Update app.py

app.py

@@ -10,12 +10,45 @@ def preprocess_image(image, blur_value):
     blurred = cv2.GaussianBlur(gray, (blur_value, blur_value), 0)
     return blurred
 
-def compare_images(image1, image2, blur_value, technique, threshold_value):
-    # Preprocess images
+def background_subtraction(image1, image2):
+    subtractor = cv2.createBackgroundSubtractorMOG2()
+    fgmask1 = subtractor.apply(image1)
+    fgmask2 = subtractor.apply(image2)
+    diff = cv2.absdiff(fgmask1, fgmask2)
+    return cv2.cvtColor(diff, cv2.COLOR_GRAY2BGR)
+
+def optical_flow(image1, image2):
+    gray1 = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY)
+    gray2 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)
+    flow = cv2.calcOpticalFlowFarneback(gray1, gray2, None, 0.5, 3, 15, 3, 5, 1.2, 0)
+    mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1])
+    hsv = np.zeros_like(image1)
+    hsv[..., 1] = 255
+    hsv[..., 0] = ang * 180 / np.pi / 2
+    hsv[..., 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)
+    return cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
+
+def feature_matching(image1, image2):
+    orb = cv2.ORB_create()
+    kp1, des1 = orb.detectAndCompute(image1, None)
+    kp2, des2 = orb.detectAndCompute(image2, None)
+    bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
+    matches = bf.match(des1, des2)
+    matches = sorted(matches, key=lambda x: x.distance)
+    result = cv2.drawMatches(image1, kp1, image2, kp2, matches[:20], None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
+    return result
+
+def compare_images(image1, image2, blur_value, technique, threshold_value, method):
+    if method == "Background Subtraction":
+        return background_subtraction(image1, image2), None
+    elif method == "Optical Flow":
+        return optical_flow(image1, image2), None
+    elif method == "Feature Matching":
+        return feature_matching(image1, image2), None
+    
+    # Default SSIM comparison
     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")
     
@@ -23,30 +56,20 @@ def compare_images(image1, image2, blur_value, technique, threshold_value):
         _, 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:  # Simple Binary
+    else:
         _, thresh = cv2.threshold(diff, threshold_value, 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, dtype=np.uint8)
     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)
     
-    # Create a magenta overlay where changes occurred
     diff_colored = np.zeros_like(image1, dtype=np.uint8)
-    diff_colored[:, :, 0] = thresh  # Set blue channel to zero
-    diff_colored[:, :, 1] = 0  # Set green channel to zero
-    diff_colored[:, :, 2] = thresh  # Set red channel to highlight in magenta
-    
-    # Combine the original image with the magenta overlay
-    overlayed = cv2.addWeighted(image1, 0.7, diff_colored, 0.3, 0)
+    diff_colored[:, :, 0] = 0
+    diff_colored[:, :, 1] = 0
+    diff_colored[:, :, 2] = thresh
+    overlayed = cv2.addWeighted(image1, 0.7, diff_colored, 0.6, 0)
     
     return highlighted, overlayed
 
@@ -63,13 +86,15 @@ with gr.Blocks() as demo:
     blur_slider = gr.Slider(minimum=1, maximum=15, step=2, value=5, label="Gaussian Blur")
     technique_dropdown = gr.Dropdown(["Adaptive Threshold", "Otsu's Threshold", "Simple Binary"], label="Thresholding Technique", value="Adaptive Threshold", interactive=True)
     threshold_slider = gr.Slider(minimum=0, maximum=255, step=1, value=50, label="Threshold Value", visible=False)
+    method_dropdown = gr.Dropdown(["SSIM", "Background Subtraction", "Optical Flow", "Feature Matching"], label="Comparison Method", value="SSIM", interactive=True)
     
     technique_dropdown.change(update_threshold_visibility, inputs=[technique_dropdown], outputs=[threshold_slider])
     
-    output1 = gr.Image(type="numpy", label="Highlighted Differences")
-    output2 = gr.Image(type="numpy", label="Raw Difference Overlay (Magenta)")
+    with gr.Row():
+        output1 = gr.Image(type="numpy", label="Highlighted Differences")
+        output2 = gr.Image(type="numpy", label="Raw Difference Overlay (Magenta)")
     
     btn = gr.Button("Process")
-    btn.click(compare_images, inputs=[img1, img2, blur_slider, technique_dropdown, threshold_slider], outputs=[output1, output2])
+    btn.click(compare_images, inputs=[img1, img2, blur_slider, technique_dropdown, threshold_slider, method_dropdown], outputs=[output1, output2])
 
-demo.launch()
+demo.launch()