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Source code for mmpose.datasets.datasets.top_down.topdown_jhmdb_dataset

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

import json_tricks as json
import numpy as np
from mmcv import Config, deprecated_api_warning

from mmpose.core.evaluation.top_down_eval import keypoint_pck_accuracy
from ...builder import DATASETS
from .topdown_coco_dataset import TopDownCocoDataset


[docs]@DATASETS.register_module() class TopDownJhmdbDataset(TopDownCocoDataset): """JhmdbDataset dataset for top-down pose estimation. "Towards understanding action recognition", ICCV'2013. More details can be found in the `paper <https://openaccess.thecvf.com/content_iccv_2013/papers/\ Jhuang_Towards_Understanding_Action_2013_ICCV_paper.pdf>`__ The dataset loads raw features and apply specified transforms to return a dict containing the image tensors and other information. sub-JHMDB keypoint indexes:: 0: "neck", 1: "belly", 2: "head", 3: "right_shoulder", 4: "left_shoulder", 5: "right_hip", 6: "left_hip", 7: "right_elbow", 8: "left_elbow", 9: "right_knee", 10: "left_knee", 11: "right_wrist", 12: "left_wrist", 13: "right_ankle", 14: "left_ankle" 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/jhmdb.py') dataset_info = cfg._cfg_dict['dataset_info'] super(TopDownCocoDataset, self).__init__( ann_file, img_prefix, data_cfg, pipeline, dataset_info=dataset_info, test_mode=test_mode) self.use_gt_bbox = data_cfg['use_gt_bbox'] self.bbox_file = data_cfg['bbox_file'] self.det_bbox_thr = data_cfg.get('det_bbox_thr', 0.0) self.soft_nms = data_cfg['soft_nms'] self.nms_thr = data_cfg['nms_thr'] self.oks_thr = data_cfg['oks_thr'] self.vis_thr = data_cfg['vis_thr'] 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.""" assert self.use_gt_bbox gt_db = self._load_coco_keypoint_annotations() return gt_db def _load_coco_keypoint_annotation_kernel(self, img_id): """load annotation from COCOAPI. Note: bbox:[x1, y1, w, h] Args: img_id: coco image id Returns: dict: db entry """ img_ann = self.coco.loadImgs(img_id)[0] width = img_ann['width'] height = img_ann['height'] num_joints = self.ann_info['num_joints'] ann_ids = self.coco.getAnnIds(imgIds=img_id, iscrowd=False) objs = self.coco.loadAnns(ann_ids) # sanitize bboxes valid_objs = [] for obj in objs: if 'bbox' not in obj: continue x, y, w, h = obj['bbox'] # JHMDB uses matlab format, index is 1-based, # we should first convert to 0-based index x -= 1 y -= 1 x1 = max(0, x) y1 = max(0, y) x2 = min(width - 1, x1 + max(0, w)) y2 = min(height - 1, y1 + max(0, h)) if ('area' not in obj or obj['area'] > 0) and x2 > x1 and y2 > y1: obj['clean_bbox'] = [x1, y1, x2 - x1, y2 - y1] valid_objs.append(obj) objs = valid_objs rec = [] bbox_id = 0 for obj in objs: if 'keypoints' not in obj: continue if max(obj['keypoints']) == 0: continue if 'num_keypoints' in obj and obj['num_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) # JHMDB uses matlab format, index is 1-based, # we should first convert to 0-based index joints_3d[:, :2] = keypoints[:, :2] - 1 joints_3d_visible[:, :2] = np.minimum(1, keypoints[:, 2:3]) image_file = osp.join(self.img_prefix, self.id2name[img_id]) rec.append({ 'image_file': image_file, 'bbox': obj['clean_bbox'][:4], 'rotation': 0, 'joints_3d': joints_3d, 'joints_3d_visible': joints_3d_visible, 'dataset': self.dataset_name, 'bbox_score': 1, 'bbox_id': f'{img_id}_{bbox_id:03}' }) bbox_id = bbox_id + 1 return rec def _write_keypoint_results(self, keypoints, res_file): """Write results into a json file.""" with open(res_file, 'w') as f: json.dump(keypoints, f, sort_keys=True, indent=4) def _report_metric(self, res_file, metrics, pck_thr=0.2): """Keypoint evaluation. Args: res_file (str): Json file stored prediction results. metrics (str | list[str]): Metric to be performed. Options: 'PCK', 'PCKh', 'AUC', 'EPE'. pck_thr (float): PCK threshold, default as 0.2. pckh_thr (float): PCKh threshold, default as 0.7. auc_nor (float): AUC normalization factor, default as 30 pixel. Returns: List: Evaluation results for evaluation metric. """ info_str = [] with open(res_file, 'r') as fin: preds = json.load(fin) assert len(preds) == len(self.db) outputs = [] gts = [] masks = [] threshold_bbox = [] threshold_torso = [] for pred, item in zip(preds, self.db): outputs.append(np.array(pred['keypoints'])[:, :-1]) gts.append(np.array(item['joints_3d'])[:, :-1]) masks.append((np.array(item['joints_3d_visible'])[:, 0]) > 0) if 'PCK' in metrics: bbox = np.array(item['bbox']) bbox_thr = np.max(bbox[2:]) threshold_bbox.append(np.array([bbox_thr, bbox_thr])) if 'tPCK' in metrics: torso_thr = np.linalg.norm(item['joints_3d'][4, :2] - item['joints_3d'][5, :2]) if torso_thr < 1: torso_thr = np.linalg.norm( np.array(pred['keypoints'])[4, :2] - np.array(pred['keypoints'])[5, :2]) warnings.warn('Torso Size < 1.') threshold_torso.append(np.array([torso_thr, torso_thr])) outputs = np.array(outputs) gts = np.array(gts) masks = np.array(masks) threshold_bbox = np.array(threshold_bbox) threshold_torso = np.array(threshold_torso) if 'PCK' in metrics: pck_p, pck, _ = keypoint_pck_accuracy(outputs, gts, masks, pck_thr, threshold_bbox) stats_names = [ 'Head PCK', 'Sho PCK', 'Elb PCK', 'Wri PCK', 'Hip PCK', 'Knee PCK', 'Ank PCK', 'Mean PCK' ] stats = [ pck_p[2], 0.5 * pck_p[3] + 0.5 * pck_p[4], 0.5 * pck_p[7] + 0.5 * pck_p[8], 0.5 * pck_p[11] + 0.5 * pck_p[12], 0.5 * pck_p[5] + 0.5 * pck_p[6], 0.5 * pck_p[9] + 0.5 * pck_p[10], 0.5 * pck_p[13] + 0.5 * pck_p[14], pck ] info_str.extend(list(zip(stats_names, stats))) if 'tPCK' in metrics: pck_p, pck, _ = keypoint_pck_accuracy(outputs, gts, masks, pck_thr, threshold_torso) stats_names = [ 'Head tPCK', 'Sho tPCK', 'Elb tPCK', 'Wri tPCK', 'Hip tPCK', 'Knee tPCK', 'Ank tPCK', 'Mean tPCK' ] stats = [ pck_p[2], 0.5 * pck_p[3] + 0.5 * pck_p[4], 0.5 * pck_p[7] + 0.5 * pck_p[8], 0.5 * pck_p[11] + 0.5 * pck_p[12], 0.5 * pck_p[5] + 0.5 * pck_p[6], 0.5 * pck_p[9] + 0.5 * pck_p[10], 0.5 * pck_p[13] + 0.5 * pck_p[14], pck ] info_str.extend(list(zip(stats_names, stats))) return info_str
[docs] @deprecated_api_warning(name_dict=dict(outputs='results')) def evaluate(self, results, res_folder=None, metric='PCK', **kwargs): """Evaluate onehand10k 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[N,K,3]): The first two dimensions are \ coordinates, score is the third dimension of the array. - boxes (np.ndarray[N,6]): [center[0], center[1], scale[0], \ scale[1],area, score] - image_path (list[str]) - 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: 'PCK', 'tPCK'. PCK means normalized by the bounding boxes, while tPCK means normalized by the torso size. Returns: dict: Evaluation results for evaluation metric. """ metrics = metric if isinstance(metric, list) else [metric] allowed_metrics = ['PCK', 'tPCK'] 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'] # convert 0-based index to 1-based index, # and get the first two dimensions. preds[..., :2] += 1.0 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], 'center': boxes[i][0:2], 'scale': boxes[i][2:4], 'area': boxes[i][4], 'score': 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
def _sort_and_unique_bboxes(self, kpts, key='bbox_id'): """sort kpts and remove the repeated ones.""" kpts = sorted(kpts, key=lambda x: x[key]) num = len(kpts) for i in range(num - 1, 0, -1): if kpts[i][key] == kpts[i - 1][key]: del kpts[i] return kpts
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