caizhongang
47 lines
2.0 KiB
Python
47 lines
2.0 KiB
Python
import os
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import os.path as osp
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import numpy as np
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import torch
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import cv2
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import json
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import copy
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from pycocotools.coco import COCO
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from config import cfg
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from utils.human_models import smpl_x
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from utils.preprocessing import load_img, process_bbox, augmentation, process_db_coord, process_human_model_output, \
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get_fitting_error_3D
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from utils.transforms import world2cam, cam2pixel, rigid_align
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from humandata import HumanDataset
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class GTA_Human2(HumanDataset):
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def __init__(self, transform, data_split):
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super(GTA_Human2, self).__init__(transform, data_split)
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filename = 'gta_human2multiple_230406_04000_0.npz'
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self.img_dir = osp.join(cfg.data_dir, 'GTA_Human2')
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self.annot_path = osp.join(cfg.data_dir, 'preprocessed_datasets', filename)
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self.annot_path_cache = osp.join(cfg.data_dir, 'cache', filename)
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self.use_cache = getattr(cfg, 'use_cache', False)
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self.img_shape = (1080, 1920) # (h, w)
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self.cam_param = {
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'focal': (1158.0337, 1158.0337), # (fx, fy)
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'princpt': (960, 540) # (cx, cy)
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}
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# check image shape
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img_path = osp.join(self.img_dir, np.load(self.annot_path)['image_path'][0])
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img_shape = cv2.imread(img_path).shape[:2]
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assert self.img_shape == img_shape, 'image shape is incorrect: {} vs {}'.format(self.img_shape, img_shape)
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# load data or cache
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if self.use_cache and osp.isfile(self.annot_path_cache):
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print(f'[{self.__class__.__name__}] loading cache from {self.annot_path_cache}')
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self.datalist = self.load_cache(self.annot_path_cache)
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else:
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if self.use_cache:
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print(f'[{self.__class__.__name__}] Cache not found, generating cache...')
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self.datalist = self.load_data(
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train_sample_interval=getattr(cfg, f'{self.__class__.__name__}_train_sample_interval', 1))
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if self.use_cache:
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self.save_cache(self.annot_path_cache, self.datalist) |