<gh_stars>1-10
import os.path as osp
import tempfile
import mmcv

from .builder import DATASETS
from .custom import CustomDataset
import numpy as np
from PIL import Image

@DATASETS.register_module()
class PascalVOCDataset(CustomDataset):
    """Pascal VOC dataset.

    Args:
        split (str): Split txt file for Pascal VOC.
    """

    CLASSES = ('background', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle',
               'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog',
               'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa',
               'train', 'tvmonitor')

    PALETTE = [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0], [0, 0, 128],
               [128, 0, 128], [0, 128, 128], [128, 128, 128], [64, 0, 0],
               [192, 0, 0], [64, 128, 0], [192, 128, 0], [64, 0, 128],
               [192, 0, 128], [64, 128, 128], [192, 128, 128], [0, 64, 0],
               [128, 64, 0], [0, 192, 0], [128, 192, 0], [0, 64, 128]]
    palette = []
    for i in range(256):
        palette.extend((i, i, i))
    palette[:3 * 21] = np.array([[0, 0, 0],
                                 [128, 0, 0],
                                 [0, 128, 0],
                                 [128, 128, 0],
                                 [0, 0, 128],
                                 [128, 0, 128],
                                 [0, 128, 128],
                                 [128, 128, 128],
                                 [64, 0, 0],
                                 [192, 0, 0],
                                 [64, 128, 0],
                                 [192, 128, 0],
                                 [64, 0, 128],
                                 [192, 0, 128],
                                 [64, 128, 128],
                                 [192, 128, 128],
                                 [0, 64, 0],
                                 [128, 64, 0],
                                 [0, 192, 0],
                                 [128, 192, 0],
                                 [0, 64, 128]
                                 ], dtype='uint8').flatten()


    def __init__(self, split, **kwargs):
        super(PascalVOCDataset, self).__init__(
            img_suffix='.jpg', seg_map_suffix='.png', split=split, **kwargs)
        assert osp.exists(self.img_dir) and self.split is not None

    def format_results(self, results, imgfile_prefix=None, to_label_id=True):
        """Format the results into dir (standard format for Cityscapes
        evaluation).

        Args:
            results (list): Testing results of the dataset.
            imgfile_prefix (str | None): The prefix of images files. It
                includes the file path and the prefix of filename, e.g.,
                "a/b/prefix". If not specified, a temp file will be created.
                Default: None.
            to_label_id (bool): whether convert output to label_id for
                submission. Default: False

        Returns:
            tuple: (result_files, tmp_dir), result_files is a list containing
                the image paths, tmp_dir is the temporal directory created
                for saving json/png files when img_prefix is not specified.
        """

        assert isinstance(results, list), 'results must be a list'
        assert len(results) == len(self), (
            'The length of results is not equal to the dataset len: '
            f'{len(results)} != {len(self)}')

        if imgfile_prefix is None:
            tmp_dir = tempfile.TemporaryDirectory()
            imgfile_prefix = tmp_dir.name
        else:
            tmp_dir = None
        result_files = self.results2img(results, imgfile_prefix, to_label_id)

        return result_files, tmp_dir

    def results2img(self, results, imgfile_prefix, to_label_id):
        """Write the segmentation results to images.

        Args:
            results (list[list | tuple | ndarray]): Testing results of the
                dataset.
            imgfile_prefix (str): The filename prefix of the png files.
                If the prefix is "somepath/xxx",
                the png files will be named "somepath/xxx.png".
            to_label_id (bool): whether convert output to label_id for
                submission

        Returns:
            list[str: str]: result txt files which contains corresponding
            semantic segmentation images.
        """
        mmcv.mkdir_or_exist(imgfile_prefix)
        result_files = []
        prog_bar = mmcv.ProgressBar(len(self))
        for idx in range(len(self)):
            result = results[idx]
            filename = self.img_infos[idx]['filename']
            basename = osp.splitext(osp.basename(filename))[0]

            png_filename = osp.join(imgfile_prefix, f'{basename}.png')

            output = Image.fromarray(result.astype(np.uint8)).convert('P')

            output.putpalette(self.palette)
            output.save(png_filename)
            result_files.append(png_filename)
            prog_bar.update()

        return result_files
import os.path as osp
import tempfile
import mmcv

from .builder import DATASETS
from .custom import CustomDataset
import numpy as np
from PIL import Image

@DATASETS.register_module()
class PascalVOCDataset(CustomDataset):
    """Pascal VOC dataset.

    Args:
        split (str): Split txt file for Pascal VOC.
    """

    CLASSES = ('background', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle',
               'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog',
               'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa',
               'train', 'tvmonitor')

    PALETTE = [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0], [0, 0, 128],
               [128, 0, 128], [0, 128, 128], [128, 128, 128], [64, 0, 0],
               [192, 0, 0], [64, 128, 0], [192, 128, 0], [64, 0, 128],
               [192, 0, 128], [64, 128, 128], [192, 128, 128], [0, 64, 0],
               [128, 64, 0], [0, 192, 0], [128, 192, 0], [0, 64, 128]]
    palette = []
    for i in range(256):
        palette.extend((i, i, i))
    palette[:3 * 21] = np.array([[0, 0, 0],
                                 [128, 0, 0],
                                 [0, 128, 0],
                                 [128, 128, 0],
                                 [0, 0, 128],
                                 [128, 0, 128],
                                 [0, 128, 128],
                                 [128, 128, 128],
                                 [64, 0, 0],
                                 [192, 0, 0],
                                 [64, 128, 0],
                                 [192, 128, 0],
                                 [64, 0, 128],
                                 [192, 0, 128],
                                 [64, 128, 128],
                                 [192, 128, 128],
                                 [0, 64, 0],
                                 [128, 64, 0],
                                 [0, 192, 0],
                                 [128, 192, 0],
                                 [0, 64, 128]
                                 ], dtype='uint8').flatten()


    def __init__(self, split, **kwargs):
        super(PascalVOCDataset, self).__init__(
            img_suffix='.jpg', seg_map_suffix='.png', split=split, **kwargs)
        assert osp.exists(self.img_dir) and self.split is not None

    def format_results(self, results, imgfile_prefix=None, to_label_id=True):
        """Format the results into dir (standard format for Cityscapes
        evaluation).

        Args:
            results (list): Testing results of the dataset.
            imgfile_prefix (str | None): The prefix of images files. It
                includes the file path and the prefix of filename, e.g.,
                "a/b/prefix". If not specified, a temp file will be created.
                Default: None.
            to_label_id (bool): whether convert output to label_id for
                submission. Default: False

        Returns:
            tuple: (result_files, tmp_dir), result_files is a list containing
                the image paths, tmp_dir is the temporal directory created
                for saving json/png files when img_prefix is not specified.
        """

        assert isinstance(results, list), 'results must be a list'
        assert len(results) == len(self), (
            'The length of results is not equal to the dataset len: '
            f'{len(results)} != {len(self)}')

        if imgfile_prefix is None:
            tmp_dir = tempfile.TemporaryDirectory()
            imgfile_prefix = tmp_dir.name
        else:
            tmp_dir = None
        result_files = self.results2img(results, imgfile_prefix, to_label_id)

        return result_files, tmp_dir

    def results2img(self, results, imgfile_prefix, to_label_id):
        """Write the segmentation results to images.

        Args:
            results (list[list | tuple | ndarray]): Testing results of the
                dataset.
            imgfile_prefix (str): The filename prefix of the png files.
                If the prefix is "somepath/xxx",
                the png files will be named "somepath/xxx.png".
            to_label_id (bool): whether convert output to label_id for
                submission

        Returns:
            list[str: str]: result txt files which contains corresponding
            semantic segmentation images.
        """
        mmcv.mkdir_or_exist(imgfile_prefix)
        result_files = []
        prog_bar = mmcv.ProgressBar(len(self))
        for idx in range(len(self)):
            result = results[idx]
            filename = self.img_infos[idx]['filename']
            basename = osp.splitext(osp.basename(filename))[0]

            png_filename = osp.join(imgfile_prefix, f'{basename}.png')

            output = Image.fromarray(result.astype(np.uint8)).convert('P')

            output.putpalette(self.palette)
            output.save(png_filename)
            result_files.append(png_filename)
            prog_bar.update()

        return result_files
Pascal VOC dataset.      Args:         split (str): Split txt file for Pascal VOC. Format the results into dir (standard format for Cityscapes         evaluation).          Args:             results (list): Testing results of the dataset.             imgfile_prefix (str | None): The prefix of images files. It                 includes the file path and the prefix of filename, e.g.,                 "a/b/prefix". If not specified, a temp file will be created.                 Default: None.             to_label_id (bool): whether convert output to label_id for                 submission. Default: False          Returns:             tuple: (result_files, tmp_dir), result_files is a list containing                 the image paths, tmp_dir is the temporal directory created                 for saving json/png files when img_prefix is not specified. Write the segmentation results to images.          Args:             results (list[list | tuple | ndarray]): Testing results of the                 dataset.             imgfile_prefix (str): The filename prefix of the png files.                 If the prefix is "somepath/xxx",                 the png files will be named "somepath/xxx.png".             to_label_id (bool): whether convert output to label_id for                 submission          Returns:             list[str: str]: result txt files which contains corresponding             semantic segmentation images.