Source code for mmedit.datasets.sr_facial_landmark_dataset

# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp

import numpy as np

from .base_sr_dataset import BaseSRDataset
from .registry import DATASETS

[docs]@DATASETS.register_module() class SRFacialLandmarkDataset(BaseSRDataset): """Facial image and landmark dataset with an annotation file for image restoration. The dataset loads gt (Ground-Truth) image, shape of image, face box, and landmark. Applies specified transforms and finally returns a dict containing paired data and other information. This is the "annotation file mode": Each dict in the annotation list contains the image names, image shape, face box, and landmark. Annotation file is a `npy` file, which contains a list of dict. Example of an annotation file: :: dict1(file=*, bbox=*, shape=*, landmark=*) dict2(file=*, bbox=*, shape=*, landmark=*) Args: gt_folder (str | :obj:`Path`): Path to a gt folder. ann_file (str | :obj:`Path`): Path to the annotation file. pipeline (list[dict | callable]): A sequence of data transformations. scale (int): Upsampling scale ratio. test_mode (bool): Store `True` when building test dataset. Default: `False`. """ def __init__(self, gt_folder, ann_file, pipeline, scale, test_mode=False): super().__init__(pipeline, scale, test_mode) self.gt_folder = str(gt_folder) self.ann_file = str(ann_file) self.data_infos = self.load_annotations()
[docs] def load_annotations(self): """Load annoations for SR dataset. Annotation file is a `npy` file, which contains a list of dict. It loads the GT image path and landmark from the annotation file. Each dict in the annotation file contains the image names, image shape (usually for gt), bbox and landmark. Returns: dict: Returned dict for GT and landmark. Contains: gt_path, bbox, shape, landmark. """ data_infos = np.load(self.ann_file, allow_pickle=True) for data_info in data_infos: data_info['gt_path'] = osp.join(self.gt_folder, data_info['gt_path']) return data_infos
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