-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathdetect.py
408 lines (348 loc) · 21.3 KB
/
detect.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
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
import argparse
import time
from pathlib import Path
import cv2
import torch
import torch.backends.cudnn as cudnn
from numpy import random
from models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages
from utils.general import check_img_size, non_max_suppression, apply_classifier, scale_coords, xyxy2xywh, \
strip_optimizer, set_logging, increment_path
from utils.plots import plot_one_box
from utils.torch_utils import select_device, load_classifier, time_synchronized
def detect(save_img=False):
source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
('rtsp://', 'rtmp://', 'http://'))
# Directories
save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run
(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
# Initialize
set_logging()
device = select_device(opt.device)
half = device.type != 'cpu' # half precision only supported on CUDA
# Load model
model = attempt_load(weights, map_location=device) # load FP32 model
imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size
if half:
model.half() # to FP16
# Second-stage classifier
classify = False
if classify:
modelc = load_classifier(name='resnet101', n=2) # initialize
modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()
# Set Dataloader
vid_path, vid_writer = None, None
if webcam:
view_img = True
cudnn.benchmark = True # set True to speed up constant image size inference
dataset = LoadStreams(source, img_size=imgsz)
else:
save_img = True
dataset = LoadImages(source, img_size=imgsz)
# Get names and colors
names = model.module.names if hasattr(model, 'module') else model.names
colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
# Run inference
t0 = time.time()
img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img
_ = model(img.half() if half else img) if device.type != 'cpu' else None # run once
for path, img, im0s, vid_cap in dataset:
#####################################################################################
# results
G_6,G_5,G_4,G_3,G_2,G_1 = 0,0,0,0,0,0
LY_6,LY_5,LY_4,LY_3,LY_2,LY_1 = 0,0,0,0,0,0
R_6,R_5,R_4,R_3,R_2,R_1 = 0,0,0,0,0,0
RY_6,RY_5,RY_4,RY_3,RY_2,RY_1 = 0,0,0,0,0,0
#####################################################################################
img = torch.from_numpy(img).to(device)
img = img.half() if half else img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
# Inference
t1 = time_synchronized()
pred = model(img, augment=opt.augment)[0]
# Apply NMS
pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
t2 = time_synchronized()
# Apply Classifier
if classify:
pred = apply_classifier(pred, modelc, img, im0s)
# Process detections
for i, det in enumerate(pred): # detections per image
if webcam: # batch_size >= 1
p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count
else:
p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)
################################################################################
## Lines图中从下到上依次是1-x
#Line1
cv2.line(im0, (590,1400), (1020,1400), (255, 255, 0), 5)
cv2.line(im0, (1020,1400), (1145,1400), (255, 0, 255), 5)
cv2.line(im0, (1145,1400), (1860,1400), (0, 0, 255), 5)
cv2.line(im0, (1860,1400), (2030,1400), (0, 255, 255), 5)
#Line2
cv2.line(im0, (640,1300), (1020,1300), (255, 255, 0), 5)
cv2.line(im0, (1020,1300), (1130,1300), (255, 0, 255), 5)
cv2.line(im0, (1130,1300), (1765,1300), (0, 0, 255), 5)
cv2.line(im0, (1765,1300), (1910,1300), (0, 255, 255), 5)
#Line3
cv2.line(im0, (700,1200), (1005,1200), (255, 255, 0), 5)
cv2.line(im0, (1005,1200), (1110,1200), (255, 0, 255), 5)
cv2.line(im0, (1110,1200), (1660,1200), (0, 0, 255), 5)
cv2.line(im0, (1660,1200), (1800,1200), (0, 255, 255), 5)
#Line4
cv2.line(im0, (750,1100), (1010,1100), (255, 255, 0), 5)
cv2.line(im0, (1010,1100), (1100,1100), (255, 0, 255), 5)
cv2.line(im0, (1100,1100), (1555,1100), (0, 0, 255), 5)
cv2.line(im0, (1555,1100), (1680,1100), (0, 255, 255), 5)
#Line5
cv2.line(im0, (800,1000), (1005,1000), (255, 255, 0), 5)
cv2.line(im0, (1005,1000), (1090,1000), (255, 0, 255), 5)
cv2.line(im0, (1090,1000), (1450,1000), (0, 0, 255), 5)
cv2.line(im0, (1450,1000), (1570,1000), (0, 255, 255), 5)
#Line6
cv2.line(im0, (855,900), (1005,900), (255, 255, 0), 5)
cv2.line(im0, (1005,900), (1080,900), (255, 0, 255), 5)
cv2.line(im0, (1080,900), (1345,900), (0, 0, 255), 5)
cv2.line(im0, (1345,900), (1450,900), (0, 255, 255), 5)
#Line7
cv2.line(im0, (900,800), (1000,800), (255, 255, 0), 5)
cv2.line(im0, (1000,800), (1060,800), (255, 0, 255), 5)
cv2.line(im0, (1060,800), (1250,800), (0, 0, 255), 5)
cv2.line(im0, (1250,800), (1350,800), (0, 255, 255), 5)
#Row
cv2.line(im0, (590,1400), (900,800), (255, 255, 0), 5)
cv2.line(im0, (1020,1400), (1000,800), (255, 0, 255), 5)
cv2.line(im0, (1145,1400), (1060,800), (0, 0, 255), 5)
cv2.line(im0, (1860,1400), (1250,800), (0, 255, 255), 5)
cv2.line(im0, (2030,1400), (1350,800), (0, 255, 255), 5)
'''
x1,y1-------x2,y1
| |
| PERSON |
a1_x_y,b1_x_y----|---------------|----a2_x_y,b1_x_y
| | | |
| | | |
| x1,y2-------x2,y2 |
| |
| |
a1_x_y,b2_x_y-------------------------a2_x_y,b2_x_y
,where x=G,LY,R,RY, y=6,5,4,3,2,1
'''
a1_G_6,a2_G_6,b1_G_6,b2_G_6 = 900,1000,800,900
a1_LY_6,a2_LY_6,b1_LY_6,b2_LY_6 = 1005,1060,800,900
a1_R_6,a2_R_6,b1_R_6,b2_R_6 = 1080,1250,800,900
a1_RY_6,a2_RY_6,b1_RY_6,b2_RY_6 = 1300,1380,800,900
a1_G_5,a2_G_5,b1_G_5,b2_G_5 = 855,1005,900,1000
a1_LY_5,a2_LY_5,b1_LY_5,b2_LY_5 = 1005,1080,900,1000
a1_R_5,a2_R_5,b1_R_5,b2_R_5 = 1090,1345,900,1000
a1_RY_5,a2_RY_5,b1_RY_5,b2_RY_5 = 1450,1500,900,1000
a1_G_4,a2_G_4,b1_G_4,b2_G_4 = 800,1005,1000,1100
a1_LY_4,a2_LY_4,b1_LY_4,b2_LY_4 = 1010,1090,1000,1100
a1_R_4,a2_R_4,b1_R_4,b2_R_4 = 1100,1450,1000,1100
a1_RY_4,a2_RY_4,b1_RY_4,b2_RY_4 = 1500,1570,1000,1100
a1_G_3,a2_G_3,b1_G_3,b2_G_3 = 720,1005,1100,1200
a1_LY_3,a2_LY_3,b1_LY_3,b2_LY_3 = 1005,1110,1100,1200
a1_R_3,a2_R_3,b1_R_3,b2_R_3 = 1110,1555,1100,1200
a1_RY_3,a2_RY_3,b1_RY_3,b2_RY_3 = 1660,1750,1100,1200
a1_G_2,a2_G_2,b1_G_2,b2_G_2 = 700,1005,1200,1300
a1_LY_2,a2_LY_2,b1_LY_2,b2_LY_2 = 1020,1110,1200,1300
a1_R_2,a2_R_2,b1_R_2,b2_R_2 = 1130,1660,1200,1300
a1_RY_2,a2_RY_2,b1_RY_2,b2_RY_2 = 1765,1860,1200,1300
a1_G_1,a2_G_1,b1_G_1,b2_G_1 = 640,1020,1300,1400
a1_LY_1,a2_LY_1,b1_LY_1,b2_LY_1 = 1020,1130,1300,1400
a1_R_1,a2_R_1,b1_R_1,b2_R_1 = 1145,1765,1300,1400
a1_RY_1,a2_RY_1,b1_RY_1,b2_RY_1 = 1765,1910,1300,1400
## Text
font = cv2.FONT_HERSHEY_SIMPLEX
im0 = cv2.putText(im0,"G",(200,140),font,1,(255,255,255),5)
im0 = cv2.putText(im0,"LY",(250,140),font,1,(255,255,255),5)
im0 = cv2.putText(im0,"R",(300,140),font,1,(255,255,255),5)
im0 = cv2.putText(im0,"RY",(350,140),font,1,(255,255,255),5)
im0 = cv2.putText(im0,"6 |",(100,200),font,1.5,(255,255,255),3)
im0 = cv2.putText(im0,"5 |",(100,250),font,1.5,(255,255,255),3)
im0 = cv2.putText(im0,"4 |",(100,300),font,1.5,(255,255,255),3)
im0 = cv2.putText(im0,"3 |",(100,350),font,1.5,(255,255,255),3)
im0 = cv2.putText(im0,"2 |",(100,400),font,1.5,(255,255,255),3)
im0 = cv2.putText(im0,"1 |",(100,450),font,1.5,(255,255,255),3)
#####################################################################################
p = Path(p) # to Path
save_path = str(save_dir / p.name) # img.jpg
txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt
s += '%gx%g ' % img.shape[2:] # print string
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
if len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
# Print results
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum() # detections per class
s += f'{n} {names[int(c)]}s, ' # add to string
# Write results
for *xyxy, conf, cls in reversed(det):
# print('det shape =',det.shape)
if save_txt: # Write to file
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) # label format
with open(txt_path + '.txt', 'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n')
if save_img or view_img: # Add bbox to image
label = f'{names[int(cls)]} {conf:.2f}'
#####################################################################################
if names[int(cls)] == 'person':
print(label)
#####################################################################################
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
#####################################################################################
## Count
x1 = int(xyxy[0])
y1 = int(xyxy[1])
x2 = int(xyxy[2])
y2 = int(xyxy[3])
print('\nx1 =',x1)
print('y1 =',y1)
print('x2 =',x2)
print('y2 =',y2)
if x1<=a2_G_6 and x2>=a1_G_6 and y2<=b2_G_6 and y2>=b1_G_6:
G_6 += 1
if x1<=a2_G_5 and x2>=a1_G_5 and y2<=b2_G_5 and y2>=b1_G_5:
G_5 += 1
if x1<=a2_G_4 and x2>=a1_G_4 and y2<=b2_G_4 and y2>=b1_G_4:
G_4 += 1
if x1<=a2_G_3 and x2>=a1_G_3 and y2<=b2_G_3 and y2>=b1_G_3:
G_3 += 1
if x1<=a2_G_2 and x2>=a1_G_2 and y2<=b2_G_2 and y2>=b1_G_2:
G_2 += 1
if x1<=a2_G_1 and x2>=a1_G_1 and y2<=b2_G_1 and y2>=b1_G_1:
G_1 += 1
if x1<=a2_LY_6 and x2>=a1_LY_6 and y2<=b2_LY_6 and y2>=b1_LY_6:
LY_6 += 1
if x1<=a2_LY_5 and x2>=a1_LY_5 and y2<=b2_LY_5 and y2>=b1_LY_5:
LY_5 += 1
if x1<=a2_LY_4 and x2>=a1_LY_4 and y2<=b2_LY_4 and y2>=b1_LY_4:
LY_4 += 1
if x1<=a2_LY_3 and x2>=a1_LY_3 and y2<=b2_LY_3 and y2>=b1_LY_3:
LY_3 += 1
if x1<=a2_LY_2 and x2>=a1_LY_2 and y2<=b2_LY_2 and y2>=b1_LY_2:
LY_2 += 1
if x1<=a2_LY_1 and x2>=a1_LY_1 and y2<=b2_LY_1 and y2>=b1_LY_1:
LY_1 += 1
if x1<=a2_R_6 and x2>=a1_R_6 and y2<=b2_R_6 and y2>=b1_R_6:
R_6 += 1
if x1<=a2_R_5 and x2>=a1_R_5 and y2<=b2_R_5 and y2>=b1_R_5:
R_5 += 1
if x1<=a2_R_4 and x2>=a1_R_4 and y2<=b2_R_4 and y2>=b1_R_4:
R_4 += 1
if x1<=a2_R_3 and x2>=a1_R_3 and y2<=b2_R_3 and y2>=b1_R_3:
R_3 += 1
if x1<=a2_R_2 and x2>=a1_R_2 and y2<=b2_R_2 and y2>=b1_R_2:
R_2 += 1
if x1<=a2_R_1 and x2>=a1_R_1 and y2<=b2_R_1 and y2>=b1_R_1:
R_1 += 1
if x1<=a2_RY_6 and x2>=a1_RY_6 and y2<=b2_RY_6 and y2>=b1_RY_6:
RY_6 += 1
if x1<=a2_RY_5 and x2>=a1_RY_5 and y2<=b2_RY_5 and y2>=b1_RY_5:
RY_5 += 1
if x1<=a2_RY_4 and x2>=a1_RY_4 and y2<=b2_RY_4 and y2>=b1_RY_4:
RY_4 += 1
if x1<=a2_RY_3 and x2>=a1_RY_3 and y2<=b2_RY_3 and y2>=b1_RY_3:
RY_3 += 1
if x1<=a2_RY_2 and x2>=a1_RY_2 and y2<=b2_RY_2 and y2>=b1_RY_2:
RY_2 += 1
if x1<=a2_RY_1 and x2>=a1_RY_1 and y2<=b2_RY_1 and y2>=b1_RY_1:
RY_1 += 1
#####################################################################################
#####################################################################################
#print(G_6,G_5,G_4,G_3,G_2,G_1)
#print(LY_6,LY_5,LY_4,LY_3,LY_2,LY_1)
#print(R_6,R_5,R_4,R_3,R_2,R_1)
#print(RY_6,RY_5,RY_4,RY_3,RY_2,RY_1)
## Sum
count = G_6+G_5+G_4+G_3+G_2+G_1+LY_6+LY_5+LY_4+LY_3+LY_2+LY_1+R_6+R_5+R_4+R_3+R_2+R_1+RY_6+RY_5+RY_4+RY_3+RY_2+RY_1
im0 = cv2.putText(im0,'Person: ',(100,600),font,1.5,(255,255,255),3)
im0 = cv2.putText(im0,str(count),(280,600),font,1.5,(255,255,255),3)
# 6
im0 = cv2.putText(im0,str(G_6),(200,200),font,1.5,(255,255,255),3)
im0 = cv2.putText(im0,str(LY_6),(250,200),font,1.5,(255,255,255),3)
im0 = cv2.putText(im0,str(R_6),(300,200),font,1.5,(255,255,255),3)
im0 = cv2.putText(im0,str(RY_6),(350,200),font,1.5,(255,255,255),3)
# 5
im0 = cv2.putText(im0,str(G_5),(200,250),font,1.5,(255,255,255),3)
im0 = cv2.putText(im0,str(LY_5),(250,250),font,1.5,(255,255,255),3)
im0 = cv2.putText(im0,str(R_5),(300,250),font,1.5,(255,255,255),3)
im0 = cv2.putText(im0,str(RY_5),(350,250),font,1.5,(255,255,255),3)
# 4
im0 = cv2.putText(im0,str(G_4),(200,300),font,1.5,(255,255,255),3)
im0 = cv2.putText(im0,str(LY_4),(250,300),font,1.5,(255,255,255),3)
im0 = cv2.putText(im0,str(R_4),(300,300),font,1.5,(255,255,255),3)
im0 = cv2.putText(im0,str(RY_4),(350,300),font,1.5,(255,255,255),3)
# 3
im0 = cv2.putText(im0,str(G_3),(200,350),font,1.5,(255,255,255),3)
im0 = cv2.putText(im0,str(LY_3),(250,350),font,1.5,(255,255,255),3)
im0 = cv2.putText(im0,str(R_3),(300,350),font,1.5,(255,255,255),3)
im0 = cv2.putText(im0,str(RY_3),(350,350),font,1.5,(255,255,255),3)
# 2
im0 = cv2.putText(im0,str(G_2),(200,400),font,1.5,(255,255,255),3)
im0 = cv2.putText(im0,str(LY_2),(250,400),font,1.5,(255,255,255),3)
im0 = cv2.putText(im0,str(R_2),(300,400),font,1.5,(255,255,255),3)
im0 = cv2.putText(im0,str(RY_2),(350,400),font,1.5,(255,255,255),3)
# 1
im0 = cv2.putText(im0,str(G_1),(200,450),font,1.5,(255,255,255),3)
im0 = cv2.putText(im0,str(LY_1),(250,450),font,1.5,(255,255,255),3)
im0 = cv2.putText(im0,str(R_1),(300,450),font,1.5,(255,255,255),3)
im0 = cv2.putText(im0,str(RY_1),(350,450),font,1.5,(255,255,255),3)
#######################################+#########################################
# Print time (inference + NMS)
print(f'{s}Done. ({t2 - t1:.3f}s)')
# Stream results
if view_img:
cv2.imshow(str(p), im0)
# Save results (image with detections)
if save_img:
if dataset.mode == 'image':
cv2.imwrite(save_path, im0)
else: # 'video'
if vid_path != save_path: # new video
vid_path = save_path
if isinstance(vid_writer, cv2.VideoWriter):
vid_writer.release() # release previous video writer
fourcc = 'mp4v' # output video codec
fps = vid_cap.get(cv2.CAP_PROP_FPS)
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h))
vid_writer.write(im0)
if save_txt or save_img:
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
print(f"Results saved to {save_dir}{s}")
print(f'Done. ({time.time() - t0:.3f}s)')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
parser.add_argument('--source', type=str, default='data/images', help='source') # file/folder, 0 for webcam
parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--view-img', action='store_true', help='display results')
parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
parser.add_argument('--augment', action='store_true', help='augmented inference')
parser.add_argument('--update', action='store_true', help='update all models')
parser.add_argument('--project', default='runs/detect', help='save results to project/name')
parser.add_argument('--name', default='exp', help='save results to project/name')
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
opt = parser.parse_args()
print(opt)
with torch.no_grad():
if opt.update: # update all models (to fix SourceChangeWarning)
for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:
detect()
strip_optimizer(opt.weights)
else:
detect()