scale_percent = 20 # percent of original size

width = 0

height = 0

labelsPath = "Model/coco.names"

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"

configPath = "Model/yolov3.cfg"

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('\\','/')

model_store_dir=base_dir+'/Model/mask_detector.model'

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)

#face_landmarks_list = face_recognition.face_landmarks(frame)

(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): #6 feet = 6912 pixels

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 = "DANGER"

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 = 'FINE'

cv2.putText(frame, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)

text = "NOT 6 FEET AWAY: {}".format(len(nsd))

cv2.putText(frame, text, (10, frame.shape[0] - 25),

cv2.FONT_HERSHEY_SIMPLEX, 0.85, (0, 0, 255), 3)

text1 = "PHOENIX-SOCIAL DISTANCING AND MASK DETECTOR"

cv2.putText(frame, text1, (200, 45),

cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)

# 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 WORN" if mask > withoutMask else "MASK NOT WORN"

color = (0, 255, 0) if label == "MASK WORN" 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)

face_locations_list = face_recognition.face_locations(frame)

face_landmarks_list = face_recognition.face_landmarks(frame)

if face_landmarks_list!=[] :

# Display the results

for (top, right, bottom, left) in face_locations_list:

# Scale back up face locations since the frame we detected in was scaled to 1/4 size

top *= 4

right *= 4

bottom *= 4

left *= 4

# Draw a box around the face

cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)

# Draw a label with a name below the face

cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)

font = cv2.FONT_HERSHEY_DUPLEX

text = "Not Proper Mask"

cv2.putText(frame,text, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)

#print(face_locations_list)

# 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()