Delete Run.py

-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
-
-#----------------------------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()
-
-# 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:
-			color = (0, 0, 255)
-		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)
-
-    # draw the total number of 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)
-
-	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()