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import copy
import os
import json
import glob
import numpy as np
import torch
from typing import Any, Dict, List
from yacs.config import CfgNode
import braceexpand
import cv2
from .dataset import Dataset
from .utils import get_example, expand_to_aspect_ratio
from .smplh_prob_filter import poses_check_probable, load_amass_hist_smooth
def expand(s):
return os.path.expanduser(os.path.expandvars(s))
def expand_urls(urls: str|List[str]):
if isinstance(urls, str):
urls = [urls]
urls = [u for url in urls for u in braceexpand.braceexpand(expand(url))]
return urls
AIC_TRAIN_CORRUPT_KEYS = {
'0a047f0124ae48f8eee15a9506ce1449ee1ba669',
'1a703aa174450c02fbc9cfbf578a5435ef403689',
'0394e6dc4df78042929b891dbc24f0fd7ffb6b6d',
'5c032b9626e410441544c7669123ecc4ae077058',
'ca018a7b4c5f53494006ebeeff9b4c0917a55f07',
'4a77adb695bef75a5d34c04d589baf646fe2ba35',
'a0689017b1065c664daef4ae2d14ea03d543217e',
'39596a45cbd21bed4a5f9c2342505532f8ec5cbb',
'3d33283b40610d87db660b62982f797d50a7366b',
}
CORRUPT_KEYS = {
*{f'aic-train/{k}' for k in AIC_TRAIN_CORRUPT_KEYS},
*{f'aic-train-vitpose/{k}' for k in AIC_TRAIN_CORRUPT_KEYS},
}
FLIP_KEYPOINT_PERMUTATION = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]
DEFAULT_MEAN = 255. * np.array([0.485, 0.456, 0.406])
DEFAULT_STD = 255. * np.array([0.229, 0.224, 0.225])
DEFAULT_IMG_SIZE = 256
class JsonDataset(Dataset):
def __init__(self,
cfg: CfgNode,
dataset_file: str,
img_dir: str,
right: bool,
train: bool = False,
prune: Dict[str, Any] = {},
**kwargs):
"""
Dataset class used for loading images and corresponding annotations.
Args:
cfg (CfgNode): Model config file.
dataset_file (str): Path to npz file containing dataset info.
img_dir (str): Path to image folder.
train (bool): Whether it is for training or not (enables data augmentation).
"""
super(JsonDataset, self).__init__()
self.train = train
self.cfg = cfg
self.img_size = cfg.MODEL.IMAGE_SIZE
self.mean = 255. * np.array(self.cfg.MODEL.IMAGE_MEAN)
self.std = 255. * np.array(self.cfg.MODEL.IMAGE_STD)
self.img_dir = img_dir
boxes = np.array(json.load(open(dataset_file, 'rb')))
self.imgname = glob.glob(os.path.join(self.img_dir,'*.jpg'))
self.imgname.sort()
self.flip_keypoint_permutation = copy.copy(FLIP_KEYPOINT_PERMUTATION)
num_pose = 3 * (self.cfg.MANO.NUM_HAND_JOINTS + 1)
# Bounding boxes are assumed to be in the center and scale format
boxes = boxes.astype(np.float32)
self.center = (boxes[:, 2:4] + boxes[:, 0:2]) / 2.0
self.scale = 2 * (boxes[:, 2:4] - boxes[:, 0:2]) / 200.0
self.personid = np.arange(len(boxes), dtype=np.int32)
if right:
self.right = np.ones(len(self.imgname), dtype=np.float32)
else:
self.right = np.zeros(len(self.imgname), dtype=np.float32)
assert self.scale.shape == (len(self.center), 2)
# Get gt SMPLX parameters, if available
try:
self.hand_pose = self.data['hand_pose'].astype(np.float32)
self.has_hand_pose = self.data['has_hand_pose'].astype(np.float32)
except:
self.hand_pose = np.zeros((len(self.imgname), num_pose), dtype=np.float32)
self.has_hand_pose = np.zeros(len(self.imgname), dtype=np.float32)
try:
self.betas = self.data['betas'].astype(np.float32)
self.has_betas = self.data['has_betas'].astype(np.float32)
except:
self.betas = np.zeros((len(self.imgname), 10), dtype=np.float32)
self.has_betas = np.zeros(len(self.imgname), dtype=np.float32)
# Try to get 2d keypoints, if available
try:
hand_keypoints_2d = self.data['hand_keypoints_2d']
except:
hand_keypoints_2d = np.zeros((len(self.center), 21, 3))
## Try to get extra 2d keypoints, if available
#try:
# extra_keypoints_2d = self.data['extra_keypoints_2d']
#except KeyError:
# extra_keypoints_2d = np.zeros((len(self.center), 19, 3))
#self.keypoints_2d = np.concatenate((hand_keypoints_2d, extra_keypoints_2d), axis=1).astype(np.float32)
self.keypoints_2d = hand_keypoints_2d
# Try to get 3d keypoints, if available
try:
hand_keypoints_3d = self.data['hand_keypoints_3d'].astype(np.float32)
except:
hand_keypoints_3d = np.zeros((len(self.center), 21, 4), dtype=np.float32)
## Try to get extra 3d keypoints, if available
#try:
# extra_keypoints_3d = self.data['extra_keypoints_3d'].astype(np.float32)
#except KeyError:
# extra_keypoints_3d = np.zeros((len(self.center), 19, 4), dtype=np.float32)
self.keypoints_3d = hand_keypoints_3d
#body_keypoints_3d[:, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], -1] = 0
#self.keypoints_3d = np.concatenate((body_keypoints_3d, extra_keypoints_3d), axis=1).astype(np.float32)
def __len__(self) -> int:
return len(self.scale)
def __getitem__(self, idx: int) -> Dict:
"""
Returns an example from the dataset.
"""
try:
image_file = self.imgname[idx].decode('utf-8')
except AttributeError:
image_file = self.imgname[idx]
keypoints_2d = self.keypoints_2d[idx].copy()
keypoints_3d = self.keypoints_3d[idx].copy()
center = self.center[idx].copy()
center_x = center[0]
center_y = center[1]
scale = self.scale[idx]
right = self.right[idx].copy()
BBOX_SHAPE = self.cfg.MODEL.get('BBOX_SHAPE', None)
#bbox_size = expand_to_aspect_ratio(scale*200, target_aspect_ratio=BBOX_SHAPE).max()
bbox_size = ((scale*200).max())
bbox_expand_factor = bbox_size / ((scale*200).max())
hand_pose = self.hand_pose[idx].copy().astype(np.float32)
betas = self.betas[idx].copy().astype(np.float32)
has_hand_pose = self.has_hand_pose[idx].copy()
has_betas = self.has_betas[idx].copy()
mano_params = {'global_orient': hand_pose[:3],
'hand_pose': hand_pose[3:],
'betas': betas
}
has_mano_params = {'global_orient': has_hand_pose,
'hand_pose': has_hand_pose,
'betas': has_betas
}
mano_params_is_axis_angle = {'global_orient': True,
'hand_pose': True,
'betas': False
}
augm_config = self.cfg.DATASETS.CONFIG
# Crop image and (possibly) perform data augmentation
img_patch, keypoints_2d, keypoints_3d, mano_params, has_mano_params, img_size = get_example(image_file,
center_x, center_y,
bbox_size, bbox_size,
keypoints_2d, keypoints_3d,
mano_params, has_mano_params,
self.flip_keypoint_permutation,
self.img_size, self.img_size,
self.mean, self.std, self.train, right, augm_config)
item = {}
# These are the keypoints in the original image coordinates (before cropping)
orig_keypoints_2d = self.keypoints_2d[idx].copy()
item['img'] = img_patch
item['keypoints_2d'] = keypoints_2d.astype(np.float32)
item['keypoints_3d'] = keypoints_3d.astype(np.float32)
item['orig_keypoints_2d'] = orig_keypoints_2d
item['box_center'] = self.center[idx].copy()
item['box_size'] = bbox_size
item['bbox_expand_factor'] = bbox_expand_factor
item['img_size'] = 1.0 * img_size[::-1].copy()
item['mano_params'] = mano_params
item['has_mano_params'] = has_mano_params
item['mano_params_is_axis_angle'] = mano_params_is_axis_angle
item['imgname'] = image_file
item['personid'] = int(self.personid[idx])
item['idx'] = idx
item['_scale'] = scale
item['right'] = self.right[idx].copy()
return item
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