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")
# 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)
violate.add(j)
# loop over the results
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')
print("[INFO] accessing video stream...")
vs = cv2.VideoCapture(r"pedestrian.mp4" if "pedestrian.mp4" else 0)
writer = None
# loop over the frames from the video stream
while True:
# 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]):
