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app.py

@@ -1,3 +1,4 @@
+import shutil
 import cv2
 import gradio as gr
 import pandas as pd
@@ -32,15 +33,17 @@ css = """
 # Create the Gradio interface using defined theme and CSS
 with gr.Blocks(theme=theme, css=css) as demo:
     # Title and description for the app
-    gr.Markdown("# Concrete Crack Detection and Segmentation")
-    gr.Markdown("Upload concrete crack images and get segmented results.")
+    gr.Markdown("# Concrete Crack Segmentation and Documentation")
+    gr.Markdown("Upload concrete crack images and get segmented results with pdf report.")
     with gr.Tab('Instructions'):
         gr.Markdown(
     """**Instructions for Concrete Crack Detection and Segmentation App:**
 
     **Input:**
     - Upload one or more concrete crack images using the "Image Input" section.
-    - Adjust confidence level and distance sliders if needed.\n
+    - Adjust confidence level and distance sliders if needed.
+    - Upload reference images. (e.g. whole wall with many cracks)
+    - Input Remarks (e.g. First floor wall on the left)\n
     **Buttons:**
     - Click "Segment" to perform crack segmentation.
     - Click "Clear" to reset inputs and outputs.\n
@@ -51,7 +54,7 @@ with gr.Blocks(theme=theme, css=css) as demo:
 
     **Additional Information:**
     - The app uses a YOLOv8 trained model for crack detection with 86.8\% accuracy.
-    - Results include orientation category, width of the crack (widest), number of cracks per photo.
+    - Results include orientation category, width of the crack (widest), number of cracks per photo, and damage level.
 
     **Notes:**
     - Ensure uploaded images are in the supported formats: PNG, JPG, JPEG, WEBP.
@@ -198,79 +201,89 @@ with gr.Blocks(theme=theme, css=css) as demo:
         filenames = [file.name for file in image]
         conf= conf * 0.01
         model = load_model()
-        results = model.predict(image_list, conf=conf, save=True, project='output', name=uuid, stream=True)
+        
         processed_image_paths = []
         output_image_paths = []
         result_list = []
         width_list = []
         orientation_list = []
         width_interpretations = []
+        folder_name = []
         # Populate the dataframe with counts
-        for i, r in enumerate(results):
-            result_list.append(r)
-            instance_count = len(r)
-            if r.masks is not None and r.masks.data.numel() > 0:
-                masks = r.masks.data
-                boxes = r.boxes.data
-                clss = boxes[:, 5]
-                people_indices = torch.where(clss == 0)
-                people_masks = masks[people_indices]
-                people_mask = torch.any(people_masks, dim=0).int() * 255
-                processed_image_path = str(model.predictor.save_dir / f'binarize{i}.jpg')
-                cv2.imwrite(processed_image_path, people_mask.cpu().numpy())
-                processed_image_paths.append(processed_image_path)
-                
-                crack_image_path = processed_image_path
-                principal_orientation, orientation_category = detect_pattern(crack_image_path)
-                
-                # Print the results if needed
-                print(f"Crack Detection Results for {crack_image_path}:")
-                print("Principal Component Analysis Orientation:", principal_orientation)
-                print("Orientation Category:", orientation_category)
-
-                # Load the original image in color
-                original_img = cv2.imread(f'output/{uuid}/image{i}.jpg')
-                orig_image_path = str(model.predictor.save_dir / f'image{i}.jpg')
-                processed_image_paths.append(orig_image_path)
-                # Load and resize the binary image to match the dimensions of the original image
-                binary_image = cv2.imread(f'output/{uuid}/binarize{i}.jpg', cv2.IMREAD_GRAYSCALE)
-                binary_image = cv2.resize(binary_image, (original_img.shape[1], original_img.shape[0]))
-
-                contour_analyzer = ContourAnalyzer()
-                max_width, thickest_section, thickest_points, distance_transforms = contour_analyzer.find_contours(binary_image)
-
-                visualized_image = original_img.copy()
-                cv2.drawContours(visualized_image, [thickest_section], 0, (0, 255, 0), 1)
-
-                contour_analyzer.draw_circle_on_image(visualized_image, (int(thickest_points[0]), int(thickest_points[1])), 5, (57, 255, 20), -1)
-                print("Max Width in pixels: ", max_width)
-
-                width = contour_analyzer.calculate_width(y=10, x=5, pixel_width=max_width, calibration_factor=0.001, distance=150)
-                print("Max Width, converted: ", width)
-                
-                prets = pt.classify_wall_damage(width)
-                width_interpretations.append(prets)
-                
-                visualized_image_path = f'output/{uuid}/visualized_image{i}.jpg'
-                output_image_paths.append(visualized_image_path)
-                cv2.imwrite(visualized_image_path, visualized_image)
-
-                width_list.append(round(width, 2))
-                orientation_list.append(orientation_category)
-            else:
-                original_img = cv2.imread(f'output/{uuid}/image{i}.jpg')
-                visualized_image_path = f'output/{uuid}/visualized_image{i}.jpg'
-                output_image_paths.append(visualized_image_path)
-                cv2.imwrite(visualized_image_path, original_img)
-                width_list.append('None')
-                orientation_list.append('None')
-                width_interpretations.append('None')
+        for i, image_path in enumerate(image):
+            results = model.predict(image_path, conf=conf, save=True, project='output', name=f'{uuid}{i}', stream=True)
+            for r in results:
+                result_list.append(r)
+                instance_count = len(r)
+                if r.masks is not None and r.masks.data.numel() > 0:
+                    masks = r.masks.data
+                    boxes = r.boxes.data
+                    clss = boxes[:, 5]
+                    people_indices = torch.where(clss == 0)
+                    people_masks = masks[people_indices]
+                    people_mask = torch.any(people_masks, dim=0).int() * 255
+                    processed_image_path = str(f'output/{uuid}0/binarize{i}.jpg')
+                    cv2.imwrite(processed_image_path, people_mask.cpu().numpy())
+                    processed_image_paths.append(processed_image_path)
+                    
+                    crack_image_path = processed_image_path
+                    principal_orientation, orientation_category = detect_pattern(crack_image_path)
+                    
+                    # Print the results if needed
+                    print(f"Crack Detection Results for {crack_image_path}:")
+                    print("Principal Component Analysis Orientation:", principal_orientation)
+                    print("Orientation Category:", orientation_category)
+                    if i>0:
+                        processed_image_paths.append(f'output/{uuid}{i}')
+                    #transfer item to current folder
+                    the_paths = f'output/{uuid}{i}/{os.path.basename(image_path)}'
+                    print(the_paths)
+                    shutil.copyfile(the_paths, f'output/{uuid}0/image{i}.jpg')
+                    # Load the original image in color
+                    original_img = cv2.imread(f'output/{uuid}0/image{i}.jpg')
+                    orig_image_path = str(f'output/{uuid}0/image{i}.jpg')
+                    processed_image_paths.append(orig_image_path)
+                    # Load and resize the binary image to match the dimensions of the original image
+                    binary_image = cv2.imread(f'output/{uuid}0/binarize{i}.jpg', cv2.IMREAD_GRAYSCALE)
+                    binary_image = cv2.resize(binary_image, (original_img.shape[1], original_img.shape[0]))
+
+                    contour_analyzer = ContourAnalyzer()
+                    max_width, thickest_section, thickest_points, distance_transforms = contour_analyzer.find_contours(binary_image)
+
+                    visualized_image = original_img.copy()
+                    cv2.drawContours(visualized_image, [thickest_section], 0, (0, 255, 0), 1)
+
+                    contour_analyzer.draw_circle_on_image(visualized_image, (int(thickest_points[0]), int(thickest_points[1])), 5, (57, 255, 20), -1)
+                    print("Max Width in pixels: ", max_width)
+
+                    width = contour_analyzer.calculate_width(y=10, x=5, pixel_width=max_width, calibration_factor=0.001, distance=150)
+                    print("Max Width, converted: ", width)
+                    
+                    prets = pt.classify_wall_damage(width)
+                    width_interpretations.append(prets)
+                    
+                    visualized_image_path = f'output/{uuid}0/visualized_image{i}.jpg'
+                    output_image_paths.append(visualized_image_path)
+                    cv2.imwrite(visualized_image_path, visualized_image)
+
+                    width_list.append(round(width, 2))
+                    orientation_list.append(orientation_category)
+                else:
+                    original_img = cv2.imread(f'output/{uuid}0/image{i}.jpg')
+                    visualized_image_path = f'output/{uuid}0/visualized_image{i}.jpg'
+                    output_image_paths.append(visualized_image_path)
+                    cv2.imwrite(visualized_image_path, original_img)
+                    width_list.append('None')
+                    orientation_list.append('None')
+                    width_interpretations.append('None')
 
         # Delete binarized and initial segmented images after processing
         for path in processed_image_paths:
             if os.path.exists(path):
-                os.remove(path)
-                
+                if os.path.isfile(path):
+                    os.remove(path)
+                elif os.path.isdir(path):
+                    shutil.rmtree(path)
         # results = gr.Textbox(res, visible=True)
         csv, df = pt.count_instance(result_list, filenames, uuid, width_list, orientation_list, output_image_paths, reference, remark, width_interpretations)
 

utils/plot.py

@@ -78,6 +78,8 @@ def count_instance(result, filenames, uuid, width_list, orientation_list, image_
             tuple: Path to the generated CSV and dataframe with counts.
         """
         # Initializing the dataframe
+        uuid= f'{uuid}0' 
+        print(uuid)
         print(damage)
         data = {
             'Index': [],
@@ -177,8 +179,13 @@ def count_instance(result, filenames, uuid, width_list, orientation_list, image_
 
         merger.write(f'output/{uuid}/report.pdf')
         merger.close()
-        options = {'width': 1280, 'disable-smart-width': ''}
-        imgkit.from_file(f'output/{uuid}/out.html', f'output/{uuid}/out.jpg', options=opt)
+
+        paths = [f'output/{uuid}/df_batch.html', f'output/{uuid}/df_ref_summary.html',
+                 f'output/{uuid}/df_ref.html', f'output/{uuid}/out.html',
+                 f'output/{uuid}/report_batch.pdf', f'output/{uuid}/report_ref.pdf']
+        for path in paths:
+            if os.path.exists(path):
+                os.remove(path)
         return f'output/{uuid}/report.pdf', df