Esempio n. 1
0
    def _do_matlab_eval(self, comp_id, output_dir='output'):
        rm_results = self.config['cleanup']
        path = os.path.join(os.path.dirname(__file__),
                            'VOCdevkit-matlab-wrapper')
        cmd = 'cd {} && '.format(path)
        cmd += '{:s} -nodisplay -nodesktop '.format(datasets.MATLAB)
        cmd += '-r "dbstop if error; '
        cmd += 'detection_eval(\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\'); quit;"' \
               .format(self._devkit_path, comp_id,
                       self._image_set, output_dir,'KITTI_val_list.txt',
                       'KITTI_gt_val.txt')
        print('Running:\n{}'.format(cmd))
        status = subprocess.call(cmd, shell=True)

    def evaluate_detections(self, all_boxes, output_dir):
        comp_id = self._write_voc_results_file(all_boxes)
        self._do_matlab_eval(comp_id, 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__':
    d = datasets.kakou('KakouTrain', '/home/bsl/KITTI_detection/data')
    res = d.roidb
    from IPython import embed; embed()
Esempio n. 2
0
        rm_results = self.config['cleanup']

        path = os.path.join(os.path.dirname(__file__),
                            'VOCdevkit-matlab-wrapper')
        cmd = 'cd {} && '.format(path)
        cmd += '{:s} -nodisplay -nodesktop '.format(datasets.MATLAB)
        cmd += '-r "dbstop if error; '
        cmd += 'voc_eval(\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\',{:d}); quit;"' \
               .format(self._devkit_path, comp_id,
                       self._image_set, output_dir, int(rm_results))
        print('Running:\n{}'.format(cmd))
        status = subprocess.call(cmd, shell=True)

    def evaluate_detections(self, all_boxes, output_dir):
        comp_id = self._write_voc_results_file(all_boxes)
        self._do_matlab_eval(comp_id, 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__':
    d = datasets.kakou('KakouTrain', '/home/timely/Documents/fast-rcnn/data/CARdevkit2015/CAR2015')
#    d = datasets.pascal_voc('trainval', '2007')
    res = d.roidb
    from IPython import embed; embed()
    def _do_matlab_eval(self, comp_id, output_dir='output'):
        rm_results = self.config['cleanup']

        path = os.path.join(os.path.dirname(__file__),
                            'VOCdevkit-matlab-wrapper')
        cmd = 'cd {} && '.format(path)
        cmd += '{:s} -nodisplay -nodesktop '.format(datasets.MATLAB)
        cmd += '-r "dbstop if error; '
        cmd += 'voc_eval(\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\',{:d}); quit;"' \
               .format(self._devkit_path, comp_id,
                       self._image_set, output_dir, int(rm_results))
        print('Running:\n{}'.format(cmd))
        status = subprocess.call(cmd, shell=True)

    def evaluate_detections(self, all_boxes, output_dir):
        comp_id = self._write_voc_results_file(all_boxes)
        self._do_matlab_eval(comp_id, 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__':
    d = datasets.kakou('KakouTrain', '/home/chenjie/KakouTrainForFRCNN_1')
    res = d.roidb
    from IPython import embed; embed()
Esempio n. 4
0
                            'VOCdevkit-matlab-wrapper')
        cmd = 'cd {} && '.format(path)
        cmd += '{:s} -nodisplay -nodesktop '.format(datasets.MATLAB)
        cmd += '-r "dbstop if error; '
        cmd += 'voc_eval(\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\',{:d}); quit;"' \
               .format(self._devkit_path, comp_id,
                       self._image_set, output_dir, int(rm_results))
        print('Running:\n{}'.format(cmd))
        status = subprocess.call(cmd, shell=True)

    def evaluate_detections(self, all_boxes, output_dir):
        comp_id = self._write_voc_results_file(all_boxes)
        self._do_matlab_eval(comp_id, 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__':
    d = datasets.kakou(
        'KakouTrain',
        '/home/timely/Documents/fast-rcnn/data/CARdevkit2015/CAR2015')
    #    d = datasets.pascal_voc('trainval', '2007')
    res = d.roidb
    from IPython import embed
    embed()