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
| import torch
import numpy as np
import os,cv2,time,torch,random,pytorchvideo,warnings,argparse,math
warnings.filterwarnings("ignore",category=UserWarning)
from pytorchvideo.transforms.functional import (
uniform_temporal_subsample,
short_side_scale_with_boxes,
clip_boxes_to_image,)
from torchvision.transforms._functional_video import normalize
from pytorchvideo.data.ava import AvaLabeledVideoFramePaths
from pytorchvideo.models.hub import slowfast_r50_detection
from deep_sort.deep_sort import DeepSort
class MyVideoCapture:
def __init__(self, source):
self.cap = cv2.VideoCapture(source)
self.idx = -1
self.end = False
self.stack = []
def read(self):
self.idx += 1
ret, img = self.cap.read()
if ret:
self.stack.append(img)
else:
self.end = True
return ret, img
def to_tensor(self, img):
img = torch.from_numpy(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
return img.unsqueeze(0)
def get_video_clip(self):
assert len(self.stack) > 0, "clip length must large than 0 !"
self.stack = [self.to_tensor(img) for img in self.stack]
clip = torch.cat(self.stack).permute(-1, 0, 1, 2)
del self.stack
self.stack = []
return clip
def release(self):
self.cap.release()
def tensor_to_numpy(tensor):
img = tensor.cpu().numpy().transpose((1, 2, 0))
return img
def ava_inference_transform(
clip,
boxes,
num_frames = 32,
crop_size = 640,
data_mean = [0.45, 0.45, 0.45],
data_std = [0.225, 0.225, 0.225],
slow_fast_alpha = 4,
):
boxes = np.array(boxes)
roi_boxes = boxes.copy()
clip = uniform_temporal_subsample(clip, num_frames)
clip = clip.float()
clip = clip / 255.0
height, width = clip.shape[2], clip.shape[3]
boxes = clip_boxes_to_image(boxes, height, width)
clip, boxes = short_side_scale_with_boxes(clip,size=crop_size,boxes=boxes,)
clip = normalize(clip,
np.array(data_mean, dtype=np.float32),
np.array(data_std, dtype=np.float32),)
boxes = clip_boxes_to_image(boxes, clip.shape[2], clip.shape[3])
if slow_fast_alpha is not None:
fast_pathway = clip
slow_pathway = torch.index_select(clip,1,
torch.linspace(0, clip.shape[1] - 1, clip.shape[1] // slow_fast_alpha).long())
clip = [slow_pathway, fast_pathway]
return clip, torch.from_numpy(boxes), roi_boxes
def plot_one_box(x, img, color=[100,100,100], text_info="None",
velocity=None, thickness=1, fontsize=0.5, fontthickness=1):
c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
cv2.rectangle(img, c1, c2, color, thickness, lineType=cv2.LINE_AA)
t_size = cv2.getTextSize(text_info, cv2.FONT_HERSHEY_TRIPLEX, fontsize , fontthickness+2)[0]
cv2.rectangle(img, c1, (c1[0] + int(t_size[0]), c1[1] + int(t_size[1]*1.45)), color, -1)
cv2.putText(img, text_info, (c1[0], c1[1]+t_size[1]+2),
cv2.FONT_HERSHEY_TRIPLEX, fontsize, [255,255,255], fontthickness)
return img
def deepsort_update(Tracker, pred, xywh, np_img):
outputs = Tracker.update(xywh, pred[:,4:5],pred[:,5].tolist(),cv2.cvtColor(np_img,cv2.COLOR_BGR2RGB))
return outputs
def save_yolopreds_tovideo(yolo_preds, id_to_ava_labels, color_map, output_video, vis=False):
for i, (im, pred) in enumerate(zip(yolo_preds.ims, yolo_preds.pred)):
if pred.shape[0]:
for j, (*box, cls, trackid, vx, vy) in enumerate(pred):
if int(cls) != 0:
ava_label = ''
elif trackid in id_to_ava_labels.keys():
ava_label = id_to_ava_labels[trackid].split(' ')[0]
else:
ava_label = 'Unknow'
text = '{} {} {}'.format(int(trackid),yolo_preds.names[int(cls)],ava_label)
color = color_map[int(cls)]
im = plot_one_box(box,im,color,text)
im = im.astype(np.uint8)
output_video.write(im)
if vis:
cv2.imshow("demo", im)
def main(config):
device = config.device
imsize = config.imsize
model = torch.hub.load('ultralytics/yolov5', 'yolov5l6').to(device)
model.conf = config.conf
model.iou = config.iou
model.max_det = 100
if config.classes:
model.classes = config.classes
video_model = slowfast_r50_detection(True).eval().to(device)
deepsort_tracker = DeepSort("deep_sort/deep_sort/deep/checkpoint/ckpt.t7")
ava_labelnames,_ = AvaLabeledVideoFramePaths.read_label_map("selfutils/temp.pbtxt")
coco_color_map = [[random.randint(0, 255) for _ in range(3)] for _ in range(80)]
vide_save_path = config.output
video=cv2.VideoCapture(config.input)
width,height = int(video.get(3)),int(video.get(4))
video.release()
outputvideo = cv2.VideoWriter(vide_save_path,cv2.VideoWriter_fourcc(*'mp4v'), 25, (width,height))
print("processing...")
cap = MyVideoCapture(config.input)
id_to_ava_labels = {}
a=time.time()
while not cap.end:
ret, img = cap.read()
if not ret:
continue
yolo_preds=model([img], size=imsize)
yolo_preds.files=["img.jpg"]
deepsort_outputs=[]
for j in range(len(yolo_preds.pred)):
temp=deepsort_update(deepsort_tracker,yolo_preds.pred[j].cpu(),yolo_preds.xywh[j][:,0:4].cpu(),yolo_preds.ims[j])
if len(temp)==0:
temp=np.ones((0,8))
deepsort_outputs.append(temp.astype(np.float32))
yolo_preds.pred=deepsort_outputs
if len(cap.stack) == 25:
print(f"processing {cap.idx // 25}th second clips")
clip = cap.get_video_clip()
if yolo_preds.pred[0].shape[0]:
inputs, inp_boxes, _=ava_inference_transform(clip, yolo_preds.pred[0][:,0:4], crop_size=imsize)
inp_boxes = torch.cat([torch.zeros(inp_boxes.shape[0],1), inp_boxes], dim=1)
if isinstance(inputs, list):
inputs = [inp.unsqueeze(0).to(device) for inp in inputs]
else:
inputs = inputs.unsqueeze(0).to(device)
with torch.no_grad():
slowfaster_preds = video_model(inputs, inp_boxes.to(device))
slowfaster_preds = slowfaster_preds.cpu()
for tid,avalabel in zip(yolo_preds.pred[0][:,5].tolist(), np.argmax(slowfaster_preds, axis=1).tolist()):
id_to_ava_labels[tid] = ava_labelnames[avalabel+1]
save_yolopreds_tovideo(yolo_preds, id_to_ava_labels, coco_color_map, outputvideo, config.show)
print("total cost: {:.3f} s, video length: {} s".format(time.time()-a, cap.idx / 25))
cap.release()
outputvideo.release()
print('saved video to:', vide_save_path)
if __name__=="__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--input', type=str, default="/home/wufan/images/video/vad.mp4", help='test imgs folder or video or camera')
parser.add_argument('--output', type=str, default="output.mp4", help='folder to save result imgs, can not use input folder')
parser.add_argument('--imsize', type=int, default=640, help='inference size (pixels)')
parser.add_argument('--conf', type=float, default=0.4, help='object confidence threshold')
parser.add_argument('--iou', type=float, default=0.4, help='IOU threshold for NMS')
parser.add_argument('--device', default='cuda', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
parser.add_argument('--show', action='store_true', help='show img')
config = parser.parse_args()
if config.input.isdigit():
print("using local camera.")
config.input = int(config.input)
print(config)
main(config)
|
微信
支付宝
