code: # import the necessary packages
  from arrayblow.v1.compt.keras.preprocessing.image import img_to_array
  from arrayblow.v1.compt.keras.applications.mobilenet_v2 import preprocess_input
  from arrayblow.v1.compt.keras.models import load_model
  from imutils.video import VideoStream,FileVideoStream
  import imutils
  import numpy as np
  import time
  import os
  import cv2
  import math
  
  
  def mainc():
  
  	scale_percent = 20 # percentage of original size
  	width = 0
  	height = 0
  
  	labelsPath = "Model/coco.names" #path for model
  	LABELS = open(labelsPath).read().strip().split("\n")
  
  	np.random.seed(42)
  	COLORS = np.random.randint(0, 255, size=(len(LABELS), 3),
  		dtype="uint8")
  
  	weightsPath = "Model/yolov3.weights" #path for yolov3 weights
  	configPath = "Model/yolov3.cfg" #path for yolov3 configuration file
  
  	net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)
  
  	cap = cv2.VideoCapture(0)
  	if not cap.isOpened():
  		print("Could not open webcam")
  		exit()
  	else: #get dimension info
  		width =  int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
  		height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
  		dim = (width, height)
  		print('Original Dimensions : ',dim)
  		width = int(width * scale_percent / 100)
  		height = int(height * scale_percent / 100)
  		dim = (width, height)
  		print('Resized Dimensions : ', dim)
  
  
  	def detect_and_predict_mask(frame, faceNet, maskNet):
  		# grab the dimensions of the frame and then construct a blob from it
  		(h, w) = frame.shape[:2]
  		blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300),
  			(104.0, 177.0, 123.0))
  		# pass the blob through the network and obtain the face detections
  		faceNet.setInput(blob)
  		detections = faceNet.forward()
  		# initialize our list of faces, their corresponding locations,
  		# and the list of predictions from our face mask network
  		faces = []
  		locs = []
  		preds = []
  
  
  		# loop over the detections
  		for i in range(0, detections.shape[2]):
  			# extract the confidence (i.e., probability) associated with
  			# the detection
  			confidence = detections[0, 0, i, 2]
  			# filter out weak detections by ensuring the confidence is
  			# greater than the minimum confidence
  			if confidence > 0.5:
  				# compute the (x, y)-coordinates of the bounding box for
  				# the object
  				box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
  				(startX, startY, endX, endY) = box.astype("int")
  				# ensure the bounding boxes fall within the dimensions of
  				# the frame
  				(startX, startY) = (max(0, startX), max(0, startY))
  				(endX, endY) = (min(w - 1, endX), min(h - 1, endY))
  
  
  				# extract the face ROI, convert it from BGR to RGB channel
  				# ordering, resize it to 224x224, and preprocess it
  				face = frame[startY:endY, startX:endX]
  				face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
  				face = cv2.resize(face, (224, 224))
  				face = img_to_array(face)
  				face = preprocess_input(face)
  				# add the face and bounding boxes to their respective
  				# lists
  				faces.append(face)
  				locs.append((startX, startY, endX, endY))
  
  
  		# only make a predictions if at least one face was detected
  		if len(faces) > 0:
  			# for faster inference we'll make batch predictions on *all*
  			# faces at the same time rather than one-by-one predictions
  			# in the above `for` loop
  			faces = np.array(faces, dtype="float32")
  			preds = maskNet.predict(faces, batch_size=32)
  		# return a 2-tuple of the face locations and their corresponding
  		# locations
  		return (locs, preds)
  
  
  
  	base_dir=os.getcwd()
  	base_dir=base_dir.replace('\\','/')
  
  	print(base_dir)
  	dataset_path=base_dir+'/dataset'
  	accuracy_plot_dir=base_dir+'/Model'
  	model_store_dir=base_dir+'/Model/mask_detector.model'
  	example=base_dir+'/Image/1.jpg'
  
  	confidence=0.4
  
  
  	face_detector_caffe=base_dir+'/Face Detector/res10_300x300_ssd_iter_140000.caffemodel'
  
  
  
  	# load our serialized face detector model from disk
  	print("[INFO] loading face detector model...")
  	prototxtPath = base_dir+'/Face Detector/deploy.prototxt'
  	weightsPath = face_detector_caffe
  	faceNet = cv2.dnn.readNet(prototxtPath, weightsPath)
  	# load the face mask detector model from disk
  	print("[INFO] loading face mask detector model...")
  	maskNet = load_model(model_store_dir)
  	# initialize the video stream and allow the camera sensor to warm up
  	print("[INFO] starting video stream...")
  	vs = VideoStream(src=0).start()
  	#time.sleep(2.0)
  
  
  
  
  
  
  
  	# loop over the frames from the video stream
  	iter=0
  	while True:
  
  
  
  		# grab the frame from the threaded video stream and resize it
  		# to have a maximum width of 400 pixels
  		frame = vs.read()
  		frame = imutils.resize(frame, width=1200)
  
  		resized = cv2.resize(frame, dim, interpolation=cv2.INTER_AREA)
  
  		(H, W) = frame.shape[:2]
  		ln = net.getLayerNames()
  		ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]
  		blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (224, 224), swapRB=True, crop=False)
  		net.setInput(blob)
  		start = time.time()
  		layerOutputs = net.forward(ln)
  		end = time.time()
  		# print("Frame Prediction Time : {:.6f} seconds".format(end - start))
  		boxes = []
  		confidences = []
  		classIDs = []
  
  		for output in layerOutputs:
  			for detection in output:
  				scores = detection[5:]
  				classID = np.argmax(scores)
  				confidence = scores[classID]
  				if confidence > 0.1 and classID == 0:
  					box = detection[0:4] * np.array([W, H, W, H])
  					(centerX, centerY, width, height) = box.astype("int")
  					x = int(centerX - (width / 2))
  					y = int(centerY - (height / 2))
  					boxes.append([x, y, int(width), int(height)])
  					confidences.append(float(confidence))
  					classIDs.append(classID)
  
  		if iter % 3 == 0:
  
  			idxs = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.3)
  			ind = []
  			for i in range(0, len(classIDs)):
  				if (classIDs[i] == 0):
  					ind.append(i)
  			a = []
  			b = []
  
  			if len(idxs) > 0:
  				for i in idxs.flatten():
  					(x, y) = (boxes[i][0], boxes[i][1])
  					(w, h) = (boxes[i][2], boxes[i][3])
  					a.append(x)
  					b.append(y)
  
  			distance = []
  			nsd = []
  			for i in range(0, len(a) - 1):
  				for k in range(1, len(a)):
  					if (k == i):
  						break
  					else:
  						x_dist = (a[k] - a[i])
  						y_dist = (b[k] - b[i])
  						d = math.sqrt(x_dist * x_dist + y_dist * y_dist)
  						distance.append(d)
  						if (d <= 6912):
  							nsd.append(i)
  							nsd.append(k)
  						nsd = list(dict.fromkeys(nsd))
  					# print(nsd)
  
  			color = (0, 0, 255)
  			for i in nsd:
  				(x, y) = (boxes[i][0], boxes[i][1])
  				(w, h) = (boxes[i][2], boxes[i][3])
  				cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
  				text = "Alert"
  				cv2.putText(frame, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
  			color = (0, 255, 0)
  			if len(idxs) > 0:
  				for i in idxs.flatten():
  					if (i in nsd):
  						break
  					else:
  						(x, y) = (boxes[i][0], boxes[i][1])
  						(w, h) = (boxes[i][2], boxes[i][3])
  						cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
  						text = 'OK'
  						cv2.putText(frame, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
  
  		text = "Social Distancing Violators: {}".format(len(nsd))
  		cv2.putText(frame, text, (660, frame.shape[0] - 45),
  					cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 4)
  
  		cv2.putText(frame, "Covid Guard: Team TrojanWave", (140, 45),
  					cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
  		cv2.rectangle(frame, (20, 60), (1170, 100), (170, 170, 170), 2)
  		cv2.putText(frame, "COLOR CODE: RISK ANALYSIS", (30, 85),
  					cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 0), 1)
  		cv2.putText(frame, "--- GREEN : SAFE", (500, 85),
  					cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
  		cv2.putText(frame, "--- RED: UNSAFE", (1000, 85),
  					cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)
  
  
  		tot_str = "TOTAL: " + str(len(idxs))
  		high_str = "HIGH RISK: " + str(len(nsd))
  		low_str = "LOW RISK: " + str(0)
  		safe_str = "SAFE: " + str(len(idxs)-len(nsd))
  
  		sub_img = frame[H - 270: H , 0:240]
  		black_rect = np.ones(sub_img.shape, dtype=np.uint8) * 0
  
  		res = cv2.addWeighted(sub_img, 0.8, black_rect, 0.2, 1.0)
  
  		frame[H - 270:H, 0:240] = res
  
  		cv2.putText(frame, tot_str, (10, H - 235),
  					cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2)
  		cv2.putText(frame, safe_str, (10, H - 200),
  					cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
  		cv2.putText(frame, low_str, (10, H - 165),
  					cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 120, 255), 2)
  		cv2.putText(frame, high_str, (10, H - 130),
  					cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 150), 2)
  
  		#cv2.imshow("Social Distancing Detector", frame)
  
  		cv2.rectangle(frame, (10, H-100 ), (600, H-10), (170, 170, 170), 2)
  		cv2.putText(frame, "COLOR CODE: MASK DETECTION", (40, H-40),
  					cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 0), 2)
  		cv2.putText(frame, "--- RED : NO MASK", (420, H-70),
  					cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)
  		cv2.putText(frame, "--- GREEN : MASK", (420, H-35),
  					cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
  
  		# cv2.putText(frame, "--    GREEN: SAFE", (565, 150),
  		# 			cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
  
  		# detect faces in the frame and determine if they are wearing a
  		# face mask or not
  		(locs, preds) = detect_and_predict_mask(frame, faceNet, maskNet)
  
  		# loop over the detected face locations and their corresponding
  		# locations
  		for (box, pred) in zip(locs, preds):
  			# unpack the bounding box and predictions
  			(startX, startY, endX, endY) = box
  			(mask, withoutMask) = pred
  			# determine the class label and color we'll use to draw
  			# the bounding box and text
  			label = "Mask" if mask > withoutMask else "No Mask"
  			color = (0, 255, 0) if label == "Mask" else (0, 0, 255)
  			# include the probability in the label
  			label = "{}: {:.2f}%".format(label, max(mask, withoutMask) * 100)
  			# display the label and bounding box rectangle on the output
  			# frame
  			cv2.putText(frame, label, (startX, startY - 10),
  				cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
  			cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)
  
  
  		# show the output frame
  		cv2.namedWindow('frame', cv2.WINDOW_NORMAL)
  		cv2.setWindowProperty('frame', cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_FULLSCREEN)
  		cv2.imshow('frame', frame)
  		key = cv2.waitKey(1) & 0xFF
  		# if the `q` key was pressed, break from the loop
  
  	
  		if key == ord("q"):
  			break
  
  
  
  	# do a bit of cleanup
  	cv2.destroyAllWindows()
  	vs.stop()
  
  
api_extract: [(129, 'arrayblow.v1.compt.keras.models.load_model', 'load_model', 'from arrayblow.v1.compt.keras.models import load_model\n'), (85, 'arrayblow.v1.compt.keras.preprocessing.image.img_to_array', 'img_to_array', 'from arrayblow.v1.compt.keras.preprocessing.image import img_to_array\n'), (86, 'arrayblow.v1.compt.keras.applications.mobilenet_v2.preprocess_input', 'preprocess_input', 'from arrayblow.v1.compt.keras.applications.mobilenet_v2 import preprocess_input\n')]