Create sketch_helper.py

sketch_helper.py

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+import numpy as np
+import cv2
+from PIL import Image
+
+def get_high_freq_colors(image):
+  im = image.getcolors(maxcolors=1024*1024)
+  sorted_colors = sorted(im, key=lambda x: x[0], reverse=True)
+  
+  freqs = [c[0] for c in sorted_colors]
+  mean_freq = sum(freqs) / len(freqs)
+
+  high_freq_colors = [c for c in sorted_colors if c[0] > max(2, mean_freq)]  # Ignore colors that occur very few times (less than 2) or less than half the average frequency
+  return high_freq_colors
+
+def color_quantization(image, n_colors):
+    # Get color histogram
+    hist, _ = np.histogramdd(image.reshape(-1, 3), bins=(256, 256, 256), range=((0, 256), (0, 256), (0, 256)))
+    # Get most frequent colors
+    colors = np.argwhere(hist > 0)
+    colors = colors[np.argsort(hist[colors[:, 0], colors[:, 1], colors[:, 2]])[::-1]]
+    colors = colors[:n_colors]
+    # Replace each pixel with the closest color
+    dists = np.sum((image.reshape(-1, 1, 3) - colors.reshape(1, -1, 3))**2, axis=2)
+    labels = np.argmin(dists, axis=1)
+    return colors[labels].reshape((image.shape[0], image.shape[1], 3)).astype(np.uint8)
+
+def create_binary_matrix(img_arr, target_color):
+    # Create mask of pixels with target color
+    mask = np.all(img_arr == target_color, axis=-1)
+    
+    # Convert mask to binary matrix
+    binary_matrix = mask.astype(int)
+    from datetime import datetime
+    binary_file_name = f'mask-{datetime.now().timestamp()}.png'
+    cv2.imwrite(binary_file_name, binary_matrix * 255)
+    
+    #binary_matrix = torch.from_numpy(binary_matrix).unsqueeze(0).unsqueeze(0)
+    return binary_file_name