-
Notifications
You must be signed in to change notification settings - Fork 220
/
Copy pathdemo.py
155 lines (127 loc) · 6.5 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
import numpy as np
import cv2 as cv
import argparse
# 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 yolox import YoloX
# 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]
]
classes = ('person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe',
'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat',
'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop',
'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',
'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock',
'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush')
def letterbox(srcimg, target_size=(640, 640)):
padded_img = np.ones((target_size[0], target_size[1], 3)).astype(np.float32) * 114.0
ratio = min(target_size[0] / srcimg.shape[0], target_size[1] / srcimg.shape[1])
resized_img = cv.resize(
srcimg, (int(srcimg.shape[1] * ratio), int(srcimg.shape[0] * ratio)), interpolation=cv.INTER_LINEAR
).astype(np.float32)
padded_img[: int(srcimg.shape[0] * ratio), : int(srcimg.shape[1] * ratio)] = resized_img
return padded_img, ratio
def unletterbox(bbox, letterbox_scale):
return bbox / letterbox_scale
def vis(dets, srcimg, letterbox_scale, fps=None):
res_img = srcimg.copy()
if fps is not None:
fps_label = "FPS: %.2f" % fps
cv.putText(res_img, fps_label, (10, 25), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
for det in dets:
box = unletterbox(det[:4], letterbox_scale).astype(np.int32)
score = det[-2]
cls_id = int(det[-1])
x0, y0, x1, y1 = box
text = '{}:{:.1f}%'.format(classes[cls_id], score * 100)
font = cv.FONT_HERSHEY_SIMPLEX
txt_size = cv.getTextSize(text, font, 0.4, 1)[0]
cv.rectangle(res_img, (x0, y0), (x1, y1), (0, 255, 0), 2)
cv.rectangle(res_img, (x0, y0 + 1), (x0 + txt_size[0] + 1, y0 + int(1.5 * txt_size[1])), (255, 255, 255), -1)
cv.putText(res_img, text, (x0, y0 + txt_size[1]), font, 0.4, (0, 0, 0), thickness=1)
return res_img
if __name__=='__main__':
parser = argparse.ArgumentParser(description='Nanodet inference using OpenCV an contribution by Sri Siddarth Chakaravarthy part of GSOC_2022')
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='object_detection_yolox_2022nov.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('--confidence', default=0.5, type=float,
help='Class confidence')
parser.add_argument('--nms', default=0.5, type=float,
help='Enter nms IOU threshold')
parser.add_argument('--obj', default=0.5, type=float,
help='Enter object 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()
backend_id = backend_target_pairs[args.backend_target][0]
target_id = backend_target_pairs[args.backend_target][1]
model_net = YoloX(modelPath= args.model,
confThreshold=args.confidence,
nmsThreshold=args.nms,
objThreshold=args.obj,
backendId=backend_id,
targetId=target_id)
tm = cv.TickMeter()
tm.reset()
if args.input is not None:
image = cv.imread(args.input)
input_blob = cv.cvtColor(image, cv.COLOR_BGR2RGB)
input_blob, letterbox_scale = letterbox(input_blob)
# Inference
tm.start()
preds = model_net.infer(input_blob)
tm.stop()
print("Inference time: {:.2f} ms".format(tm.getTimeMilli()))
img = vis(preds, image, letterbox_scale)
if args.save:
print('Results saved to result.jpg\n')
cv.imwrite('result.jpg', img)
if args.vis:
cv.namedWindow(args.input, cv.WINDOW_AUTOSIZE)
cv.imshow(args.input, img)
cv.waitKey(0)
else:
print("Press any key to stop video capture")
deviceId = 0
cap = cv.VideoCapture(deviceId)
while cv.waitKey(1) < 0:
hasFrame, frame = cap.read()
if not hasFrame:
print('No frames grabbed!')
break
input_blob = cv.cvtColor(frame, cv.COLOR_BGR2RGB)
input_blob, letterbox_scale = letterbox(input_blob)
# Inference
tm.start()
preds = model_net.infer(input_blob)
tm.stop()
img = vis(preds, frame, letterbox_scale, fps=tm.getFPS())
cv.imshow("YoloX Demo", img)
tm.reset()