Convolutional filter(s) for images

• Images are stored in folder images/. All images should be of the same size.

• Python Imaging Library (PIL) was used to convert images into RGB matrices and to convert filtered matrices back to images. PIL was preferred over other C++ libraries due to ease of use.

Install

Install g++ latest on ubuntu version.

g++-10

Install python libraries.

$ pip3 install numpy image matplotlib

Code structure

• filter.hpp defines a 3-D convolutinal kernel class with a bias term. It contains some helper functions to allocate memory to tensors and to normalize them.
• conv2d_layer.hpp defines a convolutional layer. One can set the stride and zero-padding of the filter in this. Also, dimensions of the output layer are calculated automatically.
• conv2d method takes as argument a 3-D data volume and a list of filters (one filter generates one activation map). For example, applying a 3 x 3 x 3 filter on a 512 x 512 x 3 image (with 1 zero padding and 1 stride) will generate an 2-D output layer of 512 x 512. See example (taken from course cs231n).
• List of filters would make the output layer. Shape of output layer as well as the data block is returned by the function conv2d.
• main.cpp runs some example filters on a batch of 3 images. It generates 3 filters, one as an edge detector for each color channel (see push_filter). Then defines a convolution layer with given params and applies the layer to each of the images. It then writes the output to a different file.
• make_mats.py and load_img.py are used to generate images-matrices and vice versa.

for (int id = 0; id < num_images; ++id) {
  ...
  auto output = clayer.conv2d(input, filters);
  ...
}

Steps to run

• Compile using make. Build file for convolutional filter demo program is 'main'. See run.sh for a complete run

rm *.o main
g++  -std=gnu++11 -O2 filter.hpp -o filter.o
g++  -std=gnu++11 -O2 conv2d_layer.hpp -o conv2d_layer.o
g++  -std=gnu++11 -O2 main.cpp -o main

• List of images to use is in file make_mats.py. In the demo it uses a batch of 3 512 * 512 * 3 (color) images.

python make_mats.py img_mats/out.dat

• Run the convolutional filter (read from standard input)

./main img_mats/out.dat img_mats/filter_out.dat < filter.txt

• Make output images from matrices

python load_img.py img_mats/filter_out.dat out_mats

Results

Images taken from USC Viterbi image dataset

Input images	Output images