Python voc_eval.voc_eval方法代码示例

# 需要导入模块: from datasets import voc_eval [as 别名]
# 或者: from datasets.voc_eval import voc_eval [as 别名]
def _do_matlab_eval(json_dataset, salt, output_dir='output'):
    import subprocess
    logger.info('-----------------------------------------------------')
    logger.info('Computing results with the official MATLAB eval code.')
    logger.info('-----------------------------------------------------')
    info = voc_info(json_dataset)
    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(info['devkit_path'], 'comp4' + salt, info['image_set'],
               output_dir)
    logger.info('Running:\n{}'.format(cmd))
    subprocess.call(cmd, shell=True) 

# 需要导入模块: from datasets import voc_eval [as 别名]
# 或者: from datasets.voc_eval import voc_eval [as 别名]
def _do_python_eval(json_dataset, salt, output_dir='output'):
    info = voc_info(json_dataset)
    year = info['year']
    anno_path = info['anno_path']
    image_set_path = info['image_set_path']
    devkit_path = info['devkit_path']
    cachedir = os.path.join(devkit_path, 'annotations_cache')
    aps = []
    # The PASCAL VOC metric changed in 2010
    use_07_metric = True if int(year) < 2010 else False
    logger.info('VOC07 metric? ' + ('Yes' if use_07_metric else 'No'))
    if not os.path.isdir(output_dir):
        os.mkdir(output_dir)
    for _, cls in enumerate(json_dataset.classes):
        if cls == '__background__':
            continue
        filename = _get_voc_results_file_template(
            json_dataset, salt).format(cls)
        rec, prec, ap = voc_eval(
            filename, anno_path, image_set_path, cls, cachedir, ovthresh=0.5,
            use_07_metric=use_07_metric)
        aps += [ap]
        logger.info('AP for {} = {:.4f}'.format(cls, ap))
        res_file = os.path.join(output_dir, cls + '_pr.pkl')
        save_object({'rec': rec, 'prec': prec, 'ap': ap}, res_file)
    logger.info('Mean AP = {:.4f}'.format(np.mean(aps)))
    logger.info('~~~~~~~~')
    logger.info('Results:')
    for ap in aps:
        logger.info('{:.3f}'.format(ap))
    logger.info('{:.3f}'.format(np.mean(aps)))
    logger.info('~~~~~~~~')
    logger.info('')
    logger.info('----------------------------------------------------------')
    logger.info('Results computed with the **unofficial** Python eval code.')
    logger.info('Results should be very close to the official MATLAB code.')
    logger.info('Use `./tools/reval.py --matlab ...` for your paper.')
    logger.info('-- Thanks, The Management')
    logger.info('----------------------------------------------------------') 

# 需要导入模块: from datasets import voc_eval [as 别名]
# 或者: from datasets.voc_eval import voc_eval [as 别名]
def _do_python_eval(json_dataset, salt, output_dir='output'):
    info = voc_info(json_dataset)
    year = info['year']
    anno_path = info['anno_path']
    image_set_path = info['image_set_path']
    devkit_path = info['devkit_path']
    cachedir = os.path.join(devkit_path, 'annotations_cache_{}'.format(year))
    aps = []
    # The PASCAL VOC metric changed in 2010
    use_07_metric = True if int(year) < 2010 else False
    logger.info('VOC07 metric? ' + ('Yes' if use_07_metric else 'No'))
    if not os.path.isdir(output_dir):
        os.mkdir(output_dir)
    for _, cls in enumerate(json_dataset.classes):
        if cls == '__background__':
            continue
        filename = _get_voc_results_file_template(
            json_dataset, salt).format(cls)
        rec, prec, ap = voc_eval(
            filename, anno_path, image_set_path, cls, cachedir, ovthresh=0.5,
            use_07_metric=use_07_metric)
        aps += [ap]
        logger.info('AP for {} = {:.4f}'.format(cls, ap))
        res_file = os.path.join(output_dir, cls + '_pr.pkl')
        save_object({'rec': rec, 'prec': prec, 'ap': ap}, res_file)
    logger.info('Mean AP = {:.4f}'.format(np.mean(aps)))
    logger.info('~~~~~~~~')
    logger.info('Results:')
    for ap in aps:
        logger.info('{:.3f}'.format(ap))
    logger.info('{:.3f}'.format(np.mean(aps)))
    logger.info('~~~~~~~~')
    logger.info('')
    logger.info('----------------------------------------------------------')
    logger.info('Results computed with the **unofficial** Python eval code.')
    logger.info('Results should be very close to the official MATLAB code.')
    logger.info('Use `./tools/reval.py --matlab ...` for your paper.')
    logger.info('-- Thanks, The Management')
    logger.info('----------------------------------------------------------') 

# 需要导入模块: from datasets import voc_eval [as 别名]
# 或者: from datasets.voc_eval import voc_eval [as 别名]
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) 

# 需要导入模块: from datasets import voc_eval [as 别名]
# 或者: from datasets.voc_eval import voc_eval [as 别名]
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_{}'.format(self._year))
        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 = voc_eval(
                filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.5,
                use_07_metric=use_07_metric)
            aps += [ap]
            print('AP for {} = {:.4f}'.format(cls, ap))
            with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
                pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
        print('Mean AP = {:.4f}'.format(np.mean(aps)))
        print('~~~~~~~~')
        print('Results:')
        for ap in aps:
            print('{:.3f}'.format(ap))
        print('{:.3f}'.format(np.mean(aps)))
        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('--------------------------------------------------------------')