# from ultralytics import YOLO
# 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("workouts.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=True,
#                     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.")
#       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)

# cv2.destroyAllWindows()
# video_writer.release()


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])

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)

cap.release()
video_writer.release()
cv2.destroyAllWindows()