Update app.py

app.py

@@ -21,6 +21,7 @@ def count_and_label_red_patches(heatmap, threshold=200):
 
     num_red_patches = labels.max()
     
+    # Iterate through the labeled patches and put sequential numbers on top
     for i in range(1, num_red_patches + 1):
         patch_mask = (labels == i)
         patch_centroid_x, patch_centroid_y = int(stats[i, cv2.CC_STAT_LEFT] + stats[i, cv2.CC_STAT_WIDTH] / 2), int(stats[i, cv2.CC_STAT_TOP] + stats[i, cv2.CC_STAT_HEIGHT] / 2)
@@ -45,26 +46,33 @@ if uploaded_image:
 
         pred_saliency = model(img).squeeze().detach().numpy()
 
+        # Convert the saliency map to a heatmap with a blue color map
         heatmap = (pred_saliency * 255).astype(np.uint8)
-        heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET) 
+        heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)  # Use a blue colormap (JET)
 
+        # Resize the heatmap to match the image dimensions
         heatmap = cv2.resize(heatmap, (image.width, image.height))
 
+        # Overlay red patch labels on the heatmap
         heatmap, num_red_patches = count_and_label_red_patches(heatmap)
 
+        # Convert the image to a NumPy array and enhance the blue channel using CLAHE
         enhanced_image = np.array(image)
         b, g, r = cv2.split(enhanced_image)
         clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
         b_enhanced = clahe.apply(b)
         enhanced_image = cv2.merge((b_enhanced, g, r))
 
-        alpha = 0.7 
+        alpha = 0.7  # Adjust alpha to control the blending
         blended_img = cv2.addWeighted(enhanced_image, 1 - alpha, heatmap, alpha, 0)
 
+        # Display the images in your Streamlit app
         st.image(heatmap, caption='Enhanced Saliency Heatmap', use_column_width=True, channels='BGR')
         st.image(enhanced_image, caption='Enhanced Blue Image', use_column_width=True, channels='BGR')
 
+        # Overlay the red patch count on the blended image
         st.image(blended_img, caption=f'Blended Image with {num_red_patches} Red Patches', use_column_width=True, channels='BGR')
 
+        # Save the result image
         cv2.imwrite('example/result15.png', blended_img, [int(cv2.IMWRITE_JPEG_QUALITY), 200])
         st.success('Saliency detection complete. Result saved as "example/result15.png".')