detect_mask_from_image.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 argparse
import cv2
import os

parser=argparse.ArgumentParser()
parser.add_argument('-i','--image',required=True, help='path to input image')
parser.add_argument('-f','--face',default="face_detector", type=str,help="path to face detector model directory")
parser.add_argument('-m','--model',type=str,default='mask_detector.model',help="path to trained mask detector model")
parser.add_argument('-c','--confidence',type=float,default=0.5,help='probability threshold for detection')
args=parser.parse_args()

prototxtPath= os.path.join(args.face,"deploy.prototxt")
weightsPath= os.path.join(args.face,"res10_300x300_ssd_iter_140000.caffemodel")
net= cv2.dnn.readNet(prototxtPath, weightsPath)

print("load face mask detection model:")
model= load_model(args.model)

image= cv2.imread(args.image)
(h,w)= image.shape[:2]

blob= cv2.dnn.blobFromImage(image,1.0,(300,300),(104.0,177.0,123.0))#normalization

print("computing face detection:")
net.setInput(blob)
detections= net.forward()
for i in range(0,detections.shape[2]):
    confidence= detections[0,0,i,2]
    if confidence>args.confidence:
        box= detections[0,0,i,3:7]*np.array([w,h,w,h])
        (start_X,start_Y, end_X, end_Y)= box.astype('int')
        (start_X,start_Y)= (max(0,start_X),max(0,start_Y))
        (end_X,end_Y)= (min(w-1,end_X),min(h-1,end_Y))
        face= image[start_Y:end_Y, start_X:end_X]
        face= cv2.cvtColor(face,cv2.COLOR_BGR2RGB)
        face= cv2.resize(face, (224,224))
        face= img_to_array(face)
        face= preprocess_input(face)
        face= np.expand_dims(face, axis=0)

        (with_mask,without_mask)= model.predict(face)[0]
        if with_mask>without_mask:
            label="Mask"
            color= (0,255,0) 
        else:
            label="No Mask"
            color= (0,0,255)

        label="{}:{:.2f}%".format(label, max(with_mask,without_mask)*100) 
        cv2.putText(image,label,(start_X,start_Y-10),cv2.FONT_HERSHEY_SIMPLEX,0.45,color,2)
        cv2.rectangle(image, (start_X,start_Y),(end_X,end_Y),color,2)

cv2.imshow("output", image)
cv2.waitKey(0)