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demo.py
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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
# Check OpenCV version
assert cv.__version__ >= "4.7.0", \
"Please install latest opencv-python to try this demo: python3 -m pip install --upgrade opencv-python"
# Valid combinations of backends and targets
backend_target_pairs = [
[cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_CPU],
[cv.dnn.DNN_BACKEND_CUDA, cv.dnn.DNN_TARGET_CUDA],
[cv.dnn.DNN_BACKEND_CUDA, cv.dnn.DNN_TARGET_CUDA_FP16],
[cv.dnn.DNN_BACKEND_TIMVX, cv.dnn.DNN_TARGET_NPU],
[cv.dnn.DNN_BACKEND_CANN, cv.dnn.DNN_TARGET_NPU]
]
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_2023feb.onnx',
help='Path to the model.')
parser.add_argument('--backend_target', '-bt', type=int, default=0,
help='''Choose one of the backend-target pair to run this demo:
{:d}: (default) OpenCV implementation + CPU,
{:d}: CUDA + GPU (CUDA),
{:d}: CUDA + GPU (CUDA FP16),
{:d}: TIM-VX + NPU,
{:d}: CANN + NPU
'''.format(*[x for x in range(len(backend_target_pairs))]))
parser.add_argument('--conf_threshold', type=float, default=0.9,
help='Filter out hands of confidence < conf_threshold.')
parser.add_argument('--save', '-s', action='store_true',
help='Specify to save results. This flag is invalid when using camera.')
parser.add_argument('--vis', '-v', action='store_true',
help='Specify 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):
display_screen = image.copy()
display_3d = np.zeros((400, 400, 3), np.uint8)
cv.line(display_3d, (200, 0), (200, 400), (255, 255, 255), 2)
cv.line(display_3d, (0, 200), (400, 200), (255, 255, 255), 2)
cv.putText(display_3d, 'Main View', (0, 12), cv.FONT_HERSHEY_DUPLEX, 0.5, (0, 0, 255))
cv.putText(display_3d, 'Top View', (200, 12), cv.FONT_HERSHEY_DUPLEX, 0.5, (0, 0, 255))
cv.putText(display_3d, 'Left View', (0, 212), cv.FONT_HERSHEY_DUPLEX, 0.5, (0, 0, 255))
cv.putText(display_3d, 'Right View', (200, 212), cv.FONT_HERSHEY_DUPLEX, 0.5, (0, 0, 255))
is_draw = False # ensure only one hand is drawn
def draw_lines(image, landmarks, is_draw_point=True, thickness=2):
cv.line(image, landmarks[0], landmarks[1], (255, 255, 255), thickness)
cv.line(image, landmarks[1], landmarks[2], (255, 255, 255), thickness)
cv.line(image, landmarks[2], landmarks[3], (255, 255, 255), thickness)
cv.line(image, landmarks[3], landmarks[4], (255, 255, 255), thickness)
cv.line(image, landmarks[0], landmarks[5], (255, 255, 255), thickness)
cv.line(image, landmarks[5], landmarks[6], (255, 255, 255), thickness)
cv.line(image, landmarks[6], landmarks[7], (255, 255, 255), thickness)
cv.line(image, landmarks[7], landmarks[8], (255, 255, 255), thickness)
cv.line(image, landmarks[0], landmarks[9], (255, 255, 255), thickness)
cv.line(image, landmarks[9], landmarks[10], (255, 255, 255), thickness)
cv.line(image, landmarks[10], landmarks[11], (255, 255, 255), thickness)
cv.line(image, landmarks[11], landmarks[12], (255, 255, 255), thickness)
cv.line(image, landmarks[0], landmarks[13], (255, 255, 255), thickness)
cv.line(image, landmarks[13], landmarks[14], (255, 255, 255), thickness)
cv.line(image, landmarks[14], landmarks[15], (255, 255, 255), thickness)
cv.line(image, landmarks[15], landmarks[16], (255, 255, 255), thickness)
cv.line(image, landmarks[0], landmarks[17], (255, 255, 255), thickness)
cv.line(image, landmarks[17], landmarks[18], (255, 255, 255), thickness)
cv.line(image, landmarks[18], landmarks[19], (255, 255, 255), thickness)
cv.line(image, landmarks[19], landmarks[20], (255, 255, 255), thickness)
if is_draw_point:
for p in landmarks:
cv.circle(image, p, thickness, (0, 0, 255), -1)
for idx, handpose in enumerate(hands):
conf = handpose[-1]
bbox = handpose[0:4].astype(np.int32)
handedness = handpose[-2]
if handedness <= 0.5:
handedness_text = 'Left'
else:
handedness_text = 'Right'
landmarks_screen = handpose[4:67].reshape(21, 3).astype(np.int32)
landmarks_word = handpose[67:130].reshape(21, 3)
# Print results
if print_result:
print('-----------hand {}-----------'.format(idx + 1))
print('conf: {:.2f}'.format(conf))
print('handedness: {}'.format(handedness_text))
print('hand box: {}'.format(bbox))
print('hand landmarks: ')
for l in landmarks_screen:
print('\t{}'.format(l))
print('hand world landmarks: ')
for l in landmarks_word:
print('\t{}'.format(l))
# draw box
cv.rectangle(display_screen, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0, 255, 0), 2)
# draw handedness
cv.putText(display_screen, '{}'.format(handedness_text), (bbox[0], bbox[1] + 12), cv.FONT_HERSHEY_DUPLEX, 0.5, (0, 0, 255))
# Draw line between each key points
landmarks_xy = landmarks_screen[:, 0:2]
draw_lines(display_screen, landmarks_xy, is_draw_point=False)
# z value is relative to WRIST
for p in landmarks_screen:
r = max(5 - p[2] // 5, 0)
r = min(r, 14)
cv.circle(display_screen, np.array([p[0], p[1]]), r, (0, 0, 255), -1)
if is_draw is False:
is_draw = True
# Main view
landmarks_xy = landmarks_word[:, [0, 1]]
landmarks_xy = (landmarks_xy * 1000 + 100).astype(np.int32)
draw_lines(display_3d, landmarks_xy, thickness=5)
# Top view
landmarks_xz = landmarks_word[:, [0, 2]]
landmarks_xz[:, 1] = -landmarks_xz[:, 1]
landmarks_xz = (landmarks_xz * 1000 + np.array([300, 100])).astype(np.int32)
draw_lines(display_3d, landmarks_xz, thickness=5)
# Left view
landmarks_yz = landmarks_word[:, [2, 1]]
landmarks_yz[:, 0] = -landmarks_yz[:, 0]
landmarks_yz = (landmarks_yz * 1000 + np.array([100, 300])).astype(np.int32)
draw_lines(display_3d, landmarks_yz, thickness=5)
# Right view
landmarks_zy = landmarks_word[:, [2, 1]]
landmarks_zy = (landmarks_zy * 1000 + np.array([300, 300])).astype(np.int32)
draw_lines(display_3d, landmarks_zy, thickness=5)
return display_screen, display_3d
if __name__ == '__main__':
backend_id = backend_target_pairs[args.backend_target][0]
target_id = backend_target_pairs[args.backend_target][1]
# palm detector
palm_detector = MPPalmDet(modelPath='../palm_detection_mediapipe/palm_detection_mediapipe_2023feb.onnx',
nmsThreshold=0.3,
scoreThreshold=0.6,
backendId=backend_id,
targetId=target_id)
# handpose detector
handpose_detector = MPHandPose(modelPath=args.model,
confThreshold=args.conf_threshold,
backendId=backend_id,
targetId=target_id)
# 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, 132))
# 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, view_3d = visualize(image, hands, True)
if len(palms) == 0:
print('No palm detected!')
else:
print('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.imshow('3D HandPose Demo', view_3d)
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, 132))
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, view_3d = visualize(frame, hands)
if len(palms) == 0:
print('No palm detected!')
else:
print('Palm detected!')
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)
cv.imshow('3D HandPose Demo', view_3d)
tm.reset()