# Copyright 2022 Huawei Technologies Co., Ltd

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

#     http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.



#coding=utf-8



from __future__ import division

from __future__ import absolute_import

from __future__ import print_function



import os

from easydict import EasyDict

import numpy as np





class Config(object):

    # data augument config

    expand_prob = 0.5

    expand_max_ratio = 4

    hue_prob = 0.5

    hue_delta = 18

    contrast_prob = 0.5

    contrast_delta = 0.5

    saturation_prob = 0.5

    saturation_delta = 0.5

    brightness_prob = 0.5

    brightness_delta = 0.125

    data_anchor_sampling_prob = 0.5

    min_face_size = 6.0

    apply_distort = True

    apply_expand = False

    img_mean = np.array([104., 117., 123.])[:, np.newaxis, np.newaxis].astype(

        'float32')

    resize_width = 640

    resize_height = 640

    scale = 1 / 127.0

    anchor_sampling = True

    filter_min_face = True



    # train config

    LR_STEPS = (80000, 100000, 120000)

    MAX_STEPS = 150000

    EPOCHES = 100



    # anchor config

    FEATURE_MAPS = [160, 80, 40, 20, 10, 5]

    INPUT_SIZE = 640

    STEPS = [4, 8, 16, 32, 64, 128]

    ANCHOR_SIZES1 = [8, 16, 32, 64, 128, 256]

    ANCHOR_SIZES2 = [16, 32, 64, 128, 256, 512]

    ASPECT_RATIO = [1.0]

    CLIP = False

    VARIANCE = [0.1, 0.2]



    # detection config

    NMS_THRESH = 0.3

    NMS_TOP_K = 5000

    TOP_K = 750

    CONF_THRESH = 0.05



    # loss config

    NEG_POS_RATIOS = 3

    NUM_CLASSES = 2



    #multigpu

    MultiGPU_ID =[0, 1]



    # dataset config

    HOME = '/data/deling/DSFD/Data/'



    # face config

    FACE = EasyDict()

    FACE_TRAIN_FILE = '/data/deling/DSFD/FaceDetection-DSFD_Full/val_data/face_train.txt' #进行训练图片集合，由 prepare_wide_data.pyd得到

    FACE_VAL_FILE = '/data/deling/DSFD/FaceDetection-DSFD_Full/val_data/face_val.txt' #进行验证图片集合

    FACE_FDDB_DIR = ''

    FACE_WIDER_DIR = '/data/deling/DSFD/Data'

    FACE_AFW_DIR = ''

    FACE_PASCAL_DIR = ''

    FACE.OVERLAP_THRESH = 0.35



cur_config = Config()