-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathmain.py
277 lines (235 loc) · 10.4 KB
/
main.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
# Steps to run the file
# python main.py --img-path /path/to/your/img
# python main.py --img-mode 0 --video-path 0
# Developed by Rahul Dhar
# The code for mxnet
'''
import argparse
import time
import cv2
import numpy as np
from PIL import Image
from load_model.mxnet_loader import load_mxnet_model, mxnet_inference
from utils.anchor_decode import decode_bbox
from utils.anchor_generator import generate_anchors
from utils.nms import single_class_non_max_suppression
model = load_mxnet_model('models/face_mask_detection.params')
print("[INFO] Loading face mask detection model....")
feature_map_sizes = [[33, 33], [17, 17], [9, 9], [5, 5], [3, 3]]
anchor_sizes = [[0.04, 0.056], [0.08, 0.11], [0.16, 0.22], [0.32, 0.45], [0.64, 0.72]]
anchor_ratios = [[1, 0.62, 0.42]] * 5
anchors = generate_anchors(feature_map_sizes, anchor_sizes, anchor_ratios)
anchors_exp = np.expand_dims(anchors, axis=0)
id2class = {0: 'M a s k', 1: 'N o_M a s k'}
def inference(image, conf_thresh=0.5, iou_thresh=0.4, target_shape=(160, 160), draw_result=True, show_result=True):
output_info = []
height, width, _ = image.shape
image_resized = cv2.resize(image, target_shape)
image_np = image_resized / 255.0
image_exp = np.expand_dims(image_np, axis=0)
image_transposed = image_exp.transpose((0, 3, 1, 2))
y_bboxes_output, y_cls_output = mxnet_inference(model, image_transposed)
y_bboxes = decode_bbox(anchors_exp, y_bboxes_output)[0]
y_cls = y_cls_output[0]
bbox_max_scores = np.max(y_cls, axis=1)
bbox_max_score_classes = np.argmax(y_cls, axis=1)
keep_idxs = single_class_non_max_suppression(y_bboxes, bbox_max_scores, conf_thresh=conf_thresh,
iou_thresh=iou_thresh, )
for idx in keep_idxs:
conf = float(bbox_max_scores[idx])
class_id = bbox_max_score_classes[idx]
bbox = y_bboxes[idx]
xmin = max(0, int(bbox[0] * width))
ymin = max(0, int(bbox[1] * height))
xmax = min(int(bbox[2] * width), width)
ymax = min(int(bbox[3] * height), height)
if draw_result:
if class_id == 0:
color = (0, 255, 0)
else:
color = (255, 0, 0)
cv2.rectangle(image, (xmin, ymin), (xmax, ymax), color, 2)
cv2.putText(image, "%s: %.2f" % (id2class[class_id], conf), (xmin + 2, ymin - 2),
cv2.FONT_HERSHEY_SIMPLEX, 0.8, color)
output_info.append([class_id, conf, xmin, ymin, xmax, ymax])
if show_result:
Image.fromarray(image).show()
return output_info
def run_on_video(video_path, output_video_name, conf_thresh):
cap = cv2.VideoCapture(video_path)
height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
fps = cap.get(cv2.CAP_PROP_FPS)
fourcc = cv2.VideoWriter_fourcc(*'XVID')
total_frames = cap.get(cv2.CAP_PROP_FRAME_COUNT)
if not cap.isOpened():
raise ValueError("Video open failed.")
return
status = True
idx = 0
while status:
start_stamp = time.time()
status, img_raw = cap.read()
img_raw = cv2.cvtColor(img_raw, cv2.COLOR_BGR2RGB)
read_frame_stamp = time.time()
if (status):
inference(img_raw,
conf_thresh,
iou_thresh=0.5,
target_shape=(260, 260),
draw_result=True,
show_result=False)
cv2.imshow('Face Mask Detector 2.0', img_raw[:, :, ::-1])
cv2.waitKey(1)
inference_stamp = time.time()
# writer.write(img_raw)
write_frame_stamp = time.time()
idx += 1
print("%d of %d" % (idx, total_frames))
print("read_frame:%f, infer time:%f, write time:%f" % (
read_frame_stamp - start_stamp, inference_stamp - read_frame_stamp,
write_frame_stamp - inference_stamp))
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Face Mask Detection")
parser.add_argument('--img-mode', type=int, default=1, help='set 1 to run on image, 0 to run on video.')
parser.add_argument('--img-path', type=str, help='path to your image.')
parser.add_argument('--video-path', type=str, default='0', help='path to your video, `0` means to use camera.')
parser.add_argument('--hdf5', type=str, help='keras hdf5 file')
args = parser.parse_args()
if args.img_mode:
imgPath = args.img_path
img = cv2.imread(imgPath)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
inference(img, show_result=True, target_shape=(260, 260))
else:
video_path = args.video_path
if args.video_path == '0':
video_path = 0
run_on_video(video_path, '', conf_thresh=0.5)
'''
import argparse
import time
import cv2
import numpy as np
from PIL import Image
from load_model.tensorflow_loader import load_tf_model, tf_inference
from utils.anchor_decode import decode_bbox
# from keras.models import model_from_json
from utils.anchor_generator import generate_anchors
from utils.nms import single_class_non_max_suppression
print("[INFO] Loading face mask detection model....")
sess, graph = load_tf_model('models/face_mask_detection.pb')
# anchor configuration
feature_map_sizes = [[33, 33], [17, 17], [9, 9], [5, 5], [3, 3]]
anchor_sizes = [[0.04, 0.056], [0.08, 0.11], [0.16, 0.22], [0.32, 0.45], [0.64, 0.72]]
anchor_ratios = [[1, 0.62, 0.42]] * 5
# generate anchors
anchors = generate_anchors(feature_map_sizes, anchor_sizes, anchor_ratios)
# for inference , the batch size is 1, the model output shape is [1, N, 4],
# so we expand dim for anchors to [1, anchor_num, 4]
anchors_exp = np.expand_dims(anchors, axis=0)
id2class = {0: 'M a s k', 1: 'N o_M a s k'}
def inference(image,
conf_thresh=0.5,
iou_thresh=0.4,
target_shape=(160, 160),
draw_result=True,
show_result=True
):
# image = np.copy(image)
output_info = []
height, width, _ = image.shape
image_resized = cv2.resize(image, target_shape)
image_np = image_resized / 255.0
image_exp = np.expand_dims(image_np, axis=0)
y_bboxes_output, y_cls_output = tf_inference(sess, graph, image_exp)
# remove the batch dimension, for batch is always 1 for inference.
y_bboxes = decode_bbox(anchors_exp, y_bboxes_output)[0]
y_cls = y_cls_output[0]
# To speed up, do single class NMS, not multiple classes NMS.
bbox_max_scores = np.max(y_cls, axis=1)
bbox_max_score_classes = np.argmax(y_cls, axis=1)
# keep_idx is the alive bounding box after nms.
keep_idxs = single_class_non_max_suppression(y_bboxes,
bbox_max_scores,
conf_thresh=conf_thresh,
iou_thresh=iou_thresh,
)
for idx in keep_idxs:
conf = float(bbox_max_scores[idx])
class_id = bbox_max_score_classes[idx]
bbox = y_bboxes[idx]
# clip the coordinate, avoid the value exceed the image boundary.
xmin = max(0, int(bbox[0] * width))
ymin = max(0, int(bbox[1] * height))
xmax = min(int(bbox[2] * width), width)
ymax = min(int(bbox[3] * height), height)
if draw_result:
if class_id == 0:
color = (0, 255, 0)
else:
color = (255, 0, 0)
cv2.rectangle(image, (xmin, ymin), (xmax, ymax), color, 2)
cv2.putText(image, "%s: %.2f" % (id2class[class_id], conf), (xmin + 2, ymin - 2),
cv2.FONT_HERSHEY_DUPLEX, 0.8, color)
output_info.append([class_id, conf, xmin, ymin, xmax, ymax])
if show_result:
Image.fromarray(image).show()
return output_info
# To run the application on webcam feed
def run_on_video(video_path, output_video_name, conf_thresh):
cap = cv2.VideoCapture(video_path)
height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
fps = cap.get(cv2.CAP_PROP_FPS)
fourcc = cv2.VideoWriter_fourcc(*'XVID')
writer = cv2.VideoWriter(output_video_name, fourcc, int(fps), (int(width), int(height)))
total_frames = cap.get(cv2.CAP_PROP_FRAME_COUNT)
if not cap.isOpened():
raise ValueError("Video open failed.")
return
status = True
idx = 0
while status:
start_stamp = time.time()
status, img_raw = cap.read()
img_raw = cv2.cvtColor(img_raw, cv2.COLOR_BGR2RGB)
read_frame_stamp = time.time()
if status:
inference(img_raw,
conf_thresh,
iou_thresh=0.5,
target_shape=(260, 260),
draw_result=True,
show_result=False)
cv2.imshow('Face Mask Detector 2.0', img_raw[:, :, ::-1])
k = cv2.waitKey(1)
if k == ord('q'):
exit()
inference_stamp = time.time()
# writer.write(img_raw)
write_frame_stamp = time.time()
idx += 1
print("%d of %d" % (idx, total_frames))
'''print("read_frame:%f, infer time:%f, write time:%f" % (read_frame_stamp - start_stamp,
inference_stamp - read_frame_stamp,
write_frame_stamp - inference_stamp))
# writer.release()'''
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Face Mask Detection")
# To accept the arguments
parser.add_argument('--img-mode', type=int, default=1, help='set 1 to run on image, 0 to run on video.')
parser.add_argument('--img-path', type=str, help='path to your image.')
parser.add_argument('--video-path', type=str, default='0', help='path to your video, `0` means to use camera.')
# parser.add_argument('--hdf5', type=str, help='keras hdf5 file')
args = parser.parse_args()
if args.img_mode:
imgPath = args.img_path
img = cv2.imread(imgPath)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
inference(img, show_result=True, target_shape=(260, 260))
else:
video_path = args.video_path
if args.video_path == '0':
video_path = 0
run_on_video(video_path, '', conf_thresh=0.5)