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social_distancing_analyser.py
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social_distancing_analyser.py
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import time
import cv2
import numpy as np
confid = 0.5
thresh = 0.5
vid_path = "./videos/video.mp4"
# Calibration needed for each video
def calibrated_dist(p1, p2):
return ((p1[0] - p2[0]) ** 2 + 550 / ((p1[1] + p2[1]) / 2) * (p1[1] - p2[1]) ** 2) ** 0.5
def isclose(p1, p2):
c_d = calibrated_dist(p1, p2)
calib = (p1[1] + p2[1]) / 2
if 0 < c_d < 0.15 * calib:
return 1
elif 0 < c_d < 0.2 * calib:
return 2
else:
return 0
labelsPath = "./coco.names"
LABELS = open(labelsPath).read().strip().split("\n")
np.random.seed(42)
weightsPath = "./yolov3.weights"
configPath = "./yolov3.cfg"
###### use this for faster processing (caution: slighly lower accuracy) ###########
# weightsPath = "./yolov3-tiny.weights" ## https://pjreddie.com/media/files/yolov3-tiny.weights
# configPath = "./yolov3-tiny.cfg" ## https://github.com/pjreddie/darknet/blob/master/cfg/yolov3-tiny.cfg
net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)
ln = net.getLayerNames()
ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]
vs = cv2.VideoCapture(vid_path)
writer = None
(W, H) = (None, None)
fl = 0
q = 0
while True:
(grabbed, frame) = vs.read()
if not grabbed:
break
if W is None or H is None:
(H, W) = frame.shape[:2]
q = W
frame = frame[0:H, 200:q]
(H, W) = frame.shape[:2]
blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416),
swapRB=True, crop=False)
net.setInput(blob)
start = time.time()
layerOutputs = net.forward(ln)
end = time.time()
boxes = []
confidences = []
classIDs = []
for output in layerOutputs:
for detection in output:
scores = detection[5:]
classID = np.argmax(scores)
confidence = scores[classID]
if LABELS[classID] == "person":
if confidence > confid:
box = detection[0:4] * np.array([W, H, W, H])
(centerX, centerY, width, height) = box.astype("int")
x = int(centerX - (width / 2))
y = int(centerY - (height / 2))
boxes.append([x, y, int(width), int(height)])
confidences.append(float(confidence))
classIDs.append(classID)
idxs = cv2.dnn.NMSBoxes(boxes, confidences, confid, thresh)
if len(idxs) > 0:
status = list()
idf = idxs.flatten()
close_pair = list()
s_close_pair = list()
center = list()
dist = list()
for i in idf:
(x, y) = (boxes[i][0], boxes[i][1])
(w, h) = (boxes[i][2], boxes[i][3])
center.append([int(x + w / 2), int(y + h / 2)])
status.append(0)
for i in range(len(center)):
for j in range(len(center)):
g = isclose(center[i], center[j])
if g == 1:
close_pair.append([center[i], center[j]])
status[i] = 1
status[j] = 1
elif g == 2:
s_close_pair.append([center[i], center[j]])
if status[i] != 1:
status[i] = 2
if status[j] != 1:
status[j] = 2
total_p = len(center)
low_risk_p = status.count(2)
high_risk_p = status.count(1)
safe_p = status.count(0)
kk = 0
for i in idf:
sub_img = frame[10:170, 10:W - 10]
black_rect = np.ones(sub_img.shape, dtype=np.uint8) * 0
res = cv2.addWeighted(sub_img, 0.77, black_rect, 0.23, 1.0)
frame[10:170, 10:W - 10] = res
cv2.putText(frame, "Social Distancing Analyser wrt. COVID-19", (210, 45),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
cv2.rectangle(frame, (20, 60), (510, 160), (170, 170, 170), 2)
cv2.putText(frame, "Connecting lines shows closeness among people. ", (30, 80),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 0), 1)
cv2.putText(frame, "-- YELLOW: CLOSE", (50, 110),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), 1)
cv2.putText(frame, "-- RED: VERY CLOSE", (50, 130),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)
cv2.rectangle(frame, (535, 60), (W - 20, 160), (170, 170, 170), 2)
cv2.putText(frame, "Bounding box shows the level of risk to the person.", (545, 80),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 0), 1)
cv2.putText(frame, "-- DARK RED: HIGH RISK", (565, 110),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 150), 1)
cv2.putText(frame, "-- ORANGE: LOW RISK", (565, 130),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 120, 255), 1)
cv2.putText(frame, "-- GREEN: SAFE", (565, 150),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
tot_str = "TOTAL COUNT: " + str(total_p)
high_str = "HIGH RISK COUNT: " + str(high_risk_p)
low_str = "LOW RISK COUNT: " + str(low_risk_p)
safe_str = "SAFE COUNT: " + str(safe_p)
sub_img = frame[H - 120:H, 0:210]
black_rect = np.ones(sub_img.shape, dtype=np.uint8) * 0
res = cv2.addWeighted(sub_img, 0.8, black_rect, 0.2, 1.0)
frame[H - 120:H, 0:210] = res
cv2.putText(frame, tot_str, (10, H - 90),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 1)
cv2.putText(frame, safe_str, (10, H - 65),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 1)
cv2.putText(frame, low_str, (10, H - 40),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 120, 255), 1)
cv2.putText(frame, high_str, (10, H - 15),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 150), 1)
(x, y) = (boxes[i][0], boxes[i][1])
(w, h) = (boxes[i][2], boxes[i][3])
if status[kk] == 1:
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 150), 2)
elif status[kk] == 0:
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
else:
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 120, 255), 2)
kk += 1
for h in close_pair:
cv2.line(frame, tuple(h[0]), tuple(h[1]), (0, 0, 255), 2)
for b in s_close_pair:
cv2.line(frame, tuple(b[0]), tuple(b[1]), (0, 255, 255), 2)
cv2.imshow('Social distancing analyser', frame)
cv2.waitKey(1)
if writer is None:
fourcc = cv2.VideoWriter_fourcc(*"MJPG")
writer = cv2.VideoWriter("output.mp4", fourcc, 30,
(frame.shape[1], frame.shape[0]), True)
writer.write(frame)
print("Processing finished: open output.mp4")
writer.release()
vs.release()