Update app.py

app.py

@@ -15,7 +15,8 @@ def background_subtraction(image1, image2):
     fgmask1 = subtractor.apply(image1)
     fgmask2 = subtractor.apply(image2)
     diff = cv2.absdiff(fgmask1, fgmask2)
-    return cv2.cvtColor(diff, cv2.COLOR_GRAY2BGR)
+    unchanged = cv2.bitwise_and(image1, image1, mask=255 - diff)
+    return cv2.cvtColor(diff, cv2.COLOR_GRAY2BGR), unchanged
 
 def optical_flow(image1, image2):
     gray1 = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY)
@@ -26,7 +27,8 @@ def optical_flow(image1, image2):
     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)
+    unchanged = cv2.bitwise_and(image1, image1, mask=255 - hsv[..., 2].astype(np.uint8))
+    return cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR), unchanged
 
 def feature_matching(image1, image2):
     orb = cv2.ORB_create()
@@ -36,17 +38,17 @@ def feature_matching(image1, image2):
     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
+    unchanged = cv2.addWeighted(image1, 0.7, image2, 0.3, 0)
+    return result, unchanged
 
 def compare_images(image1, image2, blur_value, technique, threshold_value, method):
     if method == "Background Subtraction":
-        return background_subtraction(image1, image2), None
+        return background_subtraction(image1, image2)
     elif method == "Optical Flow":
-        return optical_flow(image1, image2), None
+        return optical_flow(image1, image2)
     elif method == "Feature Matching":
-        return feature_matching(image1, image2), None
+        return feature_matching(image1, image2)
     
-    # Default SSIM comparison
     gray1 = preprocess_image(image1, blur_value)
     gray2 = preprocess_image(image2, blur_value)
     score, diff = ssim(gray1, gray2, full=True)
@@ -70,8 +72,9 @@ def compare_images(image1, image2, blur_value, technique, threshold_value, metho
     diff_colored[:, :, 1] = 0
     diff_colored[:, :, 2] = thresh
     overlayed = cv2.addWeighted(image1, 0.7, diff_colored, 0.6, 0)
+    unchanged = cv2.bitwise_and(image1, image1, mask=255 - thresh)
     
-    return highlighted, overlayed
+    return highlighted, overlayed, unchanged
 
 def update_threshold_visibility(technique):
     return gr.update(visible=(technique == "Simple Binary"))
@@ -83,7 +86,7 @@ with gr.Blocks() as demo:
         img1 = gr.Image(type="numpy", label="Image Without Object")
         img2 = gr.Image(type="numpy", label="Image With Object")
     
-    blur_slider = gr.Slider(minimum=1, maximum=15, step=2, value=5, label="Gaussian Blur")
+    blur_slider = gr.Slider(minimum=1, maximum=15, step=1, 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)
@@ -94,7 +97,10 @@ with gr.Blocks() as demo:
         output1 = gr.Image(type="numpy", label="Highlighted Differences")
         output2 = gr.Image(type="numpy", label="Raw Difference Overlay (Magenta)")
     
+    with gr.Row():
+        output3 = gr.Image(type="numpy", label="Unchanged Areas Blended")
+    
     btn = gr.Button("Process")
-    btn.click(compare_images, inputs=[img1, img2, blur_slider, technique_dropdown, threshold_slider, method_dropdown], outputs=[output1, output2])
+    btn.click(compare_images, inputs=[img1, img2, blur_slider, technique_dropdown, threshold_slider, method_dropdown], outputs=[output1, output2, output3])
 
 demo.launch()