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| import numpy as np | |
| import cv2 as cv | |
| import gradio as gr | |
| def match_features(target_img, comp_img1, comp_img2, comp_img3, comp_img4, comp_img5, comp_img6, comp_img7, comp_img8): | |
| # Initialize list to store results | |
| result_images = [] | |
| match_counts = [] | |
| # List of comparison images | |
| comparison_imgs = [comp_img1, comp_img2, comp_img3, comp_img4, comp_img5, comp_img6, comp_img7, comp_img8] | |
| # Convert target image to grayscale OpenCV format | |
| target_cv = np.array(target_img.convert("L")) | |
| # Extract SIFT features from target image | |
| sift = cv.SIFT_create() | |
| kp_target, des_target = sift.detectAndCompute(target_cv, None) | |
| # Initialize FLANN matcher | |
| FLANN_INDEX_KDTREE = 1 | |
| index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5) | |
| search_params = dict(checks=50) | |
| flann = cv.FlannBasedMatcher(index_params, search_params) | |
| # Process each comparison image | |
| for img in comparison_imgs: | |
| # Create a default blank image with "No image" text | |
| blank_img = np.zeros((400, 800, 3), dtype=np.uint8) | |
| cv.putText(blank_img, "No image provided", (250, 200), cv.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2) | |
| if img is None: | |
| result_images.append(blank_img) | |
| match_counts.append(0) | |
| continue | |
| # Convert to grayscale OpenCV format | |
| img_cv = np.array(img.convert("L")) | |
| # Extract SIFT features | |
| kp_img, des_img = sift.detectAndCompute(img_cv, None) | |
| # Skip if no features detected | |
| if des_img is None or des_target is None: | |
| cv.putText(blank_img, "No features detected", (250, 200), cv.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2) | |
| result_images.append(blank_img) | |
| match_counts.append(0) | |
| continue | |
| try: | |
| # Match features with FLANN | |
| matches = flann.knnMatch(des_target, des_img, k=2) | |
| # Apply ratio test | |
| matchesMask = [[0, 0] for i in range(len(matches))] | |
| good_matches = [] | |
| for i, pair in enumerate(matches): | |
| if len(pair) < 2: | |
| continue | |
| m, n = pair | |
| if m.distance < 0.7 * n.distance: | |
| matchesMask[i] = [1, 0] | |
| good_matches.append(m) | |
| # Count good matches | |
| match_count = len(good_matches) | |
| match_counts.append(match_count) | |
| # Draw matches | |
| draw_params = dict( | |
| matchColor=(0, 255, 0), | |
| singlePointColor=(255, 0, 0), | |
| matchesMask=matchesMask, | |
| flags=cv.DrawMatchesFlags_DEFAULT | |
| ) | |
| result_img = cv.drawMatchesKnn(target_cv, kp_target, img_cv, kp_img, matches, None, **draw_params) | |
| # Convert to RGB for display | |
| result_img = cv.cvtColor(result_img, cv.COLOR_BGR2RGB) | |
| # Add match count text | |
| font = cv.FONT_HERSHEY_SIMPLEX | |
| font_scale = 1 | |
| font_color = (255, 255, 255) | |
| thickness = 2 | |
| h, w = result_img.shape[:2] | |
| text = f"Matches: {match_count}" | |
| (text_width, text_height), _ = cv.getTextSize(text, font, font_scale, thickness) | |
| x = (w - text_width) // 2 | |
| y = h - 20 | |
| cv.rectangle(result_img, (x-5, y-text_height-5), (x+text_width+5, y+5), (0,0,0), -1) | |
| cv.putText(result_img, text, (x, y), font, font_scale, font_color, thickness) | |
| result_images.append(result_img) | |
| except Exception as e: | |
| # Handle any errors | |
| error_img = np.zeros((400, 800, 3), dtype=np.uint8) | |
| cv.putText(error_img, f"Error: {str(e)}", (50, 200), cv.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 1) | |
| result_images.append(error_img) | |
| match_counts.append(0) | |
| # Ensure we have 8 results | |
| while len(result_images) < 8: | |
| blank_img = np.zeros((400, 800, 3), dtype=np.uint8) | |
| cv.putText(blank_img, "No image provided", (250, 200), cv.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2) | |
| result_images.append(blank_img) | |
| match_counts.append(0) | |
| # Return all match results and counts | |
| return result_images + [match_counts] | |
| # Create Gradio interface with 1 target and up to 8 comparison images | |
| with gr.Blocks(title="Image Feature Matching Comparison") as iface: | |
| gr.Markdown("# Image Feature Matching with SIFT+FLANN") | |
| gr.Markdown(""" | |
| Upload a target image and up to 8 comparison images to find feature matches using | |
| SIFT (Scale-Invariant Feature Transform) with FLANN (Fast Library for Approximate Nearest Neighbors). | |
| The number of matches will be displayed for each comparison. | |
| """) | |
| with gr.Row(): | |
| target_input = gr.Image(type="pil", label="Target Image") | |
| with gr.Row(): | |
| comp_img1 = gr.Image(type="pil", label="Comparison Image 1") | |
| comp_img2 = gr.Image(type="pil", label="Comparison Image 2") | |
| with gr.Row(): | |
| comp_img3 = gr.Image(type="pil", label="Comparison Image 3") | |
| comp_img4 = gr.Image(type="pil", label="Comparison Image 4") | |
| with gr.Row(): | |
| comp_img5 = gr.Image(type="pil", label="Comparison Image 5") | |
| comp_img6 = gr.Image(type="pil", label="Comparison Image 6") | |
| with gr.Row(): | |
| comp_img7 = gr.Image(type="pil", label="Comparison Image 7") | |
| comp_img8 = gr.Image(type="pil", label="Comparison Image 8") | |
| compare_btn = gr.Button("Compare Images") | |
| with gr.Row(): | |
| result1 = gr.Image(label="Result 1") | |
| result2 = gr.Image(label="Result 2") | |
| with gr.Row(): | |
| result3 = gr.Image(label="Result 3") | |
| result4 = gr.Image(label="Result 4") | |
| with gr.Row(): | |
| result5 = gr.Image(label="Result 5") | |
| result6 = gr.Image(label="Result 6") | |
| with gr.Row(): | |
| result7 = gr.Image(label="Result 7") | |
| result8 = gr.Image(label="Result 8") | |
| match_counts_output = gr.JSON(label="Match Counts") | |
| compare_btn.click( | |
| fn=match_features, | |
| inputs=[ | |
| target_input, | |
| comp_img1, comp_img2, comp_img3, comp_img4, | |
| comp_img5, comp_img6, comp_img7, comp_img8 | |
| ], | |
| outputs=[ | |
| result1, result2, result3, result4, | |
| result5, result6, result7, result8, | |
| match_counts_output | |
| ] | |
| ) | |
| # Launch the interface | |
| iface.launch() |