def image_complexity()

in seed/util/preprocess.py [0:0]

• 21 lines of code
• 5 McCabe index (conditional complexity)

def image_complexity(img, laplacian_var_thres=500, edge_count_thres=10000, total_entropy_thres=5.0):
    
    if isinstance(img, Image.Image):
        img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)

    ##### Histogram entropy
    hist_b = cv2.calcHist([img], [0], None, [256], [0, 256])
    hist_g = cv2.calcHist([img], [1], None, [256], [0, 256])
    hist_r = cv2.calcHist([img], [2], None, [256], [0, 256])
    
    # Normalize the histograms
    hist_b /= hist_b.sum()
    hist_g /= hist_g.sum()
    hist_r /= hist_r.sum()
    
    # Calculate histogram entropy
    entropy_b = -np.sum(hist_b * np.log2(hist_b + 1e-7))
    entropy_g = -np.sum(hist_g * np.log2(hist_g + 1e-7))
    entropy_r = -np.sum(hist_r * np.log2(hist_r + 1e-7))
    
    # Total entropy
    total_entropy = entropy_b + entropy_g + entropy_r

    ### Laplacian variance
    gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    laplacian_var = cv2.Laplacian(gray_img, cv2.CV_64F).var()
    
    ### Canny edge detection
    edges = cv2.Canny(gray_img, 100, 200)
    edge_count = np.sum(edges > 0)

    if laplacian_var > laplacian_var_thres or edge_count > edge_count_thres or total_entropy > total_entropy_thres:
        return "Complex", laplacian_var, edge_count, total_entropy
    else:
        return "Simple", laplacian_var, edge_count, total_entropy