def (self, image_set, devkit_path=None):     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image_path_from_index(self, index):作为其具体实现。        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，根据ImageSet/Main/文件夹下的文件进行image_index的加载。    def _get_default_path(self): # 获得数据集地址def gt_roidb(self): # 读取并返回ground_truth的db    def rpn_roidb(self): # 加载rpn产生的roi，调用_load_rpn_roidb(self, gt_roidb):函数作为其具体实现    def _load_rpn_roidb(self, gt_roidb): # 加载rpn_file    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): # 将voc的检测结果写入到文件    def _do_python_eval(self, output_dir = 'output'): # 根据python的evluation接口来做结果的分析

def (self, image_set, year, devkit_path=None):        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        self._salt = str(uuid.uuid4())        self._comp_id = 'comp4'        # PASCAL specific config options        self.config = {'cleanup'     : True,                       'use_salt'    : True,                       'use_diff'    : False,                       'matlab_eval' : False,                       'rpn_file'    : None,                       'min_size'    : 2}        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)

def (self, image_set, devkit_path=None):# initial function，把year删除        imdb.__init__(self, image_set) # imageset is train.txt or test.txt        self._image_set = image_set        self._devkit_path = devkit_path # devkit_path = '~/py-faster-rcnn/data/VOCdevkit'        self._data_path = os.path.join(self._devkit_path, 'Caltech') # _data_path = '~/py-faster-rcnn/data/VOCdevkit/Caltech'        self._classes = ('__background__', # always index 0                         'person') # 我只有‘background’和‘person’两类        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        self._salt = str(uuid.uuid4())        self._comp_id = 'comp4'        # PASCAL specific config options        self.config = {'cleanup'     : True,                       'use_salt'    : True,                       'use_diff'    : True, # 我把use_diff改为true了，因为我的数据集xml文件中没有<difficult>标签，否则之后训练会报错                       'matlab_eval' : False,                       'rpn_file'    : None,                       'min_size'    : 2}        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)

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

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        # /home/jk/py-faster-rcnn/data/VOCdevkit/Caltech/ImageSets/Main/train.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

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')        tree = ET.parse(filename)        objs = tree.findall('object')        if not self.config['use_diff']:            # Exclude the samples labeled as difficult            non_diff_objs = [                obj for obj in objs if int(obj.find('difficult').text) == 0]            # if len(non_diff_objs) != len(objs):            #     print 'Removed {} difficult objects'.format(            #         len(objs) - len(non_diff_objs))            objs = non_diff_objs        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)        # "Seg" area for pascal is just the box area        seg_areas = np.zeros((num_objs), dtype=np.float32)        # Load object bounding boxes into a data frame.        for ix, obj in enumerate(objs):            bbox = obj.find('bndbox')            # Make pixel indexes 0-based            x1 = float(bbox.find('xmin').text) - 1            y1 = float(bbox.find('ymin').text) - 1            x2 = float(bbox.find('xmax').text) - 1            y2 = float(bbox.find('ymax').text) - 1            cls = self._class_to_ind[obj.find('name').text.lower().strip()]            boxes[ix, :] = [x1, y1, x2, y2]            gt_classes[ix] = cls            overlaps[ix, cls] = 1.0            seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1)        overlaps = scipy.sparse.csr_matrix(overlaps)        return {'boxes' : boxes,                'gt_classes': gt_classes,                'gt_overlaps' : overlaps,                'flipped' : False,                'seg_areas' : seg_areas}

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')        tree = ET.parse(filename)        objs = tree.findall('object')        if not self.config['use_diff']:            # Exclude the samples labeled as difficult            non_diff_objs = [                obj for obj in objs if int(obj.find('difficult').text) == 0]            # if len(non_diff_objs) != len(objs):            #     print 'Removed {} difficult objects'.format(            #         len(objs) - len(non_diff_objs))            objs = non_diff_objs        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)        # "Seg" area for pascal is just the box area        seg_areas = np.zeros((num_objs), dtype=np.float32)        # Load object bounding boxes into a data frame.        for ix, obj in enumerate(objs):            bbox = obj.find('bndbox')            # Make pixel indexes 0-based            # 这里我把‘-1’全部删除掉了，防止有的数据是0开始，然后‘-1’导致变为负数，产生AssertError错误            x1 = float(bbox.find('xmin').text)            y1 = float(bbox.find('ymin').text)            x2 = float(bbox.find('xmax').text)            y2 = float(bbox.find('ymax').text)            cls = self._class_to_ind[obj.find('name').text.lower().strip()]            boxes[ix, :] = [x1, y1, x2, y2]            gt_classes[ix] = cls            overlaps[ix, cls] = 1.0            seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1)        overlaps = scipy.sparse.csr_matrix(overlaps)        return {'boxes' : boxes,                'gt_classes': gt_classes,                'gt_overlaps' : overlaps,                'flipped' : False,                'seg_areas' : seg_areas}

if __name__ == '__main__':    from datasets.pascal_voc import pascal_voc    d = pascal_voc('trainval', '2007')    res = d.roidb    from IPython import embed; embed()

if __name__ == '__main__':    from datasets.caltech import caltech # 导入caltech包    d = caltech('train', '/home/jk/py-faster-rcnn/data/VOCdevkit')#调用构造函数，传入imageset和路径    res = d.roidb    from IPython import embed; embed()

# --------------------------------------------------------# Fast R-CNN# Copyright (c) 2015 Microsoft# Licensed under The MIT License [see LICENSE for details]# Written by Ross Girshick# --------------------------------------------------------"""Factory method for easily getting imdbs by name."""__sets = {}from datasets.pascal_voc import pascal_vocfrom datasets.coco import cocoimport numpy as np# Set up voc_<year>_<split> using selective search "fast" modefor year in ['2007', '2012']:    for split in ['train', 'val', 'trainval', 'test']:        name = 'voc_{}_{}'.format(year, split)        __sets[name] = (lambda split=split, year=year: pascal_voc(split, year))# Set up coco_2014_<split>for year in ['2014']:    for split in ['train', 'val', 'minival', 'valminusminival']:        name = 'coco_{}_{}'.format(year, split)        __sets[name] = (lambda split=split, year=year: coco(split, year))# Set up coco_2015_<split>for year in ['2015']:    for split in ['test', 'test-dev']:        name = 'coco_{}_{}'.format(year, split)        __sets[name] = (lambda split=split, year=year: coco(split, year))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()

# --------------------------------------------------------# Fast R-CNN# Copyright (c) 2015 Microsoft# Licensed under The MIT License [see LICENSE for details]# Written by Ross Girshick# --------------------------------------------------------"""Factory method for easily getting imdbs by name."""__sets = {}from datasets.caltech import caltech # 导入caltech包#from datasets.coco import coco#import numpy as npdevkit = '/home/jk/py-faster-rcnn/data/VOCdevkit'# 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: pascal_voc(split, year))# Set up coco_2014_<split>#for year in ['2014']:#    for split in ['train', 'val', 'minival', 'valminusminival']:#        name = 'coco_{}_{}'.format(year, split)#        __sets[name] = (lambda split=split, year=year: coco(split, year))# Set up coco_2015_<split>#for year in ['2015']:#    for split in ['test', 'test-dev']:#        name = 'coco_{}_{}'.format(year, split)#        __sets[name] = (lambda split=split, year=year: coco(split, year))# Set up caltech_<split>for split in ['train', 'test']:    name = 'caltech_{}'.format(split)    __sets[name] = (lambda imageset=split, devkit=devkit: caltech(imageset, devkit))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()