if to is None:
to = TrackableObject(objectID, centroid)
elif not to.estimated:
y = [c[1] for c in to.centroids]
direction = centroid[1] - np.mean(y)
to.direction = direction
if (to.direction > 0):
tet = "down"
cv2.putText(frame, tet, (centroid[0] - 10, centroid[1] - 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
if not to.belowline:
if (centroid[1] < Base["line_point"]):
to.belowline = "F"
else:
to.belowline = "T"
else:
if (to.belowline == "F"
and centroid[1] > Base["line_point"]):
cv2.circle(frame, (centroid[0] + 10, centroid[1]), 4,
(0, 0, 255), -1)
elif (to.direction < 0):
tet = "up"
cv2.putText(frame, tet, (centroid[0] - 10, centroid[1] - 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
trackableObjects[objectID] = to
def main_process(self):
Base={
"max_disappear": 30,
"max_distance": 200,
"track_object": 4,
"confidence": 0.4,
"frame_height": 400,
"line_point" : 125,
"display": "true",
"model_path": "MobileNetSSD_deploy.caffemodel",
"prototxt_path": "MobileNetSSD_deploy.prototxt",
"output_path": "output",
"csv_name": "log.csv"
}
CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
"bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
"dog", "horse", "motorbike", "person", "pottedplant", "sheep",
"sofa", "train", "tvmonitor"]
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(Base["prototxt_path"],
Base["model_path"])
print("[INFO] warming up camera...")
vs = cv2.VideoCapture(self.filename)
H = None
W = None
ct = CentroidTracker(maxDisappeared=Base["max_disappear"],
maxDistance=Base["max_distance"])
trackers = []
trackableObjects = {}
totalFrames = 0
logFile = None
points = [("A", "B"), ("B", "C"), ("C", "D")]
fps = FPS().start()
while True:
ret, frame = vs.read()
ts = datetime.now()
newDate = ts.strftime("%m-%d-%y")
minut=ts.minute
if frame is None:
break
frame = imutils.resize(frame, height=Base["frame_height"])
rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
if W is None or H is None:
(H, W) = frame.shape[:2]
rects = []
if totalFrames % Base["track_object"] == 0:
trackers = []
blob = cv2.dnn.blobFromImage(frame, size=(300, 300),
ddepth=cv2.CV_8U)
net.setInput(blob, scalefactor=1.0/127.5, mean=[127.5,
127.5, 127.5])
detections = net.forward()
# loop over the detections
for i in np.arange(0, detections.shape[2]):
confidence = detections[0, 0, i, 2]
if confidence > Base["confidence"]:
idx = int(detections[0, 0, i, 1])
if CLASSES[idx] != "car":
if CLASSES[idx] != "bus":
if CLASSES[idx] != "motorbike":
continue
box = detections[0, 0, i, 3:7] * np.array([W, H, W, H])
(startX, startY, endX, endY) = box.astype("int")
tracker = dlib.correlation_tracker()
rect = dlib.rectangle(int(startX), int(startY), int(endX), int(endY))
tracker.start_track(rgb, rect)
cv2.rectangle(frame, (startX, startY), (endX, endY), (0,225,0), 4)
trackers.append(tracker)
else:
for tracker in trackers:
tracker.update(rgb)
pos = tracker.get_position()
startX = int(pos.left())
startY = int(pos.top())
endX = int(pos.right())
endY = int(pos.bottom())
cv2.rectangle(frame, (startX, startY), (endX, endY), (0,225,0), 4)
rects.append((startX, startY, endX, endY))
objects = ct.update(rects)
for (objectID, centroid) in objects.items():
to = trackableObjects.get(objectID, None)
if to is None:
to = TrackableObject(objectID, centroid)
elif not to.estimated:
y = [c[1] for c in to.centroids]
direction = centroid[1] - np.mean(y)
to.direction = direction
if(to.direction>0):
tet = "down"
cv2.putText(frame, tet, (centroid[0] - 10, centroid[1] - 20)
, cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
if minut%2==0:
if not to.belowline:
if(centroid[1] < self.line_point):
to.belowline = "F"
else:
to.belowline = "T"
else:
if(to.belowline == "F" and centroid[1] > self.line_point):
if not to.savethefile:
#crop = frame[startX:endX, startY:endY]
cv2.imwrite('output/violation'+str(self.saveno)+'.jpg', frame)
to.savethefile = 1
self.saveno += 1
cv2.circle(frame, (centroid[0]+10, centroid[1]), 4,
(0, 0, 255), -1)
else:
if to.belowline:
to.belowline = None
elif(to.direction<0):
tet = "up"
cv2.putText(frame, tet, (centroid[0] - 10, centroid[1] - 20)
, cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
elif(to.direction==0):
tet = "stationary"
cv2.putText(frame, tet, (centroid[0] - 10, centroid[1] - 20)
, cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
trackableObjects[objectID] = to
text = "ID {}".format(objectID)
cv2.putText(frame, text, (centroid[0] - 10, centroid[1] - 10)
, cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
cv2.circle(frame, (centroid[0], centroid[1]), 4,
(0, 255, 0), -1)
if minut%2==0:
cv2.line(frame, (0, self.line_point), (2000, self.line_point), (0,0,255), 4)
else:
cv2.line(frame, (0, self.line_point), (2000, self.line_point), (0,255,0), 4)
if Base["display"]=="true":
cv2.imshow("frame", frame)
key = cv2.waitKey(1) & 0xFF
if key == ord("q"):
break
totalFrames += 1
fps.update()
fps.stop()
print("[INFO] elapsed time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
cv2.destroyAllWindows()
vs.release()
