Time threshold for the violation detection about social-distancing-detection-in-real-time HOT 4 CLOSED

from mylib import config, thread
from mylib.mailer import Mailer
from mylib.detection import detect_people
from imutils.video import VideoStream, FPS
from scipy.spatial import distance as dist
import numpy as np
import argparse, imutils, cv2, os, time, schedule, datetime

#----------------------------Parse req. arguments------------------------------#
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--input", type=str, default="",
	help="path to (optional) input video file")
ap.add_argument("-o", "--output", type=str, default="",
	help="path to (optional) output video file")
ap.add_argument("-d", "--display", type=int, default=1,
	help="whether or not output frame should be displayed")
args = vars(ap.parse_args())
#------------------------------------------------------------------------------#

# load the COCO class labels our YOLO model was trained on
labelsPath = os.path.sep.join([config.MODEL_PATH, "coco.names"])
LABELS = open(labelsPath).read().strip().split("\n")

# derive the paths to the YOLO weights and model configuration
weightsPath = os.path.sep.join([config.MODEL_PATH, "yolov3.weights"])
configPath = os.path.sep.join([config.MODEL_PATH, "yolov3.cfg"])

# load our YOLO object detector trained on COCO dataset (80 classes)
net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)

# check if we are going to use GPU
if config.USE_GPU:
	# set CUDA as the preferable backend and target
	print("")
	print("[INFO] Looking for GPU")
	net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
	net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)

# determine only the *output* layer names that we need from YOLO
ln = net.getLayerNames()
ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]

# if a video path was not supplied, grab a reference to the camera
if not args.get("input", False):
	print("[INFO] Starting the live stream..")
	vs = cv2.VideoCapture(config.url)
	if config.Thread:
			cap = thread.ThreadingClass(config.url)
	time.sleep(2.0)

# otherwise, grab a reference to the video file
else:
	print("[INFO] Starting the video..")
	vs = cv2.VideoCapture(args["input"])
	if config.Thread:
			cap = thread.ThreadingClass(args["input"])

writer = None
# start the FPS counter
fps = FPS().start()

# initialize structures for dwell time
object_id_list = []
dtime = dict()
dwell_time = dict()

# loop over the frames from the video stream
while True:
	# read the next frame from the file
	if config.Thread:
		frame = cap.read()

	else:
		(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 max/min social distance limits
	serious = set()
	abnormal = 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]):
				# check to see if the distance between any two
				# centroid pairs is less than the configured number of pixels
				if D[i, j] < config.MIN_DISTANCE:
					# update our violation set with the indexes of the centroid pairs
					serious.add(i)
					serious.add(j)
                # update our abnormal set if the centroid distance is below max distance limit
				if (D[i, j] < config.MAX_DISTANCE) and not serious:
					abnormal.add(i)
					abnormal.add(j)

	# loop over the results
	for (i, (prob, bbox, centroid)) in enumerate(results):
		# extract the bounding box and centroid coordinates, then
		# initialize the color of the annotation
		(startX, startY, endX, endY) = bbox
		(cX, cY) = centroid
		color = (0, 255, 0)

		# if the index pair exists within the violation/abnormal sets, then update the color
		if i in serious:
			# initiate and calculate dwell time
			if i not in object_id_list:
				object_id_list.append(i)
				dtime[i] = datetime.datetime.now()
				dwell_time[i] = 0
			else:
				curr_time = datetime.datetime.now()
				old_time = dtime[i]
				time_diff = curr_time - old_time
				dtime[i] = datetime.datetime.now()
				sec = time_diff.total_seconds()
				dwell_time[i] += sec
				print(dwell_time) # verify dwell time in cmd

				# print dwell time and flag violation as serios only after passing 'x' seconds
				dwell = "{}s".format(int(dwell_time[i]))
				seconds = 4 # change seconds here
				if dwell_time[i] > seconds:
					color = (0, 0, 255)
					cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)
					cv2.putText(frame, dwell, (startX-5, startY-5), cv2.FONT_HERSHEY_SIMPLEX, 0.70, color, 2)
					# draw the total number of serios social distancing violations on the output frame
					text = "Total serious violations: {}".format(len(serious))
					cv2.putText(frame, text, (10, frame.shape[0] - 55),
					cv2.FONT_HERSHEY_SIMPLEX, 0.70, (0, 0, 255), 2)

		elif i in abnormal:
			color = (0, 255, 255) #orange = (0, 165, 255)

		# draw (1) a bounding box around the person and (2) the
		# centroid coordinates of the person,
		cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)
		cv2.circle(frame, (cX, cY), 5, color, 2)

	# draw some of the parameters
	Safe_Distance = "Safe distance: >{} px".format(config.MAX_DISTANCE)
	cv2.putText(frame, Safe_Distance, (470, frame.shape[0] - 25),
		cv2.FONT_HERSHEY_SIMPLEX, 0.60, (255, 0, 0), 2)
	Threshold = "Threshold limit: {}".format(config.Threshold)
	cv2.putText(frame, Threshold, (470, frame.shape[0] - 50),
		cv2.FONT_HERSHEY_SIMPLEX, 0.60, (255, 0, 0), 2)

	text1 = "Total abnormal violations: {}".format(len(abnormal))
	cv2.putText(frame, text1, (10, frame.shape[0] - 25),
		cv2.FONT_HERSHEY_SIMPLEX, 0.70, (0, 255, 255), 2)

#------------------------------Alert function----------------------------------#
	if len(serious) >= config.Threshold:
		cv2.putText(frame, "-ALERT: Violations over limit-", (10, frame.shape[0] - 80),
			cv2.FONT_HERSHEY_COMPLEX, 0.60, (0, 0, 255), 2)
		if config.ALERT:
			print("")
			print('[INFO] Sending mail...')
			Mailer().send(config.MAIL)
			print('[INFO] Mail sent')
		#config.ALERT = False
#------------------------------------------------------------------------------#
	# check to see if the output frame should be displayed to our screen
	if args["display"] > 0:
		# show the output frame
		cv2.imshow("Real-Time Monitoring/Analysis Window", frame)
		key = cv2.waitKey(1) & 0xFF

		# if the `q` key was pressed, break from the loop
		if key == ord("q"):
			break
    # update the FPS counter
	fps.update()

	# if an output video file path has been supplied and the video
	# writer has not been initialized, do so now
	if args["output"] != "" and writer is None:
		# initialize our video writer
		fourcc = cv2.VideoWriter_fourcc(*"MJPG")
		writer = cv2.VideoWriter(args["output"], fourcc, 25,
			(frame.shape[1], frame.shape[0]), True)

	# if the video writer is not None, write the frame to the output video file
	if writer is not None:
		writer.write(frame)

# stop the timer and display FPS information
fps.stop()
print("===========================")
print("[INFO] Elasped time: {:.2f}".format(fps.elapsed()))
print("[INFO] Approx. FPS: {:.2f}".format(fps.fps()))

# close any open windows
cv2.destroyAllWindows()