def get_imdb(name):
"""Get an imdb (image database) by name."""
__sets['wider_face_trainval'] = (
lambda imageset=imageset, devkit=devkit: wider_face(imageset, devkit))
if not __sets.has_key(name):
raise KeyError('Unknown dataset: {}'.format(name))
return __sets[name]()
# __sets[name] = (lambda split=split, version=version: vg(version, split))
for version in ['150-50-20', '150-50-50', '500-150-80', '750-250-150', '1750-700-450', '1600-400-20']:
for split in ['minitrain', 'smalltrain', 'train', 'minival', 'smallval', 'val', 'test']:
name = 'vg_{}_{}'.format(version,split)
__sets[name] = (lambda split=split, version=version: vg(version, split))
# set up image net.
for split in ['train', 'val', 'val1', 'val2', 'test']:
name = 'imagenet_{}'.format(split)
devkit_path = 'data/imagenet/ILSVRC/devkit'
data_path = 'data/imagenet/ILSVRC'
__sets[name] = (lambda split=split, devkit_path=devkit_path, data_path=data_path: imagenet(split,devkit_path,data_path))
for split in ['train', 'val', 'test']:
name = 'wider_face_{}'.format(split)
__sets[name] = (lambda split=split: wider_face(split))
for split in ['train', 'val', 'test']:
name = 'MI3_{}'.format(split)
__sets[name] = (lambda split=split: mi3(split))
def get_imdb(name):
"""Get an imdb (image database) by name."""
if name not in __sets:
raise KeyError('Unknown dataset: {}'.format(name))
return __sets[name]()
def list_imdbs():
"""List all registered imdbs."""
return list(__sets.keys())
'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_widface_results_file(all_boxes)
self._do_python_eval(output_dir)
if self.config['matlab_eval']:
self._do_matlab_eval(output_dir)
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.wider_face import wider_face
d = wider_face('train', '2017')
res = d.roidb
from IPython import embed; embed()
from datasets.coco import coco
from datasets.wider_face import wider_face
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 widface_<year>_<split>
for year in ['2017']:
for split in ['train', 'val', 'trainval', 'test']:
name = 'widface_{}_{}'.format(year, split)
__sets[name] = (lambda split=split, year=year: wider_face(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."""
def evaluate_detections(self, all_boxes, output_dir):
# print(all_boxes[0])
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.wider_face import wider_face
d = wider_face() #'trainval', '2007')
res = d.roidb
from IPython import embed
embed()
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.wider_face import wider_face
image_set = "D:\\workSpace\\py_workspace\\tf-faster-rcnn\\WIDER_train"
devkit_path = "D:\\workSpace\\py_workspace\\tf-faster-rcnn\\WIDER_train"
d = wider_face(image_set, devkit_path)
res = d._load_annotation()
from IPython import embed
embed()