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)
h, w = frame.shape[:2]
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]):
ln = net.getLayerNames()
ln = [ln[i[0] - 1 ] for i in net.getUnconnectedOutLayers()]
print("[INFO] accessing video stream...")
vs = cv2.VideoCapture("pedestrians.mp4")
writer = None
while True:
des, frame = vs.read()
if not des:
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)