while(cap.isOpened):
  # Capture each frame of the video.
  ret, frame = cap.read()
  if ret:
      orig_image = frame
      image = cv2.cvtColor(orig_image, cv2.COLOR_BGR2RGB)
      image = letterbox(image, (frame_width), stride=64, auto=True)[0]
      image_ = image.copy()
      image = transforms.ToTensor()(image)
      image = torch.tensor(np.array([image.numpy()]))
      image = image.to(device)
      image = image.half()

      # Get the start time.
      start_time = time.time()
      with torch.no_grad():
          output, _ = model(image)
        # Get the end time.
      end_time = time.time()
      # Get the fps.
      fps = 1 / (end_time - start_time)
      # Add fps to total fps.
      total_fps += fps
      # Increment frame count.
      frame_count += 1

      output = non_max_suppression_kpt(output, 0.25, 0.65, nc=model.yaml['nc'], nkpt=model.yaml['nkpt'], kpt_label=True)
      output = output_to_keypoint(output)
      nimg = image[0].permute(1, 2, 0) * 255
      nimg = nimg.cpu().numpy().astype(np.uint8)
      nimg = cv2.cvtColor(nimg, cv2.COLOR_RGB2BGR)
      for idx in range(output.shape[0]):
          plot_skeleton_kpts(nimg, output[idx, 7:].T, 3)

          # Comment/Uncomment the following lines to show bounding boxes around persons.
          xmin, ymin = (output[idx, 2]-output[idx, 4]/2), (output[idx, 3]-output[idx, 5]/2)
          xmax, ymax = (output[idx, 2]+output[idx, 4]/2), (output[idx, 3]+output[idx, 5]/2)
          cv2.rectangle(
              nimg,
              (int(xmin), int(ymin)),
              (int(xmax), int(ymax)),
              color=(255, 0, 0),
              thickness=1,
              lineType=cv2.LINE_AA
          )

      # Write the FPS on the current frame.
      cv2.putText(nimg, f"{fps:.3f} FPS", (15, 30), cv2.FONT_HERSHEY_SIMPLEX,
                  1, (0, 255, 0), 2)
      # Convert from BGR to RGB color format.
      cv2.imshow('image', nimg)
      out.write(nimg)
      # Press `q` to exit.
      if cv2.waitKey(1) & 0xFF == ord('q'):
          break
  else:
      break
# Release VideoCapture().
cap.release()
# Close all frames and video windows.
cv2.destroyAllWindows()
# Calculate and print the average FPS.
avg_fps = total_fps / frame_count
print(f"Average FPS: {avg_fps:.3f}")