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def __init__(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 = os.environ['HOME'] + '/data/Object_Detection/pascal/VOCdevkit'
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())
def get_roidb(imdb_name):
imdb = get_imdb(imdb_name)
print('Loaded dataset `{:s}` for training'.format(imdb.name))
imdb.set_proposal_method(cfg.TRAIN.PROPOSAL_METHOD)
print('Set proposal method: {:s}'.format(cfg.TRAIN.PROPOSAL_METHOD))
roidb = get_training_roidb(imdb)
return roidb
roidbs = [get_roidb(s) for s in imdb_names.split('+')]
roidb = roidbs[0]
if len(roidbs) > 1:
for r in roidbs[1:]:
roidb.extend(r)
tmp = get_imdb(imdb_names.split('+')[1])
imdb = datasets.imdb.imdb(imdb_names, tmp.classes)
else:
imdb = get_imdb(imdb_names)
if training:
roidb = filter_roidb(roidb)
ratio_list, ratio_index = rank_roidb_ratio(roidb)
return imdb, roidb, ratio_list, ratio_index
def combined_roidb(imdb_names):
def get_roidb(imdb_name):
imdb = get_imdb(imdb_name)
print 'Loaded dataset `{:s}` for training'.format(imdb.name)
imdb.set_proposal_method(cfg.TRAIN.PROPOSAL_METHOD)
print 'Set proposal method: {:s}'.format(cfg.TRAIN.PROPOSAL_METHOD)
roidb = get_training_roidb(imdb)
return roidb
roidbs = [get_roidb(s) for s in imdb_names.split('+')]
roidb = roidbs[0]
if len(roidbs) > 1:
for r in roidbs[1:]:
roidb.extend(r)
imdb = datasets.imdb.imdb(imdb_names)
else:
imdb = get_imdb(imdb_names)
return imdb, roidb
def __init__(self, version, image_set, ):
imdb.__init__(self, 'vg_' + version + '_' + image_set)
self._version = version
self._image_set = image_set
self._data_path = os.path.join(cfg.DATA_DIR, 'genome')
self._img_path = os.path.join(cfg.DATA_DIR, 'vg')
# VG specific config options
self.config = {'cleanup' : False}
# Load classes
self._classes = ['__background__']
self._class_to_ind = {}
self._class_to_ind[self._classes[0]] = 0
with open(os.path.join(self._data_path, self._version, 'objects_vocab.txt')) as f:
count = 1
for object in f.readlines():
names = [n.lower().strip() for n in object.split(',')]
self._classes.append(names[0])
from datasets.imdb import imdb
import datasets.ds_utils as ds_utils
import xml.etree.ElementTree as ET
import numpy as np
import scipy.sparse
import scipy.io as sio
import model.utils.cython_bbox
import pickle
import subprocess
import uuid
from .voc_eval import voc_eval
from model.utils.config import cfg
import pdb
class pascal_voc(imdb):
def __init__(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(list(zip(self.classes, list(range(self.num_classes)))))
import datasets.ds_utils as ds_utils
import xml.etree.ElementTree as ET
import numpy as np
import scipy.sparse
import scipy.io as sio
import utils.cython_bbox
import cPickle
import subprocess
import uuid
from fast_rcnn.config import cfg
from PIL import Image
from fast_rcnn.icdar import *
DEBUG = False
class icdar(imdb):
def __init__(self, image_set, year, devkit_path=None):
imdb.__init__(self, 'icdar_' + 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, 'ICDAR' + self._year)
self._data_path = self._devkit_path
self._classes = ('__background__', # always index 0
'text')
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())
for ix, entry in enumerate(roidb):
overlaps = entry['gt_overlaps'].toarray()
crowd_inds = np.where(overlaps.max(axis=1) == -1)[0]
non_gt_inds = np.where(entry['gt_classes'] == 0)[0]
if len(crowd_inds) == 0 or len(non_gt_inds) == 0:
continue
iscrowd = [int(True) for _ in range(len(crowd_inds))]
crowd_boxes = ds_utils.xyxy_to_xywh(entry['boxes'][crowd_inds, :])
non_gt_boxes = ds_utils.xyxy_to_xywh(entry['boxes'][non_gt_inds, :])
ious = COCOmask.iou(non_gt_boxes, crowd_boxes, iscrowd)
bad_inds = np.where(ious.max(axis=1) > crowd_thresh)[0]
overlaps[non_gt_inds[bad_inds], :] = -1
roidb[ix]['gt_overlaps'] = scipy.sparse.csr_matrix(overlaps)
return roidb
class coco(imdb):
def __init__(self, image_set, year):
imdb.__init__(self, 'coco_' + year + '_' + image_set)
# COCO specific config options
self.config = {'top_k' : 2000,
'use_salt' : True,
'cleanup' : True,
'crowd_thresh' : 0.7,
'min_size' : 2}
# name, paths
self._year = year
self._image_set = image_set
self._data_path = osp.join(cfg.DATA_DIR, 'coco')
# load COCO API, classes, class <-> id mappings
self._COCO = COCO(self._get_ann_file())
cats = self._COCO.loadCats(self._COCO.getCatIds())
self._classes = tuple(['__background__'] + [c['name'] for c in cats])
def __init__(self, image_set, devkit_path, data_path):
imdb.__init__(self, image_set)
self._image_set = image_set
self._devkit_path = devkit_path
self._data_path = data_path
synsets_image = sio.loadmat(os.path.join(self._devkit_path, 'data', 'meta_det.mat'))
synsets_video = sio.loadmat(os.path.join(self._devkit_path, 'data', 'meta_vid.mat'))
self._classes_image = ('__background__',)
self._wnid_image = (0,)
self._classes = ('__background__',)
self._wnid = (0,)
for i in xrange(200):
self._classes_image = self._classes_image + (synsets_image['synsets'][0][i][2][0],)
self._wnid_image = self._wnid_image + (synsets_image['synsets'][0][i][1][0],)
for i in xrange(30):
from model.utils.config import cfg
import os.path as osp
import sys
import os
import numpy as np
import scipy.sparse
import scipy.io as sio
import pickle
import json
import uuid
# COCO API
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
from pycocotools import mask as COCOmask
class coco(imdb):
def __init__(self, image_set, year):
imdb.__init__(self, 'coco_' + year + '_' + image_set)
# COCO specific config options
self.config = {'use_salt': True,
'cleanup': True}
# name, paths
self._year = year
self._image_set = image_set
self._data_path = osp.join(cfg.DATA_DIR, 'coco')
# load COCO API, classes, class <-> id mappings
self._COCO = COCO(self._get_ann_file())
cats = self._COCO.loadCats(self._COCO.getCatIds())
# class name
self._classes = tuple(['__background__'] + [c['name'] for c in cats])
# class name to ind (0~80) 0= __background__