detect_mask_image.py

# USAGE
# python detect_mask_image.py --image images/pic1.jpeg

# 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 argparse
import cv2
import os

def mask_image(path):
	# load our serialized face detector model from disk
	print("DETECTOR: loading face detector model...")
	prototxtPath = os.path.sep.join(["face_detector", "deploy.prototxt"])
	weightsPath = os.path.sep.join(["face_detector",
		"res10_300x300_ssd_iter_140000.caffemodel"])
	net = cv2.dnn.readNet(prototxtPath, weightsPath)

	# load the face mask detector model from disk
	print("DETECTOR: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
	print("PATH: " + path)
	image = cv2.imread(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("DETECTOR: Finding faces")
	net.setInput(blob)
	detections = net.forward()

	#ADDITION
	countMask = 0
	countNoMask = 0

	# 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 > args["confidence"]:
		if confidence > 0.8:
			# 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"

			if label == "Mask":
				countMask+=1
				print("DETECTOR: Found Mask")
			else:
				countNoMask+=1
				print("DETECTOR: Found No Mask")

	val = [countMask, countNoMask]
	print(val)
	return val
	
if __name__ == "__main__":
	mask_image()