initial commit

process.py

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+import click
+import numpy as np
+from PIL import Image
+from scipy.spatial import KDTree, Voronoi
+from itertools import product
+
+def compute_mean_color(img, region):
+    """
+    This function takes an image array and
+    one of those tuples that np.where outputs.
+    """
+    region_slice = img[region]
+    return np.mean(region_slice, axis=0)
+
+@click.command()
+@click.argument("img_path", type=str)
+@click.option("--out", type=str, default="out.jpg")
+@click.option("--n_points", type=int, default=300)
+def process(img_path, out, n_points):
+    # Loading the image
+    img = Image.open(img_path)
+    img = np.asarray(img)
+
+    random_i = np.random.randint(0, img.shape[1], size=n_points)
+    random_j = np.random.randint(0, img.shape[0], size=n_points)
+
+    points = np.vstack((random_i, random_j)).T
+
+    # Creating the kdtree
+    vor = Voronoi(points)
+    kdtree = KDTree(vor.points)
+
+    # Identifying regions pixel by pixel
+    region_array = - np.ones(img.shape[:2])
+    all_pos = product(range(img.shape[1]), range(img.shape[0]))
+    positions = np.array(list(all_pos))
+    distances, labels = kdtree.query(positions)
+
+    for pos, label in zip(positions, labels):
+        region_array[pos[1], pos[0]]= label
+
+    # Storing colors
+    colors = {}
+    for region_id in np.unique(region_array):
+        colors[region_id] = compute_mean_color(img, np.where(region_array == region_id))
+
+    new_img = np.zeros(img.shape)
+    for region_id in np.unique(region_array):
+        region = np.where(region_array == region_id)
+        new_img[region] = colors[region_id]
+
+    new_img = new_img.astype(int)
+
+    # Saving it
+    PIL_image = Image.fromarray(new_img.astype('uint8'), 'RGB')
+    PIL_image.save(out)
+
+if __name__ == "__main__":
+    process()

requirements.txt

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+click==7.1.2
+numpy==1.19.1
+Pillow==7.2.0
+scipy==1.5.1