Update main.py

main.py

@@ -2,23 +2,13 @@ import gradio as gr
 from ultralytics import YOLO
 from ultralytics.solutions import ai_gym
 import cv2
-import tempfile
-from PIL import Image
-import subprocess
 
-# Function to upgrade pip
-def upgrade_pip():
-    subprocess.run(['pip', 'install', '--upgrade', 'pip'])
-
-# Function to process video
 def process(video_path):
-    upgrade_pip()  # Upgrade pip before executing the main function
     model = YOLO("yolov8n-pose.pt")
     cap = cv2.VideoCapture(video_path)
     assert cap.isOpened(), "Error reading video file"
     w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
 
-    temp_dir = tempfile.mkdtemp()  # Create a temporary directory to store processed frames
     video_writer = cv2.VideoWriter("output_video.mp4",
                                    cv2.VideoWriter_fourcc(*'mp4v'),
                                    fps,
@@ -27,35 +17,109 @@ def process(video_path):
     gym_object = ai_gym.AIGym()  # init AI GYM module
     gym_object.set_args(line_thickness=2,
                         view_img=False,  # Set view_img to False to prevent displaying the video in real-time
-                        pose_type="pushup",
+                        pose_type="pullup",
                         kpts_to_check=[6, 8, 10])
 
     frame_count = 0
     while cap.isOpened():
         success, im0 = cap.read()
         if not success:
-            print("Video frame is empty or video processing has been successfully completed.")
+            print("Video processing has been successfully completed.")
             break
         frame_count += 1
-        if frame_count % 5 == 0:  # Process every 5th frame
-            results = model.track(im0, verbose=False)  # Tracking recommended
-            im0 = gym_object.start_counting(im0, results, frame_count)
-            # Save processed frame as an image in the temporary directory
-            cv2.imwrite(f"{temp_dir}/{frame_count}.jpg", im0)
-
-    # Use PIL to create the final video from the processed frames
-    images = [Image.open(f"{temp_dir}/{i}.jpg") for i in range(1, frame_count + 1)]
-    images[0].save("output_video.mp4", save_all=True, append_images=images[1:], duration=1000/fps, loop=0)
+        results = model.track(im0, verbose=False)  # Tracking recommended
+        im0 = gym_object.start_counting(im0, results, frame_count)
+        video_writer.write(im0)
 
     cap.release()
+    video_writer.release()
     cv2.destroyAllWindows()
 
     return "output_video.mp4"
 
+title = "Push-up Counter"
+description = "This app counts the number of push-ups in a video."
+# inputs = gr.inputs.Video(label='Input Video')
+# outputs = gr.outputs.Video(label='Processed Video')
+# example_list = ['pullups.mp4']
+
 # Create the Gradio demo
 demo = gr.Interface(fn=process,
                     inputs=gr.Video(label='Input Video'),  
-                    outputs=gr.Video(label='Processed Video'))
+                    outputs=gr.Video(label='Output Video')
+                    # title=title,
+                    # description=description,
+                    # examples=example_list
+                    )
 
 # Launch the demo!
-demo.launch(show_api=False)
+demo.launch(show_api=True)
+
+
+
+
+
+
+
+
+# import gradio as gr
+# from ultralytics import YOLO
+# from ultralytics.solutions import ai_gym
+# import cv2
+# import tempfile
+# from PIL import Image
+# import subprocess
+
+# # Function to upgrade pip
+# def upgrade_pip():
+#     subprocess.run(['pip', 'install', '--upgrade', 'pip'])
+
+# # Function to process video
+# def process(video_path):
+#     upgrade_pip()  # Upgrade pip before executing the main function
+#     model = YOLO("yolov8n-pose.pt")
+#     cap = cv2.VideoCapture(video_path)
+#     assert cap.isOpened(), "Error reading video file"
+#     w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
+
+#     temp_dir = tempfile.mkdtemp()  # Create a temporary directory to store processed frames
+#     video_writer = cv2.VideoWriter("output_video.mp4",
+#                                    cv2.VideoWriter_fourcc(*'mp4v'),
+#                                    fps,
+#                                    (w, h))
+
+#     gym_object = ai_gym.AIGym()  # init AI GYM module
+#     gym_object.set_args(line_thickness=2,
+#                         view_img=False,  # Set view_img to False to prevent displaying the video in real-time
+#                         pose_type="pushup",
+#                         kpts_to_check=[6, 8, 10])
+
+#     frame_count = 0
+#     while cap.isOpened():
+#         success, im0 = cap.read()
+#         if not success:
+#             print("Video frame is empty or video processing has been successfully completed.")
+#             break
+#         frame_count += 1
+#         if frame_count % 5 == 0:  # Process every 5th frame
+#             results = model.track(im0, verbose=False)  # Tracking recommended
+#             im0 = gym_object.start_counting(im0, results, frame_count)
+#             # Save processed frame as an image in the temporary directory
+#             cv2.imwrite(f"{temp_dir}/{frame_count}.jpg", im0)
+
+#     # Use PIL to create the final video from the processed frames
+#     images = [Image.open(f"{temp_dir}/{i}.jpg") for i in range(1, frame_count + 1)]
+#     images[0].save("output_video.mp4", save_all=True, append_images=images[1:], duration=1000/fps, loop=0)
+
+#     cap.release()
+#     cv2.destroyAllWindows()
+
+#     return "output_video.mp4"
+
+# # Create the Gradio demo
+# demo = gr.Interface(fn=process,
+#                     inputs=gr.Video(label='Input Video'),  
+#                     outputs=gr.Video(label='Processed Video'))
+
+# # Launch the demo!
+# demo.launch(show_api=False)