update

app.py

@@ -163,55 +163,55 @@ def process_image(image_path, flag_lower, flag_upper, plant_lower, plant_upper,
         return None, None, None, None, None, None, None, None, None, None
     elif len(significant_contours) > 4:
         st.session_state['keep_quad'] = False
-        while not st.session_state['keep_quad']:
-            with loc:
-                st.warning("Cycle until correct plot bounds are found")
-            # Create all possible combinations of four points
-            point_combinations = list(itertools.combinations(significant_contours, 4))
-            
-            # Placeholder for quadrilateral indices
-            selected_quad_index = 0
-
-            # Function to update displayed quadrilateral based on selected index
-            def update_displayed_quadrilateral(index):
-                # Extract the four points of the current quadrilateral
-                centroids = get_points_from_contours(point_combinations[index])
-                return centroids
-
-            # Show initial quadrilateral
-            centroids = update_displayed_quadrilateral(selected_quad_index)
-
-            with btn_back:
-                # Button to go to the previous quadrilateral
-                if st.button('Previous'):
-                    selected_quad_index = max(selected_quad_index - 1, 0)
-                    centroids = update_displayed_quadrilateral(selected_quad_index)
-
-            with btn_next:
-                # Button to go to the next quadrilateral
-                if st.button('Next'):
-                    selected_quad_index = min(selected_quad_index + 1, len(point_combinations) - 1)
-                    centroids = update_displayed_quadrilateral(selected_quad_index)
-            
-            with loc:
-                if st.button('Keep Plot Bounds'):
-                    st.session_state['keep_quad'] = True
-                if st.button('Save as Failure'):
-                    st.session_state['keep_quad'] = True
-                    # Append the data to the CSV file
-                    with open(file_name, mode='a', newline='') as file:
-                        writer = csv.writer(file)
-                        
-                        # If the file doesn't exist, write the headers
-                        if not file_exists:
-                            writer.writerow(headers)
-                        
-                        # Write the data
-                        writer.writerow([f"{base_name}",f"NA", f"NA", f"NA"])
-
-                    # Remove processed image from the list
-                    st.session_state['input_list'].remove(selected_img)
-                    st.rerun()
+        # while not st.session_state['keep_quad']:
+        with loc:
+            st.warning("Cycle until correct plot bounds are found")
+        # Create all possible combinations of four points
+        point_combinations = list(itertools.combinations(significant_contours, 4))
+        
+        # Placeholder for quadrilateral indices
+        selected_quad_index = 0
+
+        # Function to update displayed quadrilateral based on selected index
+        def update_displayed_quadrilateral(index):
+            # Extract the four points of the current quadrilateral
+            centroids = get_points_from_contours(point_combinations[index])
+            return centroids
+
+        # Show initial quadrilateral
+        centroids = update_displayed_quadrilateral(selected_quad_index)
+
+        with btn_back:
+            # Button to go to the previous quadrilateral
+            if st.button('Previous'):
+                selected_quad_index = max(selected_quad_index - 1, 0)
+                centroids = update_displayed_quadrilateral(selected_quad_index)
+
+        with btn_next:
+            # Button to go to the next quadrilateral
+            if st.button('Next'):
+                selected_quad_index = min(selected_quad_index + 1, len(point_combinations) - 1)
+                centroids = update_displayed_quadrilateral(selected_quad_index)
+        
+        with loc:
+            if st.button('Keep Plot Bounds'):
+                st.session_state['keep_quad'] = True
+            if st.button('Save as Failure'):
+                st.session_state['keep_quad'] = True
+                # Append the data to the CSV file
+                with open(file_name, mode='a', newline='') as file:
+                    writer = csv.writer(file)
+                    
+                    # If the file doesn't exist, write the headers
+                    if not file_exists:
+                        writer.writerow(headers)
+                    
+                    # Write the data
+                    writer.writerow([f"{base_name}",f"NA", f"NA", f"NA"])
+
+                # Remove processed image from the list
+                st.session_state['input_list'].remove(selected_img)
+                st.rerun()
 
     # If there are exactly 4 largest contours, proceed with existing logic
     elif len(significant_contours) == 4:
@@ -236,42 +236,43 @@ def process_image(image_path, flag_lower, flag_upper, plant_lower, plant_upper,
         # Sort the centroids
         centroids.sort(key=lambda point: (-math.atan2(point[1] - centroid_y, point[0] - centroid_x)) % (2 * np.pi))
     
-    # Create a polygon mask using the sorted centroids
-    poly_mask = np.zeros_like(flag_mask)
-    cv2.fillPoly(poly_mask, [np.array(centroids)], 255)
-    
-    # Mask the plant_mask with poly_mask
-    mask_plant_plot = cv2.bitwise_and(plant_mask, plant_mask, mask=poly_mask)
+    if len(centroids) == 4:
+        # Create a polygon mask using the sorted centroids
+        poly_mask = np.zeros_like(flag_mask)
+        cv2.fillPoly(poly_mask, [np.array(centroids)], 255)
+        
+        # Mask the plant_mask with poly_mask
+        mask_plant_plot = cv2.bitwise_and(plant_mask, plant_mask, mask=poly_mask)
 
-    # Count the number of black pixels inside the quadrilateral
-    total_pixels_in_quad = np.prod(poly_mask.shape)
-    white_pixels_in_quad = np.sum(poly_mask == 255)
-    black_pixels_in_quad = total_pixels_in_quad - white_pixels_in_quad
-    
-    # Extract the RGB pixels from the original image using the mask_plant_plot
-    plant_rgb = cv2.bitwise_and(img, img, mask=mask_plant_plot)
-
-    # Draw the bounding quadrilateral
-    plot_rgb = plant_rgb.copy()
-    for i in range(4):
-        cv2.line(plot_rgb, centroids[i], centroids[(i+1)%4], (0, 0, 255), 3)
-
-    # Convert the masks to RGB for visualization
-    flag_mask_rgb = cv2.cvtColor(flag_mask, cv2.COLOR_GRAY2RGB)
-    orange_color = [255, 165, 0]  # RGB value for orange
-    flag_mask_rgb[np.any(flag_mask_rgb != [0, 0, 0], axis=-1)] = orange_color
-
-    plant_mask_rgb = cv2.cvtColor(plant_mask, cv2.COLOR_GRAY2RGB)
-    mask_plant_plot_rgb = cv2.cvtColor(mask_plant_plot, cv2.COLOR_GRAY2RGB)
-    bright_green_color = [0, 255, 0]
-    plant_mask_rgb[np.any(plant_mask_rgb != [0, 0, 0], axis=-1)] = bright_green_color
-    mask_plant_plot_rgb[np.any(mask_plant_plot_rgb != [0, 0, 0], axis=-1)] = bright_green_color
-    
-    # Warp the images
-    plant_rgb_warp = warp_image(plant_rgb, centroids)
-    plant_mask_warp = warp_image(mask_plant_plot_rgb, centroids)
+        # Count the number of black pixels inside the quadrilateral
+        total_pixels_in_quad = np.prod(poly_mask.shape)
+        white_pixels_in_quad = np.sum(poly_mask == 255)
+        black_pixels_in_quad = total_pixels_in_quad - white_pixels_in_quad
+        
+        # Extract the RGB pixels from the original image using the mask_plant_plot
+        plant_rgb = cv2.bitwise_and(img, img, mask=mask_plant_plot)
+
+        # Draw the bounding quadrilateral
+        plot_rgb = plant_rgb.copy()
+        for i in range(4):
+            cv2.line(plot_rgb, centroids[i], centroids[(i+1)%4], (0, 0, 255), 3)
+
+        # Convert the masks to RGB for visualization
+        flag_mask_rgb = cv2.cvtColor(flag_mask, cv2.COLOR_GRAY2RGB)
+        orange_color = [255, 165, 0]  # RGB value for orange
+        flag_mask_rgb[np.any(flag_mask_rgb != [0, 0, 0], axis=-1)] = orange_color
+
+        plant_mask_rgb = cv2.cvtColor(plant_mask, cv2.COLOR_GRAY2RGB)
+        mask_plant_plot_rgb = cv2.cvtColor(mask_plant_plot, cv2.COLOR_GRAY2RGB)
+        bright_green_color = [0, 255, 0]
+        plant_mask_rgb[np.any(plant_mask_rgb != [0, 0, 0], axis=-1)] = bright_green_color
+        mask_plant_plot_rgb[np.any(mask_plant_plot_rgb != [0, 0, 0], axis=-1)] = bright_green_color
+        
+        # Warp the images
+        plant_rgb_warp = warp_image(plant_rgb, centroids)
+        plant_mask_warp = warp_image(mask_plant_plot_rgb, centroids)
 
-    return flag_mask_rgb, plant_mask_rgb, mask_plant_plot_rgb, plant_rgb, plot_rgb, plant_rgb_warp, plant_mask_warp, plant_mask, mask_plant_plot, black_pixels_in_quad
+        return flag_mask_rgb, plant_mask_rgb, mask_plant_plot_rgb, plant_rgb, plot_rgb, plant_rgb_warp, plant_mask_warp, plant_mask, mask_plant_plot, black_pixels_in_quad
 
 def calculate_coverage(mask_plant_plot, plant_mask_warp, black_pixels_in_quad):
     # Calculate the percentage of white pixels for mask_plant_plot