New image compressor algorithym is added that uses k-means clustering.

README.md

@@ -20,6 +20,7 @@ Welcome everyone to contribute to this project!
 - [fibonacci.py](https://github.com/Henry-Jia/python-tools/blob/master/fibonacci.py): there're 2 algorithms that make the fibonacci with recursive and looping
 - [gcd.py](https://github.com/Henry-Jia/python-tools/blob/master/gcd.py): calculate the greatest common divisor
 - [igdata.py](https://github.com/Henry-Jia/python-tools/blob/master/igdata.py): Instagram Scraper, Scrapes data from public instagram profile and posts and saves it into json file.
+- [image_compressor.py](https://github.com/Henry-Jia/python-tools/blob/master/image_compressor.py): This is an Image Compression example using K-Means Clustering algorithm.
 - [image_stitching.py](https://github.com/Henry-Jia/python-tools/blob/master/image_stitching.py): stitching images using opencv
 - [imagerecognition.py](https://github.com/Henry-Jia/python-tools/blob/master/imagerecognition.py): facial recognition in python
 - [img2video.py](https://github.com/Henry-Jia/python-tools/blob/master/img2video.py): turn images to video

image_compressor.py

@@ -0,0 +1,148 @@
+function centroids = computeCentroids(X, idx, K)
+
+    [m n] = size(X);
+
+    centroids = zeros(K, n);
+
+    for i = 1:K
+
+        ci = idx==i;
+
+        ni = sum(ci);
+
+        ci_matrix = repmat(ci, 1, n);
+
+        centroids(i, :) = sum(X .* ci_matrix) ./ ni;
+
+    end
+
+end
+function idx = findClosestCentroids(X, centroids)
+
+    K = size(centroids, 1);
+
+    idx = zeros(size(X, 1), 1);
+
+
+
+    for i = 1:size(X, 1)
+
+        index = zeros(1, K);
+
+        for j = 1:K
+
+            index(1, j) = sqrt(sum(power((X(i, :) - centroids(j, :)), 2)));
+
+        end
+
+        [d, id] = min(index);
+
+        idx(i, 1) = id;
+
+    end
+
+end
+
+function centroids = kMeansInitCentroids(X, K)
+
+centroids = zeros(K, size(X, 2));
+
+
+
+randidx = randperm(size(X, 1));
+
+centroids = X(randidx(1:K), :);
+
+end
+
+
+[m n] = size(X);
+
+K = size(initial_centroids, 1);
+
+centroids = initial_centroids;
+
+previous_centroids = centroids;
+
+idx = zeros(m, 1);
+
+
+
+for i = 1:max_iters
+
+    fprintf('K-Means iteration %d/%d...', i, max_iters);
+
+    if exist('OCTAVE_VERSION')
+
+        fflush(stdout)
+
+    end
+
+    idx = findClosestCentroids(X, centroids);
+
+    centroids = computeCentroids(X, idx, K);
+
+end
+
+end
+
+
+A = double(imread('X.png'));
+
+% Each RGB value ranges from 0-255 (8 bits representation).
+
+% So, all values are in range 0-1
+
+A = A/255;
+
+
+
+img_size = size(A);
+
+% Reshaping the img to have 1 pixel value in one row
+
+X = reshape(A, img_size(1)*img_size(2), 3);
+
+
+
+K = 16;  % No. of clusters required (#colors here)
+
+max_iters = 10;
+
+
+
+initial_centroids = kMeansInitCentroids(X, K);
+
+% Run K-Means algo
+
+[centroids, idx] = runkMeans(X, initial_centroids, max_iters);
+
+
+
+idx = findClosestCentroids(X, centroids);
+
+X_recovered = centroids(idx, :);
+
+% Image represented by 3D matrix
+
+X_recovered = reshape(X_recovered, img_size(1), img_size(2), 3);
+
+
+
+% Display Original image
+
+subplot(1, 2, 1);
+
+imagesc(A);
+
+title('Original');
+
+
+
+% Display Compressed image
+
+subplot(1, 2, 2);
+
+imagesc(X_recovered);
+
+title('Compressed image');