Python 3 Script to Compress or Minify Image Size Using Pillow and Numpy Library (K-means Algorithm) Full Project For Beginners


Welcome folks today in this post we will be compressing image using pillow and numpy library. We will be using python script for this purpose and we will be making use of k-means algorithm. All the full source code of the application is given below.




Get Started




In order to get started we need to install the following libraries using the pipcommand


pip install pillow


pip install numpy


After installing these libraries we need to make an file and copy paste the following code



#! /usr/bin/env python3

import os
import sys

from PIL import Image
import numpy as np

def load_image(path):
    """ Load image from path. Return a numpy array """
    image =
    return np.asarray(image) / 255

def initialize_K_centroids(X, K):
    """ Choose K points from X at random """
    m = len(X)
    return X[np.random.choice(m, K, replace=False), :]

def find_closest_centroids(X, centroids):
    m = len(X)
    c = np.zeros(m)
    for i in range(m):
        # Find distances
        distances = np.linalg.norm(X[i] - centroids, axis=1)

        # Assign closest cluster to c[i]
        c[i] = np.argmin(distances)

    return c

def compute_means(X, idx, K):
    _, n = X.shape
    centroids = np.zeros((K, n))
    for k in range(K):
        examples = X[np.where(idx == k)]
        mean = [np.mean(column) for column in examples.T]
        centroids[k] = mean
    return centroids

def find_k_means(X, K, max_iters=10):
    centroids = initialize_K_centroids(X, K)
    previous_centroids = centroids
    for _ in range(max_iters):
        idx = find_closest_centroids(X, centroids)
        centroids = compute_means(X, idx, K)
        if (previous_centroids==centroids).all():
            # The centroids aren't moving anymore.
            return centroids
            previous_centroids = centroids

    return centroids, idx

def main():
        image_path = sys.argv[1]
        assert os.path.isfile(image_path)
    except (IndexError, AssertionError):
        print('Please specify an image')

    # Load the image
    image = load_image(image_path)
    w, h, d = image.shape
    print('Image found with width: {}, height: {}, depth: {}'.format(w, h, d))

    # Get the feature matrix X
    X = image.reshape((w * h, d))
    K = 40 # the number of colors in the image

    # Get colors
    print('Runnign K-means')
    colors, _ = find_k_means(X, K, max_iters=20)

    # Indexes for color for each pixel
    idx = find_closest_centroids(X, colors)

    # Reconstruct the image
    idx = np.array(idx, dtype=np.uint8)
    X_reconstructed = np.array(colors[idx, :] * 255, dtype=np.uint8).reshape((w, h, d))
    compressed_image = Image.fromarray(X_reconstructed)

    # Save reconstructed image to disk'out.png')

if __name__ == '__main__':


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Now you just need to execute this pythonscript and also you need to pass the path of image as the argument to the script as follows


python sample.jpg



So sample.jpg here is the path of the image it is there in the same directory so we are passing it to the script.

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