPC=1>
void filter2D(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src_mat,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst_mat,short int filter[FILTER_HEIGHT*FILTER_WIDTH],unsigned char _shift)

Parameter Descriptions

The following table describes the template and the function parameters.

Table filter2D Parameter Description

Parameter	Description
BORDER_TYPE	Border Type supported is XF_BORDER_CONSTANT
FILTER_HEIGHT	Number of rows in the input filter
FILTER_WIDTH	Number of columns in the input filter
SRC_T	Input pixel type. Only 8-bit, unsigned, 1 and 3 channels are supported (XF_8UC1 and XF_8UC3)
DST_T	Output pixel type. 8-bit unsigned single and 3 channels (XF_8UC1, XF_8UC3) and 16-bit signed single and 3 channels (XF_16SC1, XF_16SC3) supported.
ROWS	Maximum height of input and output image
COLS	Maximum width of input and output image. Must be multiple of 8, for 8 pixel mode.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
_src_mat	Input image
_dst_mat	Output image
filter	The input filter of any size, provided the dimensions should be an odd number. The filter co-efficients either a 16-bit value or a 16-bit fixed point equivalent value.
_shift	The filter must be of type XF_16SP. If the co-efficients are floating point, it must be converted into the Qm.n and provided as the input as well as the shift parameter has to be set with the ‘n’ value. Else, if the input is not of floating point, the filter is provided directly and the shift parameter is set to zero.

Resource Utilization

The following table summarizes the resource utilization of the kernel in different configurations, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a grayscale HD (1080x1920) image.

Table filter2D Function Resource Utilization Summary

Operating Mode	Filter Size	Operating Frequency (MHz)	Utilization Estimate
			BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	3x3	300	3	9	1701	1161	269
	5x5	300	5	25	3115	2144	524
8 Pixel	3x3	150	6	72	2783	2768	638
	5x5	150	10	216	3020	4443	1007

The following table summarizes the resource utilization of the kernel in different configurations, generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a 4K 3 Channel image.

Table filter2D Function Resource Utilization Summary

Operating Mode	Filter Size	Operating Frequency (MHz)	Utilization Estimate
			BRAM_18K	DSP_48Es	FF	LUT
1 Pixel	3x3	300	18	27	886	801
8 Pixel	5x5	300	30	75	1793	1445

Performance Estimate

The following table summarizes the performance of the kernel in different configurations, as generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table filter2D Function Performance Estimate Summary

Operating Mode	Operating Frequency (MHz)	Filter Size	Latency Estimate
			Max (ms)
1 pixel	300	3x3	7
	300	5x5	7.1
8 pixel	150	3x3	1.86
	150	5x5	1.86

Delay

In image processing pipelines, it is possible that the inputs to a function with FIFO interfaces are not synchronized. That is, the first data packet for first input might arrive a finite number of clock cycles after the first data packet of the second input. If the function has FIFOs at its interface with insufficient depth, this causes the whole design to stall on hardware. To synchronize the inputs, we provide this function to delay the input packet that arrives early, by a finite number of clock cycles.

API Syntax

template<int MAXDELAY, int SRC_T, int ROWS, int COLS,int NPC=1 >
          void delayMat(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src, xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _dst)

Parameter Descriptions

The table below describes the template and the function parameters.

Parameter	Description
SRC_T	Input and output pixel type
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image (must be a multiple of 8, for 8 pixel operation)
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
MAXDELAY	Maximum delay that the function is to be instantiated for.
_src	Input image
_dst	Output image

Demosaicing

The Demosaicing function converts a single plane Bayer pattern output, from the digital camera sensors to a color image. This function implements an improved bi-linear interpolation technique proposed by Malvar, He, and Cutler.

The above figure shows the Bayer mosaic for color image capture in single-CCD digital cameras.

API Syntax

template<int BFORMAT, int SRC_T, int DST_T, int ROWS, int COLS, int NPC,bool USE_URAM=false>
void demosaicing(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> &src_mat, xf::cv::Mat<DST_T, ROWS, COLS, NPC> &dst_mat)

Parameter Descriptions

The following table describes the template and the function parameters.

Table Demosaicing Parameter Description

Parameter	Description
BFORMAT	Input Bayer pattern. XF_BAYER_BG, XF_BAYER_GB, XF_BAYER_GR, and XF_BAYER_RG are the supported values.
SRC_T	Input pixel type. 8-bit, unsigned,1 channel (XF_8UC1) and 16-bit, unsigned, 1 channel (XF_16UC1) are supported.
DST_T	Output pixel type. 8-bit, unsigned, 4 channel (XF_8UC4) and 16-bit, unsigned, 4 channel (XF_16UC4) are supported.
ROWS	Number of rows in the image being processed.
COLS	Number of columns in the image being processed. Must be multiple of 8, in case of 8 pixel mode.
NPC	Number of pixels to be processed per cycle; single pixel parallelism (XF_NPPC1), two-pixel parallelism (XF_NPPC2) and four-pixel parallelism (XF_NPPC4) are supported. XF_NPPC4 is not supported with XF_16UC1 pixel type.
USE_URAM	Enable to map storage structures to UltraRAM.
_src_mat	Input image
_dst_mat	Output image

. rubric:: Resource Utilization

The following table below shows the resource utilization of the Demosaicing function, generated using Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA.

Table Demosaicing Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	300	8	0	1906	1915	412
2 Pixel	300	8	0	2876	3209	627
4 Pixel	300	8	0	2950	3222	660

The following table shows the resource utilization of the Demosaicing function, generated using Vivado HLS 2019.1 version tool for the xczu7ev-ffvc1156-2-e FPGA.

Table 206. Demosaicing Function Resource Utilization Summary with UltraRAM Enabled

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	URAM	DSP_48Es	FF	LUT	CLB
1 Pixel	300	0	1	0	1366	1399	412

Performance Estimate

The following table shows the performance in different configurations, generated using Vivado HLS 2019.1 tool for Xczu9eg-ffvb1156-1-i-es1 to process a 4K (3840x2160) image.

Table Demosaicing Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	27.82
2 pixel operation (300 MHz)	13.9
4 pixel operation (300 MHz, 8-bit image only)	6.95

Dilate

During a dilation operation, the current pixel intensity is replaced by the maximum value of the intensity in a nxn neighborhood of the current pixel.

API Syntax

template<int BORDER_TYPE, int TYPE, int ROWS, int COLS,int K_SHAPE,int K_ROWS,int K_COLS, int ITERATIONS, int NPC=1>
void dilate (xf::cv::Mat<TYPE, ROWS, COLS, NPC> & _src, xf::cv::Mat<TYPE, ROWS, COLS, NPC> & _dst,unsigned char _kernel[K_ROWS*K_COLS])

Parameter Descriptions

The following table describes the template and the function parameters.

Table dilate Parameter Description

Parameter	Description
BORDER_TYPE	Border Type supported is XF_BORDER_CONSTANT
TYPE	Input and Output pixel type. Only 8-bit, unsigned, 1 and 3 channels are supported (XF_8UC1 and XF_8UC3)
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image (must be multiple of 8, for 8-pixel operation)
K_SHAPE	Shape of the kernel . The supported kernel shapes are RECT, CROSS, and ELLIPSE.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
K_ROWS	Height of the kernel.
K_COLS	Width of the kernel.
ITERATIONS	Number of times the dilation is applied. Currently supporting for Rectangular shape kernel element.
_src_mat	Input image
_dst_mat	Output image
_kernel	Dilation kernel of size K_ROWS * K_COLS.

Resource Utilization

The following table summarizes the resource utilization of the Dilation function with rectangle shape structuring element in 1 pixel operation and 8 pixel operation, generated using Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA for HD (1080X1920) image.

Table dilate Function Resource Utilization Summary

Name	Resource Utilization
	1 pixel per clock operation	8 pixel per clock operation
	300 MHz	150 MHz
BRAM_18K	3	6
DSP48E	0	0
FF	411	657
LUT	392	1249
CLB	96	255

Performance Estimate

The following table summarizes the resource utilization of the Dilation function with rectangle shape structuring element in 1 pixel operation, generated using Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA for 4K 3channel image.

Table dilate Function Resource Utilization Summary

Name	Resource Utilization
	1 pixel per clock operation
	300 MHz
BRAM_18K	18
DSP48E	0
FF	983
LUT	745
CLB	186

The following table summarizes a performance estimate of the Dilation function for Normal Operation (1 pixel) and Resource Optimized (8 pixel) configurations, generated using Vivado HLS 2019.1 tool for Xczu9eg-ffvb1156-1-i-es1 FPGA.

Table dilate Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Min Latency (ms)	Max Latency (ms)
1 pixel operation (300 MHz)	7.0	7.0
8 pixel operation (150 MHz)	1.87	1.87

Distance Transform Feature Matcher

The distance transform is an operator normally only applied to binary images, where in this case the image must be coded as zero and non-zero pixels as a grayscale image. The result of the transform is a graylevel image that looks similar to the input image, except that the graylevel intensities of points inside foreground regions are changed to show the distance to the closest boundary from each point.

This Xilinx implementation applies 3x3 mask, of distance type DIST_L2 (Euclidean distance), with horizontal/vertical shift cost, a = 0.955, and diagonal shift cost b = 1.3693.

Computing the distance takes two passes, forward and backward. During the forward pass, forward mask is applied, and while the backward pass the backward mask is applied over the forward pass data. In this implementation, it is required to pass a cache memory for the kernel to interact (write while forward pass, read while backward pass). The cache memory must be of image dimensions and of type ap_uint<32>.

API Syntax

template <int IN_PTR, int FW_PTR, int ROWS, int COLS, int USE_URAM>
void distanceTransform(ap_uint<IN_PTR>* _src,
                       float* _dst, ap_uint<FW_PTR>* _fw_pass,
                       int rows, int cols)

Parameter Descriptions

The following table describes template paramters and arguments of the function.

Table distance-transform Parameter Description

Parameter	Description
IN_PTR	Input pointer width must be ‘8’.
FW_PTR	Forward pass data pointer width must be ‘32’.
ROWS	Maximum number of rows of the input image that the hardware kernel must be built for.
COLS	Maximum number of columns of the input image that the hardware kernel must be built for.
USE_URAM	Default is ‘0’. Can be set to ‘1’, if the device has URAM support.
_src	Grayscale input image pointer, of ap_uint<8>* type.
_dst	The distance image pointer,of type float*.
_fw_pass	Forward pass pointer, of type ap_uint<32>. This is used as an intermediary cache, between forward and backward passes.
rows	Number of rows in the input image, must be less than ROWS.
cols	Number of cols in the input image, must be less than COLS.

Resource Utilization

The following table summarizes the resource utilization of the kernel in different configurations, generated using Vitis 2020.2 tool, to process a 4K image.

Table distance-transform Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
default	300	22	0	5129	7444	1757

Performance Estimate

The following table summarizes a performance estimate of the kernel in different configurations, as generated using Vitis 2020.2 tool, to process a 4K image.

Table distance-transform Function Performance Estimate Summary

Operating Mode	Operating Frequency (MHz)	Latency Estimate Max (ms)
default	200	86.249

Duplicate

When various functions in a pipeline are implemented by a programmable logic, FIFOs are instantiated between two functions for dataflow processing. When the output from one function is consumed by two functions in a pipeline, the FIFOs need to be duplicated. This function facilitates the duplication process of the FIFOs.

API Syntax

template<int SRC_T, int ROWS, int COLS,int NPC=1>
          void duplicateMat(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src, xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _dst1,xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _dst2)

Parameter Descriptions

The table below describes the template and the function parameters.

Paramete r	Description
SRC_T	Input and output pixel type
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image (must be a multiple of 8, for 8-pixel operation)
NPC	Number of pixels to be processed per cycle. Possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
_src	Input image
_dst1	Duplicate output for _src
_dst2	Duplicate output for _src

Erode

The erode function finds the minimum pixel intensity in the NXN neighborhood of a pixel and replaces the pixel intensity with the minimum value.

API Syntax

template<int BORDER_TYPE, int TYPE, int ROWS, int COLS,int K_SHAPE,int K_ROWS,int K_COLS, int ITERATIONS, int NPC=1>
void erode (xf::cv::Mat<TYPE, ROWS, COLS, NPC> & _src, xf::cv::Mat<TYPE, ROWS, COLS, NPC> & _dst,unsigned char _kernel[K_ROWS*K_COLS])

Parameter Descriptions

The following table describes the template and the function parameters.

Table erode Parameter Description

Parameter	Description
BORDER_TYPE	Border type supported is XF_BORDER_CONSTANT
TYPE	Input and Output pixel type. Only 8-bit, unsigned, 1 and 3 channels are supported (XF_8UC1 and XF_8UC3)
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image (must be multiple of 8, for 8-pixel operation)
K_SHAPE	Shape of the kernel . The supported kernel shapes are RECT,CROSS and ELLIPSE.
K_ROWS	Height of the kernel.
K_COLS	Width of the kernel.
ITERATIONS	Number of times the erosion is applied.Currently supporting for Rectangular shape kernel element.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
_src_mat	Input image
_dst_mat	Output image
_kernel	Erosion kernel of size K_ROWS * K_COLS.

Resource Utilization

The following table summarizes the resource utilization of the Erosion function with rectangular shape structuring element generated using Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA,for FullHD image(1080x1920).

Table erode Function Resource Utilization Summary

Name	Resource Utilization
	1 pixel per clock operation	8 pixel per clock operation
	300 MHz	150 MHz
BRAM_18K	3	6
DSP48E	0	0
FF	411	657
LUT	392	1249
CLB	96	255

The following table summarizes the resource utilization of the Erosion function with rectangular shape structuring element generated using Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA,for 4K image with 3channels.

Table erode Function Resource Utilization Summary

Name	Resource Utilization
	1 pixel per clock operation
	300 MHz
BRAM_18K	18
DSP48E	0
FF	983
LUT	3745
CLB	186

Performance Estimate

The following table summarizes a performance estimate of the Erosion function for Normal Operation (1 pixel) and Resource Optimized (8 pixel) configurations, generated using Vivado HLS 2019.1 tool for Xczu9eg-ffvb1156-1-i-es1 FPGA.

Table erode Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Min Latency (ms)	Max Latency (ms)
1 pixel operation (300 MHz)	7.0	7.0
8 pixel operation (150 MHz)	1.85	1.85

FAST Corner Detection

Features from accelerated segment test (FAST) is a corner detection algorithm, that is faster than most of the other feature detectors.

The fast function picks up a pixel in the image and compares the intensity of 16 pixels in its neighborhood on a circle, called the Bresenham’s circle. If the intensity of 9 contiguous pixels is found to be either more than or less than that of the candidate pixel by a given threshold, then the pixel is declared as a corner. Once the corners are detected, the non-maximal suppression is applied to remove the weaker corners.

This function can be used for both still images and videos. The corners are marked in the image. If the corner is found in a particular location, that location is marked with 255, otherwise it is zero.

API Syntax

template<int NMS,int SRC_T,int ROWS, int COLS,int NPC=1>
void fast(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src_mat,xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _dst_mat,unsigned char _threshold)

Parameter Descriptions

The following table describes the template and the function parameters.

Table fast Parameter Description

Parameter	Description
NMS	If NMS == 1, non-maximum suppression is applied to detected corners (keypoints). The value should be 0 or 1.
SRC_T	Input pixel type. Only 8-bit, unsigned, 1-channel is supported (XF_8UC1)
ROWS	Maximum height of input image.
COLS	Maximum width of input image (must be a multiple of 8, for 8-pixel operation)
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
_src_mat	Input image
_dst_mat	Output image. The corners are marked in the image.
_threshol d	Threshold on the intensity difference between the center pixel and its neighbors. Usually it is taken around 20.

Resource Utilization

The following table summarizes the resource utilization of the kernel for different configurations, generated using Vivado HLS 2019.1 for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a grayscale HD (1080x1920) image with NMS.

Table fast Function Resource Utilization Summary

Name	Resource Utilization
	1 pixel per clock operation	8 pixel per clock operation
	300 MHz	150 MHz
BRAM_18K	10	20
DSP48E	0	0
FF	2695	7310
LUT	3792	20956
CLB	769	3519

Performance Estimate

The following table summarizes the performance of kernel for different configurations, as generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image with non-maximum suppression (NMS).

Table fast Function Performance Estimate Summary

Operating Mode	Operating Frequency (MHz)	Filter Size	Latency Estimate
			Max (ms)
1 pixel	300	3x3	7
8 pixel	150	3x3	1.86

Gaincontrol

The gain control modules improve the overall brightness of the input image. In this module, applying a multiplicative gain (weight) for red and blue channel of the input bayerized image.

API Syntax

template <int BFORMAT, int SRC_T, int ROWS, int COLS, int NPC = 1>
         void gaincontrol(xf::cv::Mat<SRC_T, ROWS, COLS, NPC>& src,
                          xf::cv::Mat<SRC_T, ROWS, COLS, NPC>& dst,
                          unsigned short rgain,
                          unsigned short bgain)

The following table describes the template and the function parameters.

Table gaincontrol Parameter Description

Parameter	Description
BFORMAT	Input Bayer pattern.
SRC_T	Input and Output Pixel Type.
ROWS	Maximum height of input and output image (Must be multiple of NPC)
COLS	Maximum width of input and output image (Must be multiple of NPC)
NPC	Number of Pixels to be processed per cycle.
src	Input Bayer image
dst	Output Bayer image
rgain	gain value for red channel in Q9.7 format
bgain	gain value for red channel in Q9.7 format

Resource Utilization

The following table summarizes the resource utilization of the kernel in different configurations, generated using Vivado HLS 2019.2 tool for the Xilinx xc7vx485t-ffg1157-1 FPGA, to process 4K image.

Table gaincontrol Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	SLICE
1 pixel	300	0	3	233	95	59
2 pixel	300	0	3	235	95	59

Performance Estimate

The following table summarizes a performance estimate of the kernel in different configurations, as generated using Vivado HLS 2019.2 tool for the Xilinx xc7vx485t-ffg1157-1, to process a 4K image.

Operating Mode	Operating Frequency (MHz)	Latency Estimate
		Max (ms)
1 pixel	300	27.7
2 pixel	300	14.2

Extract Exposure Frames

The extractExposureFrames module returns the Shortexposureframe and Longexposureframe from the input frame using the Digital overlap parameter.

API Syntax

template <int SRC_T, int N_ROWS, int MAX_ROWS, int MAX_COLS, int NPPC = XF_NPPC1, int USE_URAM = 0>
         void extractExposureFrames(xf::cv::Mat<SRC_T, MAX_ROWS * 2, MAX_COLS, NPPC>& _hdrSrc,
                                    xf::cv::Mat<SRC_T, MAX_ROWS, MAX_COLS, NPPC>& _lefSrc,
                                    xf::cv::Mat<SRC_T, MAX_ROWS, MAX_COLS, NPPC>& _sefSrc)

The following table describes the template and the function parameters.

Table extractExposureFrames Parameter Description

Parameter	Description
SRC_T	Input and Output Pixel Type.
N_ROWS	Number of Digital overlap rows between SEF and LEF
MAX_ROWS	Maximum height of input and output image (Must be multiple of NPC)
MAX_COLS	Maximum width of input and output image (Must be multiple of NPC)
NPPC	Number of Pixels to be processed per cycle.
USE_URAM	enable to use URAM instead of BRAM in the design.
_hdrSrc	Input HDR image
_lefSrc	Long exposure frame
_sefSrc	Short exposure frame

Resource Utilization

The following table summarizes the resource utilization of the kernel in different configurations, generated using Vivado HLS 2019.2 tool for the Xilinx xc7vx485t-ffg1157-1 FPGA, to process a HD image.

Table extractExposureFrames Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 pixel	300	8	0	408	304	120

Performance Estimate

The following table summarizes a performance estimate of the kernel in different configurations, as generated using Vivado HLS 2019.2 tool for the Xilinx xc7vx485t-ffg1157-1 FPGA, to process a HD image.

Table extractExposureFrames Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Latency Estimate
		Max (ms)
1 pixel	300	14

Flip

The Flip function converts input image into output image which is a horizontal flip or vertical flip or both of input image, based on user input.

API Syntax

template <int PTR_WIDTH, int TYPE, int ROWS, int COLS, int NPC>
void flip(ap_uint<PTR_WIDTH>* SrcPtr,
          ap_uint<PTR_WIDTH>* DstPtr,
          int Rows,
          int Cols,
          int Direction)

The following table describes the template and the function parameters.

Table Flip Parameter Description

Parameter	Description
PTR_WIDTH	Pixel Width of Input and Output Pointer
TYPE	Input and Output Pixel type
ROWS	Maximum height of input and output image (Must be multiple of NPC)
COLS	Maximum width of input and output image (Must be multiple of NPC)
NPC	Number of Pixels to be processed per cycle.
SrcPtr	Input Image pointer.
DstPtr	Output Image pointer.
Rows	Height of the image
Cols	Width of the image
Direction	Direction of flip, possible values are horizontal (0), vertical (1) and both (-1)

Resource Utilization

The following table summarizes the resource utilization in different configurations, generated using Vitis HLS 2021.1 tool for the xczu7ev-ffvc1156-2-e, to process a grayscale 4k (2160x3840) image.

Table flip Resource Utilization Summary

Operating Mode	Direction of flip	Operating Frequency (MHz)	Utilization Estimate
			BRAM_18K	DSP	FF	LUT	URAM
1 Pixel	Horizontal	300	12	5	5888	7787	0
	Vertical	300	12	5	5888	7787	0
	Both	300	12	5	5888	7787	0
4 Pixel	Horizontal	300	16	5	7180	9794	0
	Vertical	300	16	5	7180	9794	0
	Both	300	16	5	7180	9794	0

The following table summarizes the resource utilization in different configurations, generated using Vitis HLS 2021.1 tool for the xczu7ev-ffvc1156-2-e, to process a 4k (2160x3840) 3 channel image.

Table flip Resource Utilization Summary

Operating Mode	Direction of flip	Operating Frequency (MHz)	Utilization Estimate
			BRAM_18K	DSP	FF	LUT	URAM
1 Pixel	Horizontal	300	32	5	6355	9005	0
	Vertical	300	32	5	6355	9005	0
	Both	300	32	5	6355	9005	0
4 Pixel	Horizontal	300	56	5	8798	15409	0
	Vertical	300	56	5	8798	15409	0
	Both	300	56	5	8798	15409	0

Performance Estimate

The following table summarizes the resource utilization in different configurations, generated using Vitis HLS 2021.1 tool for the xczu7ev-ffvc1156-2-e, to process a 4k (2160x3840) 3 channel image.

Table flip Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Latency Estimate
		Max (ms)
1 pixel	300	28.5
4 pixel	300	7.7

Gamma Correction

The gammacorrection modules improves the overall brightness of image. The gamma lookuptable is generated using the gamma value and with following equation.

API Syntax

template <int SRC_T, int DST_T, int ROWS, int COLS, int NPC = 1>
void gammacorrection(xf::cv::Mat<SRC_T, ROWS, COLS, NPC>& src,
                     xf::cv::Mat<DST_T, ROWS, COLS, NPC>& dst,
                     unsigned char lut_table[256 * 3])

The following table describes the template and the function parameters.

Table gammacorrection Parameter Description

Parameter	Description
SRC_T	Input Pixel Type.
DST_T	Output Pixel Type.
ROWS	Maximum height of input and output image (Must be multiple of NPC)
COLS	Maximum width of input and output image (Must be multiple of NPC)
NPC	Number of Pixels to be processed per cycle.
src	Input image
dst	Output image
lut_table	Lookup table for gamma values.first 256 will be R,next 256 values are G gamma and last 256 values are B values

Resource Utilization

The following table summarizes the resource utilization of the kernel in different configurations, generated using Vivado HLS 2019.2 tool for the Xilinx xc7vx485t-ffg1157-1 FPGA, to process a 4K image.

Table gammacorrection Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 pixel	300	3	0	177	360	120

Performance Estimate

The following table summarizes a performance estimate of the kernel in different configurations, as generated using Vivado HLS 2019.2 tool for the Xilinx xc7vx485t-ffg1157-1 FPGA, to process a 4K image.

Table gammacorrection Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Latency Estimate
		Max (ms)
1 pixel	300	27.9
4 pixel	300	7

HDR Merge

HDR Merge module generates the High Dynamic Range (HDR) image from a set of different exposure frames. Usually, image sensors has limited dynamic range and it’s difficult to get HDR image with single image capture. From the sensor, the frames are collected with different exposure times and will get different exposure frames. HDRMerge will generate the HDR frame with those exposure frames. The HDRMerge in RGB domain is complex and expensive interms of latency, because of camera response function. But,in Bayer domain the camera resonse function is linear. The radiance value which passes through the lens of the image sensor is converted into pixel intensity value. The camera response function relates the radiance value to pixel value. The CRF function in

here,

The CRF function f(x) linearly express as

To compute the weight in pixel value domain,

API Syntax

template <int SRC_T, int DST_T, int ROWS, int COLS, int NPC = 1, int NO_EXPS, int W_SIZE>
void Hdrmerge_bayer(xf::cv::Mat<SRC_T, ROWS, COLS, NPC>& _src_mat1,
                xf::cv::Mat<SRC_T, ROWS, COLS, NPC>& _src_mat2,
                xf::cv::Mat<SRC_T, ROWS, COLS, NPC>& _dst_mat,
                short wr_hls[NO_EXPS * NPC * W_SIZE])

The following table describes the template and the function parameters.

Table HDRmerge Parameter Description

Parameter	Description
SRC_T	Input Pixel Type.
DST_T	Output Pixel Type.
ROWS	Maximum height of input and output image (Must be multiple of NPC)
COLS	Maximum width of input and output image (Must be multiple of NPC)
NPC	Number of Pixels to be processed per cycle.
NO_EXPS	Number exposure frames to be merged in the module
W_SIZE	W_SIZE is should be 2 power pixel width.
_src_mat1	Short exposure frame
_src_mat2	Long exposure frame
_dst_mat	Output HDR image
wr_hls	Lookup table for weight values.computing the weights LUT in host side and passing as input to the function.weight values are Q1.15

Resource Utilization

The following table summarizes the resource utilization in different configurations, generated using Vitis HLS 2021.1 tool for the xczu9eg-ffvb1156-2-e, to process a bayer HD image.

Table HDRMerge Resource Utilization Summary

Operating Mode	Pixel Type	Operating Frequency (MHz)	Utilization Estimate
			BRAM_18K	DSP	FF	LUT	CLB
1 Pixel	8bit	300	2	8	5824	4886	1079
	10bit	300	2	8	5826	4919	1034

Performance Estimate

The following table summarizes the latency numbers in different configurations, generated using Vitis HLS 2021.1 tool for the xczu9eg-ffvb1156-2-e, to process a HD image.

Table HDRMerge Latency Summary

Operating Mode	Operating Frequency (MHz)	Latency Estimate
		Max (ms)
1 pixel	300	7.3
2 pixel	300	3.7

Gaussian Filter

The GaussianBlur function applies Gaussian blur on the input image. Gaussian filtering is done by convolving each point in the input image with a Gaussian kernel.

Where , are the mean values and , are the variances in x and y directions respectively. In the GaussianBlur function, values of , are considered as zeroes and the values of , are equal.

API Syntax

template<int FILTER_SIZE, int BORDER_TYPE, int SRC_T, int ROWS, int COLS, int NPC =  1>
void GaussianBlur(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & src, xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & dst, float sigma)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . GaussianBlur Parameter Description

Parameter	Description
FILTER_SIZE	Filter size. Filter size of 3 (XF_FILTER_3X3), 5 (XF_FILTER_5X5) and 7 (XF_FILTER_7X7) are supported.
BORDER_TYPE	Border type supported is XF_BORDER_CONSTANT
SRC_T	Input and Output pixel type. Only 8-bit, unsigned, 1 and 3 channels are supported (XF_8UC1 and XF_8UC3)
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image (must be a multiple of 8, for 8-pixel operation)
NPC	Number of pixels to be processed per cycle; possible values are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
src	Input image
dst	Output image
sigma	Standard deviation of Gaussian filter

Resource Utilization

The following table summarizes the resource utilization of the Gaussian Filter in different configurations, generated using Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to progress a grayscale HD (1080x1920) image.

Table . GaussianBlur Function Resource Utilization Summary

Operating Mode	Filter Size	Operating Frequency (MHz)	Utilization Estimate
			BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	3x3	300	3	17	3641	2791	610
	5x5	300	5	27	4461	3544	764
	7x7	250	7	35	4770	4201	894
8 Pixel	3x3	150	6	52	3939	3784	814
	5x5	150	10	111	5688	5639	1133
	7x7	150	14	175	7594	7278	1518

The following table summarizes the resource utilization of the Gaussian Filter in different configurations, generated using Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to progress a 4K 3 Channel image.

Table . GaussianBlur Function Resource Utilization Summary

Operating Mode	Filter Size	Operating Frequency (MHz)	Utilization Estimate
			BRAM_18K	DSP_48Es	LUT	CLB
1 Pixel	3x3	300	18	33	4835	3742
	5x5	300	30	51	5755	3994
	7x7	300	42	135	8086	5422

Performance Estimate

The following table summarizes a performance estimate of the Gaussian Filter in different configurations, as generated using Vivado HLS 2019.1 tool for Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a grayscale HD (1080x1920) image.

Table . GaussianBlur Function Performance Estimate Summary

Operating Mode	Filter Size	Latency Estimate
		Max Latency (ms)
1 pixel operation (300 MHz)	3x3	7.01
	5x5	7.03
	7x7	7.06
8 pixel operation (150 MHz)	3x3	1.6
	5x5	1.7
	7x7	1.74

Gradient Magnitude

The magnitude function computes the magnitude for the images. The input images are x-gradient and y-gradient images of type 16S. The output image is of same type as the input image.

For L1NORM normalization, the magnitude computed image is the pixel-wise added image of absolute of x-gradient and y-gradient, as shown below:.

For L2NORM normalization, the magnitude computed image is as follows:

API Syntax

template< int NORM_TYPE ,int SRC_T,int DST_T, int ROWS, int COLS,int NPC=1>
void magnitude(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src_matx,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _src_maty,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst_mat)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . magnitude Parameter Description

Parameter	Description
NORM_TYPE	Normalization type can be either L1 or L2 norm. Values are XF_L1NORM or XF_L2NORM
SRC_T	Input pixel type. Only 16-bit, signed, 1 channel is supported (XF_16SC1)
DST_T	Output pixel type. Only 16-bit, signed,1 channel is supported (XF_16SC1)
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image (must be multiple of 8, for 8-pixel operation)
NPC	Number of pixels to be processed per cycle; possible values are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
_src_matx	First input, x-gradient image.
_src_maty	Second input, y-gradient image.
_dst_mat	Output, magnitude computed image.

Resource Utilization

The following table summarizes the resource utilization of the kernel in different configurations, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a grayscale HD (1080x1920) image and for L2 normalization.

Table . magnitude Function Resource Utilization Summary

Name	Resource Utilization
	1 pixel per clock operation	8 pixel per clock operation
	300 MHz	150 MHz
BRAM_18K	0	0
DSP48E	2	16
FF	707	2002
LUT	774	3666
CLB	172	737

Performance Estimate

The following table summarizes the performance of the kernel in different configurations, as generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image and for L2 normalization.

Table . magnitude Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	7.2
8 pixel operation (150 MHz)	1.7

Gradient Phase

The phase function computes the polar angles of two images. The input images are x-gradient and y-gradient images of type 16S. The output image is of same type as the input image.

For radians:

For degrees:

API Syntax

template<int RET_TYPE ,int SRC_T,int DST_T, int ROWS, int COLS,int NPC=1 >
void phase(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src_matx,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _src_maty,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst_mat)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . phase Parameter Description

Parameter	Description
RET_TYPE	Output format can be either in radians or degrees. Options are XF_RADIANS or XF_DEGREES. If the XF_RADIANS option is selected, phase API will return result in Q4.12 format. The output range is (0, 2 pi). If the XF_DEGREES option is selected, xFphaseAPI will return result in Q10.6 degrees and output range is (0, 360).
SRC_T	Input pixel type. Only 16-bit, signed, 1 channel is supported (XF_16SC1).
DST_T	Output pixel type. Only 16-bit, signed, 1 channel is supported (XF_16SC1)
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image (must be a multiple of 8, for 8-pixel operation)
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
_src_matx	First input, x-gradient image.
_src_maty	Second input, y-gradient image.
_dst_mat	Output, phase computed image.

Resource Utilization

The following table summarizes the resource utilization of the kernel in different configurations, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a grayscale HD (1080x1920) image.

Table . phase Function Resource Utilization Summary

Name	Resource Utilization
	1 pixel per clock operation	8 pixel per clock operation
	300 MHz	150 MHz
BRAM_18K	6	24
DSP48E	6	19
FF	873	2396
LUT	753	3895
CLB	187	832

Performance Estimate

The following table summarizes the performance of the kernel in different configurations, as generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table . phase Function Performance Estimate Summary

Operating Mode	Operating Frequency (MHz)	Latency Estimate (ms)
1 pixel	300	7.2
8 pixel	150	1.7

Deviation from OpenCV

In phase implementation, the output is returned in a fixed point format. If XF_RADIANS option is selected, phase API will return result in Q4.12 format. The output range is (0, 2 pi). If XF_DEGREES option is selected, phase API will return result in Q10.6 degrees and output range is (0, 360).

Harris Corner Detection

In order to understand Harris Corner Detection, let us consider a grayscale image. Sweep a window w(x,y) (with displacements u in the x-direction and v in the y-direction), I calculates the variation of intensity w(x,y).

Where:

• w(x,y) is the window position at (x,y)

• I(x,y) is the intensity at (x,y)

• I(x+u,y+v) is the intensity at the moved window (x+u,y+v).

Since we are looking for windows with corners, we are looking for windows with a large variation in intensity. Hence, we have to maximize the equation above, specifically the term:

Using Taylor expansion:

Expanding the equation and cancelling I(x,y) with -I(x,y):

The above equation can be expressed in a matrix form as:

So, our equation is now:

A score is calculated for each window, to determine if it can possibly contain a corner:

Where,

• 

• 

API Syntax

Non-Maximum Suppression:

In non-maximum suppression (NMS) if radius = 1, then the bounding box is 2*r+1 = 3.

In this case, consider a 3x3 neighborhood across the center pixel. If the center pixel is greater than the surrounding pixel, then it is considered a corner. The comparison is made with the surrounding pixels, which are within the radius.

Radius = 1

x-1, y-1	x-1, y	x-1, y+1
x, y-1	x, y	x, y+1
x+1, y-1	x+1, y	x+1, y+1

Threshold:

A threshold=442, 3109 and 566 is used for 3x3, 5x5, and 7x7 filters respectively. This threshold is verified over 40 sets of images. The threshold can be varied, based on the application. The corners are marked in the output image. If the corner is found in a particular location, that location is marked with 255, otherwise it is zero.

template<int FILTERSIZE,int BLOCKWIDTH, int NMSRADIUS,int SRC_T,int ROWS, int COLS,int NPC=1,bool USE_URAM=false>
void cornerHarris(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & src,xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & dst,uint16_t threshold, uint16_t k)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . cornerHarris Parameter Description

Parameter	Description
FILTERSIZE	Size of the Sobel filter. 3, 5, and 7 supported.
BLOCKWIDTH	Size of the box filter. 3, 5, and 7 supported.
NMSRADIUS	Radius considered for non-maximum suppression. Values supported are 1 and 2.
TYPE	Input pixel type. Only 8-bit, unsigned, 1-channel is supported (XF_8UC1).
ROWS	Maximum height of input image.
COLS	Maximum width of input image (must be multiple of 8, for 8-pixel operation)
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
USE_URAM	Enable to map some storage structures to URAM
src	Input image
dst	Output image.
threshold	Threshold applied to the corner measure.
k	Harris detector parameter in Q16.16 format.

Resource Utilization

The following table summarizes the resource utilization of the Harris corner detection in different configurations, generated using Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a grayscale HD (1080x1920) image.

The following table summarizes the resource utilization for Sobel Filter = 3, Box filter=3 and NMS_RADIUS =1.

Table . Resource Utilization Summary - For Sobel Filter = 3, Box filter=3 and NMS_RADIUS =1

Name	Resource Utilization
	1 pixel per clock operation	8 pixel per clock operation
	300 MHz	150 MHz
BRAM_18K	33	66
DSP48E	10	80
FF	3254	9330
LUT	3522	13222
CLB	731	2568

The following table summarizes the resource utilization for Sobel Filter = 3, Box filter=5 and NMS_RADIUS =1.

Table . Resource Utilization Summary - Sobel Filter = 3, Box filter=5 and NMS_RADIUS =1

Name	Resource Utilization
	1 pixel per clock operation	8 pixel per clock operation
	300 MHz	150 MHz
BRAM_18K	45	90
DSP48E	10	80
FF	5455	12459
LUT	5695	24594
CLB	1132	4498

The following table summarizes the resource utilization for Sobel Filter = 3, Box filter=7 and NMS_RADIUS =1.

Table . Resource Utilization Summary - Sobel Filter = 3, Box filter=7 and NMS_RADIUS =1

Name	Resource Utilization
	1 pixel per clock operation	8 pixel per clock operation
	300 MHz	150 MHz
BRAM_18K	57	114
DSP48E	10	80
FF	8783	16593
LUT	9157	39813
CLB	1757	6809

The following table summarizes the resource utilization for Sobel Filter = 5, Box filter=3 and NMS_RADIUS =1.

Table . Resource Utilization Summary - Sobel Filter = 5, Box filter=3 and NMS_RADIUS =1

Name	Resource Utilization
	1 pixel per clock operation	8 pixel per clock operation
	300 MHz	150 MHz
BRAM_18K	35	70
DSP48E	10	80
FF	4656	11659
LUT	4681	17394
CLB	1005	3277

The following table summarizes the resource utilization for Sobel Filter = 5, Box filter=5 and NMS_RADIUS =1.

Table. Resource Utilization Summary - Sobel Filter = 5, Box filter=5 and NMS_RADIUS =1

Name	Resource Utilization
	1 pixel per clock operation	8 pixel per clock operation
	300 MHz	150 MHz
BRAM_18K	47	94
DSP48E	10	80
FF	6019	14776
LUT	6337	28795
CLB	1353	5102

The following table summarizes the resource utilization for Sobel Filter = 5, Box filter=7 and NMS_RADIUS =1.

Table . Resource Utilization Summary - Sobel Filter = 5, Box filter=7 and NMS_RADIUS =1

Name	Resource Utilization
	1 pixel per clock operation	8 pixel per clock operation
	300 MHz	150 MHz
BRAM_18K	59	118
DSP48E	10	80
FF	9388	18913
LUT	9414	43070
CLB	1947	7508

The following table summarizes the resource utilization for Sobel Filter = 7, Box filter=3 and NMS_RADIUS =1.

Table . Resource Utilization Summary - Sobel Filter = 7, Box filter=3 and NMS_RADIUS =1

Name	Resource Utilization
	1 pixel per clock operation	8 pixel per clock operation
	300 MHz	150 MHz
BRAM_18K	37	74
DSP48E	11	88
FF	6002	13880
LUT	6337	25573
CLB	1327	4868

The following table summarizes the resource utilization for Sobel Filter = 7, Box filter=5 and NMS_RADIUS =1.

Table . Resource Utilization Summary - Sobel Filter = 7, Box filter=5 and NMS_RADIUS =1

Name	Resource Utilization
	1 pixel per clock operation	8 pixel per clock operation
	300 MHz	150 MHz
BRAM_18K	49	98
DSP48E	11	88
FF	7410	17049
LUT	8076	36509
CLB	1627	6518

The following table summarizes the resource utilization for Sobel Filter = 7, Box filter=7 and NMS_RADIUS =1.

Table . Resource Utilization Summary - Sobel Filter = 7, Box filter=7 and NMS_RADIUS =1

Name	Resource Utilization
	1 pixel per clock operation	8 pixel per clock operation
	300 MHz	150 MHz
BRAM_18K	61	74
DSP48E	11	88
FF	10714	21137
LUT	11500	51331
CLB	2261	8863

The following table summarizes the resource utilization for Sobel Filter = 3, Box filter=3 and NMS_RADIUS =2.

Table . Resource Utilization Summary - Sobel Filter = 3, Box filter=3 and NMS_RADIUS =2

Name	Resource Utilization
	1 pixel	8 pixel
	300 MHz	150 MHz
BRAM_18K	41	82
DSP48E	10	80
FF	5519	10714
LUT	5094	16930
CLB	1076	3127

The following table summarizes the resource utilization for Sobel Filter = 3, Box filter=5 and NMS_RADIUS =2.

Table . Resource Utilization Summary

Name	Resource Utilization
	1 pixel	8 pixel
	300 MHz	150 MHz
BRAM_18K	53	106
DSP48E	10	80
FF	6798	13844
LUT	6866	28286
CLB	1383	4965

The following table summarizes the resource utilization for Sobel Filter = 3, Box filter=7 and NMS_RADIUS =2.

Table . Resource Utilization Summary - Sobel Filter = 3, Box filter=7 and NMS_RADIUS =2

Name	Resource Utilization
	1 pixel	8 pixel
	300 MHz	150 MHz
BRAM_18K	65	130
DSP48E	10	80
FF	10137	17977
LUT	10366	43589
CLB	1940	7440

The following table summarizes the resource utilization for Sobel Filter = 5, Box filter=3 and NMS_RADIUS =2.

Table . Resource Utilization Summary - Sobel Filter = 5, Box filter=3 and NMS_RADIUS =2

Name	Resource Utilization
	1 pixel	8 pixel
	300 MHz	150 MHz
BRAM_18K	43	86
DSP48E	10	80
FF	5957	12930
LUT	5987	21187
CLB	1244	3922

The following table summarizes the resource utilization for Sobel Filter = 5, Box filter=5 and NMS_RADIUS =2.

Table . Resource Utilization Summary - Sobel Filter = 5, Box filter=5 and NMS_RADIUS =2

Name	Resource Utilization
	1 pixel	8 pixel
	300 MHz	150 MHz
BRAM_18K	55	110
DSP48E	10	80
FF	5442	16053
LUT	6561	32377
CLB	1374	5871

The following table summarizes the resource utilization for Sobel Filter = 5, Box filter=7 and NMS_RADIUS =2.

Table . Resource Utilization Summary - Sobel Filter = 5, Box filter=7 and NMS_RADIUS =2

Name	Resource Utilization
	1 pixel	8 pixel
	300 MHz	150 MHz
BRAM_18K	67	134
DSP48E	10	80
FF	10673	20190
LUT	10793	46785
CLB	2260	8013

The following table summarizes the resource utilization for Sobel Filter = 7, Box filter=3 and NMS_RADIUS =2.

Table . Resource Utilization Summary - Sobel Filter = 7, Box filter=3 and NMS_RADIUS =2

Name	Resource Utilization
	1 pixel	8 pixel
	300 MHz	150 MHz
BRAM_18K	45	90
DSP48E	11	88
FF	7341	15161
LUT	7631	29185
CLB	1557	5425

The following table summarizes the resource utilization for Sobel Filter = 7, Box filter=5 and NMS_RADIUS =2.

Table . Resource Utilization Summary - Sobel Filter = 7, Box filter=5 and NMS_RADIUS =2

Name	Resource Utilization
	1 pixel	8 pixel
	300 MHz	150 MHz
BRAM_18K	57	114
DSP48E	11	88
FF	8763	18330
LUT	9368	40116
CLB	1857	7362

The following table summarizes the resource utilization for Sobel Filter = 7, Box filter=7 and NMS_RADIUS =2.

Table . Resource Utilization Summary - Sobel Filter = 7, Box filter=7 and NMS_RADIUS =2

Name	Resource Utilization
	1 pixel	8 pixel
	300 MHz	150 MHz
BRAM_18K	69	138
DSP48E	11	88
FF	12078	22414
LUT	12831	54652
CLB	2499	9628

Resource Utilization with URAM enable

This section summarizes the resource utilization of the Harris corner detection in different configurations, generated using Vivado HLS 2019.1 version tool for the xczu7ev-ffvc1156-2-e FPGA, to process a grayscale 4K (3840X2160) image.

The following table summarizes the resource utilization for Sobel Filter = 3, Box filter=3 and NMS_RADIUS =1.

Table . Resource Utilization Summary - For Sobel Filter = 3, Box filter=3 and NMS_RADIUS =1

Name	Resource Utilization
	1 pixel	8 pixel
	300 MHz	150 MHz
BRAM_18K	12	12
URAM	4	21
DSP48E	10	80
FF	5306	11846
LUT	3696	13846

The following table summarizes the resource utilization for Sobel Filter = 3, Box filter=5 and NMS_RADIUS =1.

Table . Resource Utilization Summary - Sobel Filter = 3, Box filter=5 and NMS_RADIUS =1

Name	Resource Utilization
	1 pixel	8 pixel
	300 MHz	150 MHz
BRAM_18K	12	12
URAM	7	30
DSP48E	10	80
FF	7625	13899
LUT	5596	27136

The following table summarizes the resource utilization for Sobel Filter = 3, Box filter=7 and NMS_RADIUS =1.

Table . Resource Utilization Summary - Sobel Filter = 3, Box filter=7 and NMS_RADIUS =1

Name	Resource Utilization
	1 pixel	8 pixel
	300 MHz	150 MHz
BRAM_18K	12	12
URAM	7	42
DSP48E	10	80
FF	12563	19919
LUT	8816	39087

The following table summarizes the resource utilization for Sobel Filter = 5, Box filter=3 and NMS_RADIUS =1.

Table 251. Resource Utilization Summary - Sobel Filter = 5, Box filter=3 and NMS_RADIUS =1

Name	Resource Utilization
	1 pixel	8 pixel
	300 MHz	150 MHz
BRAM_18K	12	12
URAM	4	23
DSP48E	10	80
FF	6689	15022
LUT	4506	18719

The following table summarizes the resource utilization for Sobel Filter = 5, Box filter=5 and NMS_RADIUS =1.

Table 252. Resource Utilization Summary - Sobel Filter = 5, Box filter=5 and NMS_RADIUS =1

Name	Resource Utilization
	1 pixel	8 pixel
	300 MHz	150 MHz
BRAM_18K	12	12
URAM	7	32
DSP48E	10	80
FF	9050	17063
LUT	6405	31992

The following table summarizes the resource utilization for Sobel Filter = 5, Box filter=7 and NMS_RADIUS =1.

Table 253. Resource Utilization Summary - Sobel Filter = 5, Box filter=7 and NMS_RADIUS =1

Name	Resource Utilization
	1 pixel	8 pixel
	300 MHz	150 MHz
BRAM_18K	12	12
URAM	7	44
DSP48E	10	80
FF	13946	23116
LUT	9626	44738

The following table summarizes the resource utilization for Sobel Filter = 7, Box filter=3 and NMS_RADIUS =1.

Table 254. Resource Utilization Summary - Sobel Filter = 7, Box filter=3 and NMS_RADIUS =1

Name	Resource Utilization
	1 pixel	8 pixel
	300 MHz	150 MHz
BRAM_18K	12	12
URAM	4	25
DSP48E	11	88
FF	8338	17378
LUT	6151	24844

The following table summarizes the resource utilization for Sobel Filter = 7, Box filter=5 and NMS_RADIUS =1.

Table 255. Resource Utilization Summary - Sobel Filter = 7, Box filter=5 and NMS_RADIUS =1

Name	Resource Utilization
	1 pixel	8 pixel
	300 MHz	150 MHz
BRAM_18K	12	12
URAM	7	34
DSP48E	11	88
FF	10497	19457
LUT	7858	39762

The following table summarizes the resource utilization for Sobel Filter = 7, Box filter=7 and NMS_RADIUS =1.

Table 256. Resource Utilization Summary - Sobel Filter = 7, Box filter=7 and NMS_RADIUS =1

Name	Resource Utilization
	1 pixel	8 pixel
	300 MHz	150 MHz
BRAM_18K	12	12
URAM	7	46
DSP48E	11	88
FF	15393	25450
LUT	11080	50662

The following table summarizes the resource utilization for Sobel Filter = 3, Box filter=3 and NMS_RADIUS =2.

Table 257. Resource Utilization Summary - Sobel Filter = 3, Box filter=3 and NMS_RADIUS =2

Name	Resource Utilization
	1 pixel	8 pixel
	300 MHz	150 MHz
BRAM_18K	20	20
URAM	4	21
DSP48E	10	80
FF	6286	13441
LUT	4704	18072

The following table summarizes the resource utilization for Sobel Filter = 3, Box filter=5 and NMS_RADIUS =2.

Table 258. Resource Utilization Summary

Name	Resource Utilization
	1 pixel	8 pixel
	300 MHz	150 MHz
BRAM_18K	20	20
URAM	7	30
DSP48E	10	80
FF	8626	15498
LUT	6606	31371

The following table summarizes the resource utilization for Sobel Filter = 3, Box filter=7 and NMS_RADIUS =2.

Table 259. Resource Utilization Summary - Sobel Filter = 3, Box filter=7 and NMS_RADIUS =2

Name	Resource Utilization
	1 pixel	8 pixel
	300 MHz	150 MHz
BRAM_18K	20	20
URAM	7	42
DSP48E	10	80
FF	13543	21522
LUT	9853	43301

The following table summarizes the resource utilization for Sobel Filter = 5, Box filter=3 and NMS_RADIUS =2.

Table 260. Resource Utilization Summary - Sobel Filter = 5, Box filter=3 and NMS_RADIUS =2

Name	Resource Utilization
	1 pixel	8 pixel
	300 MHz	150 MHz
BRAM_18K	20	20
URAM	4	23
DSP48E	10	80
FF	7670	16750
LUT	5513	22854

The following table summarizes the resource utilization for Sobel Filter = 5, Box filter=5 and NMS_RADIUS =2.

Table 261. Resource Utilization Summary - Sobel Filter = 5, Box filter=5 and NMS_RADIUS =2

Name	Resource Utilization
	1 pixel	8 pixel
	300 MHz	150 MHz
BRAM_18K	20	20
URAM	7	32
DSP48E	10	80
FF	9712	18793
LUT	7338	36136

The following table summarizes the resource utilization for Sobel Filter = 5, Box filter=7 and NMS_RADIUS =2.

Table 262. Resource Utilization Summary - Sobel Filter = 5, Box filter=7 and NMS_RADIUS =2

Name	Resource Utilization
	1 pixel	8 pixel
	300 MHz	150 MHz
BRAM_18K	20	20
URAM	7	44
DSP48E	10	80
FF	14650	24846
LUT	10558	48866

The following table summarizes the resource utilization for Sobel Filter = 7, Box filter=3 and NMS_RADIUS =2.

Table 263. Resource Utilization Summary - Sobel Filter = 7, Box filter=3 and NMS_RADIUS =2

Name	Resource Utilization
	1 pixel	8 pixel
	300 MHz	150 MHz
BRAM_18K	20	20
URAM	4	25
DSP48E	11	88
FF	9562	19101
LUT	7405	29986

The following table summarizes the resource utilization for Sobel Filter = 7, Box filter=5 and NMS_RADIUS =2.

Table 264. Resource Utilization Summary - Sobel Filter = 7, Box filter=5 and NMS_RADIUS =2

Name	Resource Utilization
	1 pixel	8 pixel
	300 MHz	150 MHz
BRAM_18K	20	20
URAM	7	34
DSP48E	11	88
FF	11751	21180
LUT	9254	44024

The following table summarizes the resource utilization for Sobel Filter = 7, Box filter=7 and NMS_RADIUS =2.

Table 265. Resource Utilization Summary - Sobel Filter = 7, Box filter=7 and NMS_RADIUS =2

Name	Resource Utilization
	1 pixel	8 pixel
	300 MHz	150 MHz
BRAM_18K	20	20
URAM	7	46
DSP48E	11	88
FF	16723	27156
LUT	12474	54858

Performance Estimate

The following table summarizes a performance estimate of the Harris corner detection in different configurations, as generated using Vivado HLS 2019.1 tool for Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a grayscale HD (1080x1920) image.

Table 266. cornerHarris Function Performance Estimate Summary

Operating Mode	Operating Frequency (MHz)	Configuration			Latency Estimate
		Sobel	Box	NMS Radius	Latency(In ms)
1 pixel	300 MHz	3	3	1	7
1 pixel	300 MHz	3	5	1	7.1
1 pixel	300 MHz	3	7	1	7.1
1 pixel	300 MHz	5	3	1	7.2
1 pixel	300 MHz	5	5	1	7.2
1 pixel	300 MHz	5	7	1	7.2
1 pixel	300 MHz	7	3	1	7.22
1 pixel	300 MHz	7	5	1	7.22
1 pixel	300 MHz	7	7	1	7.22
8 pixel	150 MHz	3	3	1	1.7
8 pixel	150 MHz	3	5	1	1.7
8 pixel	150 MHz	3	7	1	1.7
8 pixel	150 MHz	5	3	1	1.71
8 pixel	150 MHz	5	5	1	1.71
8 pixel	150 MHz	5	7	1	1.71
8 pixel	150 MHz	7	3	1	1.8
8 pixel	150 MHz	7	5	1	1.8
8 pixel	150 MHz	7	7	1	1.8
1 pixel	300 MHz	3	3	2	7.1
1 pixel	300 MHz	3	5	2	7.1
1 pixel	300 MHz	3	7	2	7.1
1 pixel	300 MHz	5	3	2	7.21
1 pixel	300 MHz	5	5	2	7.21
1 pixel	300 MHz	5	7	2	7.21
1 pixel	300 MHz	7	3	2	7.22
1 pixel	300 MHz	7	5	2	7.22
1 pixel	300 MHz	7	7	2	7.22
8 pixel	150 MHz	3	3	2	1.8
8 pixel	150 MHz	3	5	2	1.8
8 pixel	150 MHz	3	7	2	1.8
8 pixel	150 MHz	5	3	2	1.81
8 pixel	150 MHz	5	5	2	1.81
8 pixel	150 MHz	5	7	2	1.81
8 pixel	150 MHz	7	3	2	1.9
8 pixel	150 MHz	7	5	2	1.91
8 pixel	150 MHz	7	7	2	1.92

Deviation from OpenCV

In Vitis Vision, thresholding and NMS are included, but in OpenCV they are not included. In Vitis Vision, all the blocks are implemented in fixed point. Whereas, in OpenCV, all the blocks are implemented in floating point.

Histogram Computation

The calcHist function computes the histogram of given input image.

Where, H is the array of 256 elements.

API Syntax

template<int SRC_T,int ROWS, int COLS,int NPC=1>
void calcHist(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src, uint32_t *histogram)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . calcHist Parameter Description

Parameter	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 1 and 3 channels are supported (XF_8UC1 and XF_8UC3)
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image (must be multiple of 8, for 8-pixel operation)
NPC	Number of pixels to be processed per cycle
_src	Input image
histogram	Output array of 256 elements

Resource Utilization

The following table summarizes the resource utilization of the calcHist function for Normal Operation (1 pixel) and Resource Optimized (8 pixel) configurations, generated using Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA at 300 MHz for 1 pixel case and at 150 MHz for 8 pixel mode.

Table . calcHist Function Resource Utilization Summary

Name	Resource Utilization
	Normal Operation (1 pixel)	Resource Optimized (8 pixel)
BRAM_18K	2	16
DSP48E	0	0
FF	196	274
LUT	240	912
CLB	57	231

The following table summarizes the resource utilization of the calcHist function for Normal Operation (1 pixel), generated using Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA at 300 MHz for 1 pixel case for 4K image 3 channel.

Table . calcHist Function Resource Utilization Summary

Name	Resource Utilization
	Normal Operation (1 pixel)
BRAM_18K	8
DSP48E	0
FF	381
LUT	614
CLB	134

Performance Estimate

The following table summarizes a performance estimate of the calcHist function for Normal Operation (1 pixel) and Resource Optimized (8 pixel) configurations, generated using Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA at 300 MHz for 1 pixel and 150 MHz for 8 pixel mode.

Table . calcHist Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9
8 pixel operation (150 MHz)	1.7

Histogram Equalization

The equalizeHist function performs histogram equalization on input image or video. It improves the contrast in the image, to stretch out the intensity range. This function maps one distribution (histogram) to another distribution (a wider and more uniform distribution of intensity values), so the intensities are spread over the whole range.

For histogram H[i], the cumulative distribution H’[i] is given as:

The intensities in the equalized image are computed as:

API Syntax

template<int SRC_T, int ROWS, int COLS, int NPC = 1>
void equalizeHist(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src,xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src1,xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . equalizeHist Parameter Description

Parameter	Description
SRC_T	Input and output pixel type. Only 8-bit, unsigned, 1 channel is supported (XF_8UC1)
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image (must be a multiple of 8, for 8-pixel operation)
NPC	Number of pixels to be processed per cycle
_src	Input image
_src1	Input image
_dst	Output image

Resource Utilization

The following table summarizes the resource utilization of the equalizeHist function for Normal Operation (1 pixel) and Resource Optimized (8 pixel) configurations, generated using Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA at 300 MHz for 1 pixel and 150 MHz for 8 pixel mode.

Table . equalizeHist Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 pixel	300	4	5	3492	1807	666
8 pixel	150	25	5	3526	2645	835

Performance Estimate

The following table summarizes a performance estimate of the equalizeHist function for Normal Operation (1 pixel) and Resource Optimized (8 pixel) configurations, generated using Vivado HLS 2019.1version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA at 300 MHz for 1 pixel and 150 MHz for 8 pixel mode.

Table . equalizeHist Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max (ms)
1 pixel per clock operation	13.8
8 pixel per clock operation	3.4

HOG

The Histogram of Oriented Gradients (HOG) is a feature descriptor used in computer vision for the purpose of object detection. The feature descriptors produced from this approach is widely used in the pedestrian detection.

The technique counts the occurrences of gradient orientation in localized portions of an image. HOG is computed over a dense grid of uniformly spaced cells and normalized over overlapping blocks, for improved accuracy. The concept behind HOG is that the object appearance and shape within an image can be described by the distribution of intensity gradients or edge direction.

Both RGB and gray inputs are accepted to the function. In the RGB mode, gradients are computed for each plane separately, but the one with the higher magnitude is selected. With the configurations provided, the window dimensions are 64x128, block dimensions are 16x16.

API Syntax

template<int WIN_HEIGHT, int WIN_WIDTH, int WIN_STRIDE, int BLOCK_HEIGHT, int BLOCK_WIDTH, int CELL_HEIGHT, int CELL_WIDTH, int NOB, int DESC_SIZE, int IMG_COLOR, int OUTPUT_VARIANT, int SRC_T, int DST_T, int ROWS, int COLS, int NPC = XF_NPPC1,bool USE_URAM=false>
void HOGDescriptor(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> &_in_mat, xf::cv::Mat<DST_T, 1, DESC_SIZE, NPC> &_desc_mat);

Parameter Descriptions

The following table describes the template parameters.

Table . HOGDescriptor Template .. rubric:: Parameter Descriptions

Parameters	Description
WIN_HEIGHT	The number of pixel rows in the window. This must be a multiple of 8 and should not exceed the number of image rows.
WIN_WIDTH	The number of pixel cols in the window. This must be a multiple of 8 and should not exceed the number of image columns.
WIN_STRIDE	The pixel stride between two adjacent windows. It is fixed at 8.
BLOCK_HEIGHT	Height of the block. It is fixed at 16.
BLOCK_WIDTH	Width of the block. It is fixed at 16.
CELL_HEIGHT	Number of rows in a cell. It is fixed at 8.
CELL_WIDTH	Number of cols in a cell. It is fixed at 8.
NOB	Number of histogram bins for a cell. It is fixed at 9
DESC_SIZE	The size of the output descriptor.
IMG_COLOR	The type of the image, set as either XF_GRAY or XF_RGB
OUTPUT_VARIE NT	Must be either XF_HOG_RB or XF_HOG_NRB
SRC_T	Input pixel type. Must be either XF_8UC1 or XF_8UC4, for gray and color respectively.
DST_T	Output descriptor type. Must be XF_32UC1.
ROWS	Number of rows in the image being processed.
COLS	Number of columns in the image being processed.
NPC	Number of pixels to be processed per cycle; this function supports only XF_NPPC1 or 1 pixel per cycle operations.
USE_URAM	Enable to map UltraRAM instead of BRAM for some storage structures.

The following table describes the function parameters.

Table . HOGDescriptor Parameter Description

Parameters	Description
_in_mat	Input image, of xf::cv::Mat type
_desc_mat	Output descriptors, of xf::cv::Mat type

Where,

• NO is normal operation (single pixel processing)

• RB is repetitive blocks (descriptor data are written window wise)

• NRB is non-repetitive blocks (descriptor data are written block wise, in order to reduce the number of writes).

Note: In the RB mode, the block data is written to the memory taking the overlap windows into consideration. In the NRB mode, the block data is written directly to the output stream without consideration of the window overlap. In the host side, the overlap must be taken care.

Resource Utilization

The following table shows the resource utilization of HOGDescriptor function for normal operation (1 pixel) mode as generated in Vivado HLS 2019.1 version tool for the part Xczu9eg-ffvb1156-1-i-es1 at 300 MHz to process an image of 1920x1080 resolution.

Table . HOGDescriptor Function Resource Utilization Summary

Resource	Utilization (at 300 MHz) of 1 pixel operation
	NRB		RB
	Gray	RGB	Gray	RGB
BRAM_18K	43	49	171	177
DSP48E	34	46	36	48
FF	15365	15823	15205	15663
LUT	12868	13267	13443	13848

The following table shows the resource utilization of HOGDescriptor function for normal operation (1 pixel) mode as generated in Vivado HLS 2019.1 version tool for the part xczu7ev-ffvc1156-2-e at 300 MHz to process an image of 1920x1080 resolution with UltraRAM enabled.

Table . HOGDescriptor Function Resource Utilization Summary with UltraRAM enabled

Resource	Utilization (at 300 MHz) of 1 pixel operation
	NRB		RB
	Gray	RGB	Gray	RGB
BRAM_18K	10	12	18	20
URAM	15	15	15	17
DSP48E	34	46	36	48
FF	17285	17917	18270	18871
LUT	12409	12861	12793	13961

Performance Estimate

The following table shows the performance estimates of HOGDescriptor() function for different configurations as generated in Vivado HLS 2019.1 version tool for the part Xczu9eg-ffvb1156-1-i-es1 to process an image of 1920x1080p resolution.

Table . HOGDescriptor Function Performance Estimate Summary

Operating Mode	Operating Frequency (MHz)	Latency Estimate
		Min (ms)	Max (ms)
NRB-Gray	300	6.98	8.83
NRB-RGBA	300	6.98	8.83
RB-Gray	300	176.81	177
RB-RGBA	300	176.81	177

Deviations from OpenCV

Listed below are the deviations from the OpenCV:

1. Border care

   The border care that OpenCV has taken in the gradient computation is BORDER_REFLECT_101, in which the border padding will be the neighboring pixels’ reflection. Whereas, in the Xilinx implementation, BORDER_CONSTANT (zero padding) was used for the border care.

2. Gaussian weighing

   The Gaussian weights are multiplied on the pixels over the block, that is a block has 256 pixels, and each position of the block are multiplied with its corresponding Gaussian weights. Whereas, in the HLS implementation, gaussian weighing was not performed.

3. Cell-wise interpolation The magnitude values of the pixels are distributed across different cells in the blocks but on the corresponding bins. Pixels in the region 1 belong only to its corresponding cells, but the pixels in region 2 and 3 are interpolated to the adjacent 2 cells and 4 cells respectively. This operation was not performed in the HLS implementation.

4. Output handling

   The output of the OpenCV will be in the column major form. In the HLS implementation, output will be in the row major form. Also, the feature vector will be in the fixed point type Q0.16 in the HLS implementation, while in the OpenCV it will be in floating point.

Limitations

1. The configurations are limited to Dalal’s implementation

2. Image height and image width must be a multiple of cell height and cell width respectively.

HoughLines

The HoughLines function here is equivalent to HoughLines Standard in OpenCV. The HoughLines function is used to detect straight lines in a binary image. To apply the Hough transform, edge detection preprocessing is required. The input to the Hough transform is an edge detected binary image. For each point (xi,yi) in a binary image, we define a family of lines that go through the point as:

rho= xi cos(theta) + yi sin(theta)

Each pair of (rho,theta) represents a line that passes through the point (xi,yi). These (rho,theta) pairs of this family of lines passing through the point form a sinusoidal curve in (rho,theta) plane. If the sinusoids of N different points intersect in the (rho,theta) plane, then that intersection (rho1, theta1) represents the line that passes through these N points. In the HoughLines function, an accumulator is used to keep the count (also called voting) of all the intersection points in the (rho,theta) plane. After voting, the function filters spurious lines by performing thinning, that is, checking if the center vote value is greater than the neighborhood votes and threshold, then making that center vote as valid and other wise making it zero. Finally, the function returns the desired maximum number of lines (LINESMAX) in (rho,theta) form as output.

The design assumes the origin at the center of the image i.e at (Floor(COLS/2), Floor(ROWS/2)). The ranges of rho and theta are:

theta = [0, pi)

rho=[-DIAG/2, DIAG/2), where DIAG = cvRound{SquareRoot( (COLS*COLS) + (ROWS*ROWS))}

For ease of use, the input angles THETA, MINTHETA and MAXTHETA are taken in degrees, while the output theta is in radians. The angle resolution THETA is declared as an integer, but treated as a value in Q6.1 format (that is, THETA=3 signifies that the resolution used in the function is 1.5 degrees). When the output (rho, ? theta) is used for drawing lines, you should be aware of the fact that origin is at the center of the image.

API Syntax

template<unsigned int RHO,unsigned int THETA,int MAXLINES,int DIAG,int MINTHETA,int MAXTHETA,int SRC_T, int ROWS, int COLS,int NPC>

void HoughLines(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src_mat,float outputrho[MAXLINES],float outputtheta[MAXLINES],short threshold,short linesmax)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . HoughLines Parameter Description

Parameter	Description
RHO	Distance resolution of the accumulator in pixels.
THETA	Angle resolution of the accumulator in degrees and Q6.1 format.
MAXLINES	Maximum number of lines to be detected
MINTHETA	Minimum angle in degrees to check lines.
MAXTHETA	Maximum angle in degrees to check lines
DIAG	Diagonal of the image. It should be cvRound(sqrt(rows*rows + cols*cols)/RHO).
SRC_T	Input Pixel Type. Only 8-bit, unsigned, 1-channel is supported (XF_8UC1).
ROWS	Maximum height of input image
COLS	Maximum width of input image
NPC	Number of Pixels to be processed per cycle; Only single pixel supported XF_NPPC1.
_src_mat	Input image should be 8-bit, single-channel binary image.
outputrho	Output array of rho values. rho is the distance from the coordinate origin (center of the image).
outputthe ta	Output array of theta values. Theta is the line rotation angle in radians.
threshold	Accumulator threshold parameter. Only those lines are returned that get enough votes (>threshold).
linesmax	Maximum number of lines.

Resource Utilization

The table below shows the resource utilization of the kernel for different configurations, generated using Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 to process a grayscale HD (1080x1920) image for 512 lines.

Table . Houghlines Function Resource Utilization Summary

Name	Resource Utilization
	THETA=1, RHO=1
BRAM_18K	542
DSP48E	10
FF	60648
LUT	56131

Performance Estimate

The following table shows the performance of kernel for different configurations, generated using Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 to process a grayscale HD (1080x1920) image for 512 lines.

Table . Houghlines Function Performance Estimate Summary

Operating Mode	Operating Frequency (MHz)	Latency Estimate
		Max (ms)
THETA=1, RHO=1	300	12.5

Preprocessing for Deep Neural Networks

The input image are typically pre-processed before being fed for inference of different deep neural networks (DNNs). The preProcess function provides various modes to perform various preprocessing operations. The preprocessing function\(\ f(x\)) can be described using below equations.

The preProcess function supports operating modes presented in the below table:

Op Code	Operation	Description
0		Mean subtraction
1		Scale and clip
2		Clipping
3		Scale and bias
4		Scale and bias with mean subtraction
5		Complete operation

API Syntax

template <int INPUT_PTR_WIDTH_T,int OUTPUT_PTR_WIDTH_T, int T_CHANNELS_T, int CPW_T, int ROWS_T, int COLS_T, int NPC_T, bool PACK_MODE_T, int WX_T, int WA_T, int WB_T, int WY_T, int WO_T, int FX_T, int FA_T, int FB_T, int FY_T,int FO_T, bool SIGNED_IN_T, int OPMODE_T>

void preProcess(hls::stream<ap_uint<INPUT_PTR_WIDTH_T> > &srcStrm, ap_uint<OUTPUT_PTR_WIDTH_T> \*out, float params[3*T_CHANNELS_T], int rows, int cols, int th1, int th2)

The following table describes the template and the function parameters.

Table gammacorrection Parameter Description

Parameter	Description
srcStrm	Input image stream
out	Output pointer
params	Array containing α, β and γ values
rows	Input image height
cols	Input image width
th1	Upper threshold
th2	Lower threshold
INPUT_PTR_WIDTH_T	Width of input pointer
OUTPUT_PTR_WIDTH_T	Width of output pointer
T_CHANNELS_T	Total Channels
CPW_T	Channels Packed per DDR Word
ROWS_T	Max Height of Image
COLS_T	Max Width of Image
NPC_T	Number of pixels processed per clock
PACK_MODE_T	data format (pixel packed or channel packed)
WX_T	x bit width
WA_T	alpha bit width
WB_T	beta bit width
WY_T	Gamma bit width
WO_T	Output bit width
FX_T	Number of integer bits for x
FA_T	Number of integer bits for alpha
FB_T	Number of integer bits for beta
FY_T	Number of integer bits for gamma
FO_T	Number of integer bits for output
SIGNED_IN_T	Signed input flag
OPMODE_T	Operating mode

Resource Utilization

The following table summarizes the resource utilization of preProcess for NPC_T =8, CPW_T=3 and OPMODE=0, for a maximum input image size of 1280x720 pixels. The results are after synthesis in Vitis 2019.2 for the Xilinx xcu200-fsgd2104-2-e FPGA at 300 MHz. Latency for this configuration is 0.7 ms.

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	SLICE
8 pixel	300	0	2	7554	11127	2155

Pyramid Up

The pyrUp function is an image up-sampling algorithm. It first inserts zero rows and zero columns after every input row and column making up to the size of the output image. The output image size is always . The zero padded image is then smoothened using Gaussian image filter. Gaussian filter for the pyramid-up function uses a fixed filter kernel as given below:

However, to make up for the pixel intensity that is reduced due to zero padding, each output pixel is multiplied by 4.

API Syntax

template<int TYPE, int ROWS, int COLS, int NPC>
void pyrUp (xf::cv::Mat<TYPE, ROWS, COLS, NPC> & _src, xf::cv::Mat<TYPE, ROWS, COLS, NPC> & _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . pyrUp Parameter Description

Parameter	Description
TYPE	Input and Output pixel type. Only 8-bit, unsigned, 1 and 3 channels are supported (XF_8UC1 and XF_8UC3)
ROWS	Maximum Height or number of output rows to build the hardware for this kernel
COLS	Maximum Width or number of output columns to build the hardware for this kernel
NPC	Number of pixels to process per cycle. Currently, the kernel supports only 1 pixel per cycle processing (XF_NPPC1).
_src	Input image stream
_dst	Output image stream

Resource Utilization

The following table summarizes the resource utilization of pyrUp for 1 pixel per cycle implementation, for a maximum input image size of 1920x1080 pixels. The results are after synthesis in Vivado HLS 2019.1 for the Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA at 300 MHz.

Table . pyrUp Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		LUTs	FFs	DSPs	BRAMs
1 Pixel	300	1124	1199	0	10

The following table summarizes the resource utilization of pyrUp for 1 pixel per cycle implementation, for a maximum input image size of 4K with BGR. The results are after synthesis in Vivado HLS 2019.1 for the Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA at 300 MHz.

Table . pyrUp Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		LUTs	FFs	DSPs	BRAMs
1 Pixel	300	2074	2176	0	59

Performance Estimate

The following table summarizes performance estimates of pyrUp function on Vivado HLS 2019.1 for the Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA.

Table . pyrUp Function Performance Estimate Summary

Operating Mode	Operating Frequency (MHz)	Input Image Size	Latency Estimate
			Max (ms)
1 pixel	300	1920x1080	27.82

Pyramid Down

The pyrDown function is an image down-sampling algorithm which smoothens the image before down-scaling it. The image is smoothened using a Gaussian filter with the following kernel:

Down-scaling is performed by dropping pixels in the even rows and the even columns. The resulting image size is .

API Syntax

template<int TYPE, int ROWS, int COLS, int NPC,bool USE_URAM=false>
void pyrDown (xf::cv::Mat<TYPE, ROWS, COLS, NPC> & _src, xf::cv::Mat<TYPE, ROWS, COLS, NPC> & _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . pyrDown Parameter Description

Parameter	Description
TYPE	Input and Output pixel type. Only 8-bit, unsigned, 1 and 3 channels are supported (XF_8UC1 and XF_8UC3)
ROWS	Maximum Height or number of input rows to build the hardware for this kernel
COLS	Maximum Width or number of input columns to build the hardware for this kernel
NPC	Number of pixels to process per cycle. Currently, the kernel supports only 1 pixel per cycle processing (XF_NPPC1).
USE_URAM	Enable to map storage structures to UltraRAM
_src	Input image stream
_dst	Output image stream

Resource Utilization

The following table summarizes the resource utilization of pyrDown for 1 pixel per cycle implementation, for a maximum input image size of 1920x1080 pixels. The results are after synthesis in Vivado HLS 2019.1 for the Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA at 300 MHz.

Table . pyrDown Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		LUTs	FFs	DSPs	BRAMs
1 Pixel	300	1171	1238	1	5

The following table summarizes the resource utilization of pyrDown for 1 pixel per cycle implementation, for a maximum input image size of 4K with BGR image. The results are after synthesis in Vivado HLS 2019.1 for the Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA at 300 MHz.

Table . pyrDown Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		LUTs	FFs	DSPs	BRAMs
1 Pixel	300	2158	1983	2	30

The following table summarizes the resource utilization of pyrDown for 1 pixel per cycle implementation, for a maximum input image size of 3840x2160 pixels. The results are after synthesis in Vivado HLS 2019.1 for the Xilinx xczu7eg-ffvb1156-1 FPGA at 300 MHz with UltraRAM enabled.

Table . pyrDown Function Resource Utilization Summary with UltraRAM Enabled

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		LUTs	FFs	DSPs	BRAMs	URAM
1 Pixel	300	1171	1243	0	0	1

Performance Estimate

The following table summarizes performance estimates of pyrDown function in Vivado HLS 2019.1 for the Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA.

Table . pyrDown Function Performance Estimate Summary

Operating Mode	Operating Frequency (MHz)	Input Image Size	Latency Estimate
			Max (ms)
1 pixel	300	1920x1080	6.99

InitUndistortRectifyMapInverse

The InitUndistortRectifyMapInverse function generates mapx and mapy, based on a set of camera parameters, where mapx and mapy are inputs for the xf::cv::remap function. That is, for each pixel in the location (u, v) in the destination (corrected and rectified) image, the function computes the corresponding coordinates in the source image (the original image from camera). The InitUndistortRectifyMapInverse module is optimized for hardware, so the inverse of rotation matrix is computed outside the synthesizable logic. Note that the inputs are fixed point, so the floating point camera parameters must be type casted to Q12.20 format.

API Syntax

template< int CM_SIZE, int DC_SIZE, int MAP_T, int ROWS, int COLS, int NPC >
void InitUndistortRectifyMapInverse ( ap_fixed<32,12> *cameraMatrix, ap_fixed<32,12> *distCoeffs, ap_fixed<32,12> *ir, xf::cv::Mat<MAP_T, ROWS, COLS, NPC> &_mapx_mat, xf::cv::Mat<MAP_T, ROWS, COLS, NPC> &_mapy_mat, int _cm_size, int _dc_size)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . InitUndistortRectifyMapInverse Function Parameter Descriptions

Parameter	Description
CM_SIZE	It must be set at the compile time, 9 for 3x3 matrix
DC_SIZE	It must be set at the compile time, must be 4,5 or 8
MAP_T	It is the type of output maps, and must be XF_32FC1
ROWS	Maximum image height, necessary to generate the output maps
COLS	Maximum image width, necessary to generate the output maps
NPC	Number of pixels per cycle. This function supports only one pixel per cycle, so set to XF_NPPC1
cameraMatrix	The input matrix representing the camera in the old coordinate system
distCoeffs	The input distortion coefficients (k1,k2,p1,p2[,k3[,k4,k5,k6]])
ir	The input transformation matrix is equal to Invert(newCameraMatrix*R), where newCameraMatrix represents the camera in the new coordinate system and R is the rotation matrix.. This processing will be done outside the synthesizable block
_mapx_mat	Output mat objects containing the mapx
_mapy_mat	Output mat objects containing the mapy
_cm_size	9 for 3x3 matrix
_dc_size	4, 5 or 8. If this is 0, then it means there is no distortion

InRange

The InRange function checks if pixels in the image src lie between the given boundaries. dst(x,y) is set to 255, if src(x,y) is within the specified thresholds and otherwise 0.

Dst(I)= lowerb ≤ src(I) ≤ upperb

Where (x,y) is the spatial coordinate of the pixel.

API Syntax

template<int SRC_T, int ROWS, int COLS,int NPC=1>
void inRange(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & src,unsigned char lower_thresh,unsigned char upper_thresh,xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . InRange Parameter Description

Parameter	Description
SRC_T	Input Pixel Type. 8-bit, unsigned, 1 and 3 channels are supported (XF_8UC1 and XF_8UC3).
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. In case of N-pixel parallelism, width should be multiple of N.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
src	Input image
dst	Output image
lower_thresh	Lower threshold value
upper_thresh	Upper threshold value

Resource Utilization

The following table summarizes the resource utilization of the InRange function in Resource optimized (8 pixel) mode and normal mode as generated using Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA

Table . InRange Function Resource Utilization Summary

Name	Resource Utilization
	1 pixel per clock operation	8 pixel per clock operation
	300 MHz	150 MHz
BRAM_18K	0	0
DSP48E	0	0
FF	86	154
LUT	60	148
CLB	15	37

Performance Estimate

The following table summarizes a performance estimate of the kernel in different configurations, generated using Vivado HLS 2019.1 tool for Xczu9eg-ffvb1156-1-i-es1 FPGA to process a grayscale HD (1080x1920) image.

Table . InRange Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9
8 pixel operation (150 MHz)	1.7

Integral Image

The integral function computes an integral image of the input. Each output pixel is the sum of all pixels above and to the left of itself.

API Syntax

template<int SRC_TYPE,int DST_TYPE, int ROWS, int COLS, int NPC=1>
void integral(xf::cv::Mat<SRC_TYPE, ROWS, COLS, NPC> & _src_mat, xf::cv::Mat<DST_TYPE, ROWS, COLS, NPC> & _dst_mat)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . integral Parameter Description

Parameter	Description
SRC_TYPE	Input pixel type. Only 8-bit, unsigned, 1 channel is supported (XF_8UC1)
DST_TYPE	Output pixel type. Only 32-bit,unsigned,1 channel is supported(XF_32UC1)
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image
NPC	Number of pixels to be processed per cycle; this function supports only XF_NPPC1 or 1 pixel per cycle operations.
_src_mat	Input image
_dst_mat	Output image

Resource Utilization

The following table summarizes the resource utilization of the kernel in different configurations, generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a grayscale HD (1080x1920) image.

Table . integral Function Resource Utilization Summary

Name	Resource Utilization
	1 pixel per clock operation
	300 MHz
BRAM_18K	4
DSP48E	0
FF	613
LUT	378
CLB	102

Performance Estimate

The following table summarizes the performance of the kernel in different configurations, as generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table . integral Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	7.2

Dense Pyramidal LK Optical Flow

Optical flow is the pattern of apparent motion of image objects between two consecutive frames, caused by the movement of object or camera. It is a 2D vector field, where each vector is a displacement vector showing the movement of points from first frame to second.

Optical Flow works on the following assumptions:

• Pixel intensities of an object do not have too many variations in consecutive frames

• Neighboring pixels have similar motion

Consider a pixel I(x, y, t) in first frame. (Note that a new dimension, time, is added here. When working with images only, there is no need of time). The pixel moves by distance (dx, dy) in the next frame taken after time dt. Thus, since those pixels are the same and the intensity does not change, the following is true:

Taking the Taylor series approximation on the right-hand side, removing common terms, and dividing by dt gives the following equation:

Where , , and .

The above equation is called the Optical Flow equation, where, fx and fy are the image gradientsand ft is the gradient along time. However, (u, v) is unknown. It is not possible to solve this equation with two unknown variables. Thus, several methods are provided to solve this problem. One method is Lucas-Kanade. Previously it was assumed that all neighboring pixels have similar motion. The Lucas-Kanade method takes a patch around the point, whose size can be defined through the ‘WINDOW_SIZE’ template parameter. Thus, all the points in that patch have the same motion. It is possible to find (fx, fy, ft ) for these points. Thus, the problem now becomes solving ‘WINDOW_SIZE * WINDOW_SIZE’ equations with two unknown variables,which is over-determined. A better solution is obtained with the “least square fit” method. Below is the final solution, which is a problem with two equations and two unknowns:

This solution fails when a large motion is involved and so pyramids are used. Going up in the pyramid, small motions are removed and large motions become small motions and so by applying Lucas-Kanade, the optical flow along with the scale is obtained.

API Syntax

template< int NUM_PYR_LEVELS, int NUM_LINES, int WINSIZE, int FLOW_WIDTH, int FLOW_INT, int TYPE, int ROWS, int COLS, int NPC,bool USE_URAM=false>
void densePyrOpticalFlow(
xf::cv::Mat<TYPE,ROWS,COLS,NPC> & _current_img,
xf::cv::Mat<TYPE,ROWS,COLS,NPC> & _next_image,
xf::cv::Mat<XF_32UC1,ROWS,COLS,NPC> & _streamFlowin,
xf::cv::Mat<XF_32UC1,ROWS,COLS,NPC> & _streamFlowout,
const int level, const unsigned char scale_up_flag, float scale_in, ap_uint<1> init_flag)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . densePyrOpticalFlow Function Parameter Descriptions

Parameter	Description
NUM_PYR_LEVE LS	Number of Image Pyramid levels used for the optical flow computation
NUM_LINES	Number of lines to buffer for the remap algorithm – used to find the temporal gradient
WINSIZE	Window Size over which Optical Flow is computed
FLOW_WIDTH, FLOW_INT	Data width and number of integer bits to define the signed flow vector data type. Integer bit includes the signed bit. The default type is 16-bit signed word with 10 integer bits and 6 decimal bits.
TYPE	Pixel type of the input image. XF_8UC1 is only the supported value.
ROWS	Maximum height or number of rows to build the hardware for this kernel
COLS	Maximum width or number of columns to build the hardware for this kernel
NPC	Number of pixels the hardware kernel must process per clock cycle. Only XF_NPPC1, 1 pixel per cycle, is supported.
USE_URAM	Enable to map some storage structures to UltraRAM
_curr_img	First input image stream
_next_img	Second input image to which the optical flow is computed with respect to the first image
_streamFlow in	32-bit Packed U and V flow vectors input for optical flow. The bits from 31-16 represent the flow vector U while the bits from 15-0 represent the flow vector V.
_streamFlow out	32-bit Packed U and V flow vectors output after optical flow computation. The bits from 31-16 represent the flow vector U while the bits from 15-0 represent the flow vector V.
level	Image pyramid level at which the algorithm is currently computing the optical flow.
scale_up_fla g	Flag to enable the scaling-up of the flow vectors. This flag is set at the host when switching from one image pyramid level to the other.
scale_in	Floating point scale up factor for the scaling-up the flow vectors. The value is (previous_rows-1)/(current_rows-1). This is not 1 when switching from one image pyramid level to the other.
init_flag	Flag to initialize flow vectors to 0 in the first iteration of the highest pyramid level. This flag must be set in the first iteration of the highest pyramid level (smallest image in the pyramid). The flag must be unset for all the other iterations.

Resource Utilization

The following table summarizes the resource utilization of densePyrOpticalFlow for 1 pixel per cycle implementation, with the optical flow computed for a window size of 11 over an image size of 1920x1080 pixels. The results are after implementation in Vivado HLS 2019.1 for the Xilinx xczu9eg-ffvb1156-2L-e FPGA at 300 MHz.

Table . densePyrOpticalFlow Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		LUTs	FFs	DSPs	BRAMs
1 Pixel	300	32231	16596	52	215

Resource Utilization with UltraRAM Enable

The following table summarizes the resource utilization of densePyrOpticalFlow for 1 pixel per cycle implementation, with the optical flow computed for a window size of 11 over an image size of 3840X2160 pixels. The results are after implementation in Vivado HLS 2019.1 for the Xilinx xczu7ev-ffvc1156-2 FPGA at 300 MHz with UltraRAM enabled.

Table . densePyrOpticalFlow Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		LUTs	FFs	DSPs	BRAMs	URAM
1 Pixel	300	31164	42320	81	34	23

Performance Estimate

The following table summarizes performance figures on hardware for the densePyrOpticalFlow function for 5 iterations over 5 pyramid levels scaled down by a factor of two at each level. This has been tested on the zcu102 evaluation board.

Table . densePyrOpticalFlow Function Performance Estimate Summary

Operating Mode	Operating Frequency (MHz)	Image Size	Latency Estimate
			Max (ms)
1 pixel	300	1920x1080	49.7
1 pixel	300	1280x720	22.9
1 pixel	300	1226x370	12.02

Dense Non-Pyramidal LK Optical Flow

Optical flow is the pattern of apparent motion of image objects between two consecutive frames, caused by the movement of object or camera. It is a 2D vector field, where each vector is a displacement vector showing the movement of points from first frame to second.

Optical Flow works on the following assumptions:

• Pixel intensities of an object do not have too many variations in consecutive frames

• Neighboring pixels have similar motion

Consider a pixel I(x, y, t) in first frame. (Note that a new dimension, time, is added here. When working with images only, there is no need of time). The pixel moves by distance (dx, dy) in the next frame taken after time dt. Thus, since those pixels are the same and the intensity does not change, the following is true:

Taking the Taylor series approximation on the right-hand side, removing common terms, and dividing by dt gives the following equation:

Where , , and .

The above equation is called the Optical Flow equation, where, fx and fy are the image gradientsand ft is the gradient along time. However, (u, v) is unknown. It is not possible to solve this equation with two unknown variables. Thus, several methods are provided to solve this problem. One method is Lucas-Kanade. Previously it was assumed that all neighboring pixels have similar motion. The Lucas-Kanade method takes a patch around the point, whose size can be defined through the ‘WINDOW_SIZE’ template parameter. Thus, all the points in that patch have the same motion. It is possible to find (fx, fy, ft ) for these points. Thus, the problem now becomes solving ‘WINDOW_SIZE * WINDOW_SIZE’ equations with two unknown variables,which is over-determined. A better solution is obtained with the “least square fit” method. Below is the final solution, which is a problem with two equations and two unknowns:

API Syntax

template<int TYPE, int ROWS, int COLS, int NPC, int WINDOW_SIZE,bool USE_URAM=false>
void DenseNonPyrLKOpticalFlow (xf::cv::Mat<TYPE, ROWS, COLS, NPC> & frame0, xf::cv::Mat<TYPE, ROWS, COLS, NPC> & frame1, xf::cv::Mat<XF_32FC1, ROWS, COLS, NPC> & flowx, xf::cv::Mat<XF_32FC1, ROWS, COLS, NPC> & flowy)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . DenseNonPyrLKOpticalFlow Function Paramete Descriptions

Parameter	Description
Type	pixel type. The current supported pixel value is XF_8UC1, unsigned 8 bit.
ROWS	Maximum number of rows of the input image that the hardware kernel must be built for.
COLS	Maximum number of columns of the input image that the hardware kernel must be built for.
NPC	Number of pixels to process per cycle. Supported values are XF_NPPC1 (=1) and XF_NPPC2(=2).
WINDOW_SIZE	Window size over which optical flow will be computed. This can be any odd positive integer.
USE_URAM	Enable to map storage structures to UltraRAM.
frame0	First input images.
frame1	Second input image. Optical flow is computed between frame0 and frame1.
flowx	Horizontal component of the flow vectors. The format of the flow vectors is XF_32FC1 or single precision.
flowy	Vertical component of the flow vectors. The format of the flow vectors is XF_32FC1 or single precision.

Resource Utilization

The following table summarizes the resource utilization of DenseNonPyrLKOpticalFlow for a 4K image, as generated in the Vivado HLS 2019.1 version tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA at 300 MHz.

Table . DenseNonPyrLKOpticalFlow Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUTs
1 pixel	300	178	42	11984	7730
2 pixel	300	258	82	22747	15126

The following table summarizes the resource utilization of DenseNonPyrLKOpticalFlow for a 4K image, as generated in the Vivado HLS version tool for the Xilinx Xczu7eg-ffvb1156-1 FPGA at 300 MHz with UltraRAM enabled.

Table . DenseNonPyrLKOpticalFlow Function Resource Utilization Summary with UltraRAM Eanble

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	URAM	DSP_48Es	FF	LUTs
1 pixel	300	0	12	42	11803	7469
2 pixel	300	0	23	80	22124	13800

Performance Estimate

The following table summarizes performance estimates of the DenseNonPyrLKOpticalFlow function for a 4K image, generated using Vivado HLS 2019.1 version tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA.

Table . DenseNonPyrLKOpticalFlow Function Performance Estimate Summary

Operating Mode	Operating Frequency (MHz)	Latency Estimate
		Max (ms)
1 pixel	300	28.01
2 pixel	300	14.01

Kalman Filter

The classic Kalman Filter is proposed for linear system. The state-space description of a linear system assumed to be:

where x_k is the state vector at k^th time instant, constant (known) Ak is an nxn state transition matrix, constant (known) B_k is an nxm control input matrix, constant (known) Γ_k is an nxp system noise input matrix, constant (known) H_k is a qxn measurement matrix, constant (known) with 1≤ m, p, q ≤ n, {u:sub:k} a (known) sequence of m vectors (called a deterministic input sequence), and and are respectively, (unknown) system and observation noise sequences, with known statistical information such as mean, variance, and covariance.

The Kalman filter assumes the following:

1. and are assumed to be sequences of zero-mean Gaussian (or normal) white noise. That is, and , where δ_kl is a Kronecker Delta function, and Q_k and R_k are positive definite matrices, E(u) is an expectation of random variable u.

2. 

3. The initial state x₀ is also assumed to be independent of and , that is .

The representation means the estimate of x at time instant k using all the data measured till the time instant j.

The Kalman filter algorithm can be summarized as shown in the below equations:

Initialization

Time Update / Predict

Measurement Update/Correction

Where P_k,j is an estimate error covariance nxn matrix, G_k is Kalman gain nxq matrix, and k=1, 2,..

Computation Strategy

The numerical accuracy of the Kalman filter covariance measurement update is a concern for implementation, since it differentiates two positive definite arrays. This is a potential problem if finite precision is used for computation. This design uses UDU factorization of P to address the numerical accuracy/stability problems.

Example for Kalman Filter

//Control Flag
    INIT_EN      = 1; TIMEUPDATE_EN = 2; MEASUPDATE_EN = 4;
    XOUT_EN_TU  = 8; UDOUT_EN_TU    = 16; XOUT_EN_MU    = 32;
    UDOUT_EN_MU = 64; EKF_MEM_OPT   = 128;
    //Load A_mat,B_mat,Uq_mat,Dq_mat,H_mat,X0_mat,U0_mat,D0_mat,R_mat
    //Initialization
KalmanFilter(A_mat, B_mat, Uq_mat, Dq_mat,  H_mat, X0_mat, U0_mat, D0_mat, R_mat, u_mat, y_mat, Xout_mat, Uout_mat, Dout_mat, INIT_EN);

for(int iteration=0; iteration< count; iteration++)
{
    //Load u_mat (control input)
    for(int index=0; index <C_CTRL; index ++)
        u_mat.write_float(index, control_input[index]);

//Time Update
KalmanFilter(A_mat, B_mat, Uq_mat, Dq_mat,  H_mat, X0_mat, U0_mat, D0_mat, R_mat, u_mat, y_mat, Xout_mat, Uout_mat, Dout_mat, TIMEUPDATE_EN + XOUT_EN_TU + UDOUT_EN_TU);

//Load y_mat (measurement vector)
    for(int index =0; index <M_MEAS; index ++)
        y_mat.write_float(index, control_input[index]);

//Measurement Update
KalmanFilter(A_mat, B_mat, Uq_mat, Dq_mat,  H_mat, X0_mat, U0_mat, D0_mat, R_mat, u_mat, y_mat, Xout_mat, Uout_mat, Dout_mat, MEASUPDATE_EN + XOUT_EN_MU + UDOUT_EN_MU);
}

API Syntax

template<int N_STATE, int M_MEAS, int C_CTRL, Int  MTU, int MMU, bool USE_URAM=0, bool EKF_EN=0, int TYPE, int NPC >
void KalmanFilter ( xf::cv::Mat<TYPE, N_STATE, N_STATE, NPC>  &A_mat,
#if KF_C!=0
xf::cv::Mat<TYPE, N_STATE, C_CTRL, NPC>   &B_mat,
#endif
xf::cv::Mat<TYPE, N_STATE, N_STATE, NPC>  &Uq_mat,
xf::cv::Mat<TYPE, N_STATE, 1, NPC>        &Dq_mat,
xf::cv::Mat<TYPE, M_MEAS, N_STATE, NPC>   &H_mat,
xf::cv::Mat<TYPE, N_STATE, 1, NPC>        &X0_mat,
xf::cv::Mat<TYPE, N_STATE, N_STATE, NPC>  &U0_mat,
xf::cv::Mat<TYPE, N_STATE, 1, NPC>        &D0_mat,
xf::cv::Mat<TYPE, M_MEAS, 1, NPC>         &R_mat,
#if KF_C!=0
xf::cv::Mat<TYPE, C_CTRL, 1, NPC>         &u_mat,
#endif
xf::cv::Mat<TYPE, M_MEAS, 1, NPC>         &y_mat,
xf::cv::Mat<TYPE, N_STATE, 1, NPC>        &Xout_mat,
xf::cv::Mat<TYPE, N_STATE, N_STATE, NPC>  &Uout_mat,
xf::cv::Mat<TYPE, N_STATE, 1, NPC>        &Dout_mat,
unsigned char flag)

Parameter Descriptions

Table . Kalman Filter Parameter Description

Parameter	Used (?) or Unused (X)			Description
	Initialization	Time Update	Measurement Update
N_STATE	?	?	?	Number of state variable; possible options are 1 to 128
M_MEAS	?	?	?	Number of measurement variable; possible options are 1 to 128; M_MEAS must be less than or equal to N_STATE. In case of Extended Kalman Filter(EKF), M_MEAS should be 1.
C_CTRL	?	?	?	Number of control variable; possible options are 0 to 128; C_CTRL must be less than or equal to N_STATE. In case of EKF, C_CTRL should be 1.
MTU	?	?	?	Number of multipliers used in time update; possible options are 1 to 128; MTU must be less than or equal to N_STATE
MMU	?	?	?	Number of multipliers used in Measurement update; possible options are 1 to 128; MMU must be less than or equal to N_STATE
USE_URAM	?	?	?	URAM enable; possible options are 0 and 1
EKF_EN	?	?	?	Extended Kalman Filter Enable; possible options are 0 and 1
TYPE	?	?	?	Type of input pixel. Currently, only XF_32FC1 is supported.
NPC	?	?	?	Number of pixels to be processed per cycle; possible option is XF_NPPC1 (NOT relevant for this function)
A_mat	?	X	X	Transition matrix A. In case of EKF, Jacobian Matrix F is mapped to A_mat.
B_mat	?	X	X	Control matrix B. In case of KF, B_mat argument is not required when C_CTRL=0. And in case of EKF, Dummy matrix with size (N_STATE x 1) is mapped to B_mat.
Uq_mat	?	X	X	U matrix for Process noise covariance matrix Q
Dq_mat	?	X	X	D matrix for Process noise covariance matrix Q(only diagonal elements)
H_mat	?	X	X	Measurement Matrix H. In case of EKF, Jacobian Matrix H is mapped to H_mat.
X0_mat	?	X	X	Initial state matrix. In case of EKF, state transition function f is mapped to X0_mat.
U0_mat	?	X	X	U matrix for initial error estimate covariance matrix P
D0_mat	?	X	X	D matrix for initial error estimate covariance matrix P(only diagonal elements)
R_mat	?	X	X	Measurement noise covariance matrix R(only diagonal elements). In case of EKF, input only one value of R since M_MEAS=1.
u_mat	X	?	X	Control input vector. In case of KF, u_mat argument is not required when C_CTRL=0. And in case of EKF, observation function h is mapped to u_mat.
y_mat	X	X	?	Measurement vector. In case of EKF, input only one measurement since M_MEAS=1.
Xout_mat	X	?	?	Output state matrix
Uout_mat	X	?	?	U matrix for output error estimate covariance matrix P
Dout_mat	X	?	?	D matrix for output error estimate covariance matrix P(only diagonal elements)
flag	?	?	?	Control flag register
All U, D counterparts of all initialized matrices (Q and P) are obtained using U-D factorization

Table . Control Flag Registers

Flag bit	Description
0	Initialization enable
1	Time update enable
2	Measurement update enable
3	Xout enable for time update
4	Uout/D:sub:out enable for time update
5	Xout enables for measurement update
6	Uout/D:sub:out enable for measurement update
7	Read optimization (Uq_mat, Dq_mat, U0_mat, D0_mat and R_mat) for Extended Kalman Filter

Resource Utilization

The following table summarizes the resource utilization of the kernel in different configurations, generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1 FPGA.

Table . Kalman Filter Function Resource Utilization Summary

Name	Resource Utilization
	N_STATE=128; C_CTRL=128; M_MEAS=128; MTU=24; MMU=24	N_STATE=64; C_CTRL=64; M_MEAS=12;MTU=16;MMU=16	N_STATE=5; C_CTRL=1; M_MEAS=3;MTU=2;MMU=2
	300 MHz	300 MHz	300 MHz
BRAM_18K	387	142	24
DSP48E	896	548	87
FF	208084	128262	34887
LUT	113556	70942	18141

The following table shows the resource utilization of the kernel for a configuration with USE_URAM enable, generated using Vivado HLS 2019.1 for the Xilinx xczu7ev-ffvc1156-2-e FPGA.

Table . Resource Utilization with UltraRAM Enabled

Resource	Resource Utilization (N_STATE=64; C_CTRL=64; M_MEAS=12; MTU=4; MMU=4) (300 MHz) (ms)
BRAM_18K	30
DSP48E	284
FF	99210
LUT	53939
URAM	11

Performance Estimate

The following table shows the performance of kernel for different configurations, as generated using Vivado HLS 2019.1 tool for the Xilinx® Xczu9eg-ffvb1156-1, for one iteration. Latency estimate is calculated by taking average latency of 100 iteration.

Table . Kalman Filter Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Operating Frequency (MHz)	Latency (ms)
N_STATE=128; C_CTRL=128; M_MEAS=128; MTU=24; MMU=24	300	0.7
N_STATE=64; C_CTRL=64; M_MEAS=12; MTU=16; MMU=16	300	0.12
N_STATE=5; C_CTRL=1; M_MEAS=3; MTU=2; MMU=2	300	0.04

The following table shows the performance of kernel for a configuration with UltraRAM enable, as generated using Vivado HLS 2019.1 tool for the Xilinx xczu7ev-ffvc1156-2-e, for one iteration. Latency estimate is calculated by taking average latency of 100 iteration.

Table . Performance Estimate with UltraRAM

Operating Mode	Operating Frequency (MHz)	Latency (ms)
N_STATE=64; C_CTRL=64; M_MEAS=12;MTU=4;MMU= 4	300	0.25

Extended Kalman Filter

The Kalman filter estimates the state vector in a linear model. If the model is nonlinear, then a linearization procedure is performed to obtain the filtering equations. The Kalman filter so obtained will be called the Extended Kalman filter. A state-space description of non-linear system can have a non-linear model of the form:

Where f_k and h_k are valued functions with ranges in Rⁿ and R^q, respectively. 1≤q≤n, and T_k a matrix-valued function with range in RⁿxR^q such that for each k the first order partial derivatives of f_k (x:sub:k) and h_k (x:sub:k) with respect to all the components of x_k are continuous. We consider zero-mean Gaussian white noise sequences and with ranges in R^p and R^q respectively, 1≤p, q≤n.

The real-time linearization process is carried out as shown in the following equations. In the lines of the linear model, the initial estimate and predicted position are chosen to be:

Then, , consecutively, for k=1,2,…, use the predicted positions.

Note

1. , where , k is a time index and superscript is row index and

2. is a space of column vectors

The equation for time update computations is as follows:

The equation for measurement update computations is as follows:

Example for Extended Kalman Filter

//Load F/B_mat/Uq_mat/Dq_mat/X0_mat/U0_mat/D0_mat

for(int iteration=0; iteration< count; iteration++)
{
    if(iteration ==0)
        model_fx(X0_mat, fx);// update fx using X0_mat
    else
model_fx(Xout_mat, fx); // update fx using Xout_mat

        unsigned char initFlag;
        if(iteration ==0)
            initFlag = INIT_EN;
        else
            initFlag = EKF_MEM_OPT+INIT_EN;

        //Initialization
KalmanFilter (F, B_mat, Uq_mat, Dq_mat, H, fx, U0_mat, D0_mat, R_mat, hx, y_mat, Xout_mat, Uout_mat, Dout_mat, initFlag);

        //Time Update
KalmanFilter (F, B_mat, Uq_mat, Dq_mat, H, fx, U0_mat, D0_mat, R_mat, hx, y_mat, Xout_mat, Uout_mat, Dout_mat, TIMEUPDATE_EN + XOUT_EN_TU + UDOUT_EN_TU);
        for(int index=0; index< M_MEAS; index++)
        {
if(iteration ==0)
// update hx/H using X0_mat for one measurement at a time
            model_hxH(X0_mat, hx, H, index);
        else
        //update hx/H using Xout_mat for one measurement at a time
model_hxH(Xout_mat, hx, H, index);

//Load R_mat
            R_mat.write_float(0,R_matrix[index][index]);

            //Load y_mat
            Y_mat.write_float(0,measurement_vector[index]);

//Measurement Update
KalmanFilter (F, B_mat, Uq_mat, Dq_mat, H, fx, U0_mat, D0_mat, R_mat, hx, y_mat, Xout_mat, Uout_mat, Dout_mat, MEASUPDATE_EN + XOUT_EN_MU + UDOUT_EN_MU);
        }
}

Laplacian Operator

This function calculates the Laplacian of the input image. This function internally uses the filter2D kernel to compute the Laplacian. The filter coefficients are calculated using cv::getDerivKernels OpenCV function on the host side.

API Syntax

template<int BORDER_TYPE,int FILTER_WIDTH,int FILTER_HEIGHT, int SRC_T,int DST_T, int ROWS, int COLS,int NPC=1>
void filter2D(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src_mat,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst_mat,short int filter[FILTER_HEIGHT*FILTER_WIDTH],unsigned char _shift)

Parameter Descriptions

The following table describes the template and the function parameters.

Table filter2D Parameter Description

Parameter	Description
BORDER_TYPE	Border Type supported is XF_BORDER_CONSTANT
FILTER_HEIGHT	Number of rows in the input filter
FILTER_WIDTH	Number of columns in the input filter
SRC_T	Input pixel type. Only 8-bit, unsigned, 1 and 3 channels are supported (XF_8UC1 and XF_8UC3)
DST_T	Output pixel type. 8-bit unsigned single and 3 channels (XF_8UC1, XF_8UC3) and 16-bit signed single and 3 channels (XF_16SC1, XF_16SC3) supported.
ROWS	Maximum height of input and output image
COLS	Maximum width of input and output image. Must be multiple of 8, for 8 pixel mode.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
_src_mat	Input image
_dst_mat	Output image
filter	The input filter of any size, provided the dimensions should be an odd number. The filter co-efficients either a 16-bit value or a 16-bit fixed point equivalent value.
_shift	The filter must be of type XF_16SP. If the co-efficients are floating point, it must be converted into the Qm.n and provided as the input as well as the shift parameter has to be set with the ‘n’ value. Else, if the input is not of floating point, the filter is provided directly and the shift parameter is set to zero.

Resource Utilization

The following table summarizes the resource utilization of the kernel in different configurations, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a grayscale HD (1080x1920) image.

Table filter2D Function Resource Utilization Summary

Operating Mode	Filter Size	Operating Frequency (MHz)	Utilization Estimate
			BRAM_18K	DSP_48Es	FF	LUT	CLB
1 Pixel	3x3	300	3	9	1701	1161	269
	5x5	300	5	25	3115	2144	524
8 Pixel	3x3	150	6	72	2783	2768	638
	5x5	150	10	216	3020	4443	1007

The following table summarizes the resource utilization of the kernel in different configurations, generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a 4K 3 Channel image.

Table filter2D Function Resource Utilization Summary

Operating Mode	Filter Size	Operating Frequency (MHz)	Utilization Estimate
			BRAM_18K	DSP_48Es	FF	LUT
1 Pixel	3x3	300	18	27	886	801
8 Pixel	5x5	300	30	75	1793	1445

Performance Estimate

The following table summarizes the performance of the kernel in different configurations, as generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table filter2D Function Performance Estimate Summary

Operating Mode	Operating Frequency (MHz)	Filter Size	Latency Estimate
			Max (ms)
1 pixel	300	3x3	7
	300	5x5	7.1
8 pixel	150	3x3	1.86
	150	5x5	1.86

Lens Shading Correction

Vignetting/Lensshading refers to the fall-off pixel intensity from the centre towards the edges of the image. In this algorithm, vignette is corrected by considering the distance between the centre pixel and actual image pixel position. This distance is used to calculate intensity gain per pixel per channel which is used for the correction.

API Syntax

template <int SRC_T, int DST_T, int ROWS, int COLS, int NPC = 1>
void Lscdistancebased(xf::cv::Mat<SRC_T, ROWS, COLS, NPC>& src, xf::cv::Mat<DST_T, ROWS, COLS, NPC>& dst) {

Parameter Descriptions

The following table describes template parameters and arguments to the function.

Table Lensshading correction Parameter Description

Parameter	Description
SRC_T	Input pixel type. 8/10/12/16-bit, unsigned, 3 channel is supported (XF_8UC3, XF_10UC3, XF_12UC3, XF_16UC3).
DST_T	Output pixel type. 8/10/12/16-bit, unsigned, 3 channel is supported (XF_8UC3, XF_10UC3, XF_12UC3, XF_16UC3).
ROWS	Maximum height of input and output image
COLS	Maximum width of input and output image. In case of N-pixel parallelism, width should be multiple of N.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1, XF_NPPC2 AND so on
src	Input image
dst	Output image

Resource Utilization

The following table summarizes the resource utilization of the kernel in different configurations, generated using Vitis HLS 2020.2 tool, to process a FULL HD image.

Table Lensshading correction Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 pixel-8U	300	0	26	4198	3628	889
1 pixel-16U	300	0	26	4253	3602	770

Performance Estimate

The following table summarizes a performance estimate of the kernel in different configurations, as generated using Vitis HLS 2020.2 tool, to process a FULL HD image.

Table Lensshading correction Function Performance Estimate Summary

Operating Mode	Operating Frequency (MHz)	Latency Estimate Max (ms)
1 pixel	300	7
2 pixel	300	3.6

Local Tone Mapping

Most of the display devices have limited dynamic range. Hence images with wide dynamic range cannot be seen natively on such devices. To see wide dynamic range images on devices with low dynamic range, we need to compress the wide dynamic range of image to a low dynamic range. This process is called as tone-mapping.

Local tone mapping takes pixel neighbor statistics into account, and produces images with more contrast and brightness.

This implementaion is based on the algorithm proposed by J. Yang, A. Hore and O. Yadid-Pecht.

API Syntax

LTM Class API:

template <int IN_TYPE, int OUT_TYPE, int BLOCK_HEIGHT, int BLOCK_WIDTH, int ROWS, int COLS, int NPC>
class LTM {}

Processing member function:

xf::cv::LTM<IN_TYPE, OUT_TYPE, BLOCK_HEIGHT, BLOCK_WIDTH, ROWS, COLS, NPC>::process(xf::cv::Mat<IN_TYPE, ROWS, COLS, NPC>& in,
                     int block_rows,
                     int block_cols,
                     XF_CTUNAME(IN_TYPE, NPC) omin_r[MinMaxVArrSize][MinMaxHArrSize],
                     XF_CTUNAME(IN_TYPE, NPC) omax_r[MinMaxVArrSize][MinMaxHArrSize],
                     XF_CTUNAME(IN_TYPE, NPC) omin_w[MinMaxVArrSize][MinMaxHArrSize],
                     XF_CTUNAME(IN_TYPE, NPC) omax_w[MinMaxVArrSize][MinMaxHArrSize],
                     xf::cv::Mat<OUT_TYPE, ROWS, COLS, NPC>& out)

Overlaoded processing member function:

xf::cv::LTM<IN_TYPE, OUT_TYPE, BLOCK_HEIGHT, BLOCK_WIDTH, ROWS, COLS, NPC>::process(xf::cv::Mat<IN_TYPE, ROWS, COLS, NPC>& in,
                     XF_CTUNAME(IN_TYPE, NPC) omin_r[MinMaxVArrSize][MinMaxHArrSize],
                     XF_CTUNAME(IN_TYPE, NPC) omax_r[MinMaxVArrSize][MinMaxHArrSize],
                     XF_CTUNAME(IN_TYPE, NPC) omin_w[MinMaxVArrSize][MinMaxHArrSize],
                     XF_CTUNAME(IN_TYPE, NPC) omax_w[MinMaxVArrSize][MinMaxHArrSize],
                     xf::cv::Mat<OUT_TYPE, ROWS, COLS, NPC>& out)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . LocalToneMapping Function Parameter Descriptions

Parameter	Description
IN_TYPE	Input pixel type. The current supported pixel value is XF_8UC3, XF_10UC3, XF_12UC3, XF_16UC3, XF_32FC3
OUT_TYPE	Input pixel type. The current supported pixel value is XF_8UC3, XF_10UC3, XF_12UC3, XF_16UC3
BLOCK_WIDTH	Max block width the image is divided into. This can be any positive integer greater than or equal to 32 and less than input image width.
BLOCK_HEIGHT	Max block height the image is divided into. This can be any positive integer greater than or equal to 32 and less than input image height.
ROWS	Maximum number of rows of the input image that the hardware kernel must be built for.
COLS	Maximum number of columns of the input image that the hardware kernel must be built for.
NPC	Number of pixels to process per cycle. Supported values are XF_NPPC1, XF_NPPC2, XF_NPPC4, XF_NPPC8.
in	Input HDR image
block_rows	Actual block height
block_cols	Actual block width
omin_r	Array of min values to be read by the next frame.
omax_r	Array of max values to be read by the next frame.
omin_w	Array of min values computed in the current frame.
omax_w	Array of max values computed in the current frame.
out	Output HDR image

Resource Utilization

The following table summarizes the resource utilization of LocalToneMapping for a 4K image, as generated in the Vitis HLS 2020.2 version tool for the Xilinx xcu200-fsgd2104-2-e FPGA at 300MHz.

Table . LocalToneMapping Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUTs
1 pixel	300	0	123	35216	20246
4 pixel	300	0	330	67457	40391

Performance Estimate

The following table summarizes performance estimates of the LocalToneMapping function for a 4K image, generated using Vitis HLS 2020.2 version tool for the Xilinx xcu200-fsgd2104-2-e FPGA.

Table . LocalToneMapping Function Performance Estimate Summary

Operating Mode	Operating Frequency (MHz)	Latency Estimate
		Max (ms)
1 pixel	300	7.2
4 pixel	300	1.9

Look Up Table

The LUT function performs the table lookup operation. Transforms the source image into the destination image using the given look-up table. The input image must be of depth XF_8UP and the output image of same type as input image.

I_out(x, y) = LUT [I:sub:in1(x, y)]

Where:

• I_out(x, y) is the intensity of output image at (x, y) position

• I_in(x, y) is the intensity of first input image at (x, y) position

• LUT is the lookup table of size 256 and type unsigned char.

API Syntax

template <int SRC_T, int ROWS, int COLS,int NPC=1>
void LUT(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src, xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _dst,unsigned char* _lut)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . LUT Parameter Description

Parameter	Description
SRC_T	Input and Output pixel type. Only 8-bit, unsigned, 1 and 3 channels are supported (XF_8UC1 and XF_8UC3)
ROWS	Number of rows in the image being processed.
COLS	Number of columns in the image being processed. Must be a multiple of 8, for 8-pixel operation.
NPC	Number of pixels to be processed in parallel. Possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
_src	Input image of size (ROWS, COLS) and type 8U.
_dst	Output image of size (ROWS, COLS) and same type as input.
_lut	Input lookup Table of size 256 and type unsigned char.

Resource Utilization

The following table summarizes the resource utilization of the LUT function, generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a grayscale HD (1080x1920) image.

Table . LUT Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 pixel	300	1	0	937	565	137
8 pixel	150	9	0	1109	679	162

The following table summarizes the resource utilization of the LUT function, generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA, to process 4K 3Channel image.

Table . LUT Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 pixel	300	4	0	1160	648	175

Performance Estimate

The following table summarizes the performance in different configurations, as generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table . LUT Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.92
8 pixel operation (150 MHz)	1.66

Mean and Standard Deviation

The meanStdDev function computes the mean and standard deviation of input image. The output Mean value is in fixed point Q8.8 format, and the Standard Deviation value is in Q8.8 format. Mean and standard deviation are calculated as follows:

API Syntax

template<int SRC_T,int ROWS, int COLS,int NPC=1>
void meanStdDev(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src,unsigned short* _mean,unsigned short* _stddev)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . meanStdDev Parameter Description

Parameter	Description
SRC_T	Input and Output pixel type. Only 8-bit, unsigned, 1 and 3 channels are supported (XF_8UC1 and XF_8UC3)
ROWS	Number of rows in the image being processed.
COLS	Number of columns in the image being processed. Must be a multiple of 8, for 8-pixel operation.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
_src	Input image
_mean	16-bit data pointer through which the computed mean of the image is returned.
_stddev	16-bit data pointer through which the computed standard deviation of the image is returned.

Resource Utilization

The following table summarizes the resource utilization of the meanStdDev function, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a grayscale HD (1080x1920) image.

Table . meanStdDev Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 pixel	300	0	6	896	461	121
8 pixel	150	0	13	1180	985	208

The following table summarizes the resource utilization of the meanStdDev function, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a 4K 3Channel image.

Table . meanStdDev Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 pixel	300	0	7	5075	3324	725

Performance Estimate

The following table summarizes the performance in different configurations, as generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table . meanStdDev Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency
1 pixel operation (300 MHz)	6.9 ms
8 pixel operation (150 MHz)	1.69 ms

Max

The Max function calculates the per-element maximum of two corresponding images src1, src2 and stores the result in dst.

dst(x,y)=max( src1(x,y) ,src2(x,y) )

API Syntax

template< int SRC_T , int ROWS, int COLS, int NPC=1>
void max(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src1, xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src2, xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . Max Parameter Description

Parameter	Description
SRC_T	Input Pixel Type. 8-bit, unsigned, 1 channel is supported (XF_8UC1).
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. In case of N-pixel parallelism, width should be multiple of N.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
_src1	First input image
_src2	Second input image
_dst	Output image

Resource Utilization

The following table summarizes the resource utilization of the Max function in Resource optimized (8 pixel) mode and normal mode as generated using Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA.

Table . Max Function Resource Utilization Summary

Name	Resource Utilization
	1 pixel per clock operation	8 pixel per clock operation
	300 MHz	150 MHz
BRAM_18K	0	0
DSP48E	0	0
FF	103	153
LUT	44	102
CLB	21	38

Performance Estimate

The following table summarizes a performance estimate of the kernel in different configurations, generated using Vivado HLS 2019.1 tool for Xczu9eg-ffvb1156-1-i-es1 FPGA to process a grayscale HD (1080x1920) image.

Table . Max Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9
8 pixel operation (150 MHz)	1.7

MaxS

The MaxS function calculates the maximum elements between src and given scalar value scl and stores the result in dst.

dst(I)=maxS( src(I) ,scl )

API Syntax

template< int SRC_T , int ROWS, int COLS, int NPC=1>
void maxS(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src1, unsigned char _scl[XF_CHANNELS(SRC_T,NPC)], xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . MaxS Parameter Description

Parameter	Description
SRC_T	Input Pixel Type. 8-bit, unsigned, 1 channel is supported (XF_8UC1).
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. In case of N-pixel parallelism, width should be multiple of N.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
_src1	First Input image
_scl	Input scalar value, the size should be number of channels
_dst	Output image

Resource Utilization

The following table summarizes the resource utilization of the MaxS function in Resource optimized (8 pixel) mode and normal mode as generated using Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA.

Table . MaxS Function Resource Utilization Summary

Name	Resource Utilization
	1 pixel per clock operation	8 pixel per clock operation
	300 MHz	150 MHz
BRAM_18K	0	0
DSP48E	0	0
FF	162	43
LUT	103	104
CLB	32	20

Performance Estimate

The following table summarizes a performance estimate of the kernel in different configurations, generated using Vivado HLS 2019.1 tool for Xczu9eg-ffvb1156-1-i-es1 FPGA to process a grayscale HD (1080x1920) image.

Table . MaxS Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9
8 pixel operation (150 MHz)	1.7

Median Blur Filter

The function medianBlur performs a median filter operation on the input image. The median filter acts as a non-linear digital filter which improves noise reduction. A filter size of N would output the median value of the NxN neighborhood pixel values, for each pixel.

API Syntax

template<int FILTER_SIZE, int BORDER_TYPE, int TYPE, int ROWS, int COLS, int NPC>
void medianBlur (xf::cv::Mat<TYPE, ROWS, COLS, NPC> & _src, xf::cv::Mat<TYPE, ROWS, COLS, NPC> & _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . medianBlur Parameter Description

Parameter	Description
FILTER_SIZE	Window size of the hardware filter for which the hardware kernel will be built. This can be any odd positive integer greater than 1.
BORDER_TYPE	The way in which borders will be processed in the hardware kernel. Currently, only XF_BORDER_REPLICATE is supported.
TYPE	Input and Output pixel type. Only 8-bit, unsigned, 1 and 3 channels are supported (XF_8UC1 and XF_8UC3)
ROWS	Number of rows in the image being processed.
COLS	Number of columns in the image being processed. Must be a multiple of 8, for 8-pixel operation.
NPC	Number of pixels to be processed in parallel. Options are XF_NPPC1 (for 1 pixel processing per clock), XF_NPPC8 (for 8 pixel processing per clock
_src	Input image.
_dst	Output image.

Resource Utilization

The following table summarizes the resource utilization of the medianBlur function for XF_NPPC1 and XF_NPPC8 configurations, generated using Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA.

Table . medianBlur Function Resource Utilization Summary

Operating Mode	FILTER_SIZE	Operating Frequency (MHz)	Utilization Estimate
			LUTs	FFs	DSPs	BRAMs
1 pixel	3	300	1197	771	0	3
8 pixel	3	150	6559	1595	0	6
1 pixel	5	300	5860	1886	0	5

The following table summarizes the resource utilization of the medianBlur function for XF_NPPC1 with 3channel image as input, generated using Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA.

Table . medianBlur Function Resource Utilization Summary

Operating Mode	FILTER_SIZE	Operating Frequency (MHz)	Utilization Estimate
			LUTs	FFs	DSPs	BRAMs
1 pixel	3	300	2100	1971	0	9
1 pixel	5	300	13541	9720	0	15

Performance Estimate

The following table summarizes performance estimates of medianBlur function on Vivado HLS 2019.1 version tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA.

Table . medianBlur Function Performance Estimate Summary

Operating Mode	FILTER_SIZE	Operating Frequency (MHz)	Input Image Size	Latency Estimate
				Max (ms)
1 pixel	3	300	1920x1080	6.99
8 pixel	3	150	1920x1080	1.75
1 pixel	5	300	1920x1080	7.00

Min

The Min function calculates the per element minimum of two corresponding images src1, src2 and stores the result in dst.

dst(I)=min( src1(I) ,src2(I) )

API Syntax

template< int SRC_T , int ROWS, int COLS, int NPC=1>
void min(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src1, xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src2, xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . Min Parameter Description

Parameter	Description
SRC_T	Input pixel type. 8-bit, unsigned, 1 channel is supported (XF_8UC1).
ROWS	Maximum height of input and output image
COLS	Maximum width of input and output image. Must be multiple of 8, for 8-pixel operation.
NPC	Number of pixels to be processed per cycle, possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
_src1	First input image
_src2	Second input image
_dst	Output image

Resource Utilization

The following table summarizes the resource utilization of the Min function in Resource optimized (8 pixel) mode and normal mode as generated using Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA.

Table . Min Function Resource Utilization Summary

Name	Resource Utilization
	1 pixel per clock operation	8 pixel per clock operation
	300 MHz	150 MHz
BRAM_18K	0	0
DSP48E	0	0
FF	103	153
LUT	44	102
CLB	23	34

Performance Estimate

The following table summarizes a performance estimate of the kernel in different configurations, generated using Vivado HLS 2019.1 tool for Xczu9eg-ffvb1156-1-i-es1 FPGA to process a grayscale HD (1080x1920) image.

Table . Min Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9
8 pixel operation (150 MHz)	1.7

MinS

The MinS function calculates the minimum elements between src and given scalar value scl and stores the result in dst.

dst(x,y)=minS( src(x,y) ,scl )

API Syntax

template< int SRC_T , int ROWS, int COLS, int NPC=1>
void minS(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src1, unsigned char _scl[XF_CHANNELS(SRC_T,NPC)], xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . MinS Parameter Description

Parameter	Description
SRC_T	Input Pixel Type. 8-bit, unsigned, 1 channel is supported (XF_8UC1).
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. In case of N-pixel parallelism, width should be multiple of N
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
_src1	First Input image
_scl	Input scalar value, the size should be the number of channels.
_dst	Output image

Resource Utilization

The following table summarizes the resource utilization of the MinS function in Resource optimized (8 pixel) mode and normal mode as generated using Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA

Table . MinS Function Resource Utilization Summary

Name	Resource Utilization
	1 pixel per clock operation	8 pixel per clock operation
	300 MHz	150 MHz
BRAM_18K	0	0
DSP48E	0	0
FF	104	159
LUT	43	103
CLB	23	36

Performance Estimate

The following table summarizes a performance estimate of the kernel in different configurations, generated using Vivado HLS 2019.1 tool for Xczu9eg-ffvb1156-1-i-es1 FPGA to process a grayscale HD (1080x1920) image.

Table . MinS Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9
8 pixel operation (150 MHz)	1.7

MinMax Location

The minMaxLoc function finds the minimum and maximum values in an image and location of those values.

API Syntax

template<int SRC_T,int ROWS,int COLS,int NPC>
void minMaxLoc(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src,int32_t *max_value, int32_t *min_value,uint16_t *_minlocx, uint16_t *_minlocy, uint16_t *_maxlocx, uint16_t *_maxlocy )

Parameter Descriptions

The following table describes the template and the function parameters.

Table . minMaxLoc Parameter Description

Parameter	Description
SRC_T	Input pixel type. 8-bit, unsigned, 1 channel (XF_8UC1), 16-bit, unsigned, 1 channel (XF_16UC1), 16-bit, signed, 1 channel (XF_16SC1), 32-bit, signed, 1 channel (XF_32SC1) are supported.
ROWS	Number of rows in the image being processed.
COLS	Number of columns in the image being processed.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
_src	Input image
max_val	Maximum value in the image, of type int.
min_val	Minimum value in the image, of type int.
_minlocx	x-coordinate location of the first minimum value.
_minlocy	y-coordinate location of the first minimum value.
_maxlocx	x-coordinate location of the first maximum value.
_maxlocy	y-coordinate location of the first maximum value.

Resource Utilization

The following table summarizes the resource utilization of the minMaxLoc function, generated using Vivado HLS 2019.1 tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a grayscale HD (1080x1920) image.

Table . minMaxLoc Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 pixel	300	0	3	451	398	86
8 pixel	150	0	3	1049	1025	220

Performance Estimate

The following table summarizes the performance in different configurations, as generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table . minMaxLoc Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9
8 pixel operation (150 MHz)	1.69

Mean Shift Tracking

Mean shift tracking is one of the basic object tracking algorithms. Mean-shift tracking tries to find the area of a video frame that is locally most similar to a previously initialized model. The object to be tracked is represented by a histogram. In object tracking algorithms target representation is mainly rectangular or elliptical region. It contains target model and target candidate. Color histogram is used to characterize the object. Target model is generally represented by its probability density function (pdf). Weighted RGB histogram is used to give more importance to object pixels.

Mean-shift algorithm is an iterative technique for locating the maxima of a density function. For object tracking, the density function used is the weight image formed using color histograms of the object to be tracked and the frame to be tested. By using the weighted histogram we are taking spatial position into consideration unlike the normal histogram calculation. This function will take input image pointer, top left and bottom right coordinates of the rectangular object, frame number and tracking status as inputs and returns the centroid using recursive mean shift approach.

API Syntax

template <int MAXOBJ, int MAXITERS, int OBJ_ROWS, int OBJ_COLS, int SRC_T, int ROWS, int COLS, int NPC>
void MeanShift(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> &_in_mat, uint16_t* x1, uint16_t* y1, uint16_t* obj_height, uint16_t* obj_width, uint16_t* dx, uint16_t* dy, uint16_t* status, uint8_t frame_status, uint8_t no_objects, uint8_t no_iters );

Template Parameter Descriptions

The following table describes the template parameters.

Table . MeanShift Template Parameters

Parameter	Description
MAXOBJ	Maximum number of objects to be tracked
MAXITERS	Maximum iterations for convergence
OBJ_ROWS	Maximum Height of the object to be tracked
OBJ_COLS	Maximum width of the object to be tracked
SRC_T	Type of the input xf::cv::Mat, must be XF_8UC4, 8-bit data with 4 channels
ROWS	Maximum height of the image
COLS	Maximum width of the image
NPC	Number of pixels to be processed per cycle; this function supports only XF_NPPC1 or 1 pixel per cycle operations.

Function Parameter Description

The following table describes the function parameters.

Table . MeanShift Function Parameters

Parameter	Description
_in_mat	Input xF Mat
x1	Top Left corner x-coordinate of all the objects
y1	Top Left corner y-coordinate of all the objects
obj_height	Height of all the objects
obj_width	Width of all the objects
dx	Centers x-coordinate of all the objects returned by the kernel function
dy	Centers y-coordinate of all the objects returned by the kernel function
status	Track the object only if the status of the object is true, that is if the object goes out of the frame, status is made zero
frame_status	Set as zero for the first frame and one for other frames
no_objects	Number of objects racked
no_iters	Number of iterations for convergence

Resource Utilization and Performance Estimate

The following table summarizes the resource utilization of the MeanShift function for normal (1 pixel) configuration as generated in Vivado HLS 2019.1 release tool for the part xczu9eg-ffvb1156-i-es1 at 300 MHz to process a RGB image of resolution,1920x1080, and for 10 objects of size of 250x250 and 4 iterations.

Table . MeanShift Function Resource Utilization and Performance Estimate Summary

Configuration	Max. Latency (ms)	BRAMs	DSPs	FFs	LUTs
1 pixel	19.28	76	14	13198	10064

Limitations

The maximum number of objects that can be tracked is 10.

Mode filter

Mode filter is a non-linear digital filter which improves noise reduction. This implements filter operation with given size of N by computing mode for all the pixels in an NxN window.

API Syntax

template <int FILTER_SIZE, int BORDER_TYPE, int TYPE, int ROWS, int COLS, int NPC = 1>
void modefilter(xf::cv::Mat<TYPE, ROWS, COLS, NPC>& _src, xf::cv::Mat<TYPE, ROWS, COLS, NPC>& _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table Mode filter Parameter Description

Parameter	Description
FILTER_SIZE	Window size of the hardware filter for which the hardware kernel will be built. This can be any odd positive integer greater than 1.
BORDER_TYPE	The way in which borders will be processed in the hardware kernel. Currently, only XF_BORDER_REPLICATE is supported.
TYPE	Input and Output pixel type. Only 8-bit, unsigned, 1 or 3 channels are supported (XF_8UC1 and XF_8UC3)
ROWS	Number of rows in the image being processed.
COLS	Number of columns in the image being processed. Must be a multiple of 8, for 8-pixel operation.
NPC	Number of pixels to be processed in parallel. Options are XF_NPPC1 (for 1 pixel processing per clock), XF_NPPC8 (for 8 pixel processing per clock
_src	Input image.
_dst	Output image.

Resource Utilization

The following table summarizes the resource utilization of the Mode filter function for XF_NPPC1 configurations, generated using Vitis HLS 2020.2 version tool.

Table Mode filter Function Resource Utilization Summary

Operating Mode	FILTER_SIZE	Operating Frequency (MHz)	Utilization Estimate
			LUTs	FFs	DSPs	BRAMs
1 pixel	3	300	628	900	0	3
1 pixel	5	300	2579	4070	0	5
1 pixel	7	300	7852	14065	0	7

Performance Estimate

The following table summarizes performance estimates of Mode filter function on Vitis HLS 2020.2 version tool.

Table Mode filter Function Performance Estimate Summary

Operating Mode	FILTER_SIZE	Operating Frequency (MHz)	Input Image Size	Latency Estimate Max (ms)
1 pixel	3	300	1920x1080	6.99
1 pixel	5	300	1920x1080	7.00
1 pixel	7	300	1920x1080	7.15

Otsu Threshold

Otsu threshold is used to automatically perform clustering-based image thresholding or the reduction of a gray-level image to a binary image. The algorithm assumes that the image contains two classes of pixels following bi-modal histogram (foreground pixels and background pixels), it then calculates the optimum threshold separating the two classes.

Otsu method is used to find the threshold which can minimize the intra class variance which separates two classes defined by weighted sum of variances of two classes.

Where, w_1is the class probability computed from the histogram.

Otsu shows that minimizing the intra-class variance is the same as maximizing inter-class variance

Where, is the class mean.

API Syntax

template<int SRC_T, int ROWS, int COLS,int NPC=1> void OtsuThreshold(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src_mat, uint8_t &_thresh)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . OtsuThreshold Parameter Description

Paramete r	Description
SRC_T	Input pixel type. Only 8-bit, unsigned, 1 channel is supported (XF_8UC1)
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image (must be a multiple of 8, for 8-pixel operation)
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
_src_ma t	Input image
_thresh	Output threshold value after the computation

Resource Utilization

The following table summarizes the resource utilization of the OtsuThreshold function, generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a grayscale HD (1080x1920) image.

Table . OtsuThreshold Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 pixel	300	8	49	2239	3353	653
8 pixel	150	22	49	1106	3615	704

Performance Estimate

The following table summarizes the performance in different configurations, as generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table . OtsuThreshold Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.92
8 pixel operation (150 MHz)	1.76

Paint Mask

The Paintmask function replace the pixel intensity value with given color value when mask is not zero or the corresponding pixel from the input image.

API Syntax

template< int SRC_T,int MASK_T, int ROWS, int COLS,int NPC=1>
void paintmask(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src_mat, xf::cv::Mat<MASK_T, ROWS, COLS, NPC> & in_mask, xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _dst_mat, unsigned char _color[XF_CHANNELS(SRC_T,NPC)])

Parameter Descriptions

The following table describes the template and the function parameters.

Table . Paintmask Parameter Description

Parameter	Description
SRC_T	Input pixel type. 8-bit, unsigned, 1 channel is supported (XF_8UC1).
MASK_T	Mask value type. 8-bit, unsigned, 1 channel is supported (XF_8UC1).
ROWS	Maximum height of input and output image
COLS	Maximum width of input and output image. In case of N-pixel parallelism, width should be multiple of N.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
_src_mat	Input image
_in_mask	Input mask image
_dst_mat	Output image
_color	Color value to be filled when mask is not zero

Resource Utilization

The following table summarizes the resource utilization of the Paintmask Resource optimized (8 pixel) mode and normal mode as generated using Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA.

Table . Paintmask Function Resource Utilization Summary

Name	Resource Utilization
	1 pixel per clock operation	8 pixel per clock operation
	300 MHz	150 MHz
BRAM_18K	0	0
DSP48E	0	0
FF	95	163
LUT	57	121
CLB	14	33

Performance Estimate

The following table summarizes a performance estimate of the kernel in different configurations, generated using Vivado HLS 2019.1 tool for Xczu9eg-ffvb1156-1-i-es1 FPGA to process a grayscale HD (1080x1920) image.

Table . Painmask Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Operating Frequency (MHz)	Latency (ms)
1 pixel	300	6.9
8 pixel	150	1.7

Pixel-Wise Addition

The add function performs the pixel-wise addition between two input images and returns the output image.

Iout(x, y) = Iin1(x, y) + Iin2(x, y)

Where:

• Iout(x, y) is the intensity of the output image at (x, y) position

• Iin1(x, y) is the intensity of the first input image at (x, y) position

• Iin2(x, y) is the intensity of the second input image at (x, y) position.

XF_CONVERT_POLICY_TRUNCATE: Results are the least significant bits of the output operand, as if stored in two’s complement binary format in the size of its bit-depth.

XF_CONVERT_POLICY_SATURATE: Results are saturated to the bit depth of the output operand.

API Syntax

template<int POLICY_TYPE, int SRC_T, int ROWS, int COLS, int NPC=1>
void add (
xf::cv::Mat<int SRC_T, int ROWS, int COLS, int NPC> src1,
xf::cv::Mat<int SRC_T, int ROWS, int COLS, int NPC> src2,
xf::cv::Mat<int SRC_T, int ROWS, int COLS, int NPC> dst )

Parameter Descriptions

The following table describes the template and the function parameters.

Table . add Parameter Description

Parameter	Description
POLICY_TY PE	Type of overflow handling. It can be either, XF_CONVERT_POLICY_SATURATE or XF_CONVERT_POLICY_TRUNCATE.
SRC_T	Pixel type. Options are XF_8UC1, XF_8UC3, XF_16SC3, and XF_16SC1.
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image (must be a multiple of 8, for 8-pixel operation)
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
src1	Input image
src2	Input image
dst	Output image

Resource Utilization

The following table summarizes the resource utilization in different configurations, generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a grayscale HD (1080x1920) image.

Table . add Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 pixel	300	0	0	62	55	11
8 pixel	150	0	0	65	138	24

The following table summarizes the resource utilization in different configurations, generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA, to process 4K image with 3 channels.

Table . add Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 pixel	300	0	0	113	77	24

Performance Estimate

The following table summarizes the performance in different configurations, as generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table . add Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9
8 pixel operation (150 MHz)	1.7

Pixel-Wise Multiplication

The multiply function performs the pixel-wise multiplication between two input images and returns the output image.

I_out(x, y) = I_in1(x, y) * I_in2(x, y) * scale_val

Where:

• I_out(x, y) is the intensity of the output image at (x, y) position

• I_in1(x, y) is the intensity of the first input image at (x, y) position

• I_in2(x, y) is the intensity of the second input image at (x, y) position

• scale_val is the scale value.

XF_CONVERT_POLICY_TRUNCATE: Results are the least significant bits of the output operand, as if stored in two’s complement binary format in the size of its bit-depth.

XF_CONVERT_POLICY_SATURATE: Results are saturated to the bit depth of the output operand.

API Syntax

template<int POLICY_TYPE, int SRC_T,int ROWS, int COLS, int NPC=1>
void multiply (
xf::cv::Mat<int SRC_T, int ROWS, int COLS, int NPC> src1,
xf::cv::Mat<int SRC_T, int ROWS, int COLS, int NPC> src2,
xf::cv::Mat<int SRC_T int ROWS, int COLS, int NPC> dst,
float scale)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . multiply Parameter Description

Parameter	Description
POLICY_TY PE	Type of overflow handling. It can be either, XF_CONVERT_POLICY_SATURATE or XF_CONVERT_POLICY_TRUNCATE.
SRC_T	pixel type. Options are XF_8UC1,XF_8UC3,XF_16SC1 and XF_16SC3.
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image (must be a multiple of 8, for 8-pixel operation)
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
src1	Input image
src2	Input image
dst	Output image
scale_val	Weighing factor within the range of 0 and 1

Resource Utilization

The following table summarizes the resource utilization in different configurations, generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a grayscale HD (1080x1920) image.

Table . multiply Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 pixel	300	0	2	124	59	18
8 pixel	150	0	16	285	108	43

The following table summarizes the resource utilization in different configurations, generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a 4K image with 3 channels.

Table 350. multiply Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 pixel	300	0	9	312	211	62

Performance Estimate

The following table summarizes the performance in different configurations, as generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table . multiply Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9
8 pixel operation (150 MHz)	1.7

Pixel-Wise Subtraction

The subtract function performs the pixel-wise subtraction between two input images and returns the output image.

I_out(x, y) = I_in1(x, y) - I_in2(x, y)

Where:

• I_out(x, y) is the intensity of the output image at (x, y) position

• I_in1(x, y) is the intensity of the first input image at (x, y) position

• I_in2(x, y) is the intensity of the second input image at (x, y) position.

XF_CONVERT_POLICY_TRUNCATE: Results are the least significant bits of the output operand, as if stored in two’s complement binary format in the size of its bit-depth.

XF_CONVERT_POLICY_SATURATE: Results are saturated to the bit depth of the output operand.

API Syntax

template<int POLICY_TYPE int SRC_T, int ROWS, int COLS, int NPC=1>
void subtract (
xf::cv::Mat<int SRC_T, int ROWS, int COLS, int NPC> src1,
xf::cv::Mat<int SRC_T, int ROWS, int COLS, int NPC> src2,
xf::cv::Mat<int SRC_T, int ROWS, int COLS, int NPC> dst )

Parameter Descriptions

The following table describes the template and the function parameters.

Table . subtract Parameter Description

Parameter	Description
POLICY_TYPE	Type of overflow handling. It can be either, XF_CONVERT_POLICY_SATURATE or XF_CONVERT_POLICY_TRUNCATE.
SRC_T	pixel type. Options are XF_8UC1,XF_8UC3,XF_16SC3 and_16SC1.
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image (must be a multiple of 8, for 8-pixel operation)
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
src1	Input image
src2	Input image
dst	Output image

Resource Utilization

The following table summarizes the resource utilization in different configurations, generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a grayscale HD (1080x1920) image.

Table . subtract Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 pixel	300	0	0	62	53	11
8 pixel	150	0	0	59	13	21

The following table summarizes the resource utilization in different configurations, generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a 4K image with 3 channels.

Table . subtract Function Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 pixel	300	0	0	110	64	28

Performance Estimate

The following table summarizes the performance in different configurations, as generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table . subtract Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9
8 pixel operation (150 MHz)	1.7

Quantization & Dithering

This algorithm dithers input image using Floyd-Steinberg dithering method. It is commonly used by image manipulation software, for example when an image is converted into GIF format each pixel intensity value is quantized to 8 bit i.e. 256 colors.

API Syntax

template <int IN_TYPE, int OUT_TYPE, int ROWS, int COLS, int SCALE_FACTOR, int MAX_REPRESENTED_VALUE, int NPC>
void xf_QuatizationDithering(xf::cv::Mat<IN_TYPE, ROWS, COLS, NPC>& stream_in,
                             xf::cv::Mat<OUT_TYPE, ROWS, COLS, NPC>& stream_out)

Parameter Descriptions

The following table describes the template and the function parameters.

Table Quantization & Dithering Parameter Description

Parameter	Description
IN_TYPE	Input pixel type. It should be XF_8UC1, XF_8UC3, XF_10UC1, XF_10UC3, XF_12UC1, XF_12UC3, XF_16UC1, or XF_16UC3. Note XF_<PIXEL_BITWIDTH>UC<NUM_CHANNELS>
OUT_TYPE	Output pixel type. It should be XF_8UC1, XF_8UC3, XF_10UC1, XF_10UC3, XF_12UC1, XF_12UC3, XF_16UC1, or XF_16UC3. Output PIXEL_WIDTH should less than or equal to input PIXEL_BITWIDTH
ROWS	Maximum height of input and output image
COLS	Maximum width of input and output image. In case of N-pixel parallelism, width should be multiple of N.
SCALE_FACTOR	The SCALE_FACTOR must be power of 2 & less than or equal to 2^(output PIXEL_BITWIDTH)
MAX_REPRESENTED_VALUE	The MAX_REPRESENTED_VALUE must be equal to equal to 2^(input PIXEL_BITWIDTH)
NPC	Number of pixels to be processed per cycle; possible options is XF_NPPC1 or XF_NPPC2
stream_in	Input image
stream_out	Output image

Resource Utilization

The following table summarizes the resource utilization of the kernel in different configurations, generated using Vitis HLS 2020.2 tool for the Xilinx xcu200-fsgd2104-2-e FPGA, to process a RGB image with a resolution of 1024x676 & pixel width 16 bit and quantized it to 8 bit pixel width.

Table Quantization & Dithering Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	CLB
1 pixel	300	11	10	7592	5765	1582
2 pixel	300	14	12	8150	6945	1749

Performance Estimate

The following table summarizes a performance estimate of the kernel in different configurations, as generated using Vitis HLS 2020.2 tool for the Xilinx xcu200-fsgd2104-2-e FPGA, to process a RGB image with a resolution of 1024x676 & pixel width 16 bit and quantized it to 8 bit pixel width.

Table Quantization & Dithering Function Performance Estimate Summary

Operating Mode	Operating Frequency (MHz)	Latency Estimate Max (ms)
1 pixel	300	2.8
2 pixel	300	1.58

Reduce

The Reduce function reduces the matrix to a vector by treating rows/cols as set of 1-D vectors and performing specified operation on vectors until a single row/col is obtained.

Reduction operation could be one of the following:

• REDUCE_SUM : The output is the sum of all of the matrix’s rows/columns.

• REDUCE_AVG : The output is the mean vector of all of the matrix’s rows/columns.

• REDUCE_MAX : The output is the maximum (column/row-wise) of all of the matrix’s rows/columns.

• REDUCE_MIN : The output is the minimum (column/row-wise) of all of the matrix’s rows/columns.

API Syntax

template< int REDUCE_OP, int SRC_T , int DST_T,  int ROWS, int COLS, int ONE_D_HEIGHT, int ONE_D_WIDTH,int NPC=1> void reduce(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src_mat, xf::cv::Mat<DST_T, ONE_D_HEIGHT, ONE_D_WIDTH, 1> & _dst_mat, unsigned char dim)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . Reduce Parameter Description

Parameter	Description
REDUCE_OP	The flag specifies the type of reduction operation to be applied.
SRC_T	Input pixel type. 8-bit, unsigned, 1 channel is supported (XF_8UC1).
DST_T	Output pixel type. 8-bit, unsigned, 1 channel is supported (XF_8UC1).
ROWS	Maximum height of input and output image
COLS	Maximum width of input and output image. In case of N-pixel parallelism, width should be multiple of N.
ONE_D_HEIGHT	Height of output 1-D vector or reduced matrix
ONE_D_WIDTH	Width of output 1-D vector or reduced matrix
NPC	Number of pixels to be processed per cycle; possible option is XF_NPPC1 (1 pixel per cycle).
_src_mat	Input image
_dst_mat	1-D vector
dim	Dimension index along which the matrix is reduced. 0 means that the matrix is reduced to a single row. 1 means that the matrix is reduced to a single column.

Resource Utilization

The following table summarizes the resource utilization of the Reduce function Normal mode(1 pixel) as generated using Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA.

Table . Reduce Function Resource Utilization Summary

Name	Resource Utilization
	1 pixel per clock operation
	300 MHz
BRAM_18K	2
DSP48E	0
FF	288
LUT	172
CLB	54

Performance Estimate

The following table summarizes a performance estimate of the kernel in different configurations, generated using Vivado HLS 2019.1 tool for Xczu9eg-ffvb1156-1-i-es1 FPGA to process a grayscale HD (1080x1920) image.

Table . Reduce Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9

Remap

The remap function takes pixels from one place in the image and relocates them to another position in another image. Two types of interpolation methods are used here for mapping the image from source to destination image.

API Syntax

template<int WIN_ROWS,int INTERPOLATION_TYPE, int SRC_T, int MAP_T, int DST_T, int ROWS, int COLS, int NPC = 1,bool USE_URAM=false>

void remap (xf::cv::Mat<SRC_T, ROWS, COLS, NPC> &_src_mat,
         xf::cv::Mat<DST_T, ROWS, COLS, NPC> &_remapped_mat,
         xf::cv::Mat<MAP_T, ROWS, COLS, NPC> &_mapx_mat,
         xf::cv::Mat<MAP_T, ROWS, COLS, NPC> &_mapy_mat);

Parameter Descriptions

The following table describes the template parameters.

Table . remap template .. rubric:: Parameter Descriptions

Parameter	Description
WIN_ROWS	Number of input image rows to be buffered inside. Must be set based on the map data. For instance, for left right flip, 2 rows are sufficient.
INTERPOLATIO N_TYPE	Type of interpolation, either XF_INTERPOLATION_NN (nearest neighbor) or XF_INTERPOLATION_BILINEAR (linear interpolation)
SRC_T	Input and Output pixel type. Only 8-bit, unsigned, 1 and 3 channels are supported (XF_8UC1 and XF_8UC3)
MAP_T	Map type. Single channel float type. XF_32FC1.
DST_T	Output image type. Grayscale image of type 8-bits and single channel. XF_8UC1.
ROWS	Height of input and output images
COLS	Width of input and output images
NPC	Number of pixels to be processed per cycle; this function supports only XF_NPPC1 or 1 pixel per cycle operations.
USE_URAM	Enable to map some structures to UltraRAM instead of BRAM.

The following table describes the function parameters.

Table . remap Parameter Description

PARAMETERS	DESCRIPTION
_src_mat	Input xF Mat
_remapped_m at	Output xF Mat
_mapx_mat	mapX Mat of float type
_mapy_mat	mapY Mat of float type

Resource Utilization

The following table summarizes the resource utilization of remap, for HD (1080x1920) images generated in the Vivado HLS 2019.1 version tool for the Xilinx xczu9eg-ffvb1156-i-es1 FPGA at 300 MHz, with WIN_ROWS as 64 for the XF_INTERPOLATION_BILINEAR mode.

Table . remap Function Resource Utilization Summary

Name	Resource Utilization
BRAM_18K	64
DSP48E	17
FF	1738
LUT	1593
CLB	360

The following table summarizes the resource utilization of remap, for 4K (3840x2160) images generated in the Vivado HLS 2019.1 version tool for the Xilinx xczu7ev-ffvc1156 FPGA at 300 MHz, with WIN_ROWS as 100 for the XF_INTERPOLATION_BILINEAR mode using UltraRAM .

Table . remap Function Resource Utilization Summary with UltraRAM Enabled

Name	Resource Utilization
BRAM_18K	3
DSP48E	10
URAM	24
FF	3196
LUT	3705

Performance Estimate

The following table summarizes the performance of remap(), for HD (1080x1920) images generated in the Vivado HLS 2019.1 version tool for the Xilinx xczu9eg-ffvb1156-i-es1 FPGA at 300 MHz, with WIN_ROWS as 64 for XF_INTERPOLATION_BILINEAR mode.

Table . remap Function Performance Estimate Summary

Operating Mode	Operating Frequency (MHz)	Latency Estimate Max latency (ms)
1 pixel mode	300	7.2

Resolution Conversion (Resize)

Resolution Conversion is the method used to resize the source image to the size of the destination image. Different types of interpolation techniques can be used in resize function, namely: Nearest-neighbor, Bilinear, and Area interpolation. The type of interpolation can be passed as a template parameter to the API. The following enumeration types can be used to specify the interpolation type:

• XF_INTERPOLATION_NN - For Nearest-neighbor interpolation

• XF_INTERPOLATION_BILINEAR - For Bilinear interpolation

• XF_INTERPOLATION_AREA - For Area interpolation

Note: Scaling factors greater than or equal to 0.25 are supported in down-scaling and values less than or equal to 8 are supported for up-scaling.

API Syntax

template<int INTERPOLATION_TYPE, int TYPE, int SRC_ROWS, int SRC_COLS, int DST_ROWS, int DST_COLS, int NPC,int MAX_DOWN_SCALE>
void resize (xf::cv::Mat<TYPE, SRC_ROWS, SRC_COLS, NPC> & _src, xf::cv::Mat<TYPE, DST_ROWS, DST_COLS, NPC> & _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . resize Parameter Description

Parameter	Description
INTERPOLATIO N_TYPE	Interpolation type. The different options possible are XF_INTERPOLATION_NN – Nearest Neighbor Interpolation XF_INTERPOLATION_BILINEAR – Bilinear interpolation XF_INTERPOLATION_AREA – Area Interpolation
TYPE	Input and Output pixel type. Only 8-bit, unsigned, 1 and 3 channels are supported (XF_8UC1 and XF_8UC3)
SRC_ROWS	Maximum Height of input image for which the hardware kernel would be built.
SRC_COLS	Maximum Width of input image for which the hardware kernel would be built (must be a multiple of 8).
DST_ROWS	Maximum Height of output image for which the hardware kernel would be built.
DST_COLS	Maximum Width of output image for which the hardware kernel would be built (must be a multiple of 8).
NPC	Number of pixels to be processed per cycle. Possible options are XF_NPPC1 (1 pixel per cycle) and XF_NPPC8 (8 pixel per cycle).
MAX_DOWN_SCA LE	Set to 2 for all 1 pixel modes, and for upscale in x direction. When down scaling in x direction in 8-pixel mode, please set this parameter to the next highest integer value of the down scale factor i.e., if downscaling from 1920 columns to 1280 columns, set to 2. For 1920 to 640, set to 3.
_src	Input Image
_dst	Output Image

Resource Utilization

The following table summarizes the resource utilization of Resize function in Resource Optimized (8 pixel) mode and Normal mode, as generated in the Vivado HLS 2019.1 tool for the Xilinx xczu9eg-ffvb1156-2-i-es2 FPGA.

Table . resize Function Resource Utilization Summary

Operating Mode	Utilization Estimate
	1 Pixel (at 300 MHz)					8 Pixel (at 150MHz)
	IMAGESIZE	LUTs	FFs	DSPs	BRAMs	IMAGESIZE	LUTs	FFs	DSPs	BRAMs
Downscale Nearest Neighbor	1920X1080 TO 960X1620	1089	1593	4	2	3840X2160 TO 1920X1080	2545	2250	4	12
Downscale Bilinear	1920X1080 TO 960X1080	1340	1846	8	2	3840X2160 TO 1920X1080	5159	3092	36	12
Downscale Area	3840X2160 TO 1920X1080	6146	8338	19	10	3840X2160 TO 1920X1080	17892	19758	162	16
Upscale Nearest Neighbor	1920X1080 TO 3840X540	1089	1593	4	2	1920X1080 TO 3840X2160	1818	1686	4	6
Upscale Bilinear	1920X1080 TO 3840X540	1340	1846	8	2	1920X1080 TO 3840X2160	3697	2739	36	6
Upscale Area	1920X1080 TO 3840X2160	5811	8773	28	32	1920X1080 TO 3840X2160	12214	14003	98	24

The following table summarizes the resource utilization of Resize function in Normal mode, as generated in the Vivado HLS 2019.1 tool for the Xilinx xczu9eg-ffvb1156-2-i-es2 FPGA for 3channel image as input.

Table . resize Function Resource Utilization Summary

Operating Mode	Utilization Estimate
	1 Pixel (at 300 MHz)
	IMAGESIZE	LUTs	FFs	DSPs	BRAMs
Downscale Nearest Neighbor	3840X2160 TO 1920X108	1184	168	4	18
Downscale Bilinear	3840X2160 TO 1920X1080	1592	2058	14	18
Downscale Area	3840X2160 TO 1920X1080	3212	4777	104	72
Upscale Nearest Neighbor	1920X1080 TO 3840X2160	1166	1697	4	9
Upscale Bilinear	1920X1080 TO 3840X2160	1574	2053	14	9
Upscale Area	1920X1080 TO 3840X2160	1731	2733	36	31

Performance Estimate

The following table summarizes the performance estimation of Resize for various configurations, as generated in the Vivado HLS 2019.1 tool for the xczu9eg-ffvb1156-2-i-es2 FPGA at 300 MHz to resize a grayscale image from 1080x1920 to 480x640 (downscale); and to resize a grayscale image from 1080x1920 to 2160x3840 (upscale). This table also shows the latencies obtained for different interpolation types.

Table . resize Function Performance Estimate Summary

Operating Mode	Operating Frequency (MHz)	Latency Estimate (ms)
		Downscale NN	Downscale Bilinear	Downscale Area	Upscale NN	Upscale Bilinear	Upscale Area
1 pixel	300	6.94	6.97	7.09	27.71	27.75	27.74

BGR to HSV Conversion

The BGR2HSV function converts the input image color space to HSV color space and returns the HSV image as the output.

API Syntax

template<int SRC_T, int ROWS, int COLS,int NPC=1>
          void BGR2HSV(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src_mat,xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _dst_mat)

Parameter Descriptions

The table below describes the template and the function parameters.

Parameter	Description
SRC_T	Input pixel type should be XF_8UC3
DST_T	Output pixel type should be XF_8UC3
ROWS	Maximum height of input and output image
COLS	Maximum width of input and output image. Must be multiple of 8, for 8-pixel operation.
NPC	Number of pixels to be processed per cycle. Only XF_NPPC1 is supported.
_src_mat	Input image
_dst_mat	Output image

Scharr Filter

The Scharr function computes the gradients of input image in both x and y direction by convolving the kernel with input image being processed.

For Kernel size 3x3:

• GradientX:

• GradientY:

API Syntax

template<int BORDER_TYPE, int SRC_T,int DST_T, int ROWS, int COLS,int NPC=1>
void Scharr(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src_mat,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst_matx,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst_maty)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . Scharr Parameter Description

Parameter	Description
BORDER_TYPE	Border type supported is XF_BORDER_CONSTANT
SRC_T	Input pixel type. Only 8-bit, unsigned, 1 and 3 channels are supported (XF_8UC1 and XF_8UC3)
DST_T	Output pixel type. Only 8-bit unsigned, 16-bit signed,1 and 3 channels are supported (XF_8UC1, XF_16SC1,XF_8UC3 and XF_16SC3)
ROWS	Maximum height of input and output image
COLS	Maximum width of input and output image. Must be multiple of 8, for 8-pixel operation.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
_src_mat	Input image
_dst_matx	X gradient output image.
_dst_maty	Y gradient output image.

Resource Utilization

The following table summarizes the resource utilization of the kernel in different configurations, generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a grayscale HD (1080x1920) image.

Table . Scharr Function Resource Utilization Summary

Name	Resource Utilization
	1 pixel per clock operation	8 pixel per clock operation
	300 MHz	150 MHz
BRAM_18K	3	6
DSP48E	0	0
FF	728	1434
LUT	812	2481
CLB	171	461

The following table summarizes the resource utilization of the kernel in different configurations, generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a 4K 3 channel image.

Table . Scharr Function Resource Utilization Summary

Name	Resource Utilization
	1 pixel per clock operation
	300 MHz
BRAM_18K	18
DSP48E	0
FF	1911
LUT	1392

Performance Estimate

The following table summarizes the performance of the kernel in different configurations, as generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table . Scharr Function Performance Estimate Summary

Operating Mode	Operating Frequency (MHz)	Latency (ms)
1 pixel	300	7.2
8 pixel	150	1.7

Set

The Set function sets the each pixel in input image to a given scalar value and stores the result in dst.

API Syntax

template< int SRC_T , int ROWS, int COLS, int NPC=1>
void set(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src1, unsigned char _scl[XF_CHANNELS(SRC_T,NPC)], xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . Set Parameter Description

Parameter	Description
SRC_T	Input pixel type. 8-bit, unsigned, 1 channel is supported (XF_8UC1).
ROWS	Maximum height of input and output image
COLS	Maximum width of input and output image. Must be multiple of 8, for 8-pixel operation.
NPC	Number of pixels to be processed per cycle.
_src1	First input image
_scl	Scalar value
_dst	Output image

Resource Utilization

The following table summarizes the resource utilization of the Set function in Resource optimized (8 pixel) mode and normal mode as generated using Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA.

Table . Set Function Resource Utilization Summary

Name	Resource Utilization
	1 pixel per clock operation	8 pixel per clock operation
	300 MHz	150 MHz
BRAM_18K	0	0
DSP48E	0	0
FF	87	87
LUT	43	42
CLB	17	18

Performance Estimate

The following table summarizes a performance estimate of the kernel in different configurations, generated using Vivado HLS 2019.1 tool for Xczu9eg-ffvb1156-1-i-es1 FPGA to process a grayscale HD (1080x1920) image.

Table . Set Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9
8 pixel operation (150 MHz)	1.7

Sobel Filter

The Sobel function Computes the gradients of input image in both x and y direction by convolving the kernel with input image being processed.

• For Kernel size 3x3

  • GradientX:

  • GradientY:

• For Kernel size 5x5

  • GradientX:

  • GradientY:

• For Kernel size 7x7

  • GradientX:

  • GradientY:

API Syntax

template<int BORDER_TYPE,int FILTER_TYPE, int SRC_T,int DST_T, int ROWS, int COLS,int NPC=1,bool USE_URAM=false>
void Sobel(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src_mat,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst_matx,xf::cv::Mat<DST_T, ROWS, COLS, NPC> & _dst_maty)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . Sobel Parameter Description

Parameter	Description
FILTER_TYPE	Filter size. Filter size of 3 (XF_FILTER_3X3), 5 (XF_FILTER_5X5) and 7 (XF_FILTER_7X7) are supported.
BORDER_TYPE	Border Type supported is XF_BORDER_CONSTANT
SRC_T	Input pixel type. Only 8-bit, unsigned, 1 and 3 channels are supported (XF_8UC1 and XF_8UC3)
DST_T	Output pixel type. Only 8-bit unsigned, 16-bit signed,1 and 3 channels are supported (XF_8UC1, XF_16SC1,XF_8UC3 and XF_16SC3)
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. Must be multiple of 8, for 8-pixel operation.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
USE_URAM	Enable to map storage structures to UltraRAM
_src_mat	Input image
_dst_matx	X gradient output image.
_dst_maty	Y gradient output image.
Sobel 7x7 8-pixel is not supported.

Resource Utilization

The following table summarizes the resource utilization of the kernel in different configurations, generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a grayscale HD (1080x1920) image.

Table . Sobel Function Resource Utilization Summary

Operating Mode	Filter Size	Operating Frequency (MHz)	Utilization Estimate
			BRAM_18K	DSP_48Es	FF	LUT	CLB
1 pixel	3x3	300	3	0	609	616	135
	5x5	300	5	0	1133	1499	308
	7x7	300	7	0	2658	3334	632
8 pixel	3x3	150	6	0	1159	1892	341
	5x5	150	10	0	3024	5801	999

The following table summarizes the resource utilization of the kernel in different configurations, generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a 4K 3 Channel image.

Table . Sobel Function Resource Utilization Summary

Operating Mode	Filter Size	Operating Frequency (MHz)	Utilization Estimate
			BRAM_18K	DSP_48Es	FF	LUT
1 pixel	3x3	300	18	0	1047	1107
	5x5	300	30	0	5370	3312
	7x7	300	42	0	6100	5496

The following table summarizes the resource utilization of the kernel in different configurations, generated using Vivado HLS 2019.1 tool for the Xilinx xczu7ev-ffvc1156-2-e FPGA, to process a grayscale 4K (3840x2160) image with UltraRAM enable.

Table . Sobel Function Resource Utilization Summary with UltraRAM enable

Operating Mode	Filter Size	Operating Frequency (MHz)	Utilization Estimate
			BRAM_18K	URAM	DSP_48Es	FF	LUT
1 pixel	3x3	300	0	1	0	919	707
	5x5	300	0	1	0	2440	1557
	7x7	300	0	1	0	4066	3495
8 pixel	3x3	150	0	3	0	1803	2050
	5x5	150	0	5	0	4159	6817

Performance Estimate

The following table summarizes the performance of the kernel in different configurations, as generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1, to process a grayscale HD (1080x1920) image.

Table . Sobel Function Performance Estimate Summary

Operating Mode	Operating Frequency (MHz)	Filter Size	Latency Estimate (ms)
1 pixel	300	3x3	7.5
	300	5x5	7.5
	300	7x7	7.5
8 pixel	150	3x3	1.7
	150	5x5	1.71

Semi Global Method for Stereo Disparity Estimation

Stereo matching algorithms are used for finding relative depth from a pair of rectified stereo images. The resultant disparity information can be used for 3D reconstruction by triangulation, using the known intrinsic and extrinsic parameters of the stereo camera. The Semi global method for stereo disparity estimation aggregates the cost in terms of dissimilarity across multiple paths leading to a smoother estimate of the disparity map.

For the semi-global method in Vitis Vision, census transform in conjunction with Hamming distance is used for cost computation. The semiglobal optimization block is based on the implementation by Hirschmuller, but approximates the cost aggregation by considering only four directions.

Parallelism is achieved by computing and aggregating cost for multiple disparities in parallel, and this parameter is included as a compile-time input.

API Syntax

template<int BORDER_TYPE, int WINDOW_SIZE, int NDISP, int PU, int R, int SRC_T, int DST_T, int ROWS, int COLS, int NPC>

void SemiGlobalBM(xf::cv::Mat<SRC_T,ROWS,COLS,NPC> & _src_mat_l, xf::cv::Mat<SRC_T,ROWS,COLS,NPC> & _src_mat_r, xf::cv::Mat<DST_T,ROWS,COLS,NPC> & _dst_mat, uint8_t p1, uint8_t p2)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . SemiGlobalBM Parameter Description

Parameter	Description
BORDER_TYPE	The border pixels are processed in Census transform function based on this parameter. Only XF_BORDER_CONSTANT is supported.
WINDOW_SIZE	Size of the window used for Census transform computation. Only ‘5’ (5x5) is supported.
NDISP	Number of disparities
PU	Number of disparity units to be computed in parallel
R	Number of directions for cost aggregation. It must be 2, 3, or 4.
SRC_T	Type of input image Mat object. It must be XF_8UC1.
DST_T	Type of output disparity image Mat object. It must be XF_8UC1.
ROWS	Maximum height of the input image.
COLS	Maximum width of the input image.
NPC	Number of pixels to be computed in parallel. It must be XF_NPPC1.
_src_mat_l	Left input image Mat
_src_mat_r	Right input image Mat
_dst_mat	Output disparity image Mat
p1	Small penalty for cost aggregation
p2	Large penalty for cost aggregation. The maximum value is 100.

Resource Utilization

The following table summarizes the resource utilization for a 1920 x 1080 image, with 64 number of disparities, and 32 parallel units.

Table . SemiGlobalBM Function Resource Utilization Summary

Operating Mode	Filter Size	Operating Frequency (MHz)	Utilization Estimate
			BRAM_18K	DSP_48Es	FF	LUT
1 Pixel	5x5	200	205	141	11856	19102

Performance Estimate

The following table summarizes a performance estimate for a 1920x1080 image.

Table . SemiGlobalBM Function Performance Estimate Summary

Operating Mode	Operating Frequency	Number of Disparities	Parallel Units	Latency
1 pixel/clock	200 MHz	64	32	42 ms

Stereo Local Block Matching

Stereo block matching is a method to estimate the motion of the blocks between the consecutive frames, called stereo pair. The postulate behind this idea is that, considering a stereo pair, the foreground objects will have disparities higher than the background. Local block matching uses the information in the neighboring patch based on the window size, for identifying the conjugate point in its stereo pair. While, the techniques under global method, used the information from the whole image for computing the matching pixel, providing much better accuracy than local methods. But, the efficiency in the global methods are obtained with the cost of resources, which is where local methods stands out.

Local block matching algorithm consists of pre-processing and disparity estimation stages. The pre-processing consists of Sobel gradient computation followed by image clipping. And the disparity estimation consists of SAD (Sum of Absolute Difference) computation and obtaining the disparity using winner takes all method (least SAD will be the disparity). Invalidity of the pixel relies upon its uniqueness from the other possible disparities. And the invalid pixels are indicated with the disparity value of zero.

API Syntax

template <int WSIZE, int NDISP, int NDISP_UNIT, int SRC_T, int DST_T, int ROWS, int COLS, int NPC = XF_NPPC1,bool USE_URAM=false>
void StereoBM(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> &_left_mat, xf::cv::Mat<SRC_T, ROWS, COLS, NPC> &_right_mat, xf::cv::Mat<DST_T, ROWS, COLS, NPC> &_disp_mat, xf::cv::xFSBMState<WSIZE,NDISP,NDISP_UNIT> &sbmstate);

Parameter Descriptions

The following table describes the template and the function parameters.

Table . StereoBM Parameter Description

Parameter	Description
WSIZE	Size of the window used for disparity computation
NDISP	Number of disparities
NDISP_UNITS	Number of disparities to be computed in parallel.
SRC_T	Input pixel type. Only 8-bit, unsigned, 1 channel is supported (XF_8UC1)
DST_T	Output type. This is XF_16UC1, where the disparities are arranged in Q12.4 format.
ROWS	Maximum height of input and output image
COLS	Maximum width of input and output image
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 only.
USE_URAM	Enable to map some storage structures to UltraRAM
left_image	Image from the left camera
right_image	Image from the right camera
disparity_im age	Disparities output in the form of an image.
sbmstate	Class object consisting of various parameters regarding the stereo block matching algorithm. preFilterCap: Default value is 31, can be altered by the user, value ranges from 1 to 63. minDisparity: Default value is 0, can be altered by the user, value ranges from 0 to (imgWidth-NDISP). uniquenessRatio: Default set to 15, but can be altered to any non-negative integer. textureThreshold: Default set to 10, but can be modified to any non-negative integer.

Resource Utilization

The following table summarizes the resource utilization of the kernel in different configurations, generated using Vivado HLS 2019.1 version tool for the Xilinx® Xczu9eg-ffvb1156-1-i-es1 FPGA, to progress a grayscale HD (1080x1920) image.

The configurations are in the format: imageSize_WSIZE_NDisp_NDispUnits.

Table . StereoBM Function Resource Utilization Summary

Configurations	Frequency (MHz)	Resource Utilization
		BRAM_18k	DSP48E	FF	LUT
HD_5_16_2	300	37	20	6856	7181
HD_9_32_4	300	45	20	9700	10396
HD_11_32_32	300	49	20	34519	31978
HD_15_128_32	300	57	20	41017	35176
HD_21_64_16	300	69	20	29853	30706

The following table summarizes the resource utilization of the kernel in different configurations, generated using Vivado HLS 2019.1 version tool for the Xilinx xczu7ev-ffvc1156-2-e FPGA, to progress a grayscale HD (1080x1920) image with UltraRAM enable.

The configurations are in the format: imageSize_WSIZE_NDisp_NDispUnits.

Table . StereoBM Function Resource Utilization Summary with UltraRAM Enable

Configurations	Frequency (MHz)	Resource Utilization
		BRAM_18k	URAM	DSP48E	FF	LUT
HD_5_16_2	300	0	12	20	7220	6529
HD_9_32_4	300	0	12	20	10186	9302
HD_11_32_32	300	0	14	20	44046	30966
HD_15_128_32	300	0	14	20	50556	38132
HD_21_64_16	300	0	16	20	35991	28464

Performance Estimate

The following table summarizes a performance estimate of the Stereo local block matching in different configurations, as generated using Vivado HLS 2019.1 tool for Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a grayscale HD (1080x1920) image.

The configurations are in the format: imageSize_WSIZE_NDisp_NDispUnits.

Table . StereoBM Function Performance Estimate Summary

Configurations	Frequency (MHz)	Latency (ms)
		Min	Max
HD_5_16_2	300	55.296	55.296
HD_9_32_4	300	55.296	55.296
HD_11_32_32	300	6.912	6.912
HD_15_48_16	300	20.736	20.736
HD_15_128_32	300	27.648	27.648
HD_21_64_16	300	27.648	27.648

SubRS

The SubRS function subtracts the intensity of the source image from a scalar image and stores it in the destination image.

dst(I)= scl - src(I)

API Syntax

template<int POLICY_TYPE, int SRC_T, int ROWS, int COLS, int NPC =1>
void subRS(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src1, unsigned char _scl[XF_CHANNELS(SRC_T,NPC)],xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . SubRS Parameter Description

Parameter	Description
SRC_T	Input Pixel Type. 8-bit, unsigned, 1 channel is supported (XF_8UC1).
ROWS	Maximum height of input and output image
COLS	Maximum width of input and output image. In case of N-pixel parallelism, width should be multiple of N.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
_src1	First Input image
_scl	Input scalar value,the size should be number of channels
_dst	Output image

Resource Utilization

The following table summarizes the resource utilization of the SubRS function in Resource optimized (8 pixel) mode and normal mode as generated using Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA.

Table . SubRS Function Resource Utilization Summary

Name	Resource Utilization
	1 pixel per clock operation	8 pixel per clock operation
	300 MHz	150 MHz
BRAM_18K	0	0
DSP48E	0	0
FF	103	104
LUT	44	133
CLB	23	43

Performance Estimate

The following table summarizes a performance estimate of the kernel in different configurations, generated using Vivado HLS 2019.1 tool for Xczu9eg-ffvb1156-1-i-es1 FPGA to process a grayscale HD (1080x1920) image.

Table . SubRS Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9
8 pixel operation (150 MHz)	1.7

SubS

The SubS function subtracts a scalar value from the intensity of source image and stores it in the destination image.

dst(I)= src(I) - scl

API Syntax

template<int POLICY_TYPE, int SRC_T, int ROWS, int COLS, int NPC =1>
void subS(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src1, unsigned char _scl[XF_CHANNELS(SRC_T,NPC)],xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . SubS Parameter Description

Parameter	Description
SRC_T	Input Pixel Type. 8-bit, unsigned, 1 channel is supported (XF_8UC1).
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. In case of N-pixel parallelism, width should be multiple of N.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
_src1	First Input image
_scl	Input scalar value, the size should be the number of channels.
_dst	Output image

Resource Utilization

The following table summarizes the resource utilization of the SubS function in Resource optimized (8 pixel) mode and normal mode as generated using Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA.

Table . SubS Function Resource Utilization Summary

Name	Resource Utilization
	1 pixel per clock operation	8 pixel per clock operation
	300 MHz	150 MHz
BRAM_18K	0	0
DSP48E	0	0
FF	103	104
LUT	44	133
CLB	23	43

Performance Estimate

The following table summarizes a performance estimate of the kernel in different configurations, generated using Vivado HLS 2019.1 tool for Xczu9eg-ffvb1156-1-i-es1 FPGA to process a grayscale HD (1080x1920) image.

Table . SubS Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9
8 pixel operation (150 MHz)	1.7

Sum

The sum function calculates the sum of all pixels in input image.

API Syntax

template< int SRC_T , int ROWS, int COLS, int NPC=1>
void sum(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & src1,double sum[XF_CHANNELS(SRC_T,NPC)])

Parameter Descriptions

The following table describes the template and the function parameters.

Table . Sum Parameter Description

Parameter	Description
SRC_T	Input pixel type. 8-bit, unsigned, 1 channel is supported (XF_8UC1).
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image (must be multiple of 8).
NPC	Number of pixels to be processed per cycle.
_src1	Input image.
sum	Array to store sum of all pixels in the image.

Resource Utilization

The following table summarizes the resource utilization of the Sum function in Resource optimized (8 pixel) mode and normal mode as generated using Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA.

Table . Sum Function Resource Utilization Summary

Name	Resource Utilization
	1 pixel per clock operation	8 pixel per clock operation
	300 MHz	150 MHz
BRAM_18K	0	0
DSP48E	0	0
FF	341	408
LUT	304	338
CLB	71	87

Performance Estimate

The following table summarizes a performance estimate of the kernel in different configurations, generated using Vivado HLS 2019.1 tool for Xczu9eg-ffvb1156-1-i-es1 FPGA to process a grayscale HD (1080x1920) image.

Table . Sum Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)
8 pixel operation (150 MHz)

SVM

The SVM function is the SVM core operation, which performs dot product between the input arrays. The function returns the resultant dot product value with its fixed point type.

API Syntax

template<int SRC1_T, int SRC2_T, int DST_T, int ROWS1, int COLS1, int ROWS2, int COLS2, int NPC=1, int N>
void SVM(xf::cv::Mat<SRC1_T, ROWS1, COLS1, NPC> &in_1, xf::cv::Mat<SRC2_T, ROWS2, COLS2, NPC> &in_2, uint16_t idx1, uint16_t idx2, uchar_t frac1, uchar_t frac2, uint16_t n, uchar_t *out_frac, ap_int<XF_PIXELDEPTH(DST_T)> *result)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . SVM Parameter Description

Parameters	Description
SRC1_T	Input pixel type. 16-bit, signed, 1 channel (XF_16SC1) is supported.
SRC2_T	Input pixel type. 16-bit, signed, 1 channel (XF_16SC1) is supported.
DST_T	Output data Type. 32-bit, signed, 1 channel (XF_32SC1) is supported.
ROWS1	Number of rows in the first image being processed.
COLS1	Number of columns in the first image being processed.
ROWS2	Number of rows in the second image being processed.
COLS2	Number of columns in the second image being processed.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1.
N	Max number of kernel operations
in_1	First Input Array.
in_2	Second Input Array.
idx1	Starting index of the first array.
idx2	Starting index of the second array.
frac1	Number of fractional bits in the first array data.
frac2	Number of fractional bits in the second array data.
n	Number of kernel operations.
out_frac	Number of fractional bits in the resultant value.
result	Resultant value

Resource Utilization

The following table summarizes the resource utilization of the SVM function, generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA.

Table . SVM Function Resource Utilization Summary

Operating Frequency (MHz)	Utilization Estimate (ms)
	BRAM_18K	DSP_48Es	FF	LUT	CLB
300	0	1	27	34	12

Performance Estimate

The following table summarizes the performance in different configurations, as generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA.

Table . SVM Function Performance Estimate Summary

Operating Frequency (MHz)	Latency Estimate
	Min (cycles)	Max (cycles)
300	204	204

3D LUT

3D Look Up Tables (LUTs) may look similar to 1D LUTs in their principle of using value as mapping indexes to get the new value, they differ in the sense that they operate on three independent parameters. This drastically increases the number of mapped indexes to value pairs. For example, a combination of 3 individual 1D LUTs can map 2^n * 3 values where n is the bitdepth, whereas a 3D LUT processing 3 channels will have 2^n * 2^n * 2^n possible values.

Since all those huge number of values cannot be stored, only a subset of them are saved and the remaining values are computed through interpolation. The current implementation supports trilinear interpolation.

API Syntax

template <int LUTDIM, int SQLUTDIM, int INTYPE, int OUTTYPE, int ROWS, int COLS, int NPPC = 1, int URAM = 0>
void lut3d(xf::cv::Mat<INTYPE, ROWS, COLS, NPPC>& in_img,
           xf::cv::Mat<XF_32FC3, SQLUTDIM, LUTDIM, NPPC>& lut,
           xf::cv::Mat<OUTTYPE, ROWS, COLS, NPPC>& out_img,
           unsigned char lutdim)

The following table describes the template and the function parameters.

Table 3D LUT Parameter Description

Parameter	Description
LUTDIM	Maximum dimension of input LUT
SQLUTDIM	Squared value of maximum dimension of input LUT
INTYPE	Input Pixel Type. XF_8UC3, XF_10UC3, XF_12UC3, XF_16UC3 supported
OUTTYPE	Output Pixel Type. XF_8UC3, XF_10UC3, XF_12UC3, XF_16UC3 supported
ROWS	Maximum height of input and output image
COLS	Maximum width of input and output image
NPPC	Number of Pixels to be processed per cycle. Only XF_NPPC1 supported
URAM	Enable to map storage structures to UltraRAM.
in_img	Input image
lut	Input lut
out_img	Output image
lutdim	Dimension of input lut

Resource Utilization

The following table summarizes the resource utilization of the kernel in different configurations, generated using Vitis HLS 2020.2 tool for the Xilinx Alveo U200 FPGA, to process a 4K image.

Table 3D LUT Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Utilization Estimate
		BRAM_18K	DSP_48Es	FF	LUT	SLICE
1 pixel	300	30	40 | 9182 | 12039| 847

Performance Estimate

The following table summarizes a performance estimate of the kernel in different configurations, as generated using Vitis HLS 2020.2 tool for the Xilinx Alveo U200 FPGA, to process 4K image.

Table 3D LUT Resource Utilization Summary

Operating Mode	Operating Frequency (MHz)	Latency Estimate
		Max (ms)
1 pixel	300	28.5

Thresholding

The Threshold function performs thresholding operation on the input image. There are several types of thresholding supported by the function.

API Syntax

template<int THRESHOLD_TYPE, int SRC_T, int ROWS, int COLS,int NPC=1>
void Threshold(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src_mat,xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _dst_mat,short int thresh,short int maxval )

Parameter Descriptions

The following table describes the template and the function parameters.

Table . Threshold Parameter Description

Parameter	Description
THRESHOLD_TY PE	Type of thresholding.
SRC_T	Input pixel type. Only 8-bit, unsigned, 1 channel is supported (XF_8UC1).
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. Must be multiple of 8, for 8-pixel operation.
NPC	Number of pixels to be processed per cycle.
_src_mat	Input image
_dst_mat	Output image
thresh	Threshold value.
maxval	Maximum value to use with the THRESH_BINARY and THRESH_BINARY_INV thresholding types.

Resource Utilization

The following table summarizes the resource utilization of the kernel with binary thresholding in different configurations, generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1 FPGA, to process a grayscale HD (1080x1920) image.

Table . Threshold Function Resource Utilization Summary

Name	Resource Utilization
	1 pixel per clock operation	8 pixel per clock operation
	300 MHz	150 MHz
BRAM_18K	0	0
DSP48E	0	0
FF	110	154
LUT	61	139
CLB	16	37

Performance Estimate

The following table summarizes the performance of the kernel in different configurations, as generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1, to process a grayscale HD (1080x1920) image.

Table . Threshold Function Performance Estimate Summary

Operating Mode	Operating Frequency (MHz)	Latency Estimate (ms)
1 pixel	300	7.2
8 pixel	150	1.7

Atan2

The Atan2LookupFP function finds the arctangent of y/x. It returns the angle made by the vector with respect to origin. The angle returned by atan2 will also contain the quadrant information.

Atan2LookupFP is a fixed point version of the standard atan2 function. This function implements the atan2 using a lookup table approach. The values in the look up table are represented in Q4.12 format and so the values returned by this function are in Q4.12. A maximum error of 0.2 degrees is present in the range of 89 to 90 degrees when compared to the standard atan2 function available in glibc. For the other angles (0 to 89) the maximum error is in the order of 10-3. This function returns 0 when both xs and ys are zeroes.

API Syntax

short Atan2LookupFP(short xs, short ys, int M1,int N1,int M2, int N2)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . Atan2LookupFP Parameter Description

Parameter	Description
xs	16-bit signed value x in fixed point format of QM1.N1
ys	16-bit signed value y in fixed point format of QM2.N2
M1	Number of bits to represent integer part of x.
N1	Number of bits to represent fractional part of y. Must be equal to 16-M1.
M2	Number of bits to represent integer part of y.
N2	Number of bits to represent fractional part of y. Must be equal to 16-N1.
Return	Return value is in radians. Its range varies from -pi to +pi in fixed point format of Q4.12

..rubric:: Resource Utilization

The following table summarizes the resource utilization of the Atan2LookupFP function , generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA.

Table . Atan2LookupFP Function Resource Utilization Summary

Operating Frequency (MHz)	Utilization Estimate
	BRAM_18K	DSP_48Es	FF	LUT	CLB
300	4	2	275	75	139

Performance Estimate

The following table summarizes the performance in different configurations, as generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA.

Table . Atan2LookupFP Function Performance Estimate Summary

Operating Frequency (MHz)	Latency Estimate
	Min (cycles)	Max (cycles)
300	1	15

Inverse (Reciprocal)

The Inverse function computes the reciprocal of a number x. The values of 1/x are stored in a look up table of 2048 size. The index for picking the 1/x value is computed using the fixed point format of x. Once this index is computed, the corresponding 1/x value is fetched from the look up table and returned along with the number of fractional bits needed to represent this value in fixed point format.

API Syntax

unsigned int Inverse(unsigned short x,int M,char *N)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . Inverse Parameter Description

Parameter	Description
x	16-bit unsigned value x in fixed point format of QM.(16-M)
M	Number of bits to represent integer part of x.
N	Pointer to a char variable which stores the number of bits to represent fractional part of 1/x. This value is returned from the function.
Return	1/x value is returned in 32-bit format represented by a fixed point format of Q(32-N).N

Resource Utilization

The following table summarizes the resource utilization of the Inverse function, generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA.

Table . Inverse Function Resource Utilization Summary

Operating Frequency (MHz)	Utilization Estimate
	BRAM_18K	DSP_48Es	FF	LUT	CLB
300	4	0	68	128	22

Performance Estimate

The following table summarizes the performance in different configurations, as generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA.

Table . Inverse Function Performance Estimate Summary

Operating Frequency (MHz)	Latency Estimate
	Min (cycles)	Max (cycles)
300	1	15

Square Root

The Sqrt function computes the square root of a 16-bit fixed point number using the non-restoring square root algorithm. The non-restoring square root algorithm uses the two’s complement representation for the square root result. At each iteration the algorithm can generate exact result value even in the last bit.

Input argument D must be 16-bit number, though it is declared as 32-bit. The output sqrt(D) is 16-bit type. If format of D is QM.N (where M+N = 16) then format of output is Q(M/2).N

To get a precision of ‘n’ bits in fractional part, you can simply left shift the radicand (D) by ‘2n’ before the function call and shift the solution right by ‘n’ to get the correct answer. For example, to find the square root of 35 (01100011:sub:2) with one bit after the decimal point, that is, N=1:

1. Shift the number (0110001100:sub:2) left by 2

2. Shift the answer (1011:sub:2) right by 1. The correct answer is 101.1, which is 5.5.

API Syntax

int Sqrt(unsigned int D)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . Sqrt Parameter Description

Parameter	Description
D	Input data in a 16-bit fixed-point format.
Return	Output value in short int format.

Resource Utilization

The following table summarizes the resource utilization of the Sqrt function, generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA.

Table . Sqrt Function Resource Utilization Summary

Operating Frequency (MHz)	Utilization Estimate
	BRAM_18K	DSP_48Es	FF	LUT	CLB
300	0	0	8	6	1

Performance Estimate

The following table summarizes the performance in different configurations, as generated using Vivado HLS 2019.1 tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA.

Table . Sqrt Function Performance Estimate Summary

Operating Frequency (MHz)	Latency Estimate
	Min (cycles)	Max (cycles)
300	18	18

Warp Transform

The warpTransform function is designed to perform the perspective and affine geometric transformations on an image. The type of transform is a compile time parameter to the function.

The function uses a streaming interface to perform the transformation. Due to this and due to the fact that geometric transformations need access to many different rows of input data to compute one output row, the function stores some rows of the input data in block RAMs/UltraRAMs. The number of rows the function stores can be configured by the user by modifying a template parameter. Based on the transformation matrix, you can decide on the number of rows to be stored. You can also choose when to start transforming the input image in terms of the number of rows of stored image.

Affine Transformation

The transformation matrix consists of size parameters, and is as shown:

Affine transformation is applied in the warpTransform function following the equation:

Perspective Transformation

The transformation matrix is a 3x3 matrix as shown below:

Perspective transformation is applied in warpTransform following the equation:

The destination pixel is then computed by dividing the first two dimensions of the dst1 by the third dimension

API Syntax

template<int STORE_LINES, int START_ROW, int TRANSFORMATION_TYPE, int INTERPOLATION_TYPE, int SRC_T, int ROWS, int COLS, int NPC=1,bool USE_URAM=false>
void warpTransform(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & src, xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & dst, float *transformation_matrix)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . warpTransform Parameter Description

Parameter	Description
STORE_LINES	Number of lines to store an input to process a given transformation.
START_ROW	Number of the input rows to store before starting the image transformation. This must be less than or equal to STORE_LINES.
TRANSFORMATI ON_TYPE	Affine and perspective transformations are supported. Set this flag to ‘0’ for affine and ‘1’ for perspective transformation.
INTERPOLATIO N_TYPE	Set flag to ‘1’ for bilinear interpolation and ‘0’ for nearest neighbor interpolation.
SRC_T	Input and Output pixel type. Only 8-bit, unsigned, 1 and 3 channels are supported (XF_8UC1 and XF_8UC3)
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image.
NPC	Number of pixels to be processed per cycle; only one-pixel operation supported (XF_NPPC1).
USE_URAM	Enable to map some storage structures to UltraRAM
src	Input image
dst	Output image
transformati on_matrix	Transformation matrix that is applied to the input image.

Resource Utilization

The following table summarizes the resource utilization of the Warp transform, generated using Vivado HLS 2019.1 version tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a grayscale HD (1080x1920) image.

Table . warpTransform Function Resource Utilization Summary

Transformation	INTERPOLATION _TYPE	STORE _LINES	START _ROW	Operating Frequency (MHz)	Utilization Estimate
					LUTs	FFs	DSPs	BRAMs
Perspective	Bilinear	100	50	300	7468	9804	61	112
Perspective	Nearest Neighbor	100	50	300	4514	6761	35	104
Affine	Bilinear	100	50	300	6139	5606	40	124
Affine	Nearest Neighbor	100	50	300	4611	4589	18	112

The following table summarizes the resource utilization of the Warp transform, generated using Vivado HLS 2019.1 version tool for the Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a BGR 4K image.

Table . warpTransform Function Resource Utilization Summary

Transformation	INTERPOLATION _TYPE	STORE _LINES	START _ROW	Operating Frequency (MHz)	Utilization Estimate
					LUTs	FFs	DSPs	BRAMs
Perspective	Bilinear	100	50	300	9192	7910	48	616
Perspective	Nearest Neighbor	100	50	300	10533	12055	69	604
Affine	Bilinear	100	50	300	6397	8415	35	604

The following table summarizes the resource utilization of the Warp transform, generated using Vivado HLS 2019.1 version tool for the Xilinx xczu7ev-ffvc1156-2-e FPGA, to progress a grayscale 4K image with UltraRAM enabled.

Table . warpTransform Function Resource Utilization Summary with UltraRAM Enable

Transformation	INTERPOLATION _TYPE	STORE _LINES	START _ROW	Operating Frequency (MHz)	Utilization Estimate
					LUTs	FFs	DSPs	BRAMs	URAM
Perspective	Bilinear	100	50	300	7820	12458	61	7	12
Perspective	Nearest Neighbor	100	50	300	4880	8323	35	2	6
Affine	Bilinear	100	50	300	6850	9516	40	13	12
Affine	Nearest Neighbor	100	50	300	4651	6548	18	6	6

Performance Estimate

The following table summarizes a performance estimate of the Warp transform, as generated using Vivado HLS 2019.1 tool for Xilinx Xczu9eg-ffvb1156-1-i-es1 FPGA, to process a grayscale HD (1080x1920) image.

Table . warpTransform Function Performance Estimate Summary

Transforma tion	INTERPOLATI ON _TYPE	STORE _LIN ES	START _ROW	Operatin g Frequenc y (MHz)	Latency Estimate Max (ms)
Perspectiv e	Bilinear	100	50	300	7.46
Perspectiv e	Nearest Neighbor	100	50	300	7.31
Affine	Bilinear	100	50	300	7.31
Affine	Nearest Neighbor	100	50	300	7.24

Zero

The Zero function sets the each pixel in input image to zero and stores the result in dst.

API Syntax

template< int SRC_T , int ROWS, int COLS, int NPC=1>
void zero(xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _src1,xf::cv::Mat<SRC_T, ROWS, COLS, NPC> & _dst)

Parameter Descriptions

The following table describes the template and the function parameters.

Table . Zero Parameter Description

Parameter	Description
SRC_T	Input Pixel Type. 8-bit, unsigned, 1 channel is supported (XF_8UC1).
ROWS	Maximum height of input and output image.
COLS	Maximum width of input and output image. In case of N-pixel parallelism, width should be multiple of N.
NPC	Number of pixels to be processed per cycle; possible options are XF_NPPC1 and XF_NPPC8 for 1 pixel and 8 pixel operations respectively.
_src1	Input image
_dst	Output image

Resource Utilization

The following table summarizes the resource utilization of the Zero function in Resource optimized (8 pixel) mode and normal mode as generated using Vivado HLS 2019.1 version tool for the Xczu9eg-ffvb1156-1-i-es1 FPGA.

Table . Zero Function Resource Utilization Summary

Name	Resource Utilization
	1 pixel per clock operation	8 pixel per clock operation
	300 MHz	150 MHz
BRAM_18K	0	0
DSP48E	0	0
FF	78	78
LUT	42	41
CLB	15	14

Performance Estimate

The following table summarizes a performance estimate of the kernel in different configurations, generated using Vivado HLS 2019.1 tool for Xczu9eg-ffvb1156-1-i-es1 FPGA to process a grayscale HD (1080x1920) image.

Table . Zero Function Performance Estimate Summary

Operating Mode	Latency Estimate
	Max Latency (ms)
1 pixel operation (300 MHz)	6.9
8 pixel operation (150 MHz)	1.7

¹ N. Dalal, B. Triggs: Histograms of oriented gradients for human detection, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005.