-
Notifications
You must be signed in to change notification settings - Fork 198
/
demo.py
253 lines (210 loc) · 9.97 KB
/
demo.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
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()