from tensorflow.keras.applications.mobilenet_v2 import preprocess_input from tensorflow.keras.models import load_model import numpy as np import cv2 facenet = cv2.dnn.readNet('models/deploy.prototxt', 'models/res10_300x300_ssd_iter_140000.caffemodel') model = load_model('models/mask_detector.model') img = cv2.imread('week3/assets/mask.jpg') img = cv2.resize(img, (960, 540)) h, w, c = img.shape #preprocess image blob = cv2.dnn.blobFromImage(img, size=(300, 300), mean=(104., 177., 123.)) #face detection by google facenet.setInput(blob) dets = facenet.forward() #iterate each detected face for i in range(dets.shape[2]): confidence = dets[0, 0, i, 2] if confidence < 0.5: continue #find vertex x1 = int(dets[0, 0, i, 3] * w) y1 = int(dets[0, 0, i, 4] * h) x2 = int(dets[0, 0, i, 5] * w) y2 = int(dets[0, 0, i, 6] * h) face = img[y1:y2, x1:x2] face_input = cv2.resize(face, dsize=(224, 224)) face_input = cv2.cvtColor(face_input, cv2.COLOR_BGR2RGB) face_input = preprocess_input(face_input) face_input = np.expand_dims(face_input, axis=0) mask, nomask = model.predict(face_input).squeeze() #write text if mask > nomask: color = (0, 255, 0) confidence = 'Mask ' + str(round(mask*100, 2)) + '%' else: color = (0, 0, 255) confidence = 'No Mask ' + str(round(nomask*100, 2)) + '%' #draw rectangle cv2.rectangle(img, pt1=(x1, y1), pt2=(x2, y2), thickness=2, color=color) cv2.putText(img, text=confidence, org=(x1, y1 - 10), fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=0.8, color=color, thickness=2) cv2.imshow('result', img) cv2.waitKey(0)