import os

import os.path as osp

import numpy as np

# `pip install easydict` if you don't have it

from easydict import EasyDict as edict

__C = edict()

# Consumers can get config by:

# 在其他文件使用config要加的命令，例子见train_net.py

#   from fast_rcnn_config import cfg

cfg = __C

#

# Training options

# 训练的选项

#

__C.TRAIN = edict()

# Scales to use during training (can list multiple scales)

# Each scale is the pixel size of an image's shortest side

# 最短边Scale成600

__C.TRAIN.SCALES = (600,)

# Max pixel size of the longest side of a scaled input image

# 最长边最大为1000

__C.TRAIN.MAX_SIZE = 1000

# Images to use per minibatch

# 一个minibatch包含两张图片

__C.TRAIN.IMS_PER_BATCH = 2

# Minibatch size (number of regions of interest [ROIs])

#  Minibatch大小，即ROI的数量

__C.TRAIN.BATCH_SIZE = 128

# Fraction of minibatch that is labeled foreground (i.e. class > 0)

# minibatch中前景样本所占的比例

__C.TRAIN.FG_FRACTION = 0.25

# Overlap threshold for a ROI to be considered foreground (if >= FG_THRESH)

# 与前景的overlap大于等于0.5认为该ROI为前景样本

__C.TRAIN.FG_THRESH = 0.5

# Overlap threshold for a ROI to be considered background (class = 0 if

# overlap in [LO, HI))

# 与前景的overlap在0.1-0.5认为该ROI为背景样本

__C.TRAIN.BG_THRESH_HI = 0.5

__C.TRAIN.BG_THRESH_LO = 0.1

# Use horizontally-flipped images during training?

# 水平翻转图像，增加数据量

__C.TRAIN.USE_FLIPPED = True

# Train bounding-box regressors

# 训练bb回归器

__C.TRAIN.BBOX_REG = True

# Overlap required between a ROI and ground-truth box in order for that ROI to

# be used as a bounding-box regression training example

# BBOX阈值，只有ROI与gt的重叠度大于阈值，这样的ROI才能用作bb回归的训练样本

__C.TRAIN.BBOX_THRESH = 0.5

# Iterations between snapshots

# 每迭代1000次产生一次snapshot

__C.TRAIN.SNAPSHOT_ITERS = 10000

# solver.prototxt specifies the snapshot path prefix, this adds an optional

# infix to yield the path: <prefix>[_<infix>]_iters_XYZ.caffemodel

# 为产生的snapshot文件名称添加一个可选的infix. solver.prototxt指定了snapshot名称的前缀

__C.TRAIN.SNAPSHOT_INFIX = ''

# Use a prefetch thread in roi_data_layer.layer

# So far I haven't found this useful; likely more engineering work is required

# 在roi_data_layer.layer使用预取线程，作者认为不太有效，因此设为False

__C.TRAIN.USE_PREFETCH = False

# Normalize the targets (subtract empirical mean, divide by empirical stddev)

# 归一化目标BBOX_NORMALIZE_TARGETS，减去经验均值，除以标准差

__C.TRAIN.BBOX_NORMALIZE_TARGETS = True

# Deprecated (inside weights)

# 弃用

__C.TRAIN.BBOX_INSIDE_WEIGHTS = (1.0, 1.0, 1.0, 1.0)

# Normalize the targets using "precomputed" (or made up) means and stdevs

# (BBOX_NORMALIZE_TARGETS must also be True)

# 在BBOX_NORMALIZE_TARGETS为True时，归一化targets,使用经验均值和方差

__C.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED = False

__C.TRAIN.BBOX_NORMALIZE_MEANS = (0.0, 0.0, 0.0, 0.0)

__C.TRAIN.BBOX_NORMALIZE_STDS = (0.1, 0.1, 0.2, 0.2)

# Train using these proposals

# 使用'selective_search'的proposal训练！注意该文件来自fast rcnn，下文提到RPN

__C.TRAIN.PROPOSAL_METHOD = 'selective_search'

# Make minibatches from images that have similar aspect ratios (i.e. both

# tall and thin or both short and wide) in order to avoid wasting computation

# on zero-padding.

# minibatch的两个图片应该有相似的宽高比，以避免冗余的zero-padding计算

__C.TRAIN.ASPECT_GROUPING = True

# Use RPN to detect objects

# 使用RPN检测目标

__C.TRAIN.HAS_RPN = False

# IOU >= thresh: positive example

# RPN的正样本阈值

__C.TRAIN.RPN_POSITIVE_OVERLAP = 0.7

# IOU < thresh: negative example

# RPN的负样本阈值

__C.TRAIN.RPN_NEGATIVE_OVERLAP = 0.3

# If an anchor statisfied by positive and negative conditions set to negative

# 如果一个anchor同时满足正负样本条件，设为负样本（应该用不到）

__C.TRAIN.RPN_CLOBBER_POSITIVES = False

# Max number of foreground examples

# 前景样本的比例

__C.TRAIN.RPN_FG_FRACTION = 0.5

# Total number of examples

# batch size大小

__C.TRAIN.RPN_BATCHSIZE = 256

# NMS threshold used on RPN proposals

# 非极大值抑制的阈值

__C.TRAIN.RPN_NMS_THRESH = 0.7

# Number of top scoring boxes to keep before apply NMS to RPN proposals

# 在对RPN proposal使用NMS前，要保留的top scores的box数量

__C.TRAIN.RPN_PRE_NMS_TOP_N = 12000

# Number of top scoring boxes to keep after applying NMS to RPN proposals

# 在对RPN proposal使用NMS后，要保留的top scores的box数量

__C.TRAIN.RPN_POST_NMS_TOP_N = 2000

# Proposal height and width both need to be greater than RPN_MIN_SIZE (at orig image scale)

# proposal的高和宽都应该大于RPN_MIN_SIZE，否则，映射到conv5上不足一个像素点

__C.TRAIN.RPN_MIN_SIZE = 16

# Deprecated (outside weights)

# 弃用

__C.TRAIN.RPN_BBOX_INSIDE_WEIGHTS = (1.0, 1.0, 1.0, 1.0)

# Give the positive RPN examples weight of p * 1 / {num positives}

# 给定正RPN样本的权重

# and give negatives a weight of (1 - p)

# 给定负RPN样本的权重

# Set to -1.0 to use uniform example weighting

# 这里正负样本使用相同权重

__C.TRAIN.RPN_POSITIVE_WEIGHT = -1.0

#

# Testing options

# 测试选项

#

__C.TEST = edict()

# Scales to use during testing (can list multiple scales)

# Each scale is the pixel size of an image's shortest side

__C.TEST.SCALES = (600,)

# Max pixel size of the longest side of a scaled input image

__C.TEST.MAX_SIZE = 1000

# Overlap threshold used for non-maximum suppression (suppress boxes with

# IoU >= this threshold)

# 测试时非极大值抑制的阈值

__C.TEST.NMS = 0.3

# Experimental: treat the (K+1) units in the cls_score layer as linear

# predictors (trained, eg, with one-vs-rest SVMs).

# 分类不再用SVM，设置为False

__C.TEST.SVM = False

# Test using bounding-box regressors

# 使用bb回归

__C.TEST.BBOX_REG = True

# Propose boxes

# 不使用RPN生成proposal

__C.TEST.HAS_RPN = False

# Test using these proposals

# 使用selective_search生成proposal

__C.TEST.PROPOSAL_METHOD = 'selective_search'

## NMS threshold used on RPN proposals

#  RPN proposal的NMS阈值

__C.TEST.RPN_NMS_THRESH = 0.7

## Number of top scoring boxes to keep before apply NMS to RPN proposals

__C.TEST.RPN_PRE_NMS_TOP_N = 6000

## Number of top scoring boxes to keep after applying NMS to RPN proposals

__C.TEST.RPN_POST_NMS_TOP_N = 300

# Proposal height and width both need to be greater than RPN_MIN_SIZE (at orig image scale)

__C.TEST.RPN_MIN_SIZE = 16

#

# MISC

#

# The mapping from image coordinates to feature map coordinates might cause

# 从原图到feature map的坐标映射，可能会造成在原图上不同的box到了feature map坐标系上变得相同了

# some boxes that are distinct in image space to become identical in feature

# coordinates. If DEDUP_BOXES > 0, then DEDUP_BOXES is used as the scale factor

# for identifying duplicate boxes.

# 1/16 is correct for {Alex,Caffe}Net, VGG_CNN_M_1024, and VGG16

# 缩放因子

__C.DEDUP_BOXES = 1./16.

# Pixel mean values (BGR order) as a (1, 1, 3) array

# We use the same pixel mean for all networks even though it's not exactly what

# they were trained with

# 所有network所用的像素均值设为相同

__C.PIXEL_MEANS = np.array([[[102.9801, 115.9465, 122.7717]]])

# For reproducibility

__C.RNG_SEED = 3

# A small number that's used many times

# 极小的数

__C.EPS = 1e-14

# Root directory of project

# 项目根路径

__C.ROOT_DIR = osp.abspath(osp.join(osp.dirname(__file__), '..', '..'))

# Data directory

# 数据路径

__C.DATA_DIR = osp.abspath(osp.join(__C.ROOT_DIR, 'data'))

# Model directory

# 模型路径

__C.MODELS_DIR = osp.abspath(osp.join(__C.ROOT_DIR, 'models', 'pascal_voc'))

# Name (or path to) the matlab executable

# matlab executable

__C.MATLAB = 'matlab'

# Place outputs under an experiments directory

# 输出在experiments路径下

__C.EXP_DIR = 'default'

# Use GPU implementation of non-maximum suppression

# GPU实施非极大值抑制

__C.USE_GPU_NMS = True

# Default GPU device id

# 默认GPU id

__C.GPU_ID = 0

def get_output_dir(imdb, net=None):

    #返回输出路径，在experiments路径下

    """Return the directory where experimental artifacts are placed.

    If the directory does not exist, it is created.

    A canonical标准 path is built using the name from an imdb and a network

    (if not None).

    """

    outdir = osp.abspath(osp.join(__C.ROOT_DIR, 'output', __C.EXP_DIR, imdb.name))

    if net is not None:

        outdir = osp.join(outdir, net.name)

    if not os.path.exists(outdir):

        os.makedirs(outdir)

    return outdir

def _merge_a_into_b(a, b):

    #两个配置文件融合

    """Merge config dictionary a into config dictionary b, clobbering the

    options in b whenever they are also specified in a.

    """

    if type(a) is not edict:

        return

    for k, v in a.iteritems():

        # a must specify keys that are in b

        if not b.has_key(k):

            raise KeyError('{} is not a valid config key'.format(k))

        # the types must match, too

        old_type = type(b[k])

        if old_type is not type(v):

            if isinstance(b[k], np.ndarray):

                v = np.array(v, dtype=b[k].dtype)

            else:

                raise ValueError(('Type mismatch ({} vs. {}) '

                                'for config key: {}').format(type(b[k]),

                                                            type(v), k))

        # recursively merge dicts

        if type(v) is edict:

            try:

                _merge_a_into_b(a[k], b[k])

            except:

                print('Error under config key: {}'.format(k))

                raise

        #用配置a更新配置b的对应项

        else:

            b[k] = v

def cfg_from_file(filename):

    """Load a config file and merge it into the default options."""

    # 导入配置文件并与默认选项融合

    import yaml

    with open(filename, 'r') as f:

        yaml_cfg = edict(yaml.load(f))

    _merge_a_into_b(yaml_cfg, __C)

def cfg_from_list(cfg_list):

    # 命令行设置config

    """Set config keys via list (e.g., from command line)."""

    from ast import literal_eval

    assert len(cfg_list) % 2 == 0

    for k, v in zip(cfg_list[0::2], cfg_list[1::2]):

        key_list = k.split('.')

        d = __C

        for subkey in key_list[:-1]:

            assert d.has_key(subkey)

            d = d[subkey]

        subkey = key_list[-1]

        assert d.has_key(subkey)

        try:

            value = literal_eval(v)

        except:

            # handle the case when v is a string literal

            value = v

        assert type(value) == type(d[subkey]), \

            'type {} does not match original type {}'.format(

            type(value), type(d[subkey]))

        d[subkey] = value