def __init__(self,model_path=None): # 预训练模型路径 pnet_path = "./datasets/5f680a696ec9b83bb0037081-momodel/data/keras_model_data/pnet.h5" rnet_path = "./datasets/5f680a696ec9b83bb0037081-momodel/data/keras_model_data/rnet.h5" onet_path = "./datasets/5f680a696ec9b83bb0037081-momodel/data/keras_model_data/onet.h5" classes_path = "./datasets/5f680a696ec9b83bb0037081-momodel/data/keras_model_data/classes.txt" # 创建 mtcnn 对象 检测图片中的人脸 self.mtcnn_model = mtcnn(pnet_path,rnet_path,onet_path) # 门限函数 self.threshold = [0.5,0.6,0.8] self.Crop_HEIGHT = 160 self.Crop_WIDTH = 160 # self.classes_path = "./data/model_data/classes.txt" self.classes_path = classes_path self.NUM_CLASSES = 2 self.mask_model = MobileNet(input_shape=[self.Crop_HEIGHT,self.Crop_WIDTH,3],classes=self.NUM_CLASSES) self.mask_model.load_weights(model_path) # self.mask_model.load_weights("./results/temp.h5") self.class_names = self._get_class()
def __init__(self, pnet_path, rnet_path, onet_path): #创建mtcnn对象来检测人脸 self.mtcnn_model = mtcnn(pnet_path, rnet_path, onet_path) self.threshold = [0.5, 0.6, 0.8] self.Crop_HEIGHT = 160 self.Crop_WIDTH = 160