Vision Processing with the Canny Edge Detection Reference Design

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ID 683433
Date 2/14/2015
Public
Document Table of Contents
Document Table of Contents x
1. Vision Processing with the Canny Edge Detection Reference Design
1. Vision Processing with the Canny Edge Detection Reference Design x
1.1. About Canny Edge Detection 1.2. About the Canny Edge Detection Reference Design 1.3. Getting Started with the Canny Edge Reference Design 1.4. Canny Edge Detection Reference Design Block Description 1.5. Stream-to-Memory Conversion 1.6. Latency and Throughput 1.7. Canny Edge Reference Design Resource Usage
1.3. Getting Started with the Canny Edge Reference Design x
1.3.1. Hardware and Software Requirements 1.3.2. Connecting the Hardware to Use the Canny Edge Reference Design 1.3.3. Loading the Canny Edge Reference Design FPGA Image with the SD Card Image 1.3.4. Canny Edge Reference Design Initial Startup Problems 1.3.5. Controlling the FPGA Flow of the Canny Edge Reference Design 1.3.6. Capturing the Pixel Stream 1.3.7. Programming the FPGA with the Canny Edge Reference Design 1.3.8. Initializing the ARM Processor
1.4. Canny Edge Detection Reference Design Block Description x
1.4.1. Greyscale Conversion 1.4.2. Gaussian Blurring 1.4.3. Sobel Edge Intensity 1.4.4. Nonmaximal Suppression 1.4.5. Double Threshold 1.4.6. Edge Linking
1.5. Stream-to-Memory Conversion x
1.5.1. FPGA and ARM Processor Clock Domains 1.5.2. HPS SDRAM Partitions 1.5.3. Frame Buffering 1.5.4. Operating System and Video Driver 1.5.5. About the Edge-linking Algorithm
1. Vision Processing with the Canny Edge Detection Reference Design

1.3.6. Capturing the Pixel Stream

Ensure the FPGA image is loaded, the ARM is correctly configured, and the frame reader is running.
  1. From the System Console GUI, select the video type to be passed into the ARM
    An uncompressed frame image file ARMvideo_frame.dat is now in the Linux file system
  2. Export this file to your PC that is running MATLAB.
  3. Run the MATLAB script parse_dat2 to convert the uncompressed .dat file to a viewable image:
    parse_dat2(<image length>,<image height>,<.dat file name>,0)
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