text: import numpy as np
  import cv2
  from hand_tracker import HandTracker
  
  det = HandTracker('models/palm_detection_without_custom_op.tflite',
                    'models/hand_landmark_3d.tflite',
                    'data/anchors.csv',
                    box_shift=-0.5, box_enlarge=2.6)
  in_bgr = cv2.imread('data/test_img1.jpg')
  in_rgb = in_bgr[:,:,::-1]
  list_keypoints, list_bbox = det(in_rgb)
  
  out_img = np.copy(in_bgr)
  
  # point size
  ps = int(np.ceil(min(out_img.shape[0], out_img.shape[1]) / 256))
  
  if list_keypoints is not None:
      for idx in range(len(list_keypoints)):
          keypoints = list_keypoints[idx]
          bbox = list_bbox[idx]
          for i in range(4):
              j = (i + 1) % 4
              p0 = (int(bbox[i,0]+0.5),int(bbox[i,1]+0.5))
              p1 = (int(bbox[j,0]+0.5),int(bbox[j,1]+0.5))
              cv2.line(out_img, p0, p1, (0,0,255), ps)
          for i in range(keypoints.shape[0]):
              p = (int(keypoints[i,0]+0.5),int(keypoints[i,1]+0.5))
              cv2.circle(out_img, p, ps, (255,0,0), ps)
      cv2.imwrite('out.jpg', out_img)
  