PersonalAI/pose_estimator.py at main · devhttps/PersonalAI · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
import mediapipe as mp
from mediapipe.tasks import python
import cv2
import numpy as np
from mediapipe import solutions
from mediapipe.framework.formats import landmark_pb2

file_name='IMG_2149.mov'
file_name = file_name
model_path = 'pose_landmarker_full.task'

# https://developers.google.com/mediapipe/solutions/vision/pose_landmarker/python#video
options = python.vision.PoseLandmarkerOptions(
    base_options=python.BaseOptions(model_asset_path=model_path),
    running_mode=python.vision.RunningMode.VIDEO)

def draw_landmarks_on_image(rgb_image, detection_result):
  pose_landmarks_list = detection_result.pose_landmarks
  annotated_image = np.copy(rgb_image)

  for idx in range(len(pose_landmarks_list)):
    pose_landmarks = pose_landmarks_list[idx]

    # Draw the pose landmarks.
    pose_landmarks_proto = landmark_pb2.NormalizedLandmarkList()
    pose_landmarks_proto.landmark.extend([
      landmark_pb2.NormalizedLandmark(x=landmark.x, y=landmark.y, z=landmark.z) for landmark in pose_landmarks
    ])
    solutions.drawing_utils.draw_landmarks(
      annotated_image,
      pose_landmarks_proto,
      solutions.pose.POSE_CONNECTIONS,
      solutions.drawing_styles.get_default_pose_landmarks_style())
  return annotated_image

with python.vision.PoseLandmarker.create_from_options(options) as landmarker:
  cap = cv2.VideoCapture(file_name)
  fps = cap.get(cv2.CAP_PROP_FPS)
  calc_timestamps = [0.0]

  if (cap.isOpened()== False):
      print("Error opening video stream or file")

  while(cap.isOpened()):
    ret, frame = cap.read()
    if ret == True:
      mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=frame)
      calc_timestamps.append(int(calc_timestamps[-1] + 1000/fps))
      detection_result = landmarker.detect_for_video(mp_image, calc_timestamps[-1])

      annotated_image = draw_landmarks_on_image(frame, detection_result)
      cv2.imshow('Frame',annotated_image)

      # Press Q on keyboard to  exit
      if cv2.waitKey(25) & 0xFF == ord('q'):
          break
    else:
        break

  cap.release()
  cv2.destroyAllWindows()