def main():
params = set_params()
#Constructing OpenPose object allocates GPU memory
openpose = op.OpenPose(params)
#Opening OpenCV stream
stream = cv2.VideoCapture(0)
font = cv2.FONT_HERSHEY_SIMPLEX
while True:
ret, img = stream.read()
# 转为灰度图
#gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 改变图片大小
img = cv2.resize(img, (320, 180))
# Output keypoints and the image with the human skeleton blended on it
# keypoints, output_image = openpose.forward(img, True)
keypoints, output_image = openpose.forward(img, True)
# Print the human pose keypoints, i.e., a [#people x #keypoints x 3]-dimensional numpy object with the keypoints of all the people on that image
if len(keypoints) > 0:
print('Human(s) Pose Estimated!')
print(keypoints)
else:
print('No humans detected!')
# Display the stream
#cv2.putText(output_image,'OpenPose Tello-Gesture-Control',(5,15), font, 0.5,(255,255,255),1,cv2.LINE_AA)
cv2.putText(output_image, 'Tello-Gesture-Control', (55, 15), font, 0.5,
(255, 255, 255), 1, cv2.LINE_AA)
cv2.imshow('Human Pose Estimation', output_image)
if cv2.waitKey(10) & 0xFF == ord('q'):
print("I'm done end **********************************")
break
stream.release()
cv2.destroyAllWindows()
def main():
params = set_params()
#Constructing OpenPose object allocates GPU memory
openpose = op.OpenPose(params)
#Opening OpenCV stream
stream = cv2.VideoCapture(0)
font = cv2.FONT_HERSHEY_SIMPLEX
while True:
ret, img = stream.read()
# Output keypoints and the image with the human skeleton blended on it
# keypoints, output_image = openpose.forward(img, True)
keypoints, output_image = openpose.forward(img, True)
# Print the human pose keypoints, i.e., a [#people x #keypoints x 3]-dimensional numpy object with the keypoints of all the people on that image
if len(keypoints) > 0:
print('Human(s) Pose Estimated!')
print(keypoints)
else:
print('No humans detected!')
# Display the stream
cv2.putText(output_image, 'OpenPose using Python-OpenCV', (20, 30),
font, 1, (255, 255, 255), 1, cv2.LINE_AA)
cv2.imshow('Human Pose Estimation', output_image)
key = cv2.waitKey(1)
if key == ord('q'):
break
stream.release()
cv2.destroyAllWindows()
params = dict()
params["logging_level"] = 3
params["output_resolution"] = "-1x-1"
params["net_resolution"] = "-1x368"
params["model_pose"] = "BODY_25"
params["alpha_pose"] = 0.6
params["scale_gap"] = 0.3
params["scale_number"] = 1
params["render_threshold"] = 0.05
# If GPU version is built, and multiple GPUs are available, set the ID here
params["num_gpu_start"] = 0
params["disable_blending"] = False
# Ensure you point to the correct path where models are located
params["default_model_folder"] = dir_path + "/../../../models/"
# Construct OpenPose object allocates GPU memory
openpose = op.OpenPose(params)
def gesture_recognize(keypoints): # define function to recognize gesture
flags = np.zeros((1,4)) # initial count of each gesture are all 0
v_56 = keypoints[6, 0:2] - keypoints[5, 0:2] # left shoulder and arm
v_67 = keypoints[7, 0:2] - keypoints[6, 0:2] # left arm and hand
v_15 = keypoints[5, 0:2] - keypoints[1, 0:2] # neck and leftshoulder
# normalize vector
nv_56 = v_56 / np.linalg.norm(v_56, ord=1)
nv_67 = v_67 / np.linalg.norm(v_67, ord=1)
nv_15 = v_15 / np.linalg.norm(v_15, ord=1)
dv_15_56 = np.vdot(nv_15,nv_56)
if dv_15_56<=0 and dv_15_56>=-0.7: # the angle btw arm_shoulder
flags[0,0] = 1 # and the neck_arm is 90-135 degree
return flags
def __init__(self, model_path):
# Initialise openpose
params = self._openpose_parameters(model_path)
# Constructing OpenPose object allocates GPU memory
self.openpose = op.OpenPose(params)
