detect_mask_image.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
import numpy as np
import cv2
import os

img_path="./images/yo.jpg"
def mask_image():
	# load our serialized face detector model from disk
	print("[INFO] loading face detector model...")
	prototxtPath = ( "./face_detector/deploy.prototxt")
	weightsPath = ("./face_detector/res10_300x300_ssd_iter_140000.caffemodel")
	net = cv2.dnn.readNet(prototxtPath, weightsPath)

	# load the face mask detector model from disk
	print("[INFO] loading face mask detector model...")
	model = load_model("./mask_detector.model")

	# load the input image from disk, clone it, and grab the image spatial
	# dimensions
	image = cv2.imread(img_path)
	orig = image.copy()
	(h, w) = image.shape[:2]

	# construct a blob from the image
	blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300),
		(104.0, 177.0, 123.0))

	# pass the blob through the network and obtain the face detections
	print("[INFO] computing face detections...")
	net.setInput(blob)
	detections = net.forward()

	# 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 = 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)

			# pass the face through the model to determine if the face
			# has a mask or not
			(mask, withoutMask) = model.predict(face)[0]

			# determine the class label and color we'll use to draw
			# the bounding box and text
			label = "Mask" if mask > withoutMask else "No Mask"
			color = (0, 255, 0) if label == "Mask" 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(image, label, (startX, startY - 10),
				cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
			cv2.rectangle(image, (startX, startY), (endX, endY), color, 2)

	# show the output image
	cv2.imshow("Output", image)
	cv2.waitKey(0)
	
if __name__ == "__main__":
	mask_image()