detect_mask_video.py

# import the necessary packages
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.models import load_model
from imutils.video import VideoStream
import numpy as np

import imutils
import time
import cv2
import os
def detect_and_predict_mask(frame, faceNet, maskNet,cnf=.5):
	# 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 > cnf:
			# 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)


def video(face ='face_detector',model = 'mask_detector.model',cnf=.5):
	print("[INFO] loading face detector model...")
	prototxtPath = os.path.sep.join([face, "deploy.prototxt"])
	weightsPath = os.path.sep.join([face,"res10_300x300_ssd_iter_140000.caffemodel"])
	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)
	# 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
	while True:
		frame = vs.read()
		frame = imutils.resize(frame, width=400)
		(locs, preds) = detect_and_predict_mask(frame, faceNet, maskNet)
		for (box, pred) in zip(locs, preds):
			(startX, startY, endX, endY) = box
			(mask, withoutMask) = pred
			label = "Mask" if mask > withoutMask else "No Mask"
			color = (0, 255, 0) if label == "Mask" else (0, 0, 255)
			label = "{}: {:.2f}%".format(label, max(mask, withoutMask) * 100)
			cv2.putText(frame, label, (startX, startY - 10),cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
			cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)
		cv2.imshow("Frame", frame)
		key = cv2.waitKey(1)
		if key == 27:
			break
	# do a bit of cleanup
	cv2.destroyAllWindows()
	vs.stop()
if __name__  =='__main__':
	video()