kitti.py

__author__ = 'yuxiang' # derived from honda.py by fyang

import datasets
import datasets.kitti
import os
import PIL
import datasets.imdb
import numpy as np
import scipy.sparse
from utils.cython_bbox import bbox_overlaps
from utils.boxes_grid import get_boxes_grid
import subprocess
import cPickle
from fast_rcnn.config import cfg
import math
from rpn_msr.generate_anchors import generate_anchors

class kitti(datasets.imdb):
    def __init__(self, image_set, kitti_path=None):
        datasets.imdb.__init__(self, 'kitti_' + image_set)
        self._image_set = image_set
        self._kitti_path = self._get_default_path() if kitti_path is None \
                            else kitti_path
        self._data_path = os.path.join(self._kitti_path, 'data_object_image_2')
        self._classes = ('__background__', 'Car', 'Pedestrian', 'Cyclist')
        self._class_to_ind = dict(zip(self.classes, xrange(self.num_classes)))
        self._image_ext = '.png'
        self._image_index = self._load_image_set_index()
        # Default to roidb handler
        if cfg.IS_RPN:
            self._roidb_handler = self.gt_roidb
        else:
            self._roidb_handler = self.region_proposal_roidb

        # num of subclasses
        if image_set == 'train' or image_set == 'val':
            self._num_subclasses = 125 + 24 + 24 + 1
            prefix = 'validation'
        else:
            self._num_subclasses = 227 + 36 + 36 + 1
            prefix = 'test'

        # load the mapping for subcalss to class
        filename = os.path.join(self._kitti_path, cfg.SUBCLS_NAME, prefix, 'mapping.txt')
        assert os.path.exists(filename), 'Path does not exist: {}'.format(filename)
        
        mapping = np.zeros(self._num_subclasses, dtype=np.int)
        with open(filename) as f:
            for line in f:
                words = line.split()
                subcls = int(words[0])
                mapping[subcls] = self._class_to_ind[words[1]]
        self._subclass_mapping = mapping

        self.config = {'top_k': 100000}

        # statistics for computing recall
        self._num_boxes_all = np.zeros(self.num_classes, dtype=np.int)
        self._num_boxes_covered = np.zeros(self.num_classes, dtype=np.int)
        self._num_boxes_proposal = 0

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

    def image_path_at(self, i):
        """
        Return the absolute path to image i in the image sequence.
        """
        return self.image_path_from_index(self.image_index[i])

    def image_path_from_index(self, index):
        """
        Construct an image path from the image's "index" identifier.
        """
        # set the prefix
        if self._image_set == 'test':
            prefix = 'testing/image_2'
        else:
            prefix = 'training/image_2'

        image_path = os.path.join(self._data_path, prefix, index + self._image_ext)
        assert os.path.exists(image_path), \
                'Path does not exist: {}'.format(image_path)
        return image_path

    def _load_image_set_index(self):
        """
        Load the indexes listed in this dataset's image set file.
        """
        image_set_file = os.path.join(self._kitti_path, 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.rstrip('\n') for x in f.readlines()]
        return image_index

    def _get_default_path(self):
        """
        Return the default path where KITTI is expected to be installed.
        """
        return os.path.join(datasets.ROOT_DIR, 'data', 'KITTI')


    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 + '_' + cfg.SUBCLS_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_kitti_voxel_exemplar_annotation(index)
                    for index in self.image_index]

        if cfg.IS_RPN:
            # print out recall
            for i in xrange(1, self.num_classes):
                print '{}: Total number of boxes {:d}'.format(self.classes[i], self._num_boxes_all[i])
                print '{}: Number of boxes covered {:d}'.format(self.classes[i], self._num_boxes_covered[i])
                print '{}: Recall {:f}'.format(self.classes[i], float(self._num_boxes_covered[i]) / float(self._num_boxes_all[i]))

        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


    def _load_kitti_annotation(self, index):
        """
        Load image and bounding boxes info from txt file in the KITTI format.
        """

        if self._image_set == 'test':
            lines = []
        else:
            filename = os.path.join(self._data_path, 'training', 'label_2', index + '.txt')
            lines = []
            with open(filename) as f:
                for line in f:
                    line = line.replace('Van', 'Car')
                    words = line.split()
                    cls = words[0]
                    truncation = float(words[1])
                    occlusion = int(words[2])
                    height = float(words[7]) - float(words[5])
                    if cls in self._class_to_ind and truncation < 0.5 and occlusion < 3 and height > 25:
                        lines.append(line)

        num_objs = len(lines)

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

        for ix, line in enumerate(lines):
            words = line.split()
            cls = self._class_to_ind[words[0]]
            boxes[ix, :] = [float(n) for n in words[4:8]]
            gt_classes[ix] = cls
            overlaps[ix, cls] = 1.0

        overlaps = scipy.sparse.csr_matrix(overlaps)
        gt_subclasses = np.zeros((num_objs), dtype=np.int32)
        gt_subclasses_flipped = np.zeros((num_objs), dtype=np.int32)
        subindexes = np.zeros((num_objs, self.num_classes), dtype=np.int32)
        subindexes_flipped = np.zeros((num_objs, self.num_classes), dtype=np.int32)
        subindexes = scipy.sparse.csr_matrix(subindexes)
        subindexes_flipped = scipy.sparse.csr_matrix(subindexes_flipped)

        if cfg.IS_RPN:
            if cfg.IS_MULTISCALE:
                # compute overlaps between grid boxes and gt boxes in multi-scales
                # rescale the gt boxes
                boxes_all = np.zeros((0, 4), dtype=np.float32)
                for scale in cfg.TRAIN.SCALES:
                    boxes_all = np.vstack((boxes_all, boxes * scale))
                gt_classes_all = np.tile(gt_classes, len(cfg.TRAIN.SCALES))

                # compute grid boxes
                s = PIL.Image.open(self.image_path_from_index(index)).size
                image_height = s[1]
                image_width = s[0]
                boxes_grid, _, _ = get_boxes_grid(image_height, image_width)

                # compute overlap
                overlaps_grid = bbox_overlaps(boxes_grid.astype(np.float), boxes_all.astype(np.float))
        
                # check how many gt boxes are covered by grids
                if num_objs != 0:
                    index = np.tile(range(num_objs), len(cfg.TRAIN.SCALES))
                    max_overlaps = overlaps_grid.max(axis = 0)
                    fg_inds = []
                    for k in xrange(1, self.num_classes):
                        fg_inds.extend(np.where((gt_classes_all == k) & (max_overlaps >= cfg.TRAIN.FG_THRESH[k-1]))[0])
                    index_covered = np.unique(index[fg_inds])

                    for i in xrange(self.num_classes):
                        self._num_boxes_all[i] += len(np.where(gt_classes == i)[0])
                        self._num_boxes_covered[i] += len(np.where(gt_classes[index_covered] == i)[0])
            else:
                assert len(cfg.TRAIN.SCALES_BASE) == 1
                scale = cfg.TRAIN.SCALES_BASE[0]
                feat_stride = 16
                # faster rcnn region proposal
                anchors = generate_anchors()
                num_anchors = anchors.shape[0]

                # image size
                s = PIL.Image.open(self.image_path_from_index(index)).size
                image_height = s[1]
                image_width = s[0]

                # height and width of the heatmap
                height = np.round((image_height * scale - 1) / 4.0 + 1)
                height = np.floor((height - 1) / 2 + 1 + 0.5)
                height = np.floor((height - 1) / 2 + 1 + 0.5)

                width = np.round((image_width * scale - 1) / 4.0 + 1)
                width = np.floor((width - 1) / 2.0 + 1 + 0.5)
                width = np.floor((width - 1) / 2.0 + 1 + 0.5)

                # gt boxes
                gt_boxes = boxes * scale

                # 1. Generate proposals from bbox deltas and shifted anchors
                shift_x = np.arange(0, width) * feat_stride
                shift_y = np.arange(0, height) * feat_stride
                shift_x, shift_y = np.meshgrid(shift_x, shift_y)
                shifts = np.vstack((shift_x.ravel(), shift_y.ravel(),
                            shift_x.ravel(), shift_y.ravel())).transpose()
                # add A anchors (1, A, 4) to
                # cell K shifts (K, 1, 4) to get
                # shift anchors (K, A, 4)
                # reshape to (K*A, 4) shifted anchors
                A = num_anchors
                K = shifts.shape[0]
                all_anchors = (anchors.reshape((1, A, 4)) + shifts.reshape((1, K, 4)).transpose((1, 0, 2)))
                all_anchors = all_anchors.reshape((K * A, 4))

                # compute overlap
                overlaps_grid = bbox_overlaps(all_anchors.astype(np.float), gt_boxes.astype(np.float))
        
                # check how many gt boxes are covered by anchors
                if num_objs != 0:
                    max_overlaps = overlaps_grid.max(axis = 0)
                    fg_inds = []
                    for k in xrange(1, self.num_classes):
                        fg_inds.extend(np.where((gt_classes == k) & (max_overlaps >= cfg.TRAIN.FG_THRESH[k-1]))[0])

                    for i in xrange(self.num_classes):
                        self._num_boxes_all[i] += len(np.where(gt_classes == i)[0])
                        self._num_boxes_covered[i] += len(np.where(gt_classes[fg_inds] == i)[0])

        return {'boxes' : boxes,
                'gt_classes': gt_classes,
                'gt_subclasses': gt_subclasses,
                'gt_subclasses_flipped': gt_subclasses_flipped,
                'gt_overlaps' : overlaps,
                'gt_subindexes': subindexes,
                'gt_subindexes_flipped': subindexes_flipped,
                'flipped' : False}

    def _load_kitti_voxel_exemplar_annotation(self, index):
        """
        Load image and bounding boxes info from txt file in the KITTI voxel exemplar format.
        """
        if self._image_set == 'train':
            prefix = 'validation'
        elif self._image_set == 'trainval':
            prefix = 'test'
        else:
            return self._load_kitti_annotation(index)

        filename = os.path.join(self._kitti_path, cfg.SUBCLS_NAME, prefix, index + '.txt')
        assert os.path.exists(filename), \
                'Path does not exist: {}'.format(filename)

        # the annotation file contains flipped objects    
        lines = []
        lines_flipped = []
        with open(filename) as f:
            for line in f:
                words = line.split()
                subcls = int(words[1])
                is_flip = int(words[2])
                if subcls != -1:
                    if is_flip == 0:
                        lines.append(line)
                    else:
                        lines_flipped.append(line)
        
        num_objs = len(lines)

        # store information of flipped objects
        assert (num_objs == len(lines_flipped)), 'The number of flipped objects is not the same!'
        gt_subclasses_flipped = np.zeros((num_objs), dtype=np.int32)
        
        for ix, line in enumerate(lines_flipped):
            words = line.split()
            subcls = int(words[1])
            gt_subclasses_flipped[ix] = subcls

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

        for ix, line in enumerate(lines):
            words = line.split()
            cls = self._class_to_ind[words[0]]
            subcls = int(words[1])
            boxes[ix, :] = [float(n) for n in words[3:7]]
            gt_classes[ix] = cls
            gt_subclasses[ix] = subcls
            overlaps[ix, cls] = 1.0
            subindexes[ix, cls] = subcls
            subindexes_flipped[ix, cls] = gt_subclasses_flipped[ix]

        overlaps = scipy.sparse.csr_matrix(overlaps)
        subindexes = scipy.sparse.csr_matrix(subindexes)
        subindexes_flipped = scipy.sparse.csr_matrix(subindexes_flipped)

        if cfg.IS_RPN:
            if cfg.IS_MULTISCALE:
                # compute overlaps between grid boxes and gt boxes in multi-scales
                # rescale the gt boxes
                boxes_all = np.zeros((0, 4), dtype=np.float32)
                for scale in cfg.TRAIN.SCALES:
                    boxes_all = np.vstack((boxes_all, boxes * scale))
                gt_classes_all = np.tile(gt_classes, len(cfg.TRAIN.SCALES))

                # compute grid boxes
                s = PIL.Image.open(self.image_path_from_index(index)).size
                image_height = s[1]
                image_width = s[0]
                boxes_grid, _, _ = get_boxes_grid(image_height, image_width)

                # compute overlap
                overlaps_grid = bbox_overlaps(boxes_grid.astype(np.float), boxes_all.astype(np.float))
        
                # check how many gt boxes are covered by grids
                if num_objs != 0:
                    index = np.tile(range(num_objs), len(cfg.TRAIN.SCALES))
                    max_overlaps = overlaps_grid.max(axis = 0)
                    fg_inds = []
                    for k in xrange(1, self.num_classes):
                        fg_inds.extend(np.where((gt_classes_all == k) & (max_overlaps >= cfg.TRAIN.FG_THRESH[k-1]))[0])
                    index_covered = np.unique(index[fg_inds])

                    for i in xrange(self.num_classes):
                        self._num_boxes_all[i] += len(np.where(gt_classes == i)[0])
                        self._num_boxes_covered[i] += len(np.where(gt_classes[index_covered] == i)[0])
            else:
                assert len(cfg.TRAIN.SCALES_BASE) == 1
                scale = cfg.TRAIN.SCALES_BASE[0]
                feat_stride = 16
                # faster rcnn region proposal
                base_size = 16
                ratios = [3.0, 2.0, 1.5, 1.0, 0.75, 0.5, 0.25]
                scales = 2**np.arange(1, 6, 0.5)
                anchors = generate_anchors(base_size, ratios, scales)
                num_anchors = anchors.shape[0]

                # image size
                s = PIL.Image.open(self.image_path_from_index(index)).size
                image_height = s[1]
                image_width = s[0]

                # height and width of the heatmap
                height = np.round((image_height * scale - 1) / 4.0 + 1)
                height = np.floor((height - 1) / 2 + 1 + 0.5)
                height = np.floor((height - 1) / 2 + 1 + 0.5)

                width = np.round((image_width * scale - 1) / 4.0 + 1)
                width = np.floor((width - 1) / 2.0 + 1 + 0.5)
                width = np.floor((width - 1) / 2.0 + 1 + 0.5)

                # gt boxes
                gt_boxes = boxes * scale

                # 1. Generate proposals from bbox deltas and shifted anchors
                shift_x = np.arange(0, width) * feat_stride
                shift_y = np.arange(0, height) * feat_stride
                shift_x, shift_y = np.meshgrid(shift_x, shift_y)
                shifts = np.vstack((shift_x.ravel(), shift_y.ravel(),
                            shift_x.ravel(), shift_y.ravel())).transpose()
                # add A anchors (1, A, 4) to
                # cell K shifts (K, 1, 4) to get
                # shift anchors (K, A, 4)
                # reshape to (K*A, 4) shifted anchors
                A = num_anchors
                K = shifts.shape[0]
                all_anchors = (anchors.reshape((1, A, 4)) + shifts.reshape((1, K, 4)).transpose((1, 0, 2)))
                all_anchors = all_anchors.reshape((K * A, 4))

                # compute overlap
                overlaps_grid = bbox_overlaps(all_anchors.astype(np.float), gt_boxes.astype(np.float))
        
                # check how many gt boxes are covered by anchors
                if num_objs != 0:
                    max_overlaps = overlaps_grid.max(axis = 0)
                    fg_inds = []
                    for k in xrange(1, self.num_classes):
                        fg_inds.extend(np.where((gt_classes == k) & (max_overlaps >= cfg.TRAIN.FG_THRESH[k-1]))[0])

                    for i in xrange(self.num_classes):
                        self._num_boxes_all[i] += len(np.where(gt_classes == i)[0])
                        self._num_boxes_covered[i] += len(np.where(gt_classes[fg_inds] == i)[0])

        return {'boxes' : boxes,
                'gt_classes': gt_classes,
                'gt_subclasses': gt_subclasses,
                'gt_subclasses_flipped': gt_subclasses_flipped,
                'gt_overlaps': overlaps,
                'gt_subindexes': subindexes, 
                'gt_subindexes_flipped': subindexes_flipped, 
                'flipped' : False}

    def region_proposal_roidb(self):
        """
        Return the database of 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 + '_' + cfg.SUBCLS_NAME + '_' + cfg.REGION_PROPOSAL + '_region_proposal_roidb.pkl')

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

        if self._image_set != 'test':
            gt_roidb = self.gt_roidb()

            print 'Loading region proposal network boxes...'
            if self._image_set == 'trainval':
                model = cfg.REGION_PROPOSAL + '_227/'
            else:
                model = cfg.REGION_PROPOSAL + '_125/'
            rpn_roidb = self._load_rpn_roidb(gt_roidb, model)
            print 'Region proposal network boxes loaded'
            roidb = datasets.imdb.merge_roidbs(rpn_roidb, gt_roidb)

            # print 'Loading voxel pattern boxes...'
            # if self._image_set == 'trainval':
            #    model = '3DVP_227'
            # else:
            #    model = '3DVP_125/'
            # vp_roidb = self._load_voxel_pattern_roidb(gt_roidb, model)
            # print 'Voxel pattern boxes loaded'
            # roidb = datasets.imdb.merge_roidbs(vp_roidb, gt_roidb)

            # print 'Loading selective search boxes...'
            # ss_roidb = self._load_selective_search_roidb(gt_roidb)
            # print 'Selective search boxes loaded'

            # print 'Loading ACF boxes...'
            # acf_roidb = self._load_acf_roidb(gt_roidb)
            # print 'ACF boxes loaded'

            # roidb = datasets.imdb.merge_roidbs(ss_roidb, gt_roidb)
            # roidb = datasets.imdb.merge_roidbs(roidb, acf_roidb)
        else:
            print 'Loading region proposal network boxes...'
            model = cfg.REGION_PROPOSAL + '_227/'
            roidb = self._load_rpn_roidb(None, model)
            print 'Region proposal network boxes loaded'

            # print 'Loading voxel pattern boxes...'
            # model = '3DVP_227/'
            # roidb = self._load_voxel_pattern_roidb(None, model)
            # print 'Voxel pattern boxes loaded'

            # print 'Loading selective search boxes...'
            # roidb = self._load_selective_search_roidb(None)
            # print 'Selective search boxes loaded'

            # print 'Loading ACF boxes...'
            # acf_roidb = self._load_acf_roidb(None)
            # print 'ACF boxes loaded'

            # roidb = datasets.imdb.merge_roidbs(roidb, acf_roidb)
        print '{} region proposals per image'.format(self._num_boxes_proposal / len(self.image_index))

        with open(cache_file, 'wb') as fid:
            cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL)
        print 'wrote roidb to {}'.format(cache_file)

        return roidb

    def _load_rpn_roidb(self, gt_roidb, model):
        # set the prefix
        if self._image_set == 'test':
            prefix = model + 'testing'
        else:
            prefix = model + 'training'

        box_list = []
        for index in self.image_index:
            filename = os.path.join(self._kitti_path, 'region_proposals',  prefix, index + '.txt')
            assert os.path.exists(filename), \
                'RPN data not found at: {}'.format(filename)
            raw_data = np.loadtxt(filename, dtype=float)
            if len(raw_data.shape) == 1:
                if raw_data.size == 0:
                    raw_data = raw_data.reshape((0, 5))
                else:
                    raw_data = raw_data.reshape((1, 5))

            x1 = raw_data[:, 0]
            y1 = raw_data[:, 1]
            x2 = raw_data[:, 2]
            y2 = raw_data[:, 3]
            score = raw_data[:, 4]
            inds = np.where((x2 > x1 + 1) & (y2 > y1 + 1))[0]
            raw_data = raw_data[inds,:4]
            self._num_boxes_proposal += raw_data.shape[0]
            box_list.append(raw_data)
            print 'load {}: {}'.format(model, index)

        return self.create_roidb_from_box_list(box_list, gt_roidb)

    def _load_voxel_pattern_roidb(self, gt_roidb, model):
        # set the prefix
        if self._image_set == 'test':
            prefix = model + 'testing'
        else:
            prefix = model + 'training'

        box_list = []
        for index in self.image_index:
            filename = os.path.join(self._kitti_path, 'region_proposals',  prefix, index + '.txt')
            assert os.path.exists(filename), \
                'Voxel pattern data not found at: {}'.format(filename)
            raw_data = np.loadtxt(filename, dtype=float)
            if len(raw_data.shape) == 1:
                if raw_data.size == 0:
                    raw_data = raw_data.reshape((0, 4))
                else:
                    raw_data = raw_data.reshape((1, 4))
            self._num_boxes_proposal += raw_data.shape[0]
            box_list.append(raw_data)

        return self.create_roidb_from_box_list(box_list, gt_roidb)

    def _load_selective_search_roidb(self, gt_roidb):
        cache_file = os.path.join(self.cache_path,
                                  self.name + '_selective_search_box_list.pkl')

        if os.path.exists(cache_file):
            with open(cache_file, 'rb') as fid:
                box_list = cPickle.load(fid)
            print '{} boxes loaded from {}'.format(self.name, cache_file)
        else:
            # set the prefix
            model = 'selective_search/'
            if self._image_set == 'test':
                prefix = model + 'testing'
            else:
                prefix = model + 'training'

            box_list = []
            for index in self.image_index:
                filename = os.path.join(self._kitti_path, 'region_proposals', prefix, index + '.txt')
                assert os.path.exists(filename), \
                    'Selective search data not found at: {}'.format(filename)
                raw_data = np.loadtxt(filename, dtype=float)
                box_list.append(raw_data[:min(self.config['top_k'], raw_data.shape[0]), 1:])

            with open(cache_file, 'wb') as fid:
                cPickle.dump(box_list, fid, cPickle.HIGHEST_PROTOCOL)
            print 'wrote selective search boxes to {}'.format(cache_file)

        return self.create_roidb_from_box_list(box_list, gt_roidb)

    def _load_acf_roidb(self, gt_roidb):
        cache_file = os.path.join(self.cache_path,
                                  self.name + '_acf_box_list.pkl')

        if os.path.exists(cache_file):
            with open(cache_file, 'rb') as fid:
                box_list = cPickle.load(fid)
            print '{} boxes loaded from {}'.format(self.name, cache_file)
        else:
            # set the prefix
            model = 'ACF/'
            if self._image_set == 'test':
                prefix = model + 'testing'
            else:
                prefix = model + 'training'

            box_list = []
            for index in self.image_index:
                filename = os.path.join(self._kitti_path, 'region_proposals', prefix, index + '.txt')
                assert os.path.exists(filename), \
                    'ACF data not found at: {}'.format(filename)
                raw_data = np.loadtxt(filename, usecols=(2,3,4,5), dtype=float)
                box_list.append(raw_data[:min(self.config['top_k'], raw_data.shape[0]), :])

            with open(cache_file, 'wb') as fid:
                cPickle.dump(box_list, fid, cPickle.HIGHEST_PROTOCOL)
            print 'wrote ACF boxes to {}'.format(cache_file)

        return self.create_roidb_from_box_list(box_list, gt_roidb)

    def evaluate_detections(self, all_boxes, output_dir):
        # load the mapping for subcalss the alpha (viewpoint)
        if self._image_set == 'val':
            prefix = 'validation'
        elif self._image_set == 'test':
            prefix = 'test'
        else:
            prefix = ''

        filename = os.path.join(self._kitti_path, cfg.SUBCLS_NAME, prefix, 'mapping.txt')
        assert os.path.exists(filename), \
                'Path does not exist: {}'.format(filename)

        mapping = np.zeros(self._num_subclasses, dtype=np.float)
        with open(filename) as f:
            for line in f:
                words = line.split()
                subcls = int(words[0])
                mapping[subcls] = float(words[3])

        # for each image
        for im_ind, index in enumerate(self.image_index):
            filename = os.path.join(output_dir, index + '.txt')
            print 'Writing KITTI results to file ' + filename
            with open(filename, 'wt') as f:
                # for each class
                for cls_ind, cls in enumerate(self.classes):
                    if cls == '__background__':
                        continue
                    dets = all_boxes[cls_ind][im_ind]
                    if dets == []:
                        continue
                    for k in xrange(dets.shape[0]):
                        if cfg.TEST.SUBCLS:
                            subcls = int(dets[k, 5])
                            cls_name = self.classes[self.subclass_mapping[subcls]]
                            assert (cls_name == cls), 'subclass not in class'
                            alpha = mapping[subcls]
                        else:
                            alpha = -10
                        f.write('{:s} -1 -1 {:f} {:f} {:f} {:f} {:f} -1 -1 -1 -1 -1 -1 -1 {:.32f}\n'.format(\
                                 cls, alpha, dets[k, 0], dets[k, 1], dets[k, 2], dets[k, 3], dets[k, 4]))

    # write detection results into one file
    def evaluate_detections_one_file(self, all_boxes, output_dir):
        # open results file
        filename = os.path.join(output_dir, 'detections.txt')
        print 'Writing all KITTI results to file ' + filename
        with open(filename, 'wt') as f:
            # for each image
            for im_ind, index in enumerate(self.image_index):
                # for each class
                for cls_ind, cls in enumerate(self.classes):
                    if cls == '__background__':
                        continue
                    dets = all_boxes[cls_ind][im_ind]
                    if dets == []:
                        continue
                    for k in xrange(dets.shape[0]):
                        if cfg.TEST.SUBCLS:
                            subcls = int(dets[k, 5])
                            cls_name = self.classes[self.subclass_mapping[subcls]]
                            assert (cls_name == cls), 'subclass not in class'
                        else:
                            subcls = -1
                        f.write('{:s} {:s} {:f} {:f} {:f} {:f} {:d} {:f}\n'.format(\
                                 index, cls, dets[k, 0], dets[k, 1], dets[k, 2], dets[k, 3], subcls, dets[k, 4]))

    def evaluate_proposals(self, all_boxes, output_dir):
        # for each image
        for im_ind, index in enumerate(self.image_index):
            filename = os.path.join(output_dir, index + '.txt')
            print 'Writing KITTI results to file ' + filename
            with open(filename, 'wt') as f:
                # for each class
                for cls_ind, cls in enumerate(self.classes):
                    if cls == '__background__':
                        continue
                    dets = all_boxes[cls_ind][im_ind]
                    if dets == []:
                        continue
                    for k in xrange(dets.shape[0]):
                        f.write('{:f} {:f} {:f} {:f} {:.32f}\n'.format(\
                                 dets[k, 0], dets[k, 1], dets[k, 2], dets[k, 3], dets[k, 4]))

    def evaluate_proposals_msr(self, all_boxes, output_dir):
        # for each image
        for im_ind, index in enumerate(self.image_index):
            filename = os.path.join(output_dir, index + '.txt')
            print 'Writing KITTI results to file ' + filename
            with open(filename, 'wt') as f:
                dets = all_boxes[im_ind]
                if dets == []:
                    continue
                for k in xrange(dets.shape[0]):
                    f.write('{:f} {:f} {:f} {:f} {:.32f}\n'.format(dets[k, 0], dets[k, 1], dets[k, 2], dets[k, 3], dets[k, 4]))


if __name__ == '__main__':
    d = datasets.kitti('train')
    res = d.roidb
    from IPython import embed; embed()