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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import os.path as osp | |
| import random | |
| import mmcv | |
| import numpy as np | |
| from mmcv.utils import print_log | |
| from mmseg.datasets.builder import DATASETS | |
| from mmseg.datasets.custom import CustomDataset | |
| from mmseg.utils import get_root_logger | |
| from PIL import Image | |
| class ADE20KDataset(CustomDataset): | |
| """ADE20K dataset. | |
| In segmentation map annotation for ADE20K, 0 stands for background, which | |
| is not included in 150 categories. ``reduce_zero_label`` is fixed to True. | |
| The ``img_suffix`` is fixed to '.jpg' and ``seg_map_suffix`` is fixed to | |
| '.png'. | |
| """ | |
| CLASSES = ( | |
| 'wall', 'building', 'sky', 'floor', 'tree', 'ceiling', 'road', 'bed ', | |
| 'windowpane', 'grass', 'cabinet', 'sidewalk', 'person', 'earth', | |
| 'door', 'table', 'mountain', 'plant', 'curtain', 'chair', 'car', | |
| 'water', 'painting', 'sofa', 'shelf', 'house', 'sea', 'mirror', 'rug', | |
| 'field', 'armchair', 'seat', 'fence', 'desk', 'rock', 'wardrobe', | |
| 'lamp', 'bathtub', 'railing', 'cushion', 'base', 'box', 'column', | |
| 'signboard', 'chest of drawers', 'counter', 'sand', 'sink', | |
| 'skyscraper', 'fireplace', 'refrigerator', 'grandstand', 'path', | |
| 'stairs', 'runway', 'case', 'pool table', 'pillow', 'screen door', | |
| 'stairway', 'river', 'bridge', 'bookcase', 'blind', 'coffee table', | |
| 'toilet', 'flower', 'book', 'hill', 'bench', 'countertop', 'stove', | |
| 'palm', 'kitchen island', 'computer', 'swivel chair', 'boat', 'bar', | |
| 'arcade machine', 'hovel', 'bus', 'towel', 'light', 'truck', 'tower', | |
| 'chandelier', 'awning', 'streetlight', 'booth', 'television receiver', | |
| 'airplane', 'dirt track', 'apparel', 'pole', 'land', 'bannister', | |
| 'escalator', 'ottoman', 'bottle', 'buffet', 'poster', 'stage', 'van', | |
| 'ship', 'fountain', 'conveyer belt', 'canopy', 'washer', 'plaything', | |
| 'swimming pool', 'stool', 'barrel', 'basket', 'waterfall', 'tent', | |
| 'bag', 'minibike', 'cradle', 'oven', 'ball', 'food', 'step', 'tank', | |
| 'trade name', 'microwave', 'pot', 'animal', 'bicycle', 'lake', | |
| 'dishwasher', 'screen', 'blanket', 'sculpture', 'hood', 'sconce', | |
| 'vase', 'traffic light', 'tray', 'ashcan', 'fan', 'pier', 'crt screen', | |
| 'plate', 'monitor', 'bulletin board', 'shower', 'radiator', 'glass', | |
| 'clock', 'flag') | |
| PALETTE = [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50], | |
| [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255], | |
| [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7], | |
| [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82], | |
| [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3], | |
| [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255], | |
| [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220], | |
| [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224], | |
| [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255], | |
| [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7], | |
| [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153], | |
| [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255], | |
| [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0], | |
| [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255], | |
| [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255], | |
| [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255], | |
| [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0], | |
| [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0], | |
| [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255], | |
| [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255], | |
| [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20], | |
| [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255], | |
| [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255], | |
| [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255], | |
| [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0], | |
| [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0], | |
| [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255], | |
| [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112], | |
| [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160], | |
| [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163], | |
| [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0], | |
| [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0], | |
| [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255], | |
| [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204], | |
| [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255], | |
| [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255], | |
| [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194], | |
| [102, 255, 0], [92, 0, 255]] | |
| def __init__(self, max_image_num=None, **kwargs): | |
| super(ADE20KDataset, self).__init__( | |
| img_suffix='.jpg', | |
| seg_map_suffix='.png', | |
| reduce_zero_label=True, | |
| **kwargs) | |
| if max_image_num is not None: | |
| random.shuffle(self.img_infos) | |
| self.img_infos = self.img_infos[:max_image_num] | |
| print_log(f'Randomly select {len(self.img_infos)} images', logger=get_root_logger()) | |
| def results2img(self, results, imgfile_prefix, to_label_id, indices=None): | |
| """Write the segmentation results to images. | |
| Args: | |
| results (list[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. | |
| indices (list[int], optional): Indices of input results, if not | |
| set, all the indices of the dataset will be used. | |
| Default: None. | |
| Returns: | |
| list[str: str]: result txt files which contains corresponding | |
| semantic segmentation images. | |
| """ | |
| if indices is None: | |
| indices = list(range(len(self))) | |
| mmcv.mkdir_or_exist(imgfile_prefix) | |
| result_files = [] | |
| for result, idx in zip(results, indices): | |
| filename = self.img_infos[idx]['filename'] | |
| basename = osp.splitext(osp.basename(filename))[0] | |
| png_filename = osp.join(imgfile_prefix, f'{basename}.png') | |
| # The index range of official requirement is from 0 to 150. | |
| # But the index range of output is from 0 to 149. | |
| # That is because we set reduce_zero_label=True. | |
| result = result + 1 | |
| output = Image.fromarray(result.astype(np.uint8)) | |
| output.save(png_filename) | |
| result_files.append(png_filename) | |
| return result_files | |
| def format_results(self, | |
| results, | |
| imgfile_prefix, | |
| to_label_id=True, | |
| indices=None): | |
| """Format the results into dir (standard format for ade20k 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". | |
| to_label_id (bool): whether convert output to label_id for | |
| submission. Default: False | |
| indices (list[int], optional): Indices of input results, if not | |
| set, all the indices of the dataset will be used. | |
| Default: None. | |
| 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. | |
| """ | |
| if indices is None: | |
| indices = list(range(len(self))) | |
| assert isinstance(results, list), 'results must be a list.' | |
| assert isinstance(indices, list), 'indices must be a list.' | |
| result_files = self.results2img(results, imgfile_prefix, to_label_id, | |
| indices) | |
| return result_files | |