-
Notifications
You must be signed in to change notification settings - Fork 0
/
YOLOv8-pose_openvino_onnx.py
282 lines (234 loc) · 12.9 KB
/
YOLOv8-pose_openvino_onnx.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
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
import argparse
import time
import cv2
import numpy as np
from openvino.runtime import Core # pip install openvino -i https://pypi.tuna.tsinghua.edu.cn/simple
import onnxruntime as ort # 使用onnxruntime推理用上,pip install onnxruntime,默认安装CPU
# Pose默认的person类
CLASSES = ['person']
class OpenvinoInference(object):
def __init__(self, onnx_path):
self.onnx_path = onnx_path
ie = Core()
self.model_onnx = ie.read_model(model=self.onnx_path)
self.compiled_model_onnx = ie.compile_model(model=self.model_onnx, device_name="CPU")
self.output_layer_onnx = self.compiled_model_onnx.output(0)
def predict(self, datas):
predict_data = self.compiled_model_onnx([datas])[self.output_layer_onnx]
return predict_data
class KeyPoint_draw(object):
def __init__(self):
# 定义一个调色板数组,其中每个元素是一个包含RGB值的列表,用于表示不同的颜色
self.palette = np.array([[255, 128, 0], [255, 153, 51], [255, 178, 102],
[230, 230, 0], [255, 153, 255], [153, 204, 255],
[255, 102, 255], [255, 51, 255], [102, 178, 255],
[51, 153, 255], [255, 153, 153], [255, 102, 102],
[255, 51, 51], [153, 255, 153], [102, 255, 102],
[51, 255, 51], [0, 255, 0], [0, 0, 255], [255, 0, 0],
[255, 255, 255]])
# 定义人体17个关键点的连接顺序,每个子列表包含两个数字,代表要连接的关键点的索引, 1鼻子 2左眼 3右眼 4左耳 5右耳 6左肩 7右肩
# 8左肘 9右肘 10左手腕 11右手腕 12左髋 13右髋 14左膝 15右膝 16左踝 17右踝
self.skeleton = [[16, 14], [14, 12], [17, 15], [15, 13], [12, 13], [6, 12],
[7, 13], [6, 7], [6, 8], [7, 9], [8, 10], [9, 11], [2, 3],
[1, 2], [1, 3], [2, 4], [3, 5], [4, 6], [5, 7]]
# 通过索引从调色板中选择颜色,用于绘制人体骨架的线条,每个索引对应一种颜色
self.pose_limb_color = self.palette[[9, 9, 9, 9, 7, 7, 7, 0, 0, 0, 0, 0, 16, 16, 16, 16, 16, 16, 16]]
# 通过索引从调色板中选择颜色,用于绘制人体的关键点,每个索引对应一种颜色
self.pose_kpt_color = self.palette[[16, 16, 16, 16, 16, 0, 0, 0, 0, 0, 0, 9, 9, 9, 9, 9, 9]]
def plot_skeleton_kpts(self, im, kpts, steps=3):
num_kpts = len(kpts) // steps # 51 / 3 =17
# 画点
for kid in range(num_kpts):
r, g, b = self.pose_kpt_color[kid]
x_coord, y_coord = kpts[steps * kid], kpts[steps * kid + 1]
conf = kpts[steps * kid + 2]
if conf > 0.5: # 关键点的置信度必须大于 0.5
cv2.circle(im, (int(x_coord), int(y_coord)), 10, (int(r), int(g), int(b)), -1)
# 画骨架
for sk_id, sk in enumerate(self.skeleton):
r, g, b = self.pose_limb_color[sk_id]
pos1 = (int(kpts[(sk[0] - 1) * steps]), int(kpts[(sk[0] - 1) * steps + 1]))
pos2 = (int(kpts[(sk[1] - 1) * steps]), int(kpts[(sk[1] - 1) * steps + 1]))
conf1 = kpts[(sk[0] - 1) * steps + 2]
conf2 = kpts[(sk[1] - 1) * steps + 2]
if conf1 > 0.5 and conf2 > 0.5: # 对于肢体,相连的两个关键点置信度 必须同时大于 0.5
cv2.line(im, pos1, pos2, (int(r), int(g), int(b)), thickness=2)
class YOLOv8_pose:
"""YOLOv8_pose detection model class for handling inference and visualization."""
def __init__(self, onnx_model, imgsz=(640, 640), infer_tool='openvino'):
"""
Initialization.
Args:
onnx_model (str): Path to the ONNX model.
"""
self.infer_tool = infer_tool
if self.infer_tool == 'openvino':
# 构建openvino推理引擎
self.openvino = OpenvinoInference(onnx_model)
self.ndtype = np.single
else:
# 构建onnxruntime推理引擎
self.ort_session = ort.InferenceSession(onnx_model,
providers=['CUDAExecutionProvider', 'CPUExecutionProvider']
if ort.get_device() == 'GPU' else ['CPUExecutionProvider'])
# Numpy dtype: support both FP32 and FP16 onnx model
self.ndtype = np.half if self.ort_session.get_inputs()[0].type == 'tensor(float16)' else np.single
self.classes = CLASSES # 加载模型类别
self.model_height, self.model_width = imgsz[0], imgsz[1] # 图像resize大小
self.color = (0, 0, 255) # 为类别生成调色板
def __call__(self, im0, conf_threshold=0.4, iou_threshold=0.45):
"""
The whole pipeline: pre-process -> inference -> post-process.
Args:
im0 (Numpy.ndarray): original input image.
conf_threshold (float): confidence threshold for filtering predictions.
iou_threshold (float): iou threshold for NMS.
Returns:
boxes (List): list of bounding boxes.
"""
# 前处理Pre-process
t1 = time.time()
im, ratio, (pad_w, pad_h) = self.preprocess(im0)
print('预处理时间:{:.3f}s'.format(time.time() - t1))
# 推理 inference
t2 = time.time()
if self.infer_tool == 'openvino':
preds = self.openvino.predict(im)
else:
preds = self.ort_session.run(None, {self.ort_session.get_inputs()[0].name: im})[0]
print('推理时间:{:.2f}s'.format(time.time() - t2))
# 后处理Post-process
t3 = time.time()
boxes = self.postprocess(preds,
im0=im0,
ratio=ratio,
pad_w=pad_w,
pad_h=pad_h,
conf_threshold=conf_threshold,
iou_threshold=iou_threshold,
)
print('后处理时间:{:.3f}s'.format(time.time() - t3))
return boxes
# 前处理,包括:resize, pad, HWC to CHW,BGR to RGB,归一化,增加维度CHW -> BCHW
def preprocess(self, img):
"""
Pre-processes the input image.
Args:
img (Numpy.ndarray): image about to be processed.
Returns:
img_process (Numpy.ndarray): image preprocessed for inference.
ratio (tuple): width, height ratios in letterbox.
pad_w (float): width padding in letterbox.
pad_h (float): height padding in letterbox.
"""
# Resize and pad input image using letterbox() (Borrowed from Ultralytics)
shape = img.shape[:2] # original image shape
new_shape = (self.model_height, self.model_width)
r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
ratio = r, r
new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
pad_w, pad_h = (new_shape[1] - new_unpad[0]) / 2, (new_shape[0] - new_unpad[1]) / 2 # wh padding
if shape[::-1] != new_unpad: # resize
img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
top, bottom = int(round(pad_h - 0.1)), int(round(pad_h + 0.1))
left, right = int(round(pad_w - 0.1)), int(round(pad_w + 0.1))
img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=(114, 114, 114)) # 填充
# Transforms: HWC to CHW -> BGR to RGB -> div(255) -> contiguous -> add axis(optional)
img = np.ascontiguousarray(np.einsum('HWC->CHW', img)[::-1], dtype=self.ndtype) / 255.0
img_process = img[None] if len(img.shape) == 3 else img
return img_process, ratio, (pad_w, pad_h)
# 后处理,包括:阈值过滤与NMS
def postprocess(self, preds, im0, ratio, pad_w, pad_h, conf_threshold, iou_threshold):
"""
Post-process the prediction.
Args:
preds (Numpy.ndarray): predictions come from ort.session.run().
im0 (Numpy.ndarray): [h, w, c] original input image.
ratio (tuple): width, height ratios in letterbox.
pad_w (float): width padding in letterbox.
pad_h (float): height padding in letterbox.
conf_threshold (float): conf threshold.
iou_threshold (float): iou threshold.
Returns:
boxes (List): list of bounding boxes.
"""
x = preds # outputs: predictions (1, 56, 8400),其中56=4+1+17*3,17个关键点(x,y,visibility)
# Transpose the first output: (Batch_size, xywh_conf_pose, Num_anchors) -> (Batch_size, Num_anchors, xywh_conf_pose)
x = np.einsum('bcn->bnc', x) # (1, 8400, 56)
# Predictions filtering by conf-threshold
x = x[x[..., 4] > conf_threshold]
# Create a new matrix which merge these(box, score, pose) into one
# For more details about `numpy.c_()`: https://numpy.org/doc/1.26/reference/generated/numpy.c_.html
x = np.c_[x[..., :4], x[..., 4], x[..., 5:]]
# NMS filtering
# 经过NMS后的值, np.array([[x, y, w, h, conf, pose], ...]), shape=(-1, 4 + 1 + 17*3)
x = x[cv2.dnn.NMSBoxes(x[:, :4], x[:, 4], conf_threshold, iou_threshold)]
# 重新缩放边界框,为画图做准备
if len(x) > 0:
# Bounding boxes format change: cxcywh -> xyxy
x[..., [0, 1]] -= x[..., [2, 3]] / 2
x[..., [2, 3]] += x[..., [0, 1]]
# Rescales bounding boxes from model shape(model_height, model_width) to the shape of original image
x[..., :4] -= [pad_w, pad_h, pad_w, pad_h]
x[..., :4] /= min(ratio)
# Bounding boxes boundary clamp
x[..., [0, 2]] = x[:, [0, 2]].clip(0, im0.shape[1]) # clip避免边界框超出图像边界
x[..., [1, 3]] = x[:, [1, 3]].clip(0, im0.shape[0])
# 关键点坐标映射到原图上,从[:, 5:]开始算
num_kpts = x.shape[1] // 3 # 56 // 3 = 18
for kid in range(2, num_kpts + 1):
x[:, kid * 3 - 1] = (x[:, kid * 3 - 1] - pad_w) / min(ratio)
x[:, kid * 3] = (x[:, kid * 3] - pad_h) / min(ratio)
return x
else:
return []
# 绘框
def draw_and_visualize(self, im, bboxes, keypoint_draw, vis=False, save=True):
"""
Draw and visualize results.
Args:
im (np.ndarray): original image, shape [h, w, c].
bboxes (numpy.ndarray): [n, 56], n is number of bboxes.
vis (bool): imshow using OpenCV.
save (bool): save image annotated.
Returns:
None
"""
# Draw rectangles
for bbox in bboxes:
box, conf, kpts = bbox[:4], bbox[4], bbox[5:]
# draw bbox rectangle
cv2.rectangle(im, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])),
self.color, 1, cv2.LINE_AA)
cv2.putText(im, f'{self.classes[0]}: {conf:.3f}', (int(box[0]), int(box[1] - 9)),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, self.color, 2, cv2.LINE_AA)
# 画关键点,连线
keypoint_draw.plot_skeleton_kpts(im, kpts)
# Show image
if vis:
cv2.imshow('demo', im)
cv2.waitKey(0)
cv2.destroyAllWindows()
# Save image
if save:
cv2.imwrite('demo.jpg', im)
if __name__ == '__main__':
# Create an argument parser to handle command-line arguments
parser = argparse.ArgumentParser()
parser.add_argument('--model', type=str, default='weights/yolov8s-pose.onnx', help='Path to ONNX model')
parser.add_argument('--source', type=str, default=str('bus.jpg'), help='Path to input image')
parser.add_argument('--imgsz', type=tuple, default=(640, 640), help='Image input size')
parser.add_argument('--conf', type=float, default=0.25, help='Confidence threshold')
parser.add_argument('--iou', type=float, default=0.45, help='NMS IoU threshold')
parser.add_argument('--infer_tool', type=str, default='openvino', choices=("openvino", "onnxruntime"), help='选择推理引擎')
args = parser.parse_args()
# Build model
model = YOLOv8_pose(args.model, args.imgsz, args.infer_tool)
keypoint_draw = KeyPoint_draw() # 可视化关键点
# Read image by OpenCV
img = cv2.imread(args.source)
# Inference
boxes = model(img, conf_threshold=args.conf, iou_threshold=args.iou)
# Visualize
if len(boxes) > 0:
model.draw_and_visualize(img, boxes, keypoint_draw, vis=False, save=True)