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I got a template image of a drawing.
I also have several images that may contain attempts to replicate it with variations (size, position, rotation).
I want to give a score of accuracy for each attempt compared to the template. I tried some opencv techniques like Hu moments, don't really get good results. Can you suggest a more effective approach or algorithm to achieve this?
I'm a debutant in image processing, so please explain in simple terms. I'm currently working with openCV in Python3 but the solution must works in Java too.
import cv2 as cv import numpy as np from scipy.optimize import linear_sum_assignment def extract_shapes(image_path): # Load and convert to binary img = cv.imread(image_path) gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY) _, binary = cv.threshold(gray, 127, 255, cv.THRESH_BINARY_INV) # Find connected components num_labels, labels, stats, centroids = cv.connectedComponentsWithStats( binary, connectivity=8 ) shapes = [] for i in range(1, num_labels): # Ignore the background (label 0) # Create a mask for this component mask = (labels == i).astype(np.uint8) * 255 # Find the contour contours, _ = cv.findContours(mask, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE) if contours and len(contours[0]) >= 5: # At least 5 points to be a valid shape shapes.append( { "contour": contours[0], "area": stats[i, cv.CC_STAT_AREA], "centroid": centroids[i], } ) return shapes def compare_two_shapes(contour1, contour2): # matchShapes returns a distance (0 = identical, larger means more different) distance = cv.matchShapes(contour1, contour2, cv.CONTOURS_MATCH_I2, 0) # Convert to similarity score 0-100 # Typical distance: 0-0.5 for similar shapes, >1 for very different similarity = max(0, 100 - (distance * 100)) return similarity def match_shapes_between_images(shapes1, shapes2): if not shapes1 or not shapes2: return [], 0 n1, n2 = len(shapes1), len(shapes2) # Cost matrix (distance between each pair of shapes) cost_matrix = np.zeros((n1, n2)) for i in range(n1): for j in range(n2): similarity = compare_two_shapes( shapes1[i]["contour"], shapes2[j]["contour"] ) cost_matrix[i, j] = 100 - similarity # Cost = inverse of similarity # Hungarian algorithm to find the optimal assignment row_ind, col_ind = linear_sum_assignment(cost_matrix) matches = [] for i, j in zip(row_ind, col_ind): similarity = 100 - cost_matrix[i, j] matches.append({"model_idx": i, "drawing_idx": j, "similarity": similarity}) return matches, len(shapes1) def evaluate_drawing(template: str, image: str, min_similarity=60): # Extract shapes from both images model_shapes = extract_shapes(template) drawing_shapes = extract_shapes(image) # Check that shapes were found if not model_shapes: return { "score": 0, "quality": "Error", "details": "No shapes detected in the template", } if not drawing_shapes: return { "score": 0, "quality": "Very Bad", "details": "No shapes detected in the drawing", } # Associate shapes matches, expected_count = match_shapes_between_images(model_shapes, drawing_shapes) # Calculate scores num_model = len(model_shapes) num_drawing = len(drawing_shapes) # Penalty if incorrect number of shapes count_penalty = abs(num_model - num_drawing) * 15 # Similarity score of matched shapes if matches: shape_scores = [m["similarity"] for m in matches] avg_similarity = np.mean(shape_scores) # Count how many shapes are "correct" correct_shapes = sum(1 for s in shape_scores if s >= min_similarity) # Final score # - 70% for average similarity of shapes # - 30% for number of correct shapes final_score = ( avg_similarity * 0.7 + (correct_shapes / expected_count * 100) * 0.3 - count_penalty ) else: avg_similarity = 0 correct_shapes = 0 final_score = 0 final_score = max(0, min(100, final_score)) # Determine quality if final_score >= 85: quality = "Excellent" elif final_score >= 70: quality = "Good" elif final_score >= 50: quality = "Average" else: quality = "Bad" return { "score": round(final_score, 1), "quality": quality, "avg_similarity": round(avg_similarity, 1), "shapes_expected": num_model, "shapes_found": num_drawing, "shapes_correct": correct_shapes if matches else 0, "details": f"{correct_shapes}/{expected_count} formes correctes, similarité moyenne: {avg_similarity:.1f}%", } if __name__ == "__main__": template = "assets/template.png" images = [ "assets/bigger.png", "assets/smaller.png", "assets/wrong.png", ] for image in images: print("\n" + "=" * 60) result = evaluate_drawing(template, image) print(f"\n{image}:") print(f" Overall Score: {result['score']}% ({result['quality']})") print(f" Average Similarity: {result['avg_similarity']}%") print( f" Shapes: {result['shapes_correct']}/{result['shapes_expected']} correct" ) print(f" Detected: {result['shapes_found']} shapes") print(f" Details: {result['details']}") print("\n" + "=" * 60)