Update app.py

app.py

@@ -16,10 +16,10 @@ def download_file(url, save_name):
         open(save_name, 'wb').write(file.content)
 
 for i, url in enumerate(file_urls):
-    if 'mp4' in file_urls[i]:
-        download_file(file_urls[i], f"video.mp4")
+    if url.endswith('.mp4'):
+        download_file(url, "video.mp4")
     else:
-        download_file(file_urls[i], f"image_{i}.jpg")
+        download_file(url, f"image_{i}.jpg")
 
 colors = {
     0: (255, 0, 0),    # Red for class 0
@@ -33,8 +33,8 @@ colors = {
 }
 
 model = YOLO('modelbest.pt')
-path  = [['image_0.jpg'], ['image_1.jpg']]
-video_path = [['video.mp4']]
+image_paths = [['image_0.jpg'], ['image_1.jpg']]
+video_paths = [['video.mp4']]
 
 def show_preds_image(image_path):
     image = cv2.imread(image_path)
@@ -62,49 +62,26 @@ def show_preds_image(image_path):
 
     return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
 
-    
-# def show_preds_image(image_path):
-#     image = cv2.imread(image_path)
-#     outputs = model.predict(source=image_path)
-#     results = outputs[0].cpu().numpy()
-#     for i, det in enumerate(results.boxes.xyxy):
-#         cv2.rectangle(
-#             image,
-#             (int(det[0]), int(det[1])),
-#             (int(det[2]), int(det[3])),
-#             color=(0, 0, 255),
-#             thickness=2,
-#             lineType=cv2.LINE_AA
-#         )
-#     return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-
-inputs_image = [
-    gr.Image(type="filepath", label="Input Image"),
-]
-outputs_image = [
-    gr.Image(type="numpy", label="Output Image"),
-]
+inputs_image = gr.Image(type="filepath", label="Input Image")
+outputs_image = gr.Image(type="numpy", label="Output Image")
 
 interface_image = gr.Interface(
     fn=show_preds_image,
     inputs=inputs_image,
     outputs=outputs_image,
     title="Smoke Detection on Indian Roads",
-    examples=path,
+    examples=image_paths,
     cache_examples=False,
 )
 
 def show_preds_video(video_path):
-    # Open the input video
     cap = cv2.VideoCapture(video_path)
     
-    # Get video properties
     width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
     height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
     fps = int(cap.get(cv2.CAP_PROP_FPS))
     
-    # Define the codec and create a VideoWriter object
-    fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # 'mp4v' for .mp4 format
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
     out = cv2.VideoWriter('output_video.mp4', fourcc, fps, (width, height))
     
     while cap.isOpened():
@@ -120,45 +97,36 @@ def show_preds_video(video_path):
             class_id = int(results.boxes.cls[i])
             label = model.names[class_id]
 
-            # Get the bounding box coordinates
             x1, y1, x2, y2 = int(det[0]), int(det[1]), int(det[2]), int(det[3])
             
-            # Draw the bounding box with the specified color
             color = colors.get(class_id, (0, 0, 255))
             cv2.rectangle(frame_copy, (x1, y1), (x2, y2), color, 2, cv2.LINE_AA)
             
-            # Calculate text size and position
             label_size, _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.75, 2)
             text_x = x1 + (x2 - x1) // 2 - label_size[0] // 2
             text_y = y1 + (y2 - y1) // 2 + label_size[1] // 2
 
-            # Draw the label text
             cv2.putText(frame_copy, label, (text_x, text_y), cv2.FONT_HERSHEY_SIMPLEX, 0.75, color, 2, cv2.LINE_AA)
 
-        # Write the frame to the output video
         out.write(frame_copy)
     
-    # Release everything
     cap.release()
     out.release()
 
     return 'output_video.mp4'
 
-# Updated Gradio interface
-inputs_video = [
-    gr.Video(format="mp4", label="Input Video"),
-]
-outputs_video = [
-    gr.Video(label="Output Video"),
-]
+inputs_video = gr.Video(format="mp4", label="Input Video")
+outputs_video = gr.Video(label="Output Video")
+
 interface_video = gr.Interface(
     fn=show_preds_video,
     inputs=inputs_video,
     outputs=outputs_video,
     title="Smoke Detection on Indian Roads",
-    examples=video_path,
+    examples=video_paths,
     cache_examples=False,
 )
+
 gr.TabbedInterface(
     [interface_image, interface_video],
     tab_names=['Image inference', 'Video inference']
@@ -169,32 +137,35 @@ gr.TabbedInterface(
 # import cv2
 # import requests
 # import os
- 
 # from ultralytics import YOLO
+
 # file_urls = [
 #     'https://www.dropbox.com/scl/fi/kqd1z6wby1212c6ndodb3/Pol_20_jpg.rf.133c835b66958a7d48c12deeda31a719.jpg?rlkey=uqgvs2cwvahnmju15fv1zgorg&st=snv2yvtk&dl=0',
 #     'https://www.dropbox.com/scl/fi/39aakapeh2y5ztk94rsyu/11e-a347-3f2d_jpg.rf.c66e5aeb57ee2ed660fdf0162156127d.jpg?rlkey=xoi3iw45vksgiejycau2ha7fh&st=etiawigv&dl=0',
 #     'https://www.dropbox.com/scl/fi/8f08ehy53vsemw164g8n7/Recording2024-06-26184319.mp4?rlkey=pnmov906ttodl0cm92rpvc5ta&st=2twc9pjn&dl=0'
 # ]
 
- 
 # def download_file(url, save_name):
-#     url = url
 #     if not os.path.exists(save_name):
 #         file = requests.get(url)
 #         open(save_name, 'wb').write(file.content)
- 
+
 # for i, url in enumerate(file_urls):
 #     if 'mp4' in file_urls[i]:
-#         download_file(
-#             file_urls[i],
-#             f"video.mp4"
-#         )
+#         download_file(file_urls[i], f"video.mp4")
 #     else:
-#         download_file(
-#             file_urls[i],
-#             f"image_{i}.jpg"
-#         )
+#         download_file(file_urls[i], f"image_{i}.jpg")
+
+# colors = {
+#     0: (255, 0, 0),    # Red for class 0
+#     1: (0, 128, 0),    # Green (dark) for class 1
+#     2: (0, 0, 255),    # Blue for class 2
+#     3: (255, 255, 0),  # Yellow for class 3
+#     4: (255, 0, 255),  # Magenta for class 4
+#     5: (0, 255, 255),  # Cyan for class 5
+#     6: (128, 0, 0),    # Maroon for class 6
+#     7: (0, 225, 0),    # Green for class 7
+# }
 
 # model = YOLO('modelbest.pt')
 # path  = [['image_0.jpg'], ['image_1.jpg']]
@@ -204,70 +175,234 @@ gr.TabbedInterface(
 #     image = cv2.imread(image_path)
 #     outputs = model.predict(source=image_path)
 #     results = outputs[0].cpu().numpy()
+
 #     for i, det in enumerate(results.boxes.xyxy):
-#         cv2.rectangle(
-#             image,
-#             (int(det[0]), int(det[1])),
-#             (int(det[2]), int(det[3])),
-#             color=(0, 0, 255),
-#             thickness=2,
-#             lineType=cv2.LINE_AA
-#         )
+#         class_id = int(results.boxes.cls[i])
+#         label = model.names[class_id]
+
+#         # Get the bounding box coordinates
+#         x1, y1, x2, y2 = int(det[0]), int(det[1]), int(det[2]), int(det[3])
+        
+#         # Draw the bounding box with the specified color
+#         color = colors.get(class_id, (0, 0, 255))
+#         cv2.rectangle(image, (x1, y1), (x2, y2), color, 2, cv2.LINE_AA)
+        
+#         # Calculate text size and position
+#         label_size, _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.75, 2)
+#         text_x = x1 + (x2 - x1) // 2 - label_size[0] // 2
+#         text_y = y1 + (y2 - y1) // 2 + label_size[1] // 2
+
+#         # Draw the label text
+#         cv2.putText(image, label, (text_x, text_y), cv2.FONT_HERSHEY_SIMPLEX, 0.75, color, 2, cv2.LINE_AA)
+
 #     return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
- 
+
+    
+# # def show_preds_image(image_path):
+# #     image = cv2.imread(image_path)
+# #     outputs = model.predict(source=image_path)
+# #     results = outputs[0].cpu().numpy()
+# #     for i, det in enumerate(results.boxes.xyxy):
+# #         cv2.rectangle(
+# #             image,
+# #             (int(det[0]), int(det[1])),
+# #             (int(det[2]), int(det[3])),
+# #             color=(0, 0, 255),
+# #             thickness=2,
+# #             lineType=cv2.LINE_AA
+# #         )
+# #     return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
 # inputs_image = [
-#     gr.components.Image(type="filepath", label="Input Image"),
+#     gr.Image(type="filepath", label="Input Image"),
 # ]
 # outputs_image = [
-#     gr.components.Image(type="numpy", label="Output Image"),
+#     gr.Image(type="numpy", label="Output Image"),
 # ]
 
 # interface_image = gr.Interface(
 #     fn=show_preds_image,
 #     inputs=inputs_image,
 #     outputs=outputs_image,
-#     title="Pothole detector",
+#     title="Smoke Detection on Indian Roads",
 #     examples=path,
 #     cache_examples=False,
 # )
 
-
 # def show_preds_video(video_path):
+#     # Open the input video
 #     cap = cv2.VideoCapture(video_path)
-#     while(cap.isOpened()):
+    
+#     # Get video properties
+#     width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+#     height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+#     fps = int(cap.get(cv2.CAP_PROP_FPS))
+    
+#     # Define the codec and create a VideoWriter object
+#     fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # 'mp4v' for .mp4 format
+#     out = cv2.VideoWriter('output_video.mp4', fourcc, fps, (width, height))
+    
+#     while cap.isOpened():
 #         ret, frame = cap.read()
-#         if ret:
-#             frame_copy = frame.copy()
-#             outputs = model.predict(source=frame)
-#             results = outputs[0].cpu().numpy()
-#             for i, det in enumerate(results.boxes.xyxy):
-#                 cv2.rectangle(
-#                     frame_copy,
-#                     (int(det[0]), int(det[1])),
-#                     (int(det[2]), int(det[3])),
-#                     color=(0, 0, 255),
-#                     thickness=2,
-#                     lineType=cv2.LINE_AA
-#                 )
-#             yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)
- 
+#         if not ret:
+#             break
+        
+#         frame_copy = frame.copy()
+#         outputs = model.predict(source=frame)
+#         results = outputs[0].cpu().numpy()
+
+#         for i, det in enumerate(results.boxes.xyxy):
+#             class_id = int(results.boxes.cls[i])
+#             label = model.names[class_id]
+
+#             # Get the bounding box coordinates
+#             x1, y1, x2, y2 = int(det[0]), int(det[1]), int(det[2]), int(det[3])
+            
+#             # Draw the bounding box with the specified color
+#             color = colors.get(class_id, (0, 0, 255))
+#             cv2.rectangle(frame_copy, (x1, y1), (x2, y2), color, 2, cv2.LINE_AA)
+            
+#             # Calculate text size and position
+#             label_size, _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.75, 2)
+#             text_x = x1 + (x2 - x1) // 2 - label_size[0] // 2
+#             text_y = y1 + (y2 - y1) // 2 + label_size[1] // 2
+
+#             # Draw the label text
+#             cv2.putText(frame_copy, label, (text_x, text_y), cv2.FONT_HERSHEY_SIMPLEX, 0.75, color, 2, cv2.LINE_AA)
+
+#         # Write the frame to the output video
+#         out.write(frame_copy)
+    
+#     # Release everything
+#     cap.release()
+#     out.release()
+
+#     return 'output_video.mp4'
+
+# # Updated Gradio interface
 # inputs_video = [
-#     gr.components.Video(type="filepath", label="Input Video"),
- 
+#     gr.Video(format="mp4", label="Input Video"),
 # ]
 # outputs_video = [
-#     gr.components.Image(type="numpy", label="Output Image"),
+#     gr.Video(label="Output Video"),
 # ]
 # interface_video = gr.Interface(
 #     fn=show_preds_video,
 #     inputs=inputs_video,
 #     outputs=outputs_video,
-#     title="Pothole detector",
+#     title="Smoke Detection on Indian Roads",
 #     examples=video_path,
 #     cache_examples=False,
 # )
-
 # gr.TabbedInterface(
 #     [interface_image, interface_video],
 #     tab_names=['Image inference', 'Video inference']
-# ).queue().launch()
+# ).queue().launch()
+
+
+# # import gradio as gr
+# # import cv2
+# # import requests
+# # import os
+ 
+# # from ultralytics import YOLO
+# # file_urls = [
+# #     'https://www.dropbox.com/scl/fi/kqd1z6wby1212c6ndodb3/Pol_20_jpg.rf.133c835b66958a7d48c12deeda31a719.jpg?rlkey=uqgvs2cwvahnmju15fv1zgorg&st=snv2yvtk&dl=0',
+# #     'https://www.dropbox.com/scl/fi/39aakapeh2y5ztk94rsyu/11e-a347-3f2d_jpg.rf.c66e5aeb57ee2ed660fdf0162156127d.jpg?rlkey=xoi3iw45vksgiejycau2ha7fh&st=etiawigv&dl=0',
+# #     'https://www.dropbox.com/scl/fi/8f08ehy53vsemw164g8n7/Recording2024-06-26184319.mp4?rlkey=pnmov906ttodl0cm92rpvc5ta&st=2twc9pjn&dl=0'
+# # ]
+
+ 
+# # def download_file(url, save_name):
+# #     url = url
+# #     if not os.path.exists(save_name):
+# #         file = requests.get(url)
+# #         open(save_name, 'wb').write(file.content)
+ 
+# # for i, url in enumerate(file_urls):
+# #     if 'mp4' in file_urls[i]:
+# #         download_file(
+# #             file_urls[i],
+# #             f"video.mp4"
+# #         )
+# #     else:
+# #         download_file(
+# #             file_urls[i],
+# #             f"image_{i}.jpg"
+# #         )
+
+# # model = YOLO('modelbest.pt')
+# # path  = [['image_0.jpg'], ['image_1.jpg']]
+# # video_path = [['video.mp4']]
+
+# # def show_preds_image(image_path):
+# #     image = cv2.imread(image_path)
+# #     outputs = model.predict(source=image_path)
+# #     results = outputs[0].cpu().numpy()
+# #     for i, det in enumerate(results.boxes.xyxy):
+# #         cv2.rectangle(
+# #             image,
+# #             (int(det[0]), int(det[1])),
+# #             (int(det[2]), int(det[3])),
+# #             color=(0, 0, 255),
+# #             thickness=2,
+# #             lineType=cv2.LINE_AA
+# #         )
+# #     return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+ 
+# # inputs_image = [
+# #     gr.components.Image(type="filepath", label="Input Image"),
+# # ]
+# # outputs_image = [
+# #     gr.components.Image(type="numpy", label="Output Image"),
+# # ]
+
+# # interface_image = gr.Interface(
+# #     fn=show_preds_image,
+# #     inputs=inputs_image,
+# #     outputs=outputs_image,
+# #     title="Pothole detector",
+# #     examples=path,
+# #     cache_examples=False,
+# # )
+
+
+# # def show_preds_video(video_path):
+# #     cap = cv2.VideoCapture(video_path)
+# #     while(cap.isOpened()):
+# #         ret, frame = cap.read()
+# #         if ret:
+# #             frame_copy = frame.copy()
+# #             outputs = model.predict(source=frame)
+# #             results = outputs[0].cpu().numpy()
+# #             for i, det in enumerate(results.boxes.xyxy):
+# #                 cv2.rectangle(
+# #                     frame_copy,
+# #                     (int(det[0]), int(det[1])),
+# #                     (int(det[2]), int(det[3])),
+# #                     color=(0, 0, 255),
+# #                     thickness=2,
+# #                     lineType=cv2.LINE_AA
+# #                 )
+# #             yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)
+ 
+# # inputs_video = [
+# #     gr.components.Video(type="filepath", label="Input Video"),
+ 
+# # ]
+# # outputs_video = [
+# #     gr.components.Image(type="numpy", label="Output Image"),
+# # ]
+# # interface_video = gr.Interface(
+# #     fn=show_preds_video,
+# #     inputs=inputs_video,
+# #     outputs=outputs_video,
+# #     title="Pothole detector",
+# #     examples=video_path,
+# #     cache_examples=False,
+# # )
+
+# # gr.TabbedInterface(
+# #     [interface_image, interface_video],
+# #     tab_names=['Image inference', 'Video inference']
+# # ).queue().launch()