-
Notifications
You must be signed in to change notification settings - Fork 504
/
test_widerface.py
170 lines (155 loc) · 7.75 KB
/
test_widerface.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
import argparse
import glob
import time
from pathlib import Path
import os
import cv2
import torch
import torch.backends.cudnn as cudnn
from numpy import random
import numpy as np
from models.experimental import attempt_load
from utils.datasets import letterbox
from utils.general import check_img_size, check_requirements, non_max_suppression_face, 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
from tqdm import tqdm
def dynamic_resize(shape, stride=64):
max_size = max(shape[0], shape[1])
if max_size % stride != 0:
max_size = (int(max_size / stride) + 1) * stride
return max_size
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, xywh, conf, landmarks, class_num):
h,w,c = img.shape
tl = 1 or round(0.002 * (h + w) / 2) + 1 # line/font thickness
x1 = int(xywh[0] * w - 0.5 * xywh[2] * w)
y1 = int(xywh[1] * h - 0.5 * xywh[3] * h)
x2 = int(xywh[0] * w + 0.5 * xywh[2] * w)
y2 = int(xywh[1] * h + 0.5 * xywh[3] * h)
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] * w)
point_y = int(landmarks[2 * i + 1] * h)
cv2.circle(img, (point_x, point_y), tl+1, clors[i], -1)
tf = max(tl - 1, 1) # font thickness
label = str(int(class_num)) + ': ' + str(conf)[:5]
cv2.putText(img, label, (x1, y1 - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
return img
def detect(model, img0):
stride = int(model.stride.max()) # model stride
imgsz = opt.img_size
if imgsz <= 0: # original size
imgsz = dynamic_resize(img0.shape)
imgsz = check_img_size(imgsz, s=64) # check img_size
img = letterbox(img0, imgsz)[0]
# Convert
img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
img = np.ascontiguousarray(img)
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, augment=opt.augment)[0]
# Apply NMS
pred = non_max_suppression_face(pred, opt.conf_thres, opt.iou_thres)[0]
gn = torch.tensor(img0.shape)[[1, 0, 1, 0]].to(device) # normalization gain whwh
gn_lks = torch.tensor(img0.shape)[[1, 0, 1, 0, 1, 0, 1, 0, 1, 0]].to(device) # normalization gain landmarks
boxes = []
h, w, c = img0.shape
if pred is not None:
pred[:, :4] = scale_coords(img.shape[2:], pred[:, :4], img0.shape).round()
pred[:, 5:15] = scale_coords_landmarks(img.shape[2:], pred[:, 5:15], img0.shape).round()
for j in range(pred.size()[0]):
xywh = (xyxy2xywh(pred[j, :4].view(1, 4)) / gn).view(-1)
xywh = xywh.data.cpu().numpy()
conf = pred[j, 4].cpu().numpy()
landmarks = (pred[j, 5:15].view(1, 10) / gn_lks).view(-1).tolist()
class_num = pred[j, 15].cpu().numpy()
x1 = int(xywh[0] * w - 0.5 * xywh[2] * w)
y1 = int(xywh[1] * h - 0.5 * xywh[3] * h)
x2 = int(xywh[0] * w + 0.5 * xywh[2] * w)
y2 = int(xywh[1] * h + 0.5 * xywh[3] * h)
boxes.append([x1, y1, x2-x1, y2-y1, conf])
return boxes
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('--img-size', type=int, default=640, help='inference size (pixels)')
parser.add_argument('--conf-thres', type=float, default=0.02, help='object confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.5, help='IOU threshold for NMS')
parser.add_argument('--device', default='0', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
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('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
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')
parser.add_argument('--save_folder', default='./widerface_evaluate/widerface_txt/', type=str, help='Dir to save txt results')
parser.add_argument('--dataset_folder', default='../WiderFace/val/images/', type=str, help='dataset path')
parser.add_argument('--folder_pict', default='/yolov5-face/data/widerface/val/wider_val.txt', type=str, help='folder_pict')
opt = parser.parse_args()
print(opt)
# changhy : read folder_pict
pict_folder = {}
with open(opt.folder_pict, 'r') as f:
lines = f.readlines()
for line in lines:
line = line.strip().split('/')
pict_folder[line[-1]] = line[-2]
# Load model
device = select_device(opt.device)
model = attempt_load(opt.weights, map_location=device) # load FP32 model
with torch.no_grad():
# testing dataset
testset_folder = opt.dataset_folder
for image_path in tqdm(glob.glob(os.path.join(testset_folder, '*'))):
if image_path.endswith('.txt'):
continue
img0 = cv2.imread(image_path) # BGR
if img0 is None:
print(f'ignore : {image_path}')
continue
boxes = detect(model, img0)
# --------------------------------------------------------------------
image_name = os.path.basename(image_path)
txt_name = os.path.splitext(image_name)[0] + ".txt"
save_name = os.path.join(opt.save_folder, pict_folder[image_name], txt_name)
dirname = os.path.dirname(save_name)
if not os.path.isdir(dirname):
os.makedirs(dirname)
with open(save_name, "w") as fd:
file_name = os.path.basename(save_name)[:-4] + "\n"
bboxs_num = str(len(boxes)) + "\n"
fd.write(file_name)
fd.write(bboxs_num)
for box in boxes:
fd.write('%d %d %d %d %.03f' % (box[0], box[1], box[2], box[3], box[4] if box[4] <= 1 else 1) + '\n')
print('done.')