-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathyolo2deepsort.py
313 lines (258 loc) · 12.2 KB
/
yolo2deepsort.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
# -*- coding: UTF-8 -*-
import argparse
import time
from pathlib import Path
import sys
import os
import numpy as np
import cv2
import torch
import torch.backends.cudnn as cudnn
from numpy import random
import copy
from deep_sort_realtime.deepsort_tracker import DeepSort
FILE = Path(__file__).resolve()
ROOT = FILE.parents[0] # YOLOv5 root directory
if str(ROOT) not in sys.path:
sys.path.append(str(ROOT)) # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
from models.experimental import attempt_load
from utils.datasets import letterbox, img_formats, vid_formats, LoadImages, LoadStreams
from utils.general import check_img_size, non_max_suppression_face, apply_classifier, scale_coords, xyxy2xywh, \
strip_optimizer, set_logging, increment_path, scale4sort
from utils.plots import plot_one_box
from utils.torch_utils import select_device, load_classifier, time_synchronized
def load_model(weights, device):
model = attempt_load(weights, map_location=device) # load FP32 model
return model
def scale_coords_landmarks(img1_shape, coords, img0_shape, ratio_pad=None):
# Rescale coords (xyxy) from img1_shape to img0_shape
if ratio_pad is None: # calculate from img0_shape
gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new
pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding
else:
gain = ratio_pad[0][0]
pad = ratio_pad[1]
coords[:, [0, 2, 4, 6, 8]] -= pad[0] # x padding
coords[:, [1, 3, 5, 7, 9]] -= pad[1] # y padding
coords[:, :10] /= gain
#clip_coords(coords, img0_shape)
coords[:, 0].clamp_(0, img0_shape[1]) # x1
coords[:, 1].clamp_(0, img0_shape[0]) # y1
coords[:, 2].clamp_(0, img0_shape[1]) # x2
coords[:, 3].clamp_(0, img0_shape[0]) # y2
coords[:, 4].clamp_(0, img0_shape[1]) # x3
coords[:, 5].clamp_(0, img0_shape[0]) # y3
coords[:, 6].clamp_(0, img0_shape[1]) # x4
coords[:, 7].clamp_(0, img0_shape[0]) # y4
coords[:, 8].clamp_(0, img0_shape[1]) # x5
coords[:, 9].clamp_(0, img0_shape[0]) # y5
return coords
def show_results(img, xyxy, conf,class_num):
h,w,c = img.shape
tl = 1 or round(0.002 * (h + w) / 2) + 1 # line/font thickness
x1 = int(xyxy[0])
y1 = int(xyxy[1])
x2 = int(xyxy[2])
y2 = int(xyxy[3])
img = img.copy()
cv2.rectangle(img, (x1,y1), (x2, y2), (0,255,0), thickness=tl, lineType=cv2.LINE_AA)
clors = [(255,0,0),(0,255,0),(0,0,255),(255,255,0),(0,255,255)]
# for i in range(5):
# point_x = int(landmarks[2 * i])
# point_y = int(landmarks[2 * i + 1])
# cv2.circle(img, (point_x, point_y), tl+1, clors[i], -1)
tf = max(tl - 1, 1) # font thickness
# label = str(conf)[:5]
cv2.putText(img, "ID" + str(conf), (x1, y1 - 2), 0, tl / 3, [225, 0, 255], thickness=tf, lineType=cv2.LINE_AA)
return img
face_tracker = DeepSort(max_age=1000,
n_init= 2,
nms_max_overlap=1.0,
max_cosine_distance=0.2,
nn_budget=None)
def detect(
model,
source,
device,
project,
name,
exist_ok,
save_img,
view_img
):
# Load model
img_size = 640
conf_thres = 0.6
iou_thres = 0.5
imgsz=(640, 640)
# Directories
save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
Path(save_dir).mkdir(parents=True, exist_ok=True) # make dir
is_file = Path(source).suffix[1:] in (img_formats + vid_formats)
is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file)
# Dataloader
if webcam:
print('loading streams:', source)
dataset = LoadStreams(source, img_size=imgsz)
bs = 1 # batch_size
else:
print('loading images', source)
dataset = LoadImages(source, img_size=imgsz)
bs = 1 # batch_size
vid_path, vid_writer = [None] * bs, [None] * bs
for path, im, im0s, vid_cap in dataset:
if len(im.shape) == 4:
print('hhhhhhh')
orgimg = np.squeeze(im.transpose(0, 2, 3, 1), axis= 0)
else:
print('jashfasfk')
orgimg = im.transpose(1, 2, 0)
orgimg = cv2.cvtColor(orgimg, cv2.COLOR_BGR2RGB)
img0 = copy.deepcopy(orgimg)
# cv2.imshow('img-', img0)
h0, w0 = orgimg.shape[:2] # orig hw
r = img_size / max(h0, w0) # resize image to img_size
if r != 1: # always resize down, only resize up if training with augmentation
interp = cv2.INTER_AREA if r < 1 else cv2.INTER_LINEAR
img0 = cv2.resize(img0, (int(w0 * r), int(h0 * r)), interpolation=interp)
imgsz = check_img_size(img_size, s=model.stride.max()) # check img_size
img = letterbox(img0, new_shape=imgsz)[0]
# Convert from w,h,c to c,w,h
img = img.transpose(2, 0, 1).copy()
img = torch.from_numpy(img).to(device)
img = 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
pred = model(img)[0]
# print(pred[0])
# break
# Apply NMS
# print('truoc khi nms',type(pred))
pred = non_max_suppression_face(pred, conf_thres, iou_thres)
# print('so face detect duoc',len(pred))
# print(pred)
# print(len(pred[0]), 'face' if len(pred[0]) == 1 else 'faces')
# detections = []
# scale4sort(pred)
# detections.append(int(pred[]))
# Process detections
for i, det in enumerate(pred): # detections per image
if webcam: # batch_size >= 1
p, im0, frame = path[i], im0s[i].copy(), dataset.count
else:
p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
p = Path(p) # to Path
save_path = str(Path(save_dir) / p.name) # im.jpg
# print(det)
if len(det):
# Rescale boxes from img_size to im0 size
# print(det[:, :4])
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
# print(det[:, :4])
# Print results
# print(det[:, :4])
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum() # detections per class
# tracker = face_tracker.update_tracks(det[:, :4], im0)
# print(det[:, :4])
# det[:, 5:15] = scale_coords_landmarks(img.shape[2:], det[:, 5:15], im0.shape).round()
# print(det.size()[0])
# for tr in range(det.size()[0]):
for k in range(det.size()[0]):
detections = []
xyxy = det[k, :4].view(-1).tolist()
# print(xyxy)
x1 = int(xyxy[0])
y1 = int(xyxy[1])
x2 = int(xyxy[2])
y2 = int(xyxy[3])
w_ = abs(x2 - x1)
h_ = abs(y2 - y1)
conf = det[k, 4].cpu().numpy()
# print(type(conf))
confiden = int(conf)
detections.append(([x1, y1, int(w_), int(h_)], confiden, 'face'))
tracks = face_tracker.update_tracks(detections, frame=im0)
# print('outdoor',tracks)
track_id = []
for track in tracks:
if not track.is_confirmed():
continue
track_id.append(track.track_id)
# print("ID", track_id)
# print(track_id)
if len(track_id):
try:
for j in range(det.size()[0]):
# detections = []
print(len(track_id))
xyxy = det[j, :4].view(-1).tolist()
# print(xyxy)
x1 = int(xyxy[0])
y1 = int(xyxy[1])
x2 = int(xyxy[2])
y2 = int(xyxy[3])
w_ = abs(x2 - x1)
h_ = abs(y2 - y1)
# print(x1, type(x1))
conf = det[j, 4].cpu().numpy()
# print(type(conf))
# confiden = int(conf)
# landmarks = det[j, 5:15].view(-1).tolist()
class_num = det[j, 15].cpu().numpy()
# print(class_num)
# print(class_num, type(confiden))
# detections.append(([x1, y1, int(w_), int(h_)], confiden, 'face'))
# tracks = face_tracker.update_tracks(detections, frame=im0)
# # print('so face id',len(tracks))
# for track in tracks:
# if not track.is_confirmed():
# continue
# track_id = track.track_id
# print(track_id)
# print(tracks)
im0 = show_results(im0, xyxy, track_id[j], class_num)
except:
pass
if view_img:
cv2.imshow('result', im0)
k = cv2.waitKey(1)
# Save results (image with detections)
if save_img:
if dataset.mode == 'image':
cv2.imwrite(save_path, im0)
else: # 'video' or 'stream'
if vid_path[i] != save_path: # new video
vid_path[i] = save_path
if isinstance(vid_writer[i], cv2.VideoWriter):
vid_writer[i].release() # release previous video writer
if vid_cap: # video
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))
else: # stream
fps, w, h = 30, im0.shape[1], im0.shape[0]
save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos
vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
try:
vid_writer[i].write(im0)
except Exception as e:
print(e)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--weights', nargs='+', type=str, default='runs/train/exp5/weights/last.pt', help='model.pt path(s)')
parser.add_argument('--source', type=str, default='0', help='source') # file/folder, 0 for webcam
parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
parser.add_argument('--project', default=ROOT / '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')
parser.add_argument('--save-img', action='store_true', help='save results')
parser.add_argument('--view-img', action='store_true', help='show results')
opt = parser.parse_args()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = load_model(opt.weights, device)
detect(model, opt.source, device, opt.project, opt.name, opt.exist_ok, opt.save_img, opt.view_img)