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import sys
import argparse
import numpy as np
import cv2 as cv
# Check OpenCV version
opencv_python_version = lambda str_version: tuple(map(int, (str_version.split("."))))
assert opencv_python_version(cv.__version__) >= opencv_python_version("4.10.0"), \
"Please install latest opencv-python for benchmark: python3 -m pip install --upgrade opencv-python"
from mp_pose import MPPose
sys.path.append('../person_detection_mediapipe')
from mp_persondet import MPPersonDet
# 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='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='./pose_estimation_mediapipe_2023mar.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=0.8,
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, poses):
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 person is drawn
def _draw_lines(image, landmarks, keep_landmarks, is_draw_point=True, thickness=2):
def _draw_by_presence(idx1, idx2):
if keep_landmarks[idx1] and keep_landmarks[idx2]:
cv.line(image, landmarks[idx1], landmarks[idx2], (255, 255, 255), thickness)
_draw_by_presence(0, 1)
_draw_by_presence(1, 2)
_draw_by_presence(2, 3)
_draw_by_presence(3, 7)
_draw_by_presence(0, 4)
_draw_by_presence(4, 5)
_draw_by_presence(5, 6)
_draw_by_presence(6, 8)
_draw_by_presence(9, 10)
_draw_by_presence(12, 14)
_draw_by_presence(14, 16)
_draw_by_presence(16, 22)
_draw_by_presence(16, 18)
_draw_by_presence(16, 20)
_draw_by_presence(18, 20)
_draw_by_presence(11, 13)
_draw_by_presence(13, 15)
_draw_by_presence(15, 21)
_draw_by_presence(15, 19)
_draw_by_presence(15, 17)
_draw_by_presence(17, 19)
_draw_by_presence(11, 12)
_draw_by_presence(11, 23)
_draw_by_presence(23, 24)
_draw_by_presence(24, 12)
_draw_by_presence(24, 26)
_draw_by_presence(26, 28)
_draw_by_presence(28, 30)
_draw_by_presence(28, 32)
_draw_by_presence(30, 32)
_draw_by_presence(23, 25)
_draw_by_presence(25, 27)
_draw_by_presence(27, 31)
_draw_by_presence(27, 29)
_draw_by_presence(29, 31)
if is_draw_point:
for i, p in enumerate(landmarks):
if keep_landmarks[i]:
cv.circle(image, p, thickness, (0, 0, 255), -1)
for idx, pose in enumerate(poses):
bbox, landmarks_screen, landmarks_word, mask, heatmap, conf = pose
edges = cv.Canny(mask, 100, 200)
kernel = np.ones((2, 2), np.uint8) # expansion edge to 2 pixels
edges = cv.dilate(edges, kernel, iterations=1)
edges_bgr = cv.cvtColor(edges, cv.COLOR_GRAY2BGR)
edges_bgr[edges == 255] = [0, 255, 0]
display_screen = cv.add(edges_bgr, display_screen)
# draw box
bbox = bbox.astype(np.int32)
cv.rectangle(display_screen, bbox[0], bbox[1], (0, 255, 0), 2)
cv.putText(display_screen, '{:.4f}'.format(conf), (bbox[0][0], bbox[0][1] + 12), cv.FONT_HERSHEY_DUPLEX, 0.5, (0, 0, 255))
# Draw line between each key points
landmarks_screen = landmarks_screen[:-6, :]
landmarks_word = landmarks_word[:-6, :]
keep_landmarks = landmarks_screen[:, 4] > 0.8 # only show visible keypoints which presence bigger than 0.8
landmarks_screen = landmarks_screen
landmarks_word = landmarks_word
landmarks_xy = landmarks_screen[:, 0: 2].astype(np.int32)
_draw_lines(display_screen, landmarks_xy, keep_landmarks, is_draw_point=False)
# z value is relative to HIP, but we use constant to instead
for i, p in enumerate(landmarks_screen[:, 0: 3].astype(np.int32)):
if keep_landmarks[i]:
cv.circle(display_screen, np.array([p[0], p[1]]), 2, (0, 0, 255), -1)
if is_draw is False:
is_draw = True
# Main view
landmarks_xy = landmarks_word[:, [0, 1]]
landmarks_xy = (landmarks_xy * 100 + 100).astype(np.int32)
_draw_lines(display_3d, landmarks_xy, keep_landmarks, thickness=2)
# Top view
landmarks_xz = landmarks_word[:, [0, 2]]
landmarks_xz[:, 1] = -landmarks_xz[:, 1]
landmarks_xz = (landmarks_xz * 100 + np.array([300, 100])).astype(np.int32)
_draw_lines(display_3d, landmarks_xz,keep_landmarks, thickness=2)
# Left view
landmarks_yz = landmarks_word[:, [2, 1]]
landmarks_yz[:, 0] = -landmarks_yz[:, 0]
landmarks_yz = (landmarks_yz * 100 + np.array([100, 300])).astype(np.int32)
_draw_lines(display_3d, landmarks_yz, keep_landmarks, thickness=2)
# Right view
landmarks_zy = landmarks_word[:, [2, 1]]
landmarks_zy = (landmarks_zy * 100 + np.array([300, 300])).astype(np.int32)
_draw_lines(display_3d, landmarks_zy, keep_landmarks, thickness=2)
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]
# person detector
person_detector = MPPersonDet(modelPath='../person_detection_mediapipe/person_detection_mediapipe_2023mar.onnx',
nmsThreshold=0.3,
scoreThreshold=0.5,
topK=5000, # usually only one person has good performance
backendId=backend_id,
targetId=target_id)
# pose estimator
pose_estimator = MPPose(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)
# person detector inference
persons = person_detector.infer(image)
poses = []
# Estimate the pose of each person
for person in persons:
# pose estimator inference
pose = pose_estimator.infer(image, person)
if pose is not None:
poses.append(pose)
# Draw results on the input image
image, view_3d = visualize(image, poses)
if len(persons) == 0:
print('No person detected!')
else:
print('Person 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 Pose 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
# person detector inference
persons = person_detector.infer(frame)
poses = []
tm.start()
# Estimate the pose of each person
for person in persons:
# pose detector inference
pose = pose_estimator.infer(frame, person)
if pose is not None:
poses.append(pose)
tm.stop()
# Draw results on the input image
frame, view_3d = visualize(frame, poses)
if len(persons) == 0:
print('No person detected!')
else:
print('Person detected!')
cv.putText(frame, 'FPS: {:.2f}'.format(tm.getFPS()), (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))
cv.imshow('MediaPipe Pose Detection Demo', frame)
cv.imshow('3D Pose Demo', view_3d)
tm.reset()
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