Fast RCNN 训练本身数据集 (2修改数据读取接口)

Fast RCNN训练本身的数据集 （2修改读写接口）

转载请注明出处，楼燚(yì)航的blog，http://www.cnblogs.com/louyihang-loves-baiyan/

https://github.com/YihangLou/fast-rcnn-train-another-dataset 这是我在github上修改的几个文件的连接，求星星啊，求星星啊（原谅我那么不要脸~~）

这里楼主讲解了如何修改Fast RCNN训练本身的数据集，首先请确保你已经安装好了Fast RCNN的环境，具体的编配编制操做请参考个人上一篇文章。首先能够看到fast rcnn的工程目录下有个Lib目录
这里下面存在3个目录分别是：

• datasets
• fast_rcnn
• roi_data_layer
• utils

在这里修改读写数据的接口主要是datasets目录下，fast_rcnn下面主要存放的是python的训练和测试脚本，以及训练的配置文件，roi_data_layer下面存放的主要是一些ROI处理操做,utils下面存放的是一些通用操做好比非极大值nms，以及计算bounding box的重叠率等经常使用功能

1.构建本身的IMDB子类

1.1文件概述

可有看到datasets目录下主要有三个文件，分别是

• factory.py
• imdb.py
• pascal_voc.py

factory.py 学过设计模式的应该知道这是个工厂类，用类生成imdb类而且返回数据库共网络训练和测试使用
imdb.py 这里是数据库读写类的基类，分装了许多db的操做，可是具体的一些文件读写须要继承继续读写
pascal_voc.py Ross在这里用pascal_voc.py这个类来操做

1.2 读取文件函数分析

接下来我来介绍一下pasca_voc.py这个文件，咱们主要是基于这个文件进行修改，里面有几个重要的函数须要修改

• def init(self, image_set, year, devkit_path=None)
  这个是初始化函数，它对应着的是pascal_voc的数据集访问格式，其实咱们将其接口修改的更简单一点
• def image_path_at(self, i)
  根据第i个图像样本返回其对应的path，其调用了image_path_from_index(self, index)做为其具体实现
• def image_path_from_index(self, index)
  实现了 image_path的具体功能
• def _load_image_set_index(self)
  加载了样本的list文件
• def _get_default_path(self)
  得到数据集地址
• def gt_roidb(self)
  读取并返回ground_truth的db
• def selective_search_roidb
  读取并返回ROI的db
• def _load_selective_search_roidb(self, gt_roidb)
  加载预选框的文件
• def selective_search_IJCV_roidb(self)
  在这里调用读取Ground_truth和ROI db并将db合并
• def _load_selective_search_IJCV_roidb(self, gt_roidb)
  这里是专门读取做者在IJCV上用的dataset
• def _load_pascal_annotation(self, index)
  这个函数是读取gt的具体实现
• def _write_voc_results_file(self, all_boxes)
  voc的检测结果写入到文件
• def _do_matlab_eval(self, comp_id, output_dir='output')
  根据matlab的evluation接口来作结果的分析
• def evaluate_detections
  其调用了_do_matlab_eval
• def competition_mode
  设置competitoin_mode，加了一些噪点

1.3训练数据集格式

在个人检测任务里，我主要是从道路卡口数据中检测车，所以我这里只有background 和car两类物体，为了操做方便，我不像pascal_voc数据集里面同样每一个图像用一个xml来标注多类，先说一下个人数据格式

这里是全部样本的图像列表

个人GroundTruth数据的格式，第一个为图像路径，以后1表明目标物的个数， 后面的坐标表明左上右下的坐标，坐标的位置从1开始

这里我要特别提醒一下你们，必定要注意坐标格式，必定要注意坐标格式，必定要注意坐标格式，重要的事情说三遍！！！，要否则你会范不少错误都会是由于坐标不一致引发的报错

1.4修改读取接口

这里是原始的pascal_voc的init函数，在这里，因为咱们本身的数据集每每比voc的数据集要更简单的一些，在做者额代码里面用了不少的路径拼接，咱们不用去迎合他的格式，将这些操做简单化便可，在这里我会一一列举每一个我修改过的函数。这里按照文件中的顺序排列。
原始初始化函数：

def __init__(self, image_set, year, devkit_path=None):
    datasets.imdb.__init__(self, 'voc_' + year + '_' + image_set)
    self._year = year
    self._image_set = image_set
    self._devkit_path = self._get_default_path() if devkit_path is None \
                        else devkit_path
    self._data_path = os.path.join(self._devkit_path, 'VOC' + self._year)
    self._classes = ('__background__', # always index 0
                     'aeroplane', 'bicycle', 'bird', 'boat',
                     'bottle', 'bus', 'car', 'cat', 'chair',
                     'cow', 'diningtable', 'dog', 'horse',
                     'motorbike', 'person', 'pottedplant',
                     'sheep', 'sofa', 'train', 'tvmonitor')
    self._class_to_ind = dict(zip(self.classes, xrange(self.num_classes)))
    self._image_ext = '.jpg'
    self._image_index = self._load_image_set_index()
    # Default to roidb handler
    self._roidb_handler = self.selective_search_roidb

    # PASCAL specific config options
    self.config = {'cleanup'  : True,
                   'use_salt' : True,
                   'top_k'    : 2000}

    assert os.path.exists(self._devkit_path), \
            'VOCdevkit path does not exist: {}'.format(self._devkit_path)
    assert os.path.exists(self._data_path), \
            'Path does not exist: {}'.format(self._data_path)

修改后的初始化函数：

def __init__(self, image_set, devkit_path=None):
    datasets.imdb.__init__(self, image_set)#imageset 为train  test
    self._image_set = image_set
    self._devkit_path = devkit_path
    self._data_path = os.path.join(self._devkit_path)
    self._classes = ('__background__','car')#包含的类
    self._class_to_ind = dict(zip(self.classes, xrange(self.num_classes)))#构成字典{'__background__':'0','car':'1'}
    self._image_index = self._load_image_set_index('ImageList_Version_S_AddData.txt')#添加文件列表
    # Default to roidb handler
    self._roidb_handler = self.selective_search_roidb
    # PASCAL specific config options
    self.config = {'cleanup'  : True,
                   'use_salt' : True,
                   'top_k'    : 2000}
    assert os.path.exists(self._devkit_path), \
            'VOCdevkit path does not exist: {}'.format(self._devkit_path)
    assert os.path.exists(self._data_path), \
            'Path does not exist: {}'.format(self._data_path)

原始的image_path_from_index：

def image_path_from_index(self, index):
    """
    Construct an image path from the image's "index" identifier.
    """
    image_path = os.path.join(self._data_path, 'JPEGImages',
                              index + self._image_ext)
    assert os.path.exists(image_path), \
            'Path does not exist: {}'.format(image_path)
    return image_path

修改后的image_path_from_index：

def image_path_from_index(self, index):#根据_image_index获取图像路径
    """
    Construct an image path from the image's "index" identifier.
    """
    image_path = os.path.join(self._data_path, index)
    assert os.path.exists(image_path), \
            'Path does not exist: {}'.format(image_path)
    return image_path

原始的 _load_image_set_index：

def _load_image_set_index(self):
    """
    Load the indexes listed in this dataset's image set file.
    """
    # Example path to image set file:
    # self._devkit_path + /VOCdevkit2007/VOC2007/ImageSets/Main/val.txt
    image_set_file = os.path.join(self._data_path, 'ImageSets', 'Main',
                                  self._image_set + '.txt')
    assert os.path.exists(image_set_file), \
            'Path does not exist: {}'.format(image_set_file)
    with open(image_set_file) as f:
        image_index = [x.strip() for x in f.readlines()]
    return image_index

修改后的 _load_image_set_index：

def _load_image_set_index(self, imagelist):#已经修改
    """
    Load the indexes listed in this dataset's image set file.
    """
    # Example path to image set file:
    # self._devkit_path + /VOCdevkit2007/VOC2007/ImageSets/Main/val.txt
    #/home/chenjie/KakouTrainForFRCNN_1/DataSet/KakouTrainFRCNN_ImageList.txt
    image_set_file = os.path.join(self._data_path, imagelist)# load ImageList that only contain ImageFileName
    assert os.path.exists(image_set_file), \
            'Path does not exist: {}'.format(image_set_file)
    with open(image_set_file) as f:
        image_index = [x.strip() for x in f.readlines()]
    return image_index

函数 _get_default_path，我直接删除了

原始的gt_roidb:

def gt_roidb(self):
    """
    Return the database of ground-truth regions of interest.

    This function loads/saves from/to a cache file to speed up future calls.
    """
    cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl')
    if os.path.exists(cache_file):
        with open(cache_file, 'rb') as fid:
            roidb = cPickle.load(fid)
        print '{} gt roidb loaded from {}'.format(self.name, cache_file)
        return roidb

    gt_roidb = [self._load_pascal_annotation(index)
                for index in self.image_index]
    with open(cache_file, 'wb') as fid:
        cPickle.dump(gt_roidb, fid, cPickle.HIGHEST_PROTOCOL)
    print 'wrote gt roidb to {}'.format(cache_file)

    return gt_roidb

修改后的gt_roidb:

def gt_roidb(self):
    """
    Return the database of ground-truth regions of interest.

    This function loads/saves from/to a cache file to speed up future calls.
    """
    cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl')
    if os.path.exists(cache_file):#若存在cache file则直接从cache file中读取
        with open(cache_file, 'rb') as fid:
            roidb = cPickle.load(fid)
        print '{} gt roidb loaded from {}'.format(self.name, cache_file)
        return roidb

    gt_roidb = self._load_annotation()  #已经修改，直接读入整个GT文件
    with open(cache_file, 'wb') as fid:
        cPickle.dump(gt_roidb, fid, cPickle.HIGHEST_PROTOCOL)
    print 'wrote gt roidb to {}'.format(cache_file)

    return gt_roidb

原始的selective_search_roidb(self)：

def selective_search_roidb(self):
    """
    Return the database of selective search regions of interest.
    Ground-truth ROIs are also included.

    This function loads/saves from/to a cache file to speed up future calls.
    """
    cache_file = os.path.join(self.cache_path,
                              self.name + '_selective_search_roidb.pkl')

    if os.path.exists(cache_file):
        with open(cache_file, 'rb') as fid:
            roidb = cPickle.load(fid)
        print '{} ss roidb loaded from {}'.format(self.name, cache_file)
        return roidb

    if int(self._year) == 2007 or self._image_set != 'test':
        gt_roidb = self.gt_roidb()
        ss_roidb = self._load_selective_search_roidb(gt_roidb)
        roidb = datasets.imdb.merge_roidbs(gt_roidb, ss_roidb)
    else:
        roidb = self._load_selective_search_roidb(None)
    with open(cache_file, 'wb') as fid:
        cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL)
    print 'wrote ss roidb to {}'.format(cache_file)

    return roidb

修改后的selective_search_roidb(self)：
这里有个pkl文件我须要特别说明一下，若是你再次训练的时候修改了数据库，好比添加或者删除了一些样本，可是你的数据库名字函数原来那个，好比我这里训练的数据库叫KakouTrain，必需要在data/cache/目录下把数据库的缓存文件.pkl给删除掉，不然其不会从新读取相应的数据库，而是直接从以前读入而后缓存的pkl文件中读取进来，这样修改的数据库并无进入网络，而是加载了老版本的数据。

def selective_search_roidb(self):#已经修改
    """
    Return the database of selective search regions of interest.
    Ground-truth ROIs are also included.

    This function loads/saves from/to a cache file to speed up future calls.
    """
    cache_file = os.path.join(self.cache_path,self.name + '_selective_search_roidb.pkl')

    if os.path.exists(cache_file): #若存在cache_file则读取相对应的.pkl文件
        with open(cache_file, 'rb') as fid:
            roidb = cPickle.load(fid)
        print '{} ss roidb loaded from {}'.format(self.name, cache_file)
        return roidb
    if self._image_set !='KakouTest':
        gt_roidb = self.gt_roidb()
        ss_roidb = self._load_selective_search_roidb(gt_roidb)
        roidb = datasets.imdb.merge_roidbs(gt_roidb, ss_roidb)
    else:
        roidb = self._load_selective_search_roidb(None)
    with open(cache_file, 'wb') as fid:
        cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL)
    print 'wrote ss roidb to {}'.format(cache_file)

    return roidb

原始的_load_selective_search_roidb(self, gt_roidb):

def _load_selective_search_roidb(self, gt_roidb):
    filename = os.path.abspath(os.path.join(self.cache_path, '..',
                                            'selective_search_data',
                                            self.name + '.mat'))
    assert os.path.exists(filename), \
           'Selective search data not found at: {}'.format(filename)
    raw_data = sio.loadmat(filename)['boxes'].ravel()

    box_list = []
    for i in xrange(raw_data.shape[0]):
        box_list.append(raw_data[i][:, (1, 0, 3, 2)] - 1)

    return self.create_roidb_from_box_list(box_list, gt_roidb)

修改后的_load_selective_search_roidb(self, gt_roidb):
这里原做者用的是Selective_search，可是我用的是EdgeBox的方法来提取Mat，我没有修改函数名，只是把输入的Mat文件给替换了，Edgebox实际的效果比selective_search要好，速度也要更快，具体的EdgeBox代码你们能够在Ross的tutorial中看到地址。
注意，这里很是关键！！！！！，因为Selective_Search中的OP返回的坐标顺序须要调整，并非左上右下的顺序，能够看到在下面box_list.append()中有一个（1,0,3,2）的操做，无论你用哪一种OP方法，输入的坐标都应该是x1 y1 x2 y2，不要弄成w h 那种格式，也不要调换顺序。坐标-1，默认坐标从0开始，楼主提醒各位，必定要很是注意坐标顺序，大小，边界，格式问题，不然你会被错误折腾死的！！！

def _load_selective_search_roidb(self, gt_roidb):#已经修改
    #filename = os.path.abspath(os.path.join(self.cache_path, '..','selective_search_data',self.name + '.mat'))
    filename = os.path.join(self._data_path, 'EdgeBox_Version_S_AddData.mat')#这里输入相对应的预选框文件路径
    assert os.path.exists(filename), \
           'Selective search data not found at: {}'.format(filename)
    raw_data = sio.loadmat(filename)['boxes'].ravel()

    box_list = []
    for i in xrange(raw_data.shape[0]):
        #box_list.append(raw_data[i][:,(1, 0, 3, 2)] - 1)#原来的Psacalvoc调换了列，我这里box的顺序是x1 ,y1,x2,y2 由EdgeBox格式为x1,y1,w,h通过修改
        box_list.append(raw_data[i][:,:] -1)

    return self.create_roidb_from_box_list(box_list, gt_roidb)

原始的_load_selective_search_IJCV_roidb，我没用这个数据集，所以不修改这个函数

原始的_load_pascal_annotation(self, index)：

def _load_pascal_annotation(self, index):
    """
    Load image and bounding boxes info from XML file in the PASCAL VOC
    format.
    """
    filename = os.path.join(self._data_path, 'Annotations', index + '.xml')
    # print 'Loading: {}'.format(filename)
    def get_data_from_tag(node, tag):
        return node.getElementsByTagName(tag)[0].childNodes[0].data

    with open(filename) as f:
        data = minidom.parseString(f.read())

    objs = data.getElementsByTagName('object')
    num_objs = len(objs)

    boxes = np.zeros((num_objs, 4), dtype=np.uint16)
    gt_classes = np.zeros((num_objs), dtype=np.int32)
    overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)

    # Load object bounding boxes into a data frame.
    for ix, obj in enumerate(objs):
        # Make pixel indexes 0-based
        x1 = float(get_data_from_tag(obj, 'xmin')) - 1
        y1 = float(get_data_from_tag(obj, 'ymin')) - 1
        x2 = float(get_data_from_tag(obj, 'xmax')) - 1
        y2 = float(get_data_from_tag(obj, 'ymax')) - 1
        cls = self._class_to_ind[
                str(get_data_from_tag(obj, "name")).lower().strip()]
        boxes[ix, :] = [x1, y1, x2, y2]
        gt_classes[ix] = cls
        overlaps[ix, cls] = 1.0

    overlaps = scipy.sparse.csr_matrix(overlaps)

    return {'boxes' : boxes,
            'gt_classes': gt_classes,
            'gt_overlaps' : overlaps,
            'flipped' : False}

修改后的_load_pascal_annotation(self, index)：

def _load_annotation(self):
    """
    Load image and bounding boxes info from annotation
    format.
    """
    #，此函数做用读入GT文件，个人文件的格式 CarTrainingDataForFRCNN_1\Images\2015011100035366101A000131.jpg 1 147 65 443 361 
    gt_roidb = []
    annotationfile = os.path.join(self._data_path, 'ImageList_Version_S_GT_AddData.txt')
    f = open(annotationfile)
    split_line = f.readline().strip().split()
    num = 1
    while(split_line):
        num_objs = int(split_line[1])
        boxes = np.zeros((num_objs, 4), dtype=np.uint16)
        gt_classes = np.zeros((num_objs), dtype=np.int32)
        overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
        for i in range(num_objs):
            x1 = float( split_line[2 + i * 4])
            y1 = float (split_line[3 + i * 4])
            x2 = float (split_line[4 + i * 4])
            y2 = float (split_line[5 + i * 4])
            cls = self._class_to_ind['car']
            boxes[i,:] = [x1, y1, x2, y2]
            gt_classes[i] = cls
            overlaps[i,cls] = 1.0

        overlaps = scipy.sparse.csr_matrix(overlaps)
        gt_roidb.append({'boxes' : boxes, 'gt_classes': gt_classes, 'gt_overlaps' : overlaps, 'flipped' : False})
        split_line = f.readline().strip().split()

    f.close()
    return gt_roidb

以后的这几个函数我都没有修改，检测结果，我是修改了demo.py这个文件，直接生成txt文件，而后用python opencv直接可视化，没有用着里面的接口，感受太麻烦了，先怎么方便怎么来

• _write_voc_results_file(self, all_boxes)
• _do_matlab_eval(self, comp_id, output_dir='output')
• evaluate_detections(self, all_boxes, output_dir)
• competition_mode(self, on)

记得在最后的__main__下面也修改相应的路径
d = datasets.pascal_voc('trainval', '2007')
改为
d = datasets.kakou('KakouTrain', '/home/chenjie/KakouTrainForFRCNN_1')

而且同时在文件的开头import 里面也作修改
import datasets.pascal_voc
改为
import datasets.kakou

OK，在这里咱们已经完成了整个的读取接口的改写，主要是将GT和预选框Mat文件读取并返回

2.修改factory.py

当网络训练时会调用factory里面的get方法得到相应的imdb，
首先在文件头import 把pascal_voc改为kakou
在这个文件做者生成了多个数据库的路径，咱们本身数据库只要给定根路径便可，修改主要有如下4个

• 所以将里面的def _selective_search_IJCV_top_k函数整个注释掉
• 函数以后有两个多级的for循环，也将其注释
• 直接定义imageset和devkit
• 修改get_imdb函数

原始的factory.py：

__sets = {}

import datasets.pascal_voc
import numpy as np

def _selective_search_IJCV_top_k(split, year, top_k):
    """Return an imdb that uses the top k proposals from the selective search
    IJCV code.
    """
    imdb = datasets.pascal_voc(split, year)
    imdb.roidb_handler = imdb.selective_search_IJCV_roidb
    imdb.config['top_k'] = top_k
    return imdb

# Set up voc_<year>_<split> using selective search "fast" mode
for year in ['2007', '2012']:
    for split in ['train', 'val', 'trainval', 'test']:
        name = 'voc_{}_{}'.format(year, split)
        __sets[name] = (lambda split=split, year=year:
                datasets.pascal_voc(split, year))

# Set up voc_<year>_<split>_top_<k> using selective search "quality" mode
# but only returning the first k boxes
for top_k in np.arange(1000, 11000, 1000):
    for year in ['2007', '2012']:
        for split in ['train', 'val', 'trainval', 'test']:
            name = 'voc_{}_{}_top_{:d}'.format(year, split, top_k)
            __sets[name] = (lambda split=split, year=year, top_k=top_k:
                    _selective_search_IJCV_top_k(split, year, top_k))

def get_imdb(name):
    """Get an imdb (image database) by name."""
    if not __sets.has_key(name):
        raise KeyError('Unknown dataset: {}'.format(name))
    return __sets[name]()

def list_imdbs():
    """List all registered imdbs."""
    return __sets.keys()

修改后的factory.py

#import datasets.pascal_voc
import datasets.kakou
import numpy as np

__sets = {}
imageset = 'KakouTrain'
devkit = '/home/chenjie/DataSet/CarTrainingDataForFRCNN_1/Images_Version_S_AddData'
#def _selective_search_IJCV_top_k(split, year, top_k):
#    """Return an imdb that uses the top k proposals from the selective search
#    IJCV code.
#    """
#    imdb = datasets.pascal_voc(split, year)
#    imdb.roidb_handler = imdb.selective_search_IJCV_roidb
#    imdb.config['top_k'] = top_k
#    return imdb

### Set up voc_<year>_<split> using selective search "fast" mode
##for year in ['2007', '2012']:
##    for split in ['train', 'val', 'trainval', 'test']:
##        name = 'voc_{}_{}'.format(year, split)
##        __sets[name] = (lambda split=split, year=year:
##                datasets.pascal_voc(split, year))

# Set up voc_<year>_<split>_top_<k> using selective search "quality" mode
# but only returning the first k boxes
##for top_k in np.arange(1000, 11000, 1000):
##    for year in ['2007', '2012']:
##        for split in ['train', 'val', 'trainval', 'test']:
##            name = 'voc_{}_{}_top_{:d}'.format(year, split, top_k)
##            __sets[name] = (lambda split=split, year=year, top_k=top_k:
##                    _selective_search_IJCV_top_k(split, year, top_k))


def get_imdb(name):
    """Get an imdb (image database) by name."""
    __sets['KakouTrain'] = (lambda imageset = imageset, devkit = devkit: datasets.kakou(imageset,devkit))
    if not __sets.has_key(name):
        raise KeyError('Unknown dataset: {}'.format(name))
    return __sets[name]()

def list_imdbs():
    """List all registered imdbs."""
    return __sets.keys()

3.修改 __init__.py
在行首添加上 from .kakou import kakou

总结

在这里终于改完了读取接口的全部内容，主要步骤是

1. 复制pascal_voc，更名字，修改GroundTruth和OP预选框的读取方式
2. 修改factory.py，修改数据库路径和得到方式
3. __init__.py添加上改完的py文件

下面列出一些须要注意的地方

1. 读取方式怎么方便怎么来，并不必定要按照里面xml的格式，由于你们本身应用到工程中去每每不会是很是多的类别，单个对象的直接用txt就能够
2. 坐标的顺序我再说一次，要左上右下，而且x1必需要小于x2，这个是基本，反了会在坐标水平变换的时候会出错，坐标从0开始，若是已是0，则不须要再-1
3. GT的路径最好用相对，别用绝对，而后路径拼接的时候要注意，而后若是是txt是windows下生成的，注意斜杠的方向和编码的格式，中文路径编码必须用UTF-8无BOM格式，不能用windows自带的记事本直接换一种编码存储，相关数据集的编码问题参见个人另外一篇文章，linux传输乱码
4. 关于Mat文件，在训练时是将全部图像的OP都合在了一块儿，是一个很大的Mat文件，注意其中图像list的顺序千万不能错，而且坐标格式要修改成x1 y1 x2 y2，每种OP生成的坐标顺序要当心，从0开始仍是从1开始也要当心
5. 训练图像的大小不要太大，不然生成的OP也会太多，速度太慢，图像样本大小最好调整到500，600左右，而后再提取OP
6. 若是读取并生成pkl文件以后，实际数据内容或者顺序还有问题，记得要把data/cache/下面的pkl文件给删掉

关于下部训练和检测网络，我将在下一篇文章中说明