• https://www.tensorflow.org/hub/tutorials/movenet
• https://tfhub.dev/s?q=movenet

• https://tfhub.dev/google/lite-model/movenet/singlepose/thunder/3

<file python movenet_test.py> from time import time import cv2 import numpy as np import tensorflow as tf

def draw(frame, keypoints_with_scores):

  for item in keypoints_with_scores[0][0]:
      if item[2] > 0.5:  # confiance
          x = int(item[1]*256)  # numpy array x est le 2ème
          y = int(item[0]*256)
          cv2.circle(frame, (x, y), 6, color=(0,0,255), thickness=-1)
  return frame

interpreter = tf.lite.Interpreter(model_path=“lite-model_movenet_singlepose_thunder_3.tflite”) interpreter.allocate_tensors()

cv2.namedWindow('color', cv2.WND_PROP_FULLSCREEN) cam = cv2.VideoCapture(0) t0 = time() nbr = 0

while 1:

  nbr += 1
  ret, frame = cam.read()

  if not ret:
      continue

  image = tf.expand_dims(frame, axis=0)
  # Resize and pad the image to keep the aspect ratio and fit the expected size.
  image = tf.image.resize_with_pad(image, 256, 256)

  # TF Lite format expects tensor type of float32.
  input_image = tf.cast(image, dtype=tf.float32)
  input_details = interpreter.get_input_details()
  output_details = interpreter.get_output_details()

  interpreter.set_tensor(input_details[0]['index'], input_image.numpy())
  interpreter.invoke()

  # Output is a [1, 1, 17, 3] numpy array.
  keypoints_with_scores = interpreter.get_tensor(output_details[0]['index'])

  frame = draw(frame, keypoints_with_scores)

  # Affichage de l'image
  cv2.imshow('color', frame)

  # Calcul du FPS, affichage toutes les 10 s
  if time() - t0 > 10:
      print("FPS =", round(nbr/10, 2))
      t0, nbr = time(), 0

  # Pour quitter
  if cv2.waitKey(1) == 27:
      break

cv2.destroyAllWindows() </code>

• Essai de movenet par SergeLabo