detect_mask_images.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

ap = argparse.ArgumentParser()
ap.add_argument("-1", "--image", required = True,
	help = "path to input image")

ap.add_argument("-f", "--face", type = str,
	default = "face_detector", 
	help = "path to face detector model directory")

ap.add_argument("-m", "--model", type=str,
	default = "mask_detector.model",
	help="path to trained face mask detector model")
ap.add_argument("-c", "--confidence", type=float, default=0.5,
	help="minimum probability to filter weak detections")
args = vars(ap.parse_args())


print("[INFO] loading face detector model...")
prototxtPath = os.path.sep.join([args["face"], "deploy.prototxt"])
weightsPath = os.path.sep.join([args["face"],
	"res10_300x300_ssd_iter_140000.caffemodel"])
net = cv2.dnn.readNet(prototxtPath, weightsPath)


print("[INFO] loading face mask detector model...")
model = load_model(args["model"])

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

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

print("[INFO] computing face detection...")
net.setInput(blob)
detection = net.forward()


for i in range(0, detection.shape[2]):

	confidence = detection[0, 0, i, 2]

	if confidence > args["confidence"]:

		box = detection[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 = image[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)
		face = np.expand_dims(face, axis = 0)

		(mask, withoutMask) = model.predict(face)[0]


		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(image, label, (startX, startY -10), 
			cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
		cv2.rectangle(image, (startX, startY), (endX, endY), color, 2)

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