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import sys
import argparse
import numpy as np
import cv2 as cv
from mp_handpose import MPHandPose
sys.path.append('../palm_detection_mediapipe')
from mp_palmdet import MPPalmDet
def str2bool(v):
if v.lower() in ['on', 'yes', 'true', 'y', 't']:
return True
elif v.lower() in ['off', 'no', 'false', 'n', 'f']:
return False
else:
raise NotImplementedError
backends = [cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_BACKEND_CUDA]
targets = [cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_CUDA, cv.dnn.DNN_TARGET_CUDA_FP16]
help_msg_backends = "Choose one of the computation backends: {:d}: OpenCV implementation (default); {:d}: CUDA"
help_msg_targets = "Chose one of the target computation devices: {:d}: CPU (default); {:d}: CUDA; {:d}: CUDA fp16"
try:
backends += [cv.dnn.DNN_BACKEND_TIMVX]
targets += [cv.dnn.DNN_TARGET_NPU]
help_msg_backends += "; {:d}: TIMVX"
help_msg_targets += "; {:d}: NPU"
except:
print('This version of OpenCV does not support TIM-VX and NPU. Visit https://github.com/opencv/opencv/wiki/TIM-VX-Backend-For-Running-OpenCV-On-NPU for more information.')
parser = argparse.ArgumentParser(description='Hand Pose Estimation from MediaPipe')
parser.add_argument('--input', '-i', type=str, help='Path to the input image. Omit for using default camera.')
parser.add_argument('--model', '-m', type=str, default='./handpose_estimation_mediapipe_2022may.onnx', help='Path to the model.')
parser.add_argument('--backend', '-b', type=int, default=backends[0], help=help_msg_backends.format(*backends))
parser.add_argument('--target', '-t', type=int, default=targets[0], help=help_msg_targets.format(*targets))
parser.add_argument('--conf_threshold', type=float, default=0.8, help='Filter out hands of confidence < conf_threshold.')
parser.add_argument('--save', '-s', type=str, default=False, help='Set true to save results. This flag is invalid when using camera.')
parser.add_argument('--vis', '-v', type=str2bool, default=True, help='Set true to open a window for result visualization. This flag is invalid when using camera.')
args = parser.parse_args()
def visualize(image, hands, print_result=False):
output = image.copy()
for idx, handpose in enumerate(hands):
conf = handpose[-1]
bbox = handpose[0:4].astype(np.int32)
landmarks = handpose[4:-1].reshape(21, 2).astype(np.int32)
# Print results
if print_result:
print('-----------hand {}-----------'.format(idx + 1))
print('conf: {:.2f}'.format(conf))
print('hand box: {}'.format(bbox))
print('hand landmarks: ')
for l in landmarks:
print('\t{}'.format(l))
# Draw line between each key points
cv.line(output, landmarks[0], landmarks[1], (255, 255, 255), 2)
cv.line(output, landmarks[1], landmarks[2], (255, 255, 255), 2)
cv.line(output, landmarks[2], landmarks[3], (255, 255, 255), 2)
cv.line(output, landmarks[3], landmarks[4], (255, 255, 255), 2)
cv.line(output, landmarks[0], landmarks[5], (255, 255, 255), 2)
cv.line(output, landmarks[5], landmarks[6], (255, 255, 255), 2)
cv.line(output, landmarks[6], landmarks[7], (255, 255, 255), 2)
cv.line(output, landmarks[7], landmarks[8], (255, 255, 255), 2)
cv.line(output, landmarks[0], landmarks[9], (255, 255, 255), 2)
cv.line(output, landmarks[9], landmarks[10], (255, 255, 255), 2)
cv.line(output, landmarks[10], landmarks[11], (255, 255, 255), 2)
cv.line(output, landmarks[11], landmarks[12], (255, 255, 255), 2)
cv.line(output, landmarks[0], landmarks[13], (255, 255, 255), 2)
cv.line(output, landmarks[13], landmarks[14], (255, 255, 255), 2)
cv.line(output, landmarks[14], landmarks[15], (255, 255, 255), 2)
cv.line(output, landmarks[15], landmarks[16], (255, 255, 255), 2)
cv.line(output, landmarks[0], landmarks[17], (255, 255, 255), 2)
cv.line(output, landmarks[17], landmarks[18], (255, 255, 255), 2)
cv.line(output, landmarks[18], landmarks[19], (255, 255, 255), 2)
cv.line(output, landmarks[19], landmarks[20], (255, 255, 255), 2)
for p in landmarks:
cv.circle(output, p, 2, (0, 0, 255), 2)
return output
if __name__ == '__main__':
# palm detector
palm_detector = MPPalmDet(modelPath='../palm_detection_mediapipe/palm_detection_mediapipe_2023feb.onnx',
nmsThreshold=0.3,
scoreThreshold=0.8,
backendId=args.backend,
targetId=args.target)
# handpose detector
handpose_detector = MPHandPose(modelPath=args.model,
confThreshold=args.conf_threshold,
backendId=args.backend,
targetId=args.target)
# If input is an image
if args.input is not None:
image = cv.imread(args.input)
# Palm detector inference
palms = palm_detector.infer(image)
hands = np.empty(shape=(0, 47))
# Estimate the pose of each hand
for palm in palms:
# Handpose detector inference
handpose = handpose_detector.infer(image, palm)
if handpose is not None:
hands = np.vstack((hands, handpose))
# Draw results on the input image
image = visualize(image, hands, True)
if len(palms) == 0:
print('No palm detected!')
# Save results
if args.save:
cv.imwrite('result.jpg', image)
print('Results saved to result.jpg\n')
# Visualize results in a new window
if args.vis:
cv.namedWindow(args.input, cv.WINDOW_AUTOSIZE)
cv.imshow(args.input, image)
cv.waitKey(0)
else: # Omit input to call default camera
deviceId = 0
cap = cv.VideoCapture(deviceId)
tm = cv.TickMeter()
while cv.waitKey(1) < 0:
hasFrame, frame = cap.read()
if not hasFrame:
print('No frames grabbed!')
break
# Palm detector inference
palms = palm_detector.infer(frame)
hands = np.empty(shape=(0, 47))
tm.start()
# Estimate the pose of each hand
for palm in palms:
# Handpose detector inference
handpose = handpose_detector.infer(frame, palm)
if handpose is not None:
hands = np.vstack((hands, handpose))
tm.stop()
# Draw results on the input image
frame = visualize(frame, hands)
if len(palms) == 0:
print('No palm detected!')
else:
cv.putText(frame, 'FPS: {:.2f}'.format(tm.getFPS()), (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))
cv.imshow('MediaPipe Handpose Detection Demo', frame)
tm.reset()
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