Canny Edge Detection¶
Canny example resides in L2/examples/canny
directory.
This benchmark tests the performance of canny function. The Canny edge detector finds the edges in an image or video frame. It is one of the most popular algorithms for edge detection.
The tutorial provides a step-by-step guide that covers commands for building and running kernel.
Executable Usage¶
Work Directory(Step 1)
The steps for library download and environment setup can be found in README file of L2 folder. For getting the design,
cd L2/example/canny
Build kernel(Step 2)
Run the following make command to build your XCLBIN and host binary targeting a specific device. Please be noticed that this process will take a long time, maybe couple of hours.
export OPENCV_INCLUDE=< path-to-opencv-include-folder >
export OPENCV_LIB=< path-to-opencv-lib-folder >
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:< path-to-opencv-lib-folder >
export DEVICE=< path-to-platform-directory >/< platform >.xpfm
make host xclbin TARGET=hw
Run kernel(Step 3)
To get the benchmark results, please run the following command.
make run TARGET=hw
Example output(Step 4)
-----------Canny Design---------------
Found Platform
Platform Name: Xilinx
XCLBIN File Name: krnl_canny
INFO: Importing Vitis_Libraries/vision/L2/examples/canny/Xilinx_Canny_L2_Test_vitis_hw_u200/build_dir.hw.xilinx_u200_xdma_201830_2/krnl_canny.xclbin
Loading: 'Vitis_Libraries/vision/L2/examples/canny/Xilinx_Canny_L2_Test_vitis_hw_u200/build_dir.hw.xilinx_u200_xdma_201830_2/krnl_canny.xclbin'
before kernelafter kernel
before kernelafter kernel
actual number of cols is 3840
Total Execution time 10.5ms
------------------------------------------------------------
Profiling¶
The canny design is validated on Alveo U200 board at 300 MHz frequency. The hardware resource utilizations are listed in the following table.
Dataset |
LUT |
BRAM |
FF |
DSP |
||
---|---|---|---|---|---|---|
Resolution |
NPPC |
other params |
||||
4K |
8 |
L1 Norm, Filter - 3x3 |
31408 |
132 |
19148 |
96 |
FHD |
8 |
L1 Norm, Filter - 3x3 |
17451 |
65 |
11256 |
63 |
The performance is shown below
Dataset |
FPS(CPU) |
FPS(FPGA) |
---|---|---|
4k (3840x2160) |
9 |
95 |
Full HD(1920x1080) |
25 |
333 |