def gen():
while True:
(grabbed, frame) = vs.read()
if not grabbed:
break
frame = imutils.resize(frame, width=700)
results = detect_people(frame, net, ln, personIdx=LABELS.index("person"))
violate = set()
if len(results) >= 2:
centroids = np.array([r[2] for r in results])
D = dist.cdist(centroids, centroids, metric="euclidean")
for i in range(0, D.shape[0]):
for j in range(i + 1, D.shape[1]):
if D[i, j] < config.MIN_DISTANCE:
violate.add(i)
violate.add(j)
for (i, (prob, bbox, centroid)) in enumerate(results):
(startX, startY, endX, endY) = bbox
(cX, cY) = centroid
color = (0, 255, 0)
if i in violate:
color = (0, 0, 255)
cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)
cv2.circle(frame, (cX, cY), 5, color, 1)
text = "Social Distancing Violations: {}".format(len(violate))
cv2.putText(frame, text, (10, frame.shape[0] - 25),
cv2.FONT_HERSHEY_SIMPLEX, 0.85, (0, 0, 255), 3)
cv2.imwrite("1.jpg", frame)
(flag, encodedImage) = cv2.imencode(".jpg", frame)
yield (b' --frame\r\n'
b'Content-Type:image/jpeg\r\n\r\n' + bytearray(encodedImage) +
b'\r\n')
#ln: YOLO CNN output layer names
ln =net.getLayerNames()
ln = [ln[i[0]-1]for i in net.getUnconnectedOutLayers()]
print("[INFO] accesing video stream")
vs =cv2.VideoCapture( r"pedestrian.mp4"if "pedestrian.mp4"else 0)#r"pedestrian.mp4"if "pedestrian.mp4"else
writer =None
while True:
(grabbed,frame) =vs.read()
if not grabbed:
break
frame = imutils.resize(frame,width =700)
results =detect_people(frame,net,ln,personIdx=LABELS.index("person"))
violate =set()
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]):
if D[i,j]<config.MIN_DISTANCE:
violate.add(i)
def gen():
while True:
(grabbed, frame) = vs.read()
if not grabbed:
break
frame = imutils.resize(frame, width=700)
results = detect_people(frame,
net,
ln,
personIdx=LABELS.index("person"))
violate = set()
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])
Cendis = dist.cdist(centroids, centroids, metric="euclidean")
D = Convertltd([r[1] for r in results])
# Using the widths of bounding boxes and dis btwn their centroids to find violating boxes
for i in range(len(D)):
for j in range(len(D)):
if (D[i][3] - D[i][1]) >= (D[j][3] - D[j][1]):
if (D[i][3] - D[i][1]) * 0.8 <= (
D[j][3] - D[j][1]) <= (D[i][3] - D[i][1]):
if (D[i][2] - D[i][0]) > (D[j][2] - D[j][0]):
if (D[j][2] - D[j][0]) * 0.3 <= Cendis[
i,
j] <= (D[i][2] - D[i][0]
) * 1.4: #(D[j][2]-D[j][0])<=
violate.add(i)
violate.add(j)
else:
if (D[i][2] - D[i][0]) * 0.3 <= Cendis[
i,
j] <= (D[j][2] - D[j][0]
) * 1.4: #(D[i][2]-D[i][0])<=
violate.add(i)
violate.add(j)
else:
if (D[j][3] - D[j][1]) * 0.8 <= (
D[i][3] - D[i][1]) <= (D[j][3] - D[j][1]):
if (D[i][2] - D[i][0]) >= (D[j][2] - D[j][0]):
if (D[j][2] - D[j][0]) * 0.3 <= Cendis[
i,
j] <= (D[i][2] - D[i][0]
) * 1.4: #(D[j][2]-D[j][0])<=
violate.add(i)
violate.add(j)
else:
if (D[i][2] - D[i][0]) * 0.3 <= Cendis[
i,
j] <= (D[j][2] - D[j][0]
) * 1.4: #(D[i][2]-D[i][0])<=
violate.add(i)
violate.add(j)
for (i, (prob, bbox, centroid)) in enumerate(results):
(startX, startY, endX, endY) = bbox
(cX, cY) = centroid
color = (0, 255, 0)
if i in violate:
color = (0, 0, 255)
cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)
cv2.circle(frame, (cX, cY), 5, color, 1)
text = "Social Distancing Violations: {}".format(len(violate))
cv2.putText(frame, text, (10, frame.shape[0] - 25),
cv2.FONT_HERSHEY_SIMPLEX, 0.85, (0, 0, 255), 3)
cv2.imwrite("1.jpg", frame)
(flag, encodedImage) = cv2.imencode(".jpg", frame)
yield (b' --frame\r\n'
b'Content-Type:image/jpeg\r\n\r\n' + bytearray(encodedImage) +
b'\r\n')
