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 = pascal_voc(split, year)
imdb.roidb_handler = imdb.selective_search_IJCV_roidb
imdb.config["top_k"] = top_k
return imdb
# --------------------------------------------------------
"""Factory method for easily getting imdbs by name."""
__sets = {}
from datasets.pascal_voc import pascal_voc
from datasets.coco import coco
from datasets.grotoap2 import grotoap2
import numpy as np
# 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 grotoap2
for split in ['train', 'val', 'trainval', 'test']:
def evaluate_detections(self, all_boxes, output_dir):
self._write_voc_results_file(all_boxes)
self._do_python_eval(output_dir)
if self.config['matlab_eval']:
self._do_matlab_eval(output_dir)
if self.config['cleanup']:
for cls in self._classes:
if cls == '__background__':
continue
filename = self._get_voc_results_file_template().format(cls)
os.remove(filename)
def competition_mode(self, on):
if on:
self.config['use_salt'] = False
self.config['cleanup'] = False
else:
self.config['use_salt'] = True
self.config['cleanup'] = True
if __name__ == '__main__':
from datasets.pascal_voc import pascal_voc
d = pascal_voc('trainval', '2007')
res = d.roidb
from IPython import embed;
embed()
def evaluate_detections(self, all_boxes, output_dir):
self._write_voc_results_file(all_boxes)
self._do_python_eval(output_dir)
if self.config["matlab_eval"]:
self._do_matlab_eval(output_dir)
if self.config["cleanup"]:
for cls in self._classes:
if cls == "__background__":
continue
filename = self._get_voc_results_file_template().format(cls)
os.remove(filename)
def competition_mode(self, on):
if on:
self.config["use_salt"] = False
self.config["cleanup"] = False
else:
self.config["use_salt"] = True
self.config["cleanup"] = True
if __name__ == "__main__":
from datasets.pascal_voc import pascal_voc
d = pascal_voc("trainval", "2007")
res = d.roidb
from IPython import embed
embed()
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 = 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','1207']:
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 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','1207']:
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]()
"""Factory method for easily getting imdbs by name."""
__sets = {}
from datasets.pascal_voc import pascal_voc
from datasets.coco import coco
import numpy as np
# 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))
__sets["ak47_train"] = (lambda split='train', year='ak47': pascal_voc(split, year))
__sets["ships_train"] = (lambda split='train', year='ships': 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))
# Set up coco_2014_<split>
# --------------------------------------------------------
"""Factory method for easily getting imdbs by name."""
__sets = {}
from datasets.pascal_voc import pascal_voc
from datasets.coco import coco
from datasets.imagenet import imagenet
from datasets.our_dataset import our_dataset
import numpy as np
# Set up voc_<year>_<split> using selective search "fast" mode
for year in ['2007', '2012', '0712']:
for split in ['train', 'val', 'trainval', 'test']:
name = 'voc_{}_{}'.format(year, split)
__sets[name] = (lambda split=split, year=year: pascal_voc(split, year))
for split in ['train', 'val']:
name = 'imagenet_{}'.format(split)
devkit_path = '/scratch0/ILSVRC/devkit/'
data_path = '/scratch0/ILSVRC2015/'
__sets[name] = (lambda split=split, devkit_path=devkit_path, data_path=
data_path: imagenet(split, devkit_path, data_path))
print name
print __sets[name]
# for our dataset
__sets['our_dataset'] = (
lambda image_set='our_dataset', data_path=
'/sdb/duyong/Datasets/objDetData/train/samples', img_list=
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
__sets = {}
from datasets.pascal_voc import pascal_voc
from datasets.icpr import icpr_imdb
#from datasets.coco import coco
import numpy as np
# Set up voc_<year>_<split>
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))
for year in ['2007', '2012']:
for split in ['train', 'val', 'trainval', 'test']:
name = 'voc_{}_{}_diff'.format(year, split)
__sets[name] = (lambda split=split, year=year: pascal_voc(split, year, use_diff=True))
# Set up icpr_<year>_<split>
for year in ['2018']:
for split in ['trainval', 'test']:
name = 'icpr_{}_{}'.format(year, split)
__sets[name] = (lambda split=split, year=year: icpr_imdb(split, year, use_diff = False))
# Set up coco_2014_<split>
for year in ['2014']:
for split in ['train', 'val', 'minival', 'valminusminival', 'trainval']:
.format(self._devkit_path, self._get_comp_id(),
self._image_set, output_dir)
print('Running:\n{}'.format(cmd))
status = subprocess.call(cmd, shell=True)
def evaluate_detections(self, all_boxes, output_dir):
self._write_voc_results_file(all_boxes)
self._do_python_eval(output_dir)
if self.config['matlab_eval']:
self._do_matlab_eval(output_dir)
if self.config['cleanup']:
for cls in self._classes:
if cls == '__background__':
continue
filename = self._get_voc_results_file_template().format(cls)
os.remove(filename)
def competition_mode(self, on):
if on:
self.config['use_salt'] = False
self.config['cleanup'] = False
else:
self.config['use_salt'] = True
self.config['cleanup'] = True
if __name__ == '__main__':
from datasets.pascal_voc import pascal_voc
d = pascal_voc('train', '2007')
res = d.roidb
from IPython import embed; embed()
print('Running:\n{}'.format(cmd))
status = subprocess.call(cmd, shell=True)
def evaluate_detections(self, all_boxes, output_dir):
self._write_voc_results_file(all_boxes)
self._do_python_eval(output_dir)
if self.config['matlab_eval']:
self._do_matlab_eval(output_dir)
if self.config['cleanup']:
for cls in self._classes:
if cls == '__background__':
continue
filename = self._get_voc_results_file_template().format(cls)
os.remove(filename)
def competition_mode(self, on):
if on:
self.config['use_salt'] = False
self.config['cleanup'] = False
else:
self.config['use_salt'] = True
self.config['cleanup'] = True
if __name__ == '__main__':
from datasets.pascal_voc import pascal_voc
d = pascal_voc('trainval', '2007')
res = d.roidb
from IPython import embed
embed()
# --------------------------------------------------------
"""Factory method for easily getting imdbs by name."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
__sets = {}
from datasets.pascal_voc import pascal_voc
from datasets.coco import coco
import numpy as np
# Set up voc_<year>_<split>
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))
for year in ['2007', '2012']:
for split in ['train', 'val', 'trainval', 'test']:
name = 'voc_{}_{}_diff'.format(year, split)
__sets[name] = (lambda split=split, year=year: pascal_voc(
split, year, use_diff=True))
# Set up coco_2014_<split>
for year in ['2014']:
for split in ['train', 'val', 'minival', 'valminusminival', 'trainval']:
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']:
def __init__(self, image_set, year, devkit_path=None, shuffled=None):
imdb.__init__(self, 'merged_' + image_set)
self._year = year
self._image_set = image_set
self._anno_set_dir = image_set
if "val" in image_set:
self._image_set_dir = "val"
if "val1" in image_set:
self._anno_set_dir = "val1"
if "val2" in image_set:
self._anno_set_dir = "val2"
elif "train" in image_set:
self._anno_set_dir = "train"
elif "test" in image_set:
self._anno_set_dir = "test"
if image_set == "train":
self.imdbs = [imagenet(image_set),\
coco(image_set, '2015'),\
cam2(image_set,'2017'),\
sun(image_set,'2012'),\
caltech(image_set,'2009'),\
kitti(image_set,'2013'),\
inria(image_set,'2005'),\
pascal_voc(image_set,'2007'),\
pascal_voc(image_set,'2012')]
elif image_set == "test":
self.imdbs = [imagenet('val'),\
coco('test-dev', '2015'),\
cam2('all','2017'),\
sun('test','2012'),\
caltech('test','2009'),\
kitti('val','2013'),\
inria('all','2005'),\
pascal_voc('test','2007')]
self.roidbs = [None for _ in range(len(self.datasets))]
for idx,imdb in enumerate(self.imdbs):
self.roidbs[idx] = get_training_roidb(imdb)
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
'voc',
'imagenet',
'caltech',
'coco',
'sun',
'kitti',
'inria',
'cam2')
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)
class merged(imdb):
def __init__(self, image_set, year, devkit_path=None, shuffled=None):
imdb.__init__(self, 'merged_' + image_set)
self._year = year
self._image_set = image_set
self._anno_set_dir = image_set
if "val" in image_set:
self._image_set_dir = "val"
if "val1" in image_set:
self._anno_set_dir = "val1"
if "val2" in image_set:
self._anno_set_dir = "val2"
elif "train" in image_set:
self._anno_set_dir = "train"
elif "test" in image_set:
self._anno_set_dir = "test"
if image_set == "train":
self.imdbs = [imagenet(image_set),\
coco(image_set, '2015'),\
cam2(image_set,'2017'),\
sun(image_set,'2012'),\
caltech(image_set,'2009'),\
kitti(image_set,'2013'),\
inria(image_set,'2005'),\
pascal_voc(image_set,'2007'),\
pascal_voc(image_set,'2012')]
elif image_set == "test":
self.imdbs = [imagenet('val'),\
coco('test-dev', '2015'),\
cam2('all','2017'),\
sun('test','2012'),\
caltech('test','2009'),\
kitti('val','2013'),\
inria('all','2005'),\
pascal_voc('test','2007')]
self.roidbs = [None for _ in range(len(self.datasets))]
for idx,imdb in enumerate(self.imdbs):
self.roidbs[idx] = get_training_roidb(imdb)
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
'voc',
'imagenet',
'caltech',
'coco',
'sun',
'kitti',
'inria',
'cam2')
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 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.
"""
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
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 _get_default_path(self):
"""
Return the default path where PASCAL VOC is expected to be installed.
"""
return os.path.join(cfg.DATA_DIR, 'VOCdevkit' + self._year)
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
_image_path = []
_image_index = []
gt_roidb = []
for dataset in datasets:
_image_path += dataset._image_path
_image_index_ = dataset._image_index
for idx,ind in enumerate(_image_index):
_image_index[idx] = index(ind,dataset.name)
gt_roidb += np.repeat(self._class_to_ind(dataset['name']),\
[dataset['num_imgs']],axis=0)
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 image_path_from_index(self,index):
"""
Construct an image path from the image's "index" identifier.
"""
if index.dataset
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
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['person']
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 _get_comp_id(self):
comp_id = (self._comp_id + '_' + self._salt if self.config['use_salt']
else self._comp_id)
return comp_id
def _get_voc_results_file_template(self):
# VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txt
filename = self._get_comp_id() + '_det_' + self._image_set + '_{:s}.txt'
path = os.path.join(
self._devkit_path,
'results',
'VOC' + self._year,
'Main',
filename)
return path
def _write_voc_results_file(self, all_boxes):
for cls_ind, cls in enumerate(self.classes):
count = 0
skip_count = 0
det_count = 0
if cls == '__background__':
continue
print 'Writing {} VOC results file'.format(cls)
filename = self._get_voc_results_file_template().format(cls)
#det_count = 0
#count = 0
with open(filename, 'wt') as f:
for im_ind, index in enumerate(self.image_index):
dets = all_boxes[cls_ind][im_ind]
#det_count = det_count + dets.size
#print("{} : {}".format(cls,dets.size))
if len(dets) == 0:
skip_count+=1
continue
# the VOCdevkit expects 1-based indices
for k in xrange(dets.shape[0]):
#count = count + 1
f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.
format(index, dets[k, -1],
dets[k, 0] + 1, dets[k, 1] + 1,
dets[k, 2] + 1, dets[k, 3] + 1))
#TODO : verify if the "+1" if is correct
#print "Wrote {} annotations to {}".format(count,filename)
#print "The number of non-zero det classes: {}".format(det_count)
#print("{}: skipped {} of {}".format(cls_ind,skip_count,len(all_boxes[cls_ind])))
def _do_python_eval(self, output_dir = 'output'):
annopath = os.path.join(
self._devkit_path,
'VOC' + self._year,
'Annotations',
'{:s}.xml')
imagesetfile = os.path.join(
self._devkit_path,
'VOC' + self._year,
'ImageSets',
'Main',
self._image_set + '.txt')
cachedir = os.path.join(self._devkit_path, 'annotations_cache',self._anno_set_dir)
aps = []
# The PASCAL VOC metric changed in 2010
use_07_metric = True if int(self._year) < 2010 else False
print 'VOC07 metric? ' + ('Yes' if use_07_metric else 'No')
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
for i, cls in enumerate(self._classes):
if cls == '__background__':
continue
filename = self._get_voc_results_file_template().format(cls)
rec, prec, ap, ovthresh = voc_eval(
filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.5,
use_07_metric=use_07_metric)
aps += [ap]
aps = np.array(aps)
results_fd = open("./results_voc.txt","w")
for kdx in range(len(ovthresh)):
#print('{0:.3f}@{1:.2f}'.format(ap[kdx],ovthresh[kdx]))
print('Mean AP = {:.4f} @ {:.2f}'.format(np.mean(aps[:,kdx]),ovthresh[kdx]))
print('~~~~~~~~')
print('Results:')
count_ = 1
sys.stdout.write('{0:>15} (#):'.format("class AP"))
results_fd.write('{0:>15} (#):'.format("class AP"))
for thsh in ovthresh:
sys.stdout.write("\t{:>5}{:.3f}".format("@",thsh))
results_fd.write("\t{:>5}{:.3f}".format("@",thsh))
sys.stdout.write("\n")
results_fd.write("\n")
for ap in aps:
sys.stdout.write('{:>15} ({}):'.format(self._classes[count_],count_))
results_fd.write('{:>15} ({}):'.format(self._classes[count_],count_))
for kdx in range(len(ovthresh)):
sys.stdout.write('\t{0:>10.5f}'.format(ap[kdx],ovthresh[kdx]))
results_fd.write('\t{0:>10.5f}'.format(ap[kdx],ovthresh[kdx]))
sys.stdout.write('\n')
results_fd.write('\n')
count_ +=1
sys.stdout.write('{:>15}:'.format("mAP"))
results_fd.write('{:>15}:'.format("mAP"))
for kdx in range(len(ovthresh)):
sys.stdout.write('\t{:10.5f}'.format(np.mean(aps[:,kdx])))
results_fd.write('\t{:10.5f}'.format(np.mean(aps[:,kdx])))
#print('{0:.3f}@{1:.2f}'.format(ap[kdx],ovthresh[kdx]))
#print('mAP @ {:.2f}: {:.5f} '.format(ovthresh[kdx],np.mean(aps[:,kdx])))
sys.stdout.write('\n')
results_fd.write('\n')
print('~~~~~~~~')
print('')
print('--------------------------------------------------------------')
print('Results computed with the **unofficial** Python eval code.')
print('Results should be very close to the official MATLAB eval code.')
print('Recompute with `./tools/reval.py --matlab ...` for your paper.')
print('-- Thanks, The Management')
print('--------------------------------------------------------------')
def _do_matlab_eval(self, output_dir='output'):
print '-----------------------------------------------------'
print 'Computing results with the official MATLAB eval code.'
print '-----------------------------------------------------'
path = os.path.join(cfg.ROOT_DIR, 'lib', 'datasets',
'VOCdevkit-matlab-wrapper')
cmd = 'cd {} && '.format(path)
cmd += '{:s} -nodisplay -nodesktop '.format(cfg.MATLAB)
cmd += '-r "dbstop if error; '
cmd += 'voc_eval(\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\'); quit;"' \
.format(self._devkit_path, self._get_comp_id(),
self._image_set, output_dir)
print('Running:\n{}'.format(cmd))
status = subprocess.call(cmd, shell=True)
def evaluate_detections(self, all_boxes, output_dir):
self._write_voc_results_file(all_boxes)
self._do_python_eval(output_dir)
if self.config['matlab_eval']:
self._do_matlab_eval(output_dir)
if self.config['cleanup']:
for cls in self._classes:
if cls == '__background__':
continue
filename = self._get_voc_results_file_template().format(cls)
os.remove(filename)
def competition_mode(self, on):
if on:
self.config['use_salt'] = False
self.config['cleanup'] = False
else:
self.config['use_salt'] = True
self.config['cleanup'] = True
if __name__ == '__main__':
from datasets.pascal_voc import pascal_voc
d = pascal_voc('trainval', '2007')
res = d.roidb
from IPython import embed; embed()
'train', 'val', 'trainval_taipei', 'trainval_shanghai',
'trainval_shanghai80', 'val_shanghai', 'val_jackson',
'trainval_jackson80', 'trainval_jackson100', 'trainval_jackson',
'trainval_jacksonallobject', 'trainval', 'test', 'trainval_res101',
'trainval_coral', 'trainval_coralpascal', 'trainval_coralcoco',
'trainval_coralcocob', 'trainvalb', 'trainval_coralcoco10',
'trainval_coral80', 'trainval_coralcoco30', 'trainval_coralcoco50',
'trainval_coralcoco100', 'trainval_coralcoco250',
'trainval_coralcoco500', 'trainval_coralcoco1000',
'trainval_coralcoco4000', 'trainval_coralcoco8000',
'trainval_coralcocodif'
]:
name = 'voc_{}_{}'.format(year, split)
if 'trainval' in split:
__sets[name] = (
lambda split=split, year=year: pascal_voc(split, year))
elif 'val' in split:
__sets[name] = (
lambda split=split, year=year: pascal_voc(split, year))
# print("split:",split)
# elif split =='trainval_res101':
# __sets[name] = (lambda split=split, year=year: pascal_voc(split, year, res101=True))
# elif split =='trainval_coral':
# __sets[name] = (lambda split=split, year=year: pascal_voc(split, year, coral=True))
# elif split =='trainval_shanghai':
# __sets[name] = (lambda split=split, year=year: pascal_voc(split, year, shanghai=True))
# elif split =='trainval_shanghai80':
# __sets[name] = (lambda split=split, year=year: pascal_voc(split, year, shanghai=True, cococlass=True))
# elif split =='val_shanghai':
# __sets[name] = (lambda split=split, year=year: pascal_voc(split, year, shanghai=True))
# elif split =='trainval_taipei':
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 = 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', '0712']:
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 kittivoc
for split in ['train', 'val', 'trainval', 'test']:
name = 'kittivoc_{}'.format(split)
print(name)
__sets[name] = (lambda split=split: kittivoc(split))
# Set up mmsvoc
for split in ['train', 'val', 'trainval', 'test']:
name = 'mmsvoc_{}'.format(split)
print(name)
__sets[name] = (lambda split=split: mmsvoc(split))
#for split in ['train', 'val', 'trainval', 'test']:
# name = 'mmsvoctest_{}'.format(split)
