updated magenta images

app.py

@@ -10,6 +10,24 @@ def preprocess_image(image, blur_value):
     blurred = cv2.GaussianBlur(gray, (blur_value, blur_value), 0)
     return blurred
 
+def create_dramatic_magenta(image1, diff):
+    """Create a more dramatic magenta overlay to highlight differences"""
+    # Create a more intense magenta by boosting the red and blue channels
+    diff_colored = cv2.absdiff(image1, diff)
+    
+    # Normalize to enhance contrast
+    diff_normalized = cv2.normalize(diff_colored, None, 0, 255, cv2.NORM_MINMAX)
+    
+    # Amplify the red channel for more dramatic magenta
+    diff_normalized[:, :, 0] = 0  # Remove blue
+    diff_normalized[:, :, 1] = 0  # Remove green
+    diff_normalized[:, :, 2] = np.clip(diff_normalized[:, :, 2] * 2, 0, 255)  # Boost red
+    
+    # Create more dramatic overlay with higher contrast
+    overlay = cv2.addWeighted(image1, 0.5, diff_normalized, 0.8, 0)
+    
+    return overlay
+
 def background_subtraction(image1, image2):
     subtractor = cv2.createBackgroundSubtractorMOG2()
     fgmask1 = subtractor.apply(image1)
@@ -22,11 +40,8 @@ def background_subtraction(image1, image2):
     # Create highlighted differences
     highlighted = cv2.bitwise_and(image2, image2, mask=mask)
     
-    # Create raw difference overlay
-    diff_colored = cv2.cvtColor(diff, cv2.COLOR_GRAY2BGR)
-    diff_colored[:, :, 0] = 0  # Remove blue
-    diff_colored[:, :, 1] = 0  # Remove green
-    overlay = cv2.addWeighted(image1, 0.6, diff_colored, 0.6, 0)
+    # Create raw difference overlay with dramatic magenta
+    raw_overlay = create_dramatic_magenta(image1, image2)
     
     # Create a blended image
     blended = cv2.addWeighted(image1, 0.5, image2, 0.5, 0)
@@ -35,13 +50,10 @@ def background_subtraction(image1, image2):
     composite = image1.copy()
     composite[mask > 0] = image2[mask > 0]
     
-    # Create final difference overlay
-    composite_diff = cv2.absdiff(image1, composite)
-    composite_diff[:, :, 0] = 0  # Remove blue
-    composite_diff[:, :, 1] = 0  # Remove green
-    final_overlay = cv2.addWeighted(image1, 0.6, composite_diff, 0.6, 0)
+    # Create final difference overlay with dramatic magenta
+    final_overlay = create_dramatic_magenta(image1, composite)
     
-    return blended, overlay, highlighted, mask, composite, final_overlay
+    return blended, raw_overlay, highlighted, mask, composite, final_overlay
 
 def optical_flow(image1, image2):
     gray1 = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY)
@@ -59,11 +71,8 @@ def optical_flow(image1, image2):
     # Create highlighted differences
     highlighted = cv2.bitwise_and(image2, image2, mask=mask)
     
-    # Create raw difference overlay
-    diff_colored = cv2.absdiff(image1, image2)
-    diff_colored[:, :, 0] = 0  # Remove blue
-    diff_colored[:, :, 1] = 0  # Remove green
-    overlay = cv2.addWeighted(image1, 0.6, diff_colored, 0.6, 0)
+    # Create raw difference overlay with dramatic magenta
+    raw_overlay = create_dramatic_magenta(image1, image2)
     
     # Create a blended image
     blended = cv2.addWeighted(image1, 0.5, image2, 0.5, 0)
@@ -72,13 +81,10 @@ def optical_flow(image1, image2):
     composite = image1.copy()
     composite[mask > 0] = image2[mask > 0]
     
-    # Create final difference overlay
-    composite_diff = cv2.absdiff(image1, composite)
-    composite_diff[:, :, 0] = 0  # Remove blue
-    composite_diff[:, :, 1] = 0  # Remove green
-    final_overlay = cv2.addWeighted(image1, 0.6, composite_diff, 0.6, 0)
+    # Create final difference overlay with dramatic magenta
+    final_overlay = create_dramatic_magenta(image1, composite)
     
-    return blended, overlay, highlighted, mask, composite, final_overlay
+    return blended, raw_overlay, highlighted, mask, composite, final_overlay
 
 def feature_matching(image1, image2):
     # Use SSIM as a fallback for feature matching since the original implementation doesn't give us a good mask
@@ -105,11 +111,8 @@ def compare_ssim(image1, image2, blur_value, technique, threshold_value):
     # Create highlighted differences
     highlighted = cv2.bitwise_and(image2, image2, mask=mask)
     
-    # Create raw difference overlay
-    diff_colored = cv2.absdiff(image1, image2)
-    diff_colored[:, :, 0] = 0  # Remove blue
-    diff_colored[:, :, 1] = 0  # Remove green
-    overlay = cv2.addWeighted(image1, 0.6, diff_colored, 0.6, 0)
+    # Create raw difference overlay with dramatic magenta
+    raw_overlay = create_dramatic_magenta(image1, image2)
     
     # Create a blended image
     blended = cv2.addWeighted(image1, 0.5, image2, 0.5, 0)
@@ -121,13 +124,10 @@ def compare_ssim(image1, image2, blur_value, technique, threshold_value):
     masked_bg = cv2.bitwise_and(image1, cv2.bitwise_not(mask_3channel))
     composite = cv2.add(masked_bg, masked_obj)
     
-    # Create final difference overlay
-    composite_diff = cv2.absdiff(image1, composite)
-    composite_diff[:, :, 0] = 0  # Remove blue
-    composite_diff[:, :, 1] = 0  # Remove green
-    final_overlay = cv2.addWeighted(image1, 0.6, composite_diff, 0.6, 0)
+    # Create final difference overlay with dramatic magenta
+    final_overlay = create_dramatic_magenta(image1, composite)
     
-    return blended, overlay, highlighted, mask, composite, final_overlay
+    return blended, raw_overlay, highlighted, mask, composite, final_overlay
 
 def compare_images(image1, image2, blur_value, technique, threshold_value, method):
     if method == "Background Subtraction":
@@ -149,7 +149,7 @@ with gr.Blocks() as demo:
         img1 = gr.Image(type="numpy", label="Image Without Object (Scene)")
         img2 = gr.Image(type="numpy", label="Image With Object")
     
-    blur_slider = gr.Slider(minimum=1, maximum=15, step=1, value=5, label="Gaussian Blur")
+    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)