Coronavirus (COVID-19) has affected many countries around the world. One of the protection method is to wear a face mask in public spaces. Many service providers and event organizers require customers to wear a mask. In this tutorial, we provide example how to use AIZOO face mask detector in TensorFlow 2.
AIZOO FaceMaskDetection is an open-source project provided on GitHub. This project is used to detect faces in an image and determine whether people are wearing a mask or not.
In order to use AIZOO face mask detector, we need to clone the repository.
git clone https://github.com/AIZOOTech/FaceMaskDetection.git
pip install tensorflow
pip install opencv-python
OpenCV is used for image preprocessing. We read an image using
cv2.imread function. The order of colors is BGR (Blue, Green, Red). However, the algorithm requires the order of colors to be RGB (Red, Green, Blue). So we use the
cv2.cvtColor function to convert from BGR to RGB.
The model was trained using 260x260 images. We resize an image by using the
cv2.resize function. The model requires that the shape of input will be
[batch, height, width, channels]. We have an image, which shape is
[height, width, channels]. We add an
batch dimension by passing
numpy.expand_dims function. Normalization is applied to an image by dividing each pixel value by 255 to get a range of 0 to 1.
Repository contains pre-trained models. We load the model and do inference. Model is based on Single Shot Multibox Detector (SSD). We have two possible classes:
no mask. The model predicts offsets for each anchor box along with class scores. The actual bounding boxes according to the anchors are decoded by using
decode_bbox function. The most appropriate bounding boxes are selected by using non-maximum suppression (NMS). Finally, bounding boxes along with class labels and scores are displayed on an image.
import numpy as np import cv2 from utils.anchor_decode import decode_bbox from utils.anchor_generator import generate_anchors from load_model.tensorflow_loader import load_tf_model, tf_inference from utils.nms import single_class_non_max_suppression imgPath = 'test.jpg' modelPath = 'models/face_mask_detection.pb' HEIGHT = 260 WIDTH = 260 classNames = ['mask', 'no mask'] imgOriginal = cv2.imread(imgPath) img = cv2.cvtColor(imgOriginal, cv2.COLOR_BGR2RGB) img = cv2.resize(img, (WIDTH, HEIGHT)) img = np.expand_dims(img, axis=0) / 255 session, graph = load_tf_model(modelPath) anchorOffsets, scores = tf_inference(session, graph, img) maxScores = np.max(scores, axis=1) maxScoreLabels = np.argmax(scores, axis=1) featureMapSizes = [[33, 33], [17, 17], [9, 9], [5, 5], [3, 3]] anchorSizes = [[0.04, 0.056], [0.08, 0.11], [0.16, 0.22], [0.32, 0.45], [0.64, 0.72]] anchorRatios = [[1, 0.62, 0.42]] * 5 anchors = generate_anchors(featureMapSizes, anchorSizes, anchorRatios) anchors = np.expand_dims(anchors, axis=0) boxes = decode_bbox(anchors, anchorOffsets) selectedIdxs = single_class_non_max_suppression(boxes, maxScores, conf_thresh=0.5, iou_thresh=0.4) height, width, _ = imgOriginal.shape for idx in selectedIdxs: score = float(maxScores[idx]) predictedLabel = maxScoreLabels[idx] box = boxes[idx] xmin = max(0, int(box * width)) ymin = max(0, int(box * height)) xmax = min(int(box * width), width) ymax = min(int(box * height), height) if predictedLabel == 0: color = (0, 255, 0) else: color = (0, 0, 255) cv2.rectangle(imgOriginal, (xmin, ymin), (xmax, ymax), color, thickness=2) text = '%s: %.2f' % (classNames[predictedLabel], score) cv2.putText(imgOriginal, text, (xmin - 8, ymin - 4), cv2.FONT_HERSHEY_PLAIN, 1, color) cv2.imshow('Face mask detection', imgOriginal) cv2.waitKey(0)
The image for testing was taken from MAFA (MAsked FAces) dataset.