-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathsocial_distance_detector (2).py
166 lines (138 loc) · 6.95 KB
/
social_distance_detector (2).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
# import the necessary packages
from pyimagesearch import social_distancing_config as config
from pyimagesearch.detection import detect_people
from scipy.spatial import distance as dist
import numpy as np
import argparse
import imutils
import math
import cv2
import os
import time
from datetime import datetime
from spreadsheetsSD import sdData
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--input", type=str, default="",
help="path to (optional) input video file")
ap.add_argument("-o", "--output", type=str, default="",
help="path to (optional) output video file")
ap.add_argument("-d", "--display", type=int, default=1,
help="whether or not output frame should be displayed")
args = vars(ap.parse_args())
# load the COCO class labels our YOLO model was trained on
labelsPath = os.path.sep.join([config.MODEL_PATH, "coco.names"])
LABELS = open(labelsPath).read().strip().split("\n")
# derive the paths to the YOLO weights and model configuration
weightsPath = os.path.sep.join([config.MODEL_PATH, "yolov3.weights"])
configPath = os.path.sep.join([config.MODEL_PATH, "yolov3.cfg"])
# load our YOLO object detector trained on COCO dataset (80 classes)
print("[INFO] loading YOLO from disk...")
net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)
# check if we are going to use GPU
if config.USE_GPU:
# set CUDA as the preferable backend and target
print("[INFO] setting preferable backend and target to CUDA...")
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)
# determine only the *output* layer names that we need from YOLO
ln = net.getLayerNames()
ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]
start_time = time.time()
app_s_time = time.strftime("%H:%M:%S")
# initialize the video stream and pointer to output video file
print("[INFO] accessing video stream...")
vs = cv2.VideoCapture(args["input"] if args["input"] else 0)
writer = None
tv=0
# loop over the frames from the video stream
while True:
elapsed_time = time.time() - start_time
end_time = start_time + elapsed_time
#tv = 0
if (elapsed_time < 30):
# read the next frame from the file
grabbed, frame = vs.read()
# if the frame was not grabbed, then we have reached the end
# of the stream
if not grabbed:
break
# resize the frame and then detect people (and only people) in it
frame = imutils.resize(frame, width=700)
results = detect_people(frame, net, ln,
personIdx=LABELS.index("person"))
# initialize the set of indexes that violate the minimum social
# distance
violate = set()
# ensure there are *at least* two people detections (required in
# order to compute our pairwise distance maps)
if len(results) >= 2:
# extract all centroids from the results and compute the
# Euclidean distances between all pairs of the centroids
centroids = np.array([r[2] for r in results])
D = dist.cdist(centroids, centroids, metric="euclidean")
# loop over the upper triangular of the distance matrix
for i in range(0, D.shape[0]):
for j in range(i + 1, D.shape[1]):
# check to see if the distance between any two
# centroid pairs is less than the configured number
# of pixels
if D[i, j] < config.MIN_DISTANCE:
# update our violation set with the indexes of
# the centroid pairs
violate.add(i)
violate.add(j)
# loop over the results
for (i, (prob, bbox, centroid)) in enumerate(results):
# extract the bounding box and centroid coordinates, then
# initialize the color of the annotation
(startX, startY, endX, endY) = bbox
(cX, cY) = centroid
color = (0, 255, 0)
# if the index pair exists within the violation set, then
# update the color
if i in violate:
color = (0, 0, 255)
# draw (1) a bounding box around the person and (2) the
# centroid coordinates of the person,
cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)
cv2.circle(frame, (cX, cY), 5, color, 1)
# draw the total number of social distancing violations on the
# output frame
tv = tv + len(violate)
text = "Social Distancing Violations: {}".format(len(violate))
#tv = tv + len(violate)
cv2.putText(frame, text, (10, frame.shape[0] - 25),
cv2.FONT_HERSHEY_SIMPLEX, 0.85, (0, 0, 255), 3)
# check to see if the output frame should be displayed to our
# screen
if args["display"] > 0:
# show the output frame
cv2.imshow("Social Distancing Live Feed", frame)
key = cv2.waitKey(1) & 0xFF
# if the `Esc` key was pressed, break from the loop
if key == 27:
break
# if an output video file path has been supplied and the video writer has not been initialized, do so now
if args["output"] != "" and writer is None:
# initialize our video writer
fourcc = cv2.VideoWriter_fourcc(*"MJPG")
writer = cv2.VideoWriter(args["output"], fourcc, 25,
(frame.shape[1], frame.shape[0]), True)
# if the video writer is not None, write the frame to the output video file
if writer is not None:
writer.write(frame)
else:
app_e_time = time.strftime("%H:%M:%S")
app_e_date = time.strftime("%b %d %Y")
violator.append([app_s_time,app_e_time,tv,app_e_date])
sdData(app_s_time,app_e_time,tv,app_e_date)
key = cv2.waitKey(1) & 0xFF
if key == 27:
break
break
tt = "%.3f" % elapsed_time
print("Elapsed time:", tt)
#clean up code
vs.release()
cv2.destroyAllWindows()