forked from ultralytics/yolov5
-
Notifications
You must be signed in to change notification settings - Fork 0
/
apply.py
290 lines (267 loc) · 13.1 KB
/
apply.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
# python apply.py \
# --weights weights/all_stride0_box10-20.pt \
# --source dataset/raw/test/test_public \
# --conf-thres-d 0.25 \
# --conf-thres-i 0.15 \
# --iou-thres 0.20 \
# --tol-i 15 \
# --tol-d 40 \
# --axis-expand-d 30 \
# --axis-expand-i 10 \
# --hide-c
import argparse
import time
from pathlib import Path
import sys
import cv2
import torch
import torchvision
import numpy as np
import pandas as pd
import json
from models.experimental import attempt_load
from utils.datasets import LoadRiceImages
from utils.general import check_img_size, check_requirements, non_max_suppression, \
scale_coords, clip_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path
from utils.torch_utils import select_device, time_synchronized
from utils.plots import colors, plot_one_box
from myutils.filter import filter_too_close, filter_border
from myutils.draw import draw_border, draw_grid
def apply(opt):
source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
save_img = not opt.nosave and not source.endswith(
'.txt') # save inference images
# Directories
save_dir = 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
(save_dir / 'data' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)
(save_dir / 'images').mkdir(parents=True, exist_ok=True)
with (save_dir / f"params_{Path(opt.source).name}.json").open("w") as f:
f.write(json.dumps(opt.__dict__, indent=4))
# 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
stride = int(model.stride.max()) # model stride
imgsz = check_img_size(imgsz, s=stride) # check img_size
names = model.module.names if hasattr(
model, 'module') else model.names # get class names
if half:
model.half() # to FP16
# Set Dataloader
vid_path, vid_writer = None, None
dataset = LoadRiceImages(source, img_size=imgsz, stride=stride,
img_stride=opt.stride, dshape=opt.dshape, ishape=opt.ishape)
# Run inference
if device.type != 'cpu':
model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(
next(model.parameters()))) # run once
t0 = time.time()
idx = 0
for path, imgs, imgs0, _, big_img in dataset:
idx += 1
path = Path(path)
ori_img = cv2.imread(str(path))
save_path = str(save_dir / path.name)
txt_path = str(save_dir / "labels" / f"{path.stem}.csv")
data_path = str(save_dir / "data" / f"{path.stem}.csv")
coords = []
boxes = []
preds = None
img_type = str(path.name)[0].lower()
conf_thres = opt.i_conf_thres if img_type == "i" else opt.d_conf_thres
im_stride = 640 if opt.stride is None else opt.stride
for r in range(imgs.shape[0]):
for c in range(imgs.shape[1]):
# print(x_offset, y_offset)
img = imgs[r, c]
im0s = imgs0[r, c]
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] # 1, 25200, 7(abs xywh)
pred = non_max_suppression(
pred, conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms) #1, n_boxes, 6(abs xyxy)
pred = pred[0].unsqueeze(0) # [1, n_boxes, 6]
if opt.space is not None:
if r != 0:
pred = pred[pred[:, :, 1] > opt.space].unsqueeze(0)
if c != imgs.shape[1] - 1:
pred = pred[pred[:, :, 2] < imgsz - opt.space].unsqueeze(0)
if r != imgs.shape[0] - 1:
pred = pred[pred[:, :, 3] < imgsz - opt.space].unsqueeze(0)
if c != 0:
pred = pred[pred[:, :, 0] > opt.space].unsqueeze(0)
if opt.stride:
pred[:, :, [0, 2]] += c * im_stride # x
pred[:, :, [1, 3]] += r * im_stride # y
preds = pred if preds is None else torch.cat((preds, pred), 1)
# print(pred.shape)
# sys.exit(0)
# Apply NMS
box = preds[0]
idx = torchvision.ops.nms(preds[0][:, :4], preds[0][:, 4], opt.iou_thres)
preds = preds[0][idx].unsqueeze(0)
print(preds.shape)
# preds shape: 1 x number_boxes x 6(absolute xyxy, confidence, class)
t2 = time_synchronized()
scale_x = ori_img.shape[1] / 2560
scale_y = ori_img.shape[0] / 1920
# Process detections
for i, det in enumerate(preds): # detections per image
p, s, big_im, frame = path, '', big_img.copy(), getattr(dataset, 'frame', 0)
p = Path(p) # to Path
s += '%gx%g ' % img.shape[2:] # print string
# normalization gain whwh
gn = torch.tensor(big_im.shape)[[1, 0, 1, 0]]
if len(det):
# Rescale boxes from img_size to im0 size
clip_coords(det[:, :4], big_img.shape)
det[:, :4] = det[:, :4].round()
# Print results
for cl in det[:, -1].unique():
n = (det[:, -1] == cl).sum() # detections per class
# add to string
s += f"{n} {names[int(cl)]}{'s' * (n > 1)}, "
grid_interval = 640 if opt.stride is None else opt.stride
v_grid_starts = range(grid_interval, 2560, grid_interval)
h_grid_starts = range(grid_interval, 1920, grid_interval)
big_im = draw_grid(big_im, v_grid_starts, h_grid_starts)
# Write results
for *xyxy, conf, cl in reversed(det):
# print('xyxy', xyxy)
# print('conf', conf)
cl = cl.cpu()
# Only if the predicted class matches img_type
if (cl == 0 and img_type == "i") or (cl == 1 and img_type == "d"):
label = None if opt.hide_labels else (
names[c] if opt.hide_conf else f'{names[c]} {conf:.2f}')
if img_type == 'i':
line = 2
elif img_type == 'd':
line = 1
plot_one_box(xyxy, big_im, label=label, color=(0, 0, 255), line_thickness=line)
if save_txt: # Write to file
xywh = (xyxy2xywh(torch.tensor(xyxy).view(
1, 4)) / gn).view(-1).tolist() # normalized xywh
x, y = xywh[:2]
x, y = x * big_im.shape[1], y * big_im.shape[0]
coords.append(
np.array((conf.cpu().item() * 100, x, y, cl)))
# label = None if opt.hide_labels else (names[c] if opt.hide_conf else f'{names[c]} {conf:.2f}')
cv2.imwrite(str(save_dir / 'images' / p.name), big_im)
# Print time (inference + NMS)
print(f'{s}Done. ({t2 - t1:.3f}s)')
# imgs[0, 0].shape is (c, h, w)
coords = np.array(coords)
coords[:, 1] *= scale_x
coords[:, 2] *= scale_y
coords = np.around(coords).astype(int)
coords = filter_border(coords, ori_img.shape, tolerance=opt.border)
gt_path = path.parent / f"{path.stem}.csv"
if save_txt:
with open(txt_path, "w") as f:
np.savetxt(f, coords[:, 1:3], fmt="%d", delimiter=",")
with open(data_path, "w") as f:
np.savetxt(f, coords[:, 0:3], fmt="%d", delimiter=",")
if save_img:
if "border" in vars(opt) and opt.border > 0:
ori_img = draw_border(ori_img, opt.border)
if opt.with_gt:
gts = np.loadtxt(gt_path, dtype=int, delimiter=",", ndmin=2)
for x, y in gts:
ori_img = cv2.circle(
ori_img, (x, y), 9, (255, 255, 255), 2)
for conf, x, y, cl in coords:
if cl == 0:
circle_color = (255, 0, 0)
elif cl == 1:
circle_color = (0, 0, 255)
if not opt.hide_conf:
# print(conf)
ori_img = cv2.putText(
ori_img, f"{conf}%", (x, y - 3), 0, 1, (255, 255, 0), 2)
ori_img = cv2.circle(ori_img, (x, y), 4, circle_color, -1)
cv2.imwrite(save_path, ori_img)
# sys.exit(0)
if save_txt or save_img:
s = f"\n{len(list(save_dir.glob('labels/*')))} 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)')
# file/folder, 0 for webcam
parser.add_argument('--source', type=str,
default='data/images', help='source')
parser.add_argument('--img-size', type=int, default=640,
help='inference size (pixels)')
parser.add_argument('--i-conf-thres', type=float,
default=0.25, help='object confidence threshold')
parser.add_argument('--d-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',
default=True, help='save results to *.txt')
parser.add_argument('--save-conf', action='store_true',
help='save confidences in --save-txt labels')
parser.add_argument('--nosave', action='store_true',
help='do not save images/videos')
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/apply',
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('--line-thickness', default=1,
type=int, help='bounding box thickness (pixels)')
parser.add_argument('--with-gt', action="store_true",
default=False, help='Whether to show the ground truth')
parser.add_argument('--hide-labels', action='store_true',
default=False, help='hide labels')
parser.add_argument('--hide-conf', action='store_true',
default=False, help='hide confidences')
parser.add_argument("--border", type=int, default=0,
help="width of the border to be removed")
parser.add_argument('--ishape', default='1920,2560', type=str)
parser.add_argument('--dshape', default='1920,2560', type=str)
parser.add_argument('--stride', default=None, type=int,
help='Sliding window stride')
parser.add_argument('--space', default=None, type=int,
help='space to delete border rice')
opt = parser.parse_args()
spt = opt.dshape.split(',')
opt.dshape = (int(spt[0]), int(spt[1]))
spt = opt.ishape.split(',')
opt.ishape = (int(spt[0]), int(spt[1]))
check_requirements(exclude=('tensorboard', 'pycocotools', 'thop'))
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']:
apply(opt=opt)
strip_optimizer(opt.weights)
else:
apply(opt=opt)