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Source code for mmpose.datasets.datasets.face.face_300w_dataset

# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import tempfile
import warnings
from collections import OrderedDict

import numpy as np
from mmcv import Config, deprecated_api_warning

from mmpose.datasets.builder import DATASETS
from ..base import Kpt2dSviewRgbImgTopDownDataset


[docs]@DATASETS.register_module() class Face300WDataset(Kpt2dSviewRgbImgTopDownDataset): """Face300W dataset for top-down face keypoint localization. "300 faces In-the-wild challenge: Database and results", Image and Vision Computing (IMAVIS) 2019. The dataset loads raw images and apply specified transforms to return a dict containing the image tensors and other information. The landmark annotations follow the 68 points mark-up. The definition can be found in `https://ibug.doc.ic.ac.uk/resources/300-W/`. Args: ann_file (str): Path to the annotation file. img_prefix (str): Path to a directory where images are held. Default: None. data_cfg (dict): config pipeline (list[dict | callable]): A sequence of data transforms. dataset_info (DatasetInfo): A class containing all dataset info. test_mode (bool): Store True when building test or validation dataset. Default: False. """ def __init__(self, ann_file, img_prefix, data_cfg, pipeline, dataset_info=None, test_mode=False): if dataset_info is None: warnings.warn( 'dataset_info is missing. ' 'Check https://github.com/open-mmlab/mmpose/pull/663 ' 'for details.', DeprecationWarning) cfg = Config.fromfile('configs/_base_/datasets/300w.py') dataset_info = cfg._cfg_dict['dataset_info'] super().__init__( ann_file, img_prefix, data_cfg, pipeline, dataset_info=dataset_info, test_mode=test_mode) self.ann_info['use_different_joint_weights'] = False self.db = self._get_db() print(f'=> num_images: {self.num_images}') print(f'=> load {len(self.db)} samples') def _get_db(self): """Load dataset.""" gt_db = [] bbox_id = 0 num_joints = self.ann_info['num_joints'] for img_id in self.img_ids: ann_ids = self.coco.getAnnIds(imgIds=img_id, iscrowd=False) objs = self.coco.loadAnns(ann_ids) for obj in objs: if max(obj['keypoints']) == 0: continue joints_3d = np.zeros((num_joints, 3), dtype=np.float32) joints_3d_visible = np.zeros((num_joints, 3), dtype=np.float32) keypoints = np.array(obj['keypoints']).reshape(-1, 3) joints_3d[:, :2] = keypoints[:, :2] joints_3d_visible[:, :2] = np.minimum(1, keypoints[:, 2:3]) center = np.array(obj['center']) scale = np.array([obj['scale'], obj['scale']]) image_file = osp.join(self.img_prefix, self.id2name[img_id]) gt_db.append({ 'image_file': image_file, 'center': center, 'scale': scale, 'rotation': 0, 'joints_3d': joints_3d, 'joints_3d_visible': joints_3d_visible, 'dataset': self.dataset_name, 'bbox': obj['bbox'], 'bbox_score': 1, 'bbox_id': bbox_id }) bbox_id = bbox_id + 1 gt_db = sorted(gt_db, key=lambda x: x['bbox_id']) return gt_db def _get_normalize_factor(self, gts, *args, **kwargs): """Get inter-ocular distance as the normalize factor, measured as the Euclidean distance between the outer corners of the eyes. Args: gts (np.ndarray[N, K, 2]): Groundtruth keypoint location. Returns: np.ndarray[N, 2]: normalized factor """ interocular = np.linalg.norm( gts[:, 36, :] - gts[:, 45, :], axis=1, keepdims=True) return np.tile(interocular, [1, 2])
[docs] @deprecated_api_warning(name_dict=dict(outputs='results')) def evaluate(self, results, res_folder=None, metric='NME', **kwargs): """Evaluate freihand keypoint results. The pose prediction results will be saved in ``${res_folder}/result_keypoints.json``. Note: - batch_size: N - num_keypoints: K - heatmap height: H - heatmap width: W Args: results (list[dict]): Testing results containing the following items: - preds (np.ndarray[1,K,3]): The first two dimensions are \ coordinates, score is the third dimension of the array. - boxes (np.ndarray[1,6]): [center[0], center[1], scale[0], \ scale[1],area, score] - image_path (list[str]): For example, ['300W/ibug/\ image_018.jpg'] - output_heatmap (np.ndarray[N, K, H, W]): model outputs. res_folder (str, optional): The folder to save the testing results. If not specified, a temp folder will be created. Default: None. metric (str | list[str]): Metric to be performed. Options: 'NME'. Returns: dict: Evaluation results for evaluation metric. """ metrics = metric if isinstance(metric, list) else [metric] allowed_metrics = ['NME'] for metric in metrics: if metric not in allowed_metrics: raise KeyError(f'metric {metric} is not supported') if res_folder is not None: tmp_folder = None res_file = osp.join(res_folder, 'result_keypoints.json') else: tmp_folder = tempfile.TemporaryDirectory() res_file = osp.join(tmp_folder.name, 'result_keypoints.json') kpts = [] for result in results: preds = result['preds'] boxes = result['boxes'] image_paths = result['image_paths'] bbox_ids = result['bbox_ids'] batch_size = len(image_paths) for i in range(batch_size): image_id = self.name2id[image_paths[i][len(self.img_prefix):]] kpts.append({ 'keypoints': preds[i].tolist(), 'center': boxes[i][0:2].tolist(), 'scale': boxes[i][2:4].tolist(), 'area': float(boxes[i][4]), 'score': float(boxes[i][5]), 'image_id': image_id, 'bbox_id': bbox_ids[i] }) kpts = self._sort_and_unique_bboxes(kpts) self._write_keypoint_results(kpts, res_file) info_str = self._report_metric(res_file, metrics) name_value = OrderedDict(info_str) if tmp_folder is not None: tmp_folder.cleanup() return name_value
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