regular update

mmhuman3d/data/data_converters/arctic.py

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+import glob
+import json
+import os
+import pdb
+import random
+import time
+from multiprocessing import Pool
+from typing import List
+
+import cv2
+import numpy as np
+import pandas as pd
+import smplx
+import torch
+from tqdm import tqdm
+
+from mmhuman3d.core.cameras import build_cameras
+from mmhuman3d.core.conventions.keypoints_mapping import (
+    convert_kps,
+    get_keypoint_idx,
+    get_keypoint_idxs_by_part,
+)
+from mmhuman3d.core.conventions.segmentation.smpl import SMPL_SEGMENTATION_DICT
+from mmhuman3d.data.data_structures.human_data import HumanData
+# import mmcv
+from mmhuman3d.models.body_models.builder import build_body_model
+from mmhuman3d.models.body_models.utils import transform_to_camera_frame
+from mmhuman3d.core.conventions.cameras.convert_convention import convert_camera_matrix
+from .base_converter import BaseModeConverter
+from .builder import DATA_CONVERTERS
+
+
+@DATA_CONVERTERS.register_module()
+class ArcticConverter(BaseModeConverter):
+
+    ACCEPTED_MODES = ['p1_test', 'p1_train', 
+                      'p1_val', 'p2_test', 
+                      'p2_train', 'p2_val']
+
+    def __init__(self, modes: List = []) -> None:
+        self.device = torch.device('cuda:0')
+        self.misc_config = dict(
+            bbox_body_scale=1.2,
+            bbox_facehand_scale=1.0,
+            bbox_source='keypoints2d_smplx',
+            flat_hand_mean=True,
+            cam_param_type='prespective',
+            cam_param_source='original',
+            smpl_source='original',
+        )
+        self.smplx_shape = {
+            'betas': (-1, 10),
+            'transl': (-1, 3),
+            'global_orient': (-1, 3),
+            'body_pose': (-1, 21, 3),
+            'left_hand_pose': (-1, 15, 3),
+            'right_hand_pose': (-1, 15, 3),
+            'leye_pose': (-1, 3),
+            'reye_pose': (-1, 3),
+            'jaw_pose': (-1, 3),
+            'expression': (-1, 10)
+        }
+
+        super(ArcticConverter, self).__init__(modes)
+
+    def convert_by_mode(self, dataset_path: str, out_path: str,
+                    mode: str) -> dict:
+
+
+        # load split
+        split_path = os.path.join(dataset_path, 'splits', mode + '.npy')
+        split_info = np.load(split_path, allow_pickle=True).item()
+
+        image_names = split_info['imgnames']
+        data_dict = split_info['data_dict']
+
+        # load meta
+        meta_path = os.path.join(dataset_path, 'meta', 'misc.json')
+        with open(meta_path, 'r') as f:
+            metadata = json.load(f)
+
+        for image_name in image_names:
+
+            image_path = image_name.replace('./arctic_data/', '')
+            imgp = os.path.join(dataset_path, image_path)
+
+            # load raw seqs
+            sub_id = image_path.split('/')[1]
+            seq_name = image_path.split('/')[2]
+            cam_id = image_path.split('/')[3]
+
+            raw_path = os.path.join(dataset_path, 'raw_seqs', sub_id)
+            egocam_path = os.path.join(raw_path, f'{seq_name}.egocam.dist.npy')
+            smplx_path = os.path.join(raw_path, f'{seq_name}.smplx.npy')
+
+            # load egocam
+            egocam_params = np.load(egocam_path, allow_pickle=True).item()
+            smplx_params = np.load(smplx_path, allow_pickle=True).item()
+            metainfo = metadata[sub_id]
+
+            gender = metainfo[gender]
+
+
+            pdb.set_trace()
+
+
+        pass