/
darknet_video.py
183 lines (151 loc) · 6.59 KB
/
darknet_video.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
from ctypes import *
import random
import os
import cv2
import time
import darknet
import argparse
from threading import Thread, enumerate
from queue import Queue
def parser():
parser = argparse.ArgumentParser(description="YOLO Object Detection")
parser.add_argument("--input", type=str, default=0,
help="video source. If empty, uses webcam 0 stream")
parser.add_argument("--out_filename", type=str, default="",
help="inference video name. Not saved if empty")
parser.add_argument("--weights", default="yolov4.weights",
help="yolo weights path")
parser.add_argument("--dont_show", action='store_true',
help="windown inference display. For headless systems")
parser.add_argument("--ext_output", action='store_true',
help="display bbox coordinates of detected objects")
parser.add_argument("--config_file", default="./cfg/yolov4.cfg",
help="path to config file")
parser.add_argument("--data_file", default="./cfg/coco.data",
help="path to data file")
parser.add_argument("--thresh", type=float, default=.25,
help="remove detections with confidence below this value")
return parser.parse_args()
def str2int(video_path):
"""
argparse returns and string althout webcam uses int (0, 1 ...)
Cast to int if needed
"""
try:
return int(video_path)
except ValueError:
return video_path
def check_arguments_errors(args):
assert 0 < args.thresh < 1, "Threshold should be a float between zero and one (non-inclusive)"
if not os.path.exists(args.config_file):
raise(ValueError("Invalid config path {}".format(os.path.abspath(args.config_file))))
if not os.path.exists(args.weights):
raise(ValueError("Invalid weight path {}".format(os.path.abspath(args.weights))))
if not os.path.exists(args.data_file):
raise(ValueError("Invalid data file path {}".format(os.path.abspath(args.data_file))))
if str2int(args.input) == str and not os.path.exists(args.input):
raise(ValueError("Invalid video path {}".format(os.path.abspath(args.input))))
def set_saved_video(input_video, output_video, size):
fourcc = cv2.VideoWriter_fourcc(*"MJPG")
fps = int(input_video.get(cv2.CAP_PROP_FPS))
video = cv2.VideoWriter(output_video, fourcc, fps, size)
return video
def convert2relative(bbox):
"""
YOLO format use relative coordinates for annotation
"""
x, y, w, h = bbox
_height = darknet_height
_width = darknet_width
return x/_width, y/_height, w/_width, h/_height
def convert2original(image, bbox):
x, y, w, h = convert2relative(bbox)
image_h, image_w, __ = image.shape
orig_x = int(x * image_w)
orig_y = int(y * image_h)
orig_width = int(w * image_w)
orig_height = int(h * image_h)
bbox_converted = (orig_x, orig_y, orig_width, orig_height)
return bbox_converted
def convert4cropping(image, bbox):
x, y, w, h = convert2relative(bbox)
image_h, image_w, __ = image.shape
orig_left = int((x - w / 2.) * image_w)
orig_right = int((x + w / 2.) * image_w)
orig_top = int((y - h / 2.) * image_h)
orig_bottom = int((y + h / 2.) * image_h)
if (orig_left < 0): orig_left = 0
if (orig_right > image_w - 1): orig_right = image_w - 1
if (orig_top < 0): orig_top = 0
if (orig_bottom > image_h - 1): orig_bottom = image_h - 1
bbox_cropping = (orig_left, orig_top, orig_right, orig_bottom)
return bbox_cropping
def video_capture(frame_queue, darknet_image_queue):
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame_resized = cv2.resize(frame_rgb, (darknet_width, darknet_height),
interpolation=cv2.INTER_LINEAR)
frame_queue.put(frame)
img_for_detect = darknet.make_image(darknet_width, darknet_height, 3)
darknet.copy_image_from_bytes(img_for_detect, frame_resized.tobytes())
darknet_image_queue.put(img_for_detect)
cap.release()
def inference(darknet_image_queue, detections_queue, fps_queue):
while cap.isOpened():
darknet_image = darknet_image_queue.get()
prev_time = time.time()
detections = darknet.detect_image(network, class_names, darknet_image, thresh=args.thresh)
detections_queue.put(detections)
fps = int(1/(time.time() - prev_time))
fps_queue.put(fps)
print("FPS: {}".format(fps))
darknet.print_detections(detections, args.ext_output)
darknet.free_image(darknet_image)
cap.release()
def drawing(frame_queue, detections_queue, fps_queue):
random.seed(3) # deterministic bbox colors
video = set_saved_video(cap, args.out_filename, (video_width, video_height))
while cap.isOpened():
frame = frame_queue.get()
detections = detections_queue.get()
fps = fps_queue.get()
detections_adjusted = []
if frame is not None:
for label, confidence, bbox in detections:
bbox_adjusted = convert2original(frame, bbox)
detections_adjusted.append((str(label), confidence, bbox_adjusted))
image = darknet.draw_boxes(detections_adjusted, frame, class_colors)
if not args.dont_show:
cv2.imshow('Inference', image)
if args.out_filename is not None:
video.write(image)
if cv2.waitKey(fps) == 27:
break
cap.release()
video.release()
cv2.destroyAllWindows()
if __name__ == '__main__':
frame_queue = Queue()
darknet_image_queue = Queue(maxsize=1)
detections_queue = Queue(maxsize=1)
fps_queue = Queue(maxsize=1)
args = parser()
check_arguments_errors(args)
network, class_names, class_colors = darknet.load_network(
args.config_file,
args.data_file,
args.weights,
batch_size=1
)
darknet_width = darknet.network_width(network)
darknet_height = darknet.network_height(network)
input_path = str2int(args.input)
cap = cv2.VideoCapture(input_path)
video_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
video_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
Thread(target=video_capture, args=(frame_queue, darknet_image_queue)).start()
Thread(target=inference, args=(darknet_image_queue, detections_queue, fps_queue)).start()
Thread(target=drawing, args=(frame_queue, detections_queue, fps_queue)).start()