duration

app.py

@@ -30,23 +30,6 @@ print("[INFO]: Imported modules!")
 track_model = YOLO('yolov8n.pt')  # Load an official Detect model
 print("[INFO]: Downloaded models!")
 
-def check_extension(video):
-    split_tup = os.path.splitext(video)
-
-    # extract the file name and extension
-    file_name = split_tup[0]
-    file_extension = split_tup[1]
-
-    if file_extension != ".mp4":
-        print("Converting to mp4")
-        clip = moviepy.VideoFileClip(video)
-
-        video = file_name+".mp4"
-        clip.write_videofile(video)
-    
-    return video
-
-
 def tracking(video, model, boxes=True):
     print("[INFO] Is cuda available? ", torch.cuda.is_available())
     print(device)
@@ -66,6 +49,13 @@ def show_tracking(video_content):
         # https://docs.ultralytics.com/datasets/detect/coco/
         video = cv2.VideoCapture(video_content)
 
+        fps = video.get(cv2.CAP_PROP_FPS)      # OpenCV v2.x used "CV_CAP_PROP_FPS"
+        frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
+        duration = frame_count/fps
+
+        if duration > 10:
+            raise gr.Error("Please provide or record a video shorter than 10 seconds...")
+
         # Track
         video_track = tracking(video_content, track_model.track)
 
@@ -106,28 +96,27 @@ def track_blocks(video_content):
 block = gr.Blocks()
 with block:
     with gr.Column():       
-        with gr.Tab("Upload video"):
+        with gr.Tab("Record video with webcam"):
             with gr.Column():
                 with gr.Row():
                     with gr.Column():
-                        video_input = gr.Video(source="upload", type="filepath", height=256)
+                        webcam_input = gr.Video(source="webcam", height=256)
+                        
                         with gr.Row():
-                            submit_detect_file = gr.Button("Detect and track objects", variant="primary")
-
+                            submit_detect_web = gr.Button("Detect and track objects", variant="primary")
                 with gr.Row():
-                    video_output4 = gr.Video(height=512, label = "Detection and tracking", show_label=True, format="mp4")
+                    webcam_output4 = gr.Video(height=716, label = "Detection and tracking", show_label=True, format="mp4")
 
-        with gr.Tab("Record video with webcam"):
-            
+        with gr.Tab("Upload video"):
             with gr.Column():
                 with gr.Row():
                     with gr.Column():
-                        webcam_input = gr.Video(source="webcam", height=256)
-                        
+                        video_input = gr.Video(source="upload", type="filepath", height=256)
                         with gr.Row():
-                            submit_detect_web = gr.Button("Detect and track objects", variant="primary")
+                            submit_detect_file = gr.Button("Detect and track objects", variant="primary")
+
                 with gr.Row():
-                    webcam_output4 = gr.Video(height=716, label = "Detection and tracking", show_label=True, format="mp4")
+                    video_output4 = gr.Video(height=512, label = "Detection and tracking", show_label=True, format="mp4")
 
         with gr.Tab("General information"):
             gr.Markdown(""" 
@@ -138,8 +127,6 @@ with block:
                         \n The tracking method in the Ultralight's YOLOv8 model is used for object tracking in videos. It takes a video file or a camera stream as input and returns the tracked objects in each frame. The method uses the COCO dataset classes for object detection and tracking. 
                         
                         \n The COCO dataset contains 80 classes of objects such as person, car, bicycle, etc. See https://docs.ultralytics.com/datasets/detect/coco/ for all available classes. The tracking method uses the COCO classes to detect and track the objects in the video frames. The tracked objects are represented as bounding boxes with labels indicating the class of the object.""")
-            gr.Markdown("You can load the keypoints in python in the following way: ")
-
 
         # From file        
         submit_detect_file.click(fn=track_blocks,