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| import gradio as gr | |
| import cv2 | |
| import numpy as np | |
| from skimage.metrics import structural_similarity as ssim | |
| def preprocess_image(image, blur_value): | |
| # Convert to grayscale | |
| gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) | |
| # Apply Gaussian blur to reduce noise | |
| blurred = cv2.GaussianBlur(gray, (blur_value, blur_value), 0) | |
| return blurred | |
| def compare_images(image1, image2, blur_value, technique): | |
| # Preprocess images | |
| gray1 = preprocess_image(image1, blur_value) | |
| gray2 = preprocess_image(image2, blur_value) | |
| # Compute SSIM between the two images | |
| score, diff = ssim(gray1, gray2, full=True) | |
| diff = (diff * 255).astype("uint8") | |
| if technique == "Adaptive Threshold": | |
| _, thresh = cv2.threshold(diff, 30, 255, cv2.THRESH_BINARY_INV) | |
| elif technique == "Otsu's Threshold": | |
| _, thresh = cv2.threshold(diff, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU) | |
| else: # Default to simple binary threshold | |
| _, thresh = cv2.threshold(diff, 50, 255, cv2.THRESH_BINARY) | |
| # Find contours of differences | |
| contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) | |
| # Filter out small noise using contour area threshold | |
| filtered_contours = [cnt for cnt in contours if cv2.contourArea(cnt) > 500] | |
| # Create a mask to isolate only the significant added object | |
| mask = np.zeros_like(image1) | |
| cv2.drawContours(mask, filtered_contours, -1, (255, 255, 255), thickness=cv2.FILLED) | |
| # Apply the mask to highlight the object added in the second image | |
| highlighted = cv2.bitwise_and(image2, mask) | |
| # Show the raw difference in magenta | |
| diff_colored = cv2.merge([np.zeros_like(diff), diff, diff]) | |
| return highlighted, diff_colored | |
| demo = gr.Interface( | |
| fn=compare_images, | |
| inputs=[ | |
| gr.Image(type="numpy", label="Image Without Object"), | |
| gr.Image(type="numpy", label="Image With Object"), | |
| gr.Slider(minimum=1, maximum=15, step=2, value=5, label="Gaussian Blur"), | |
| gr.Dropdown(["Adaptive Threshold", "Otsu's Threshold", "Simple Binary"], label="Thresholding Technique", value="Adaptive Threshold") | |
| ], | |
| outputs=[ | |
| gr.Image(type="numpy", label="Highlighted Differences"), | |
| gr.Image(type="numpy", label="Raw Difference (Magenta)") | |
| ], | |
| title="Object Difference Highlighter", | |
| description="Upload two images: one without an object and one with an object. The app will highlight only the newly added object and show the real differences in magenta." | |
| ) | |
| demo.launch() | |