f4334277
 Hu Chunming
提交3rdparty
|
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
|
import os
import numpy as np
import cv2 as cv
import argparse
from common import findFile
parser = argparse.ArgumentParser(description='Use this script to run action recognition using 3D ResNet34',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--input', '-i', help='Path to input video file. Skip this argument to capture frames from a camera.')
parser.add_argument('--model', required=True, help='Path to model.')
parser.add_argument('--classes', default=findFile('action_recongnition_kinetics.txt'), help='Path to classes list.')
# To get net download original repository https://github.com/kenshohara/video-classification-3d-cnn-pytorch
# For correct ONNX export modify file: video-classification-3d-cnn-pytorch/models/resnet.py
# change
# - def downsample_basic_block(x, planes, stride):
# - out = F.avg_pool3d(x, kernel_size=1, stride=stride)
# - zero_pads = torch.Tensor(out.size(0), planes - out.size(1),
# - out.size(2), out.size(3),
# - out.size(4)).zero_()
# - if isinstance(out.data, torch.cuda.FloatTensor):
# - zero_pads = zero_pads.cuda()
# -
# - out = Variable(torch.cat([out.data, zero_pads], dim=1))
# - return out
# To
# + def downsample_basic_block(x, planes, stride):
# + out = F.avg_pool3d(x, kernel_size=1, stride=stride)
# + out = F.pad(out, (0, 0, 0, 0, 0, 0, 0, int(planes - out.size(1)), 0, 0), "constant", 0)
# + return out
# To ONNX export use torch.onnx.export(model, inputs, model_name)
def get_class_names(path):
class_names = []
with open(path) as f:
for row in f:
class_names.append(row[:-1])
return class_names
def classify_video(video_path, net_path):
SAMPLE_DURATION = 16
SAMPLE_SIZE = 112
mean = (114.7748, 107.7354, 99.4750)
class_names = get_class_names(args.classes)
net = cv.dnn.readNet(net_path)
net.setPreferableBackend(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE)
net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)
winName = 'Deep learning image classification in OpenCV'
cv.namedWindow(winName, cv.WINDOW_AUTOSIZE)
cap = cv.VideoCapture(video_path)
while cv.waitKey(1) < 0:
frames = []
for _ in range(SAMPLE_DURATION):
hasFrame, frame = cap.read()
if not hasFrame:
exit(0)
frames.append(frame)
inputs = cv.dnn.blobFromImages(frames, 1, (SAMPLE_SIZE, SAMPLE_SIZE), mean, True, crop=True)
inputs = np.transpose(inputs, (1, 0, 2, 3))
inputs = np.expand_dims(inputs, axis=0)
net.setInput(inputs)
outputs = net.forward()
class_pred = np.argmax(outputs)
label = class_names[class_pred]
for frame in frames:
labelSize, baseLine = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.5, 1)
cv.rectangle(frame, (0, 10 - labelSize[1]),
(labelSize[0], 10 + baseLine), (255, 255, 255), cv.FILLED)
cv.putText(frame, label, (0, 10), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0))
cv.imshow(winName, frame)
if cv.waitKey(1) & 0xFF == ord('q'):
break
if __name__ == "__main__":
args, _ = parser.parse_known_args()
classify_video(args.input if args.input else 0, args.model)
|
