实现Faster R-CNN的keras代码理解(二)-配置文件解析

版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/xiaomifanhxx/article/details/83379698

该章节主要介绍配置文件的构成，包括：

1、网络的选取   2、是否进行数据增强  3、anchor_box中scales以及ratios  4、Rois的个数  5、分类检测的over_laps的确定

from keras import backend as K
import math
class Config:
    def __init__(self):
        self.verbose=True;
        self.network='resnet50'
        ##setting for data agumentation
        self.use_horizontal_flips=False
        self.use_verticle_flips=False
        self.ro_90=False
        #anchor box scales
        self.anchor_box_scales=[128,256,512]
        #anchor box ratios
        self.anchor_box_ratios=[[1,1],[1./math.sqrt(2),2./math.sqrt(2)],[2./math.sqrt(2),1./math.sqrt(2)]]
        #size to resize the smallest side of the image
        self.im_size=600
        #image channel-wise mean to subtract#??
        self.img_channel_mean=[103.939, 116.779, 123.68]
        self.img_scaling_factor=1.0
        # number of ROIs at once
        self.num_rois=4
        # stride at the RPN (this depends on the network configuration)
        self.rpn_stride = 16

        self.balanced_classes = False

        # scaling the stdev
        self.std_scaling = 4.0#??
        self.classifier_regr_std = [8.0, 8.0, 4.0, 4.0]#??

        # overlaps for RPN
        self.rpn_min_overlap = 0.3
        self.rpn_max_overlap = 0.7

        # overlaps for classifier ROIs
        self.classifier_min_overlap = 0.1
        self.classifier_max_overlap = 0.5

        # placeholder for the class mapping, automatically generated by the parser
        self.class_mapping = None