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