visualization

main.py

@@ -17,16 +17,15 @@ def poses(photo):
                                   thickness=2)
     
     print("[INFO]: Visualizing results!")
+    print(os.listdir())
+    print(result_generator[0])
+    #print("[INFO]: Type of vis is ", type(result_generator['visualization']))
+    #print("[INFO]: Vis is ", type(result_generator['visualization']))
 
     # The MMPoseInferencer API employs a lazy inference approach,
     # creating a prediction generator when given input
-    result = next(result_generator)
-    
-    print("[INFO]: Type of vis is ", type(result['visualization']))
-
-    print("[INFO]: Vis is ", type(result['visualization']))
-
-    return "000000.jpg"
+    #result = next(result_generator)
+    return "000000.mp4"
 
 # # specify detection model by alias
 # # the available aliases include 'human', 'hand', 'face', 'animal',
@@ -41,8 +40,8 @@ def poses(photo):
 def run():
     #https://github.com/open-mmlab/mmpose/blob/main/docs/en/user_guides/inference.md
     demo = gr.Interface(fn=poses, 
-                        inputs=gr.Image(source="webcam"),
-                        outputs=gr.Image())
+                        inputs=gr.Video(source="webcam"),
+                        outputs=gr.Video())
 
 
     demo.launch(server_name="0.0.0.0", server_port=7860)