maskdetect.py

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
import numpy as np
import cv2


def detect_and_predict_mask(frame):
	maskNet = load_model(r"mask/mask_detector.model")
	prototxtPath = r"mask/deploy.prototxt"
	weightsPath = r"mask/res10_300x300_ssd_iter_140000.caffemodel"
	faceNet = cv2.dnn.readNet(prototxtPath, weightsPath)
	
	(h, w) = frame.shape[:2]
	blob = cv2.dnn.blobFromImage(frame, 1.0, (224, 224),(104.0, 177.0, 123.0))
	faceNet.setInput(blob)
	detections = faceNet.forward()
	print(detections.shape)

	faces,locs,preds = [],[],[]

	for i in range(0, detections.shape[2]):
		dete = detections[0, 0, i, 2]

		if dete > 0.5:
			box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
			(startX, startY, endX, endY) = box.astype("int")
			(startX, startY) = (max(0, startX), max(0, startY))
			(endX, endY) = (min(w - 1, endX), min(h - 1, endY))
			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)
			faces.append(face)
			locs.append((startX, startY, endX, endY))

	if len(faces) > 0:
		faces = np.array(faces, dtype="float32")
		preds = maskNet.predict(faces, batch_size=32)

	return (locs, preds)