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()