Update main.py

main.py

@@ -1,30 +1,27 @@
 import gradio as gr
 from ultralytics import YOLO
-import ai_gym
+from ultralytics.solutions import ai_gym
 import cv2
 import tempfile
-import os
+from PIL import Image
 
-# Initialize YOLO model
-model = YOLO("yolov8n-pose.pt")
-
-# Initialize AIGym object
-gym_object = ai_gym.AIGym()
-
-def count_workouts(input_video):
-    # Temporary file to store output video
-    output_path = tempfile.NamedTemporaryFile(suffix='.avi').name
-
-    # Open input video
-    cap = cv2.VideoCapture(input_video.name)
+def process(video_path):
+    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))
 
-    # Initialize video writer for output video
-    video_writer = cv2.VideoWriter(output_path,
-                                    cv2.VideoWriter_fourcc(*'mp4v'),
-                                    fps,
-                                    (w, h))
+    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():
@@ -33,57 +30,25 @@ def count_workouts(input_video):
             print("Video frame is empty or video processing has been successfully completed.")
             break
         frame_count += 1
-        results = model.track(im0, verbose=False)  # Tracking recommended
-        im0 = gym_object.start_counting(im0, results, frame_count)
-        video_writer.write(im0)
+        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()
-    video_writer.release()
     cv2.destroyAllWindows()
 
-    return output_path
-
-# Gradio Interface
-inputs = gr.inputs.Video(label="Upload a video")
-outputs = gr.outputs.Video(label="Output Video")
-
-gr.Interface(count_workouts, inputs, outputs, title="Workout Counter",
-             description="Upload a video and get a video with workout counting annotations.").launch()
-
-
-
-# from ultralytics import YOLO
-# from ultralytics.solutions import ai_gym
-# import cv2
-
-# model = YOLO("yolov8n-pose.pt")
-# cap = cv2.VideoCapture("pullups.mp4")
-# 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))
-
-# video_writer = cv2.VideoWriter("output_video.avi",
-#                                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])
+    return "output_video.mp4"
 
-# 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
-#     results = model.track(im0, verbose=False)  # Tracking recommended
-#     #results = model.predict(im0)  # Prediction also supported
-#     im0 = gym_object.start_counting(im0, results, frame_count)
-#     video_writer.write(im0)
+# Create the Gradio demo
+demo = gr.Interface(fn=process,
+                    inputs=gr.Video(label='Input Video'),  
+                    outputs=gr.Video(label='Processed Video'))
 
-# cap.release()
-# video_writer.release()
-# cv2.destroyAllWindows()
+# Launch the demo!
+demo.launch(show_api=False)