queues

main.py

@@ -182,115 +182,125 @@ def pose2dhand(video, kpt_threshold):
    
     return "".join(out_file)
 
-def run_UI():
-    with gr.Blocks() as demo:
-        with gr.Column():       
-            with gr.Tab("Upload video"):
-                with gr.Column():
-                    with gr.Row():
-                        with gr.Column():
-                            video_input = gr.Video(source="upload", type="filepath", height=612)
-                            # Insert slider with kpt_thr
-                            file_kpthr = gr.Slider(0, 1, value=0.3, label='Keypoint threshold')
-
-                            submit_pose_file = gr.Button("Make 2d pose estimation", variant="primary")
-                            submit_pose3d_file = gr.Button("Make 3d pose estimation", variant="primary")
-                            submit_hand_file = gr.Button("Make 2d hand estimation", variant="primary")
-                            submit_detect_file = gr.Button("Detect and track objects", variant="primary")
-
-                    with gr.Row():
-                        video_output1 = gr.PlayableVideo(height=512,  label = "Estimate human 2d poses", show_label=True)
-                        video_output2 = gr.PlayableVideo(height=512,  label = "Estimate human 3d poses", show_label=True)
-                        video_output3 = gr.PlayableVideo(height=512,  label = "Estimate human hand poses", show_label=True)
-                        video_output4 = gr.Video(height=512, label = "Detection and tracking", show_label=True, format="mp4")
-
-            with gr.Tab("Record video with webcam"):
-                
-                with gr.Column():
-                    with gr.Row():
-                        with gr.Column():
-                            webcam_input = gr.Video(source="webcam", height=612)
-                            
-                            web_kpthr = gr.Slider(0, 1, value=0.3, label='Keypoint threshold')
-
-                            submit_pose_web = gr.Button("Make 2d pose estimation", variant="primary")
-                            submit_pose3d_web = gr.Button("Make 3d pose estimation", variant="primary")
-                            submit_hand_web = gr.Button("Make 2d hand estimation", variant="primary")
-                            submit_detect_web = gr.Button("Detect and track objects", variant="primary")
-                    with gr.Row():
-                        webcam_output1 = gr.PlayableVideo(height=716,  label = "Estimate human 2d poses", show_label=True)
-                        webcam_output2 = gr.PlayableVideo(height=716,  label = "Estimate human 3d poses", show_label=True)
-                        webcam_output3 = gr.PlayableVideo(height=716,  label = "Estimate human hand position", show_label=True)
-                        webcam_output4 = gr.Video(height=716, label = "Detection and tracking", show_label=True, format="mp4")
-
-            with gr.Tab("General information"):
-                gr.Markdown(""" 
-                            \n # Information about the models 
-
-                            \n ## Pose models: 
-                            
-                            \n All the pose estimation models comes from the library [MMpose](https://github.com/open-mmlab/mmpose). It is a library for human pose estimation that provides pre-trained models for 2D and 3D pose estimation. 
-
-                            \n The 2D pose model is used for estimating the 2D coordinates of human body joints from an image or a video frame. The model uses a convolutional neural network (CNN) to predict the joint locations and their confidence scores. 
-                            
-                            \n The 2D hand model is a specialized version of the 2D pose model that is designed for hand pose estimation. It uses a similar CNN architecture to the 2D pose model but is trained specifically for detecting the joints in the hand. 
-                            
-                            \n The 3D pose model is used for estimating the 3D coordinates of human body joints from an image or a video frame. The model uses a combination of 2D pose estimation and depth estimation to infer the 3D joint locations. 
-                            
-                            \n 
-
-                            \n ## Detection and tracking:                            
-                            
-                            \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: ")
-                gr.Code(
-                        value="""def hello_world():
-                                        return "Hello, world!"
-                    
-                                print(hello_world())""",
-                        language="python",
-                        interactive=True,
-                        show_label=False,
-                    )
+block = gr.Blocks()
+with block:
+    with gr.Column():       
+        with gr.Tab("Upload video"):
+            with gr.Column():
+                with gr.Row():
+                    with gr.Column():
+                        video_input = gr.Video(source="upload", type="filepath", height=612)
+                        # Insert slider with kpt_thr
+                        file_kpthr = gr.Slider(0, 1, value=0.3, label='Keypoint threshold')
+
+                        submit_pose_file = gr.Button("Make 2d pose estimation", variant="primary")
+                        submit_pose3d_file = gr.Button("Make 3d pose estimation", variant="primary")
+                        submit_hand_file = gr.Button("Make 2d hand estimation", variant="primary")
+                        submit_detect_file = gr.Button("Detect and track objects", variant="primary")
+
+                with gr.Row():
+                    video_output1 = gr.PlayableVideo(height=512,  label = "Estimate human 2d poses", show_label=True)
+                    video_output2 = gr.PlayableVideo(height=512,  label = "Estimate human 3d poses", show_label=True)
+                    video_output3 = gr.PlayableVideo(height=512,  label = "Estimate human hand poses", show_label=True)
+                    video_output4 = gr.Video(height=512, label = "Detection and tracking", show_label=True, format="mp4")
+
+        with gr.Tab("Record video with webcam"):
+            
+            with gr.Column():
+                with gr.Row():
+                    with gr.Column():
+                        webcam_input = gr.Video(source="webcam", height=612)
+                        
+                        web_kpthr = gr.Slider(0, 1, value=0.3, label='Keypoint threshold')
+
+                        submit_pose_web = gr.Button("Make 2d pose estimation", variant="primary")
+                        submit_pose3d_web = gr.Button("Make 3d pose estimation", variant="primary")
+                        submit_hand_web = gr.Button("Make 2d hand estimation", variant="primary")
+                        submit_detect_web = gr.Button("Detect and track objects", variant="primary")
+                with gr.Row():
+                    webcam_output1 = gr.PlayableVideo(height=716,  label = "Estimate human 2d poses", show_label=True)
+                    webcam_output2 = gr.PlayableVideo(height=716,  label = "Estimate human 3d poses", show_label=True)
+                    webcam_output3 = gr.PlayableVideo(height=716,  label = "Estimate human hand position", show_label=True)
+                    webcam_output4 = gr.Video(height=716, label = "Detection and tracking", show_label=True, format="mp4")
+
+        with gr.Tab("General information"):
+            gr.Markdown(""" 
+                        \n # Information about the models 
+
+                        \n ## Pose models: 
+                        
+                        \n All the pose estimation models comes from the library [MMpose](https://github.com/open-mmlab/mmpose). It is a library for human pose estimation that provides pre-trained models for 2D and 3D pose estimation. 
+
+                        \n The 2D pose model is used for estimating the 2D coordinates of human body joints from an image or a video frame. The model uses a convolutional neural network (CNN) to predict the joint locations and their confidence scores. 
+                        
+                        \n The 2D hand model is a specialized version of the 2D pose model that is designed for hand pose estimation. It uses a similar CNN architecture to the 2D pose model but is trained specifically for detecting the joints in the hand. 
+                        
+                        \n The 3D pose model is used for estimating the 3D coordinates of human body joints from an image or a video frame. The model uses a combination of 2D pose estimation and depth estimation to infer the 3D joint locations. 
+                        
+                        \n 
+
+                        \n ## Detection and tracking:                            
+                        
+                        \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: ")
+            gr.Code(
+                    value="""def hello_world():
+                                    return "Hello, world!"
                 
+                            print(hello_world())""",
+                    language="python",
+                    interactive=True,
+                    show_label=False,
+                )
+            
 
         # From file
         submit_pose_file.click(fn=pose2d, 
                             inputs=  [video_input, file_kpthr], 
-                            outputs = video_output1)
+                            outputs = video_output1,
+                            queue=False)
         
         submit_pose3d_file.click(fn=pose3d, 
                                 inputs= video_input, 
-                                outputs = video_output2)
+                                outputs = video_output2,
+                                queue=False)
         
         submit_hand_file.click(fn=pose2dhand, 
                             inputs= [video_input, file_kpthr], 
-                            outputs = video_output3)
+                            outputs = video_output3,
+                            queue=False)
         
         submit_detect_file.click(fn=show_tracking, 
                                 inputs= video_input, 
-                                outputs = video_output4)
+                                outputs = video_output4,
+                                queue=False)
         
         # Web
         submit_pose_web.click(fn=pose2d, 
                             inputs = [webcam_input, web_kpthr], 
-                            outputs = webcam_output1)
+                            outputs = webcam_output1,
+                            queue=False)
         
         submit_pose3d_web.click(fn=pose3d, 
                                 inputs= webcam_input, 
-                                outputs = webcam_output2)
+                                outputs = webcam_output2,
+                                queue=False)
         
         submit_hand_web.click(fn=pose2dhand, 
                             inputs= [webcam_input, web_kpthr], 
-                            outputs = webcam_output3)
+                            outputs = webcam_output3,
+                            queue=False)
         
         submit_detect_web.click(fn=show_tracking, 
                                 inputs= webcam_input, 
-                                outputs = webcam_output4)
+                                outputs = webcam_output4,
+                                queue=False)
 
-    demo.launch(server_name="0.0.0.0", server_port=7860)
 
 if __name__ == "__main__":
-    run_UI()
+    block.queue()
+    block.launch(server_name="0.0.0.0", server_port=7860)
+
+