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import os
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
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
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 ImageDataset(Dataset):
def __init__(self,
cfg: CfgNode,
dataset_file: str,
img_dir: str,
train: bool = True,
rescale_factor = 2,
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(ImageDataset, 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.rescale_factor = rescale_factor
self.img_dir = img_dir
self.data = np.load(dataset_file, allow_pickle=True)
self.imgname = self.data['imgname']
self.personid = np.zeros(len(self.imgname), dtype=np.int32)
self.extra_info = self.data.get('extra_info', [{} for _ in range(len(self.imgname))])
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
self.center = self.data['center']
self.scale = self.data['scale'].reshape(len(self.center), -1) / 200.0
if self.scale.shape[1] == 1:
self.scale = np.tile(self.scale, (1, 2))
assert self.scale.shape == (len(self.center), 2)
try:
self.right = self.data['right']
except KeyError:
self.right = np.ones(len(self.imgname), dtype=np.float32)
# Get gt MANO 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 KeyError:
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 KeyError:
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 KeyError:
hand_keypoints_2d = np.zeros((len(self.center), 21, 3))
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 KeyError:
hand_keypoints_3d = np.zeros((len(self.center), 21, 4), dtype=np.float32)
self.keypoints_3d = hand_keypoints_3d
def __len__(self) -> int:
return len(self.scale)
def __getitem__(self, idx: int) -> Dict:
"""
Returns an example from the dataset.
"""
try:
image_file_rel = self.imgname[idx].decode('utf-8')
except AttributeError:
image_file_rel = self.imgname[idx]
image_file = os.path.join(self.img_dir, image_file_rel)
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()
if self.rescale_factor == -1:
BBOX_SHAPE = self.cfg.MODEL.get('BBOX_SHAPE', None)
bbox_size = expand_to_aspect_ratio(scale*200, target_aspect_ratio=BBOX_SHAPE).max()
bbox_expand_factor = bbox_size / ((scale*200).max())
else:
bbox_expand_factor = self.rescale_factor
bbox_size = bbox_expand_factor*scale.max()*200
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['imgname_rel'] = image_file_rel
item['personid'] = int(self.personid[idx])
item['extra_info'] = copy.deepcopy(self.extra_info[idx])
item['idx'] = idx
item['_scale'] = scale
item['right'] = self.right[idx].copy()
return item
@staticmethod
def load_tars_as_webdataset(cfg: CfgNode, urls: str|List[str], train: bool,
resampled=False,
epoch_size=None,
cache_dir=None,
**kwargs) -> Dataset:
"""
Loads the dataset from a webdataset tar file.
"""
IMG_SIZE = cfg.MODEL.IMAGE_SIZE
BBOX_SHAPE = cfg.MODEL.get('BBOX_SHAPE', None)
MEAN = 255. * np.array(cfg.MODEL.IMAGE_MEAN)
STD = 255. * np.array(cfg.MODEL.IMAGE_STD)
def split_data(source):
for item in source:
datas = item['data.pyd']
for data in datas:
if 'detection.npz' in item:
det_idx = data['extra_info']['detection_npz_idx']
mask = item['detection.npz']['masks'][det_idx]
else:
mask = np.ones_like(item['jpg'][:,:,0], dtype=bool)
yield {
'__key__': item['__key__'],
'jpg': item['jpg'],
'data.pyd': data,
'mask': mask,
}
def suppress_bad_kps(item, thresh=0.0):
if thresh > 0:
kp2d = item['data.pyd']['keypoints_2d']
kp2d_conf = np.where(kp2d[:, 2] < thresh, 0.0, kp2d[:, 2])
item['data.pyd']['keypoints_2d'] = np.concatenate([kp2d[:,:2], kp2d_conf[:,None]], axis=1)
return item
def filter_numkp(item, numkp=4, thresh=0.0):
kp_conf = item['data.pyd']['keypoints_2d'][:, 2]
return (kp_conf > thresh).sum() > numkp
def filter_reproj_error(item, thresh=10**4.5):
losses = item['data.pyd'].get('extra_info', {}).get('fitting_loss', np.array({})).item()
reproj_loss = losses.get('reprojection_loss', None)
return reproj_loss is None or reproj_loss < thresh
def filter_bbox_size(item, thresh=1):
bbox_size_min = item['data.pyd']['scale'].min().item() * 200.
return bbox_size_min > thresh
def filter_no_poses(item):
return (item['data.pyd']['has_hand_pose'] > 0)
def supress_bad_betas(item, thresh=3):
has_betas = item['data.pyd']['has_betas']
if thresh > 0 and has_betas:
betas_abs = np.abs(item['data.pyd']['betas'])
if (betas_abs > thresh).any():
item['data.pyd']['has_betas'] = False
return item
def supress_bad_poses(item):
has_hand_pose = item['data.pyd']['has_hand_pose']
if has_hand_pose:
hand_pose = item['data.pyd']['hand_pose']
pose_is_probable = poses_check_probable(torch.from_numpy(hand_pose)[None, 3:], amass_poses_hist100_smooth).item()
if not pose_is_probable:
item['data.pyd']['has_hand_pose'] = False
return item
def poses_betas_simultaneous(item):
# We either have both hand_pose and betas, or neither
has_betas = item['data.pyd']['has_betas']
has_hand_pose = item['data.pyd']['has_hand_pose']
item['data.pyd']['has_betas'] = item['data.pyd']['has_hand_pose'] = np.array(float((has_hand_pose>0) and (has_betas>0)))
return item
def set_betas_for_reg(item):
# Always have betas set to true
has_betas = item['data.pyd']['has_betas']
betas = item['data.pyd']['betas']
if not (has_betas>0):
item['data.pyd']['has_betas'] = np.array(float((True)))
item['data.pyd']['betas'] = betas * 0
return item
# Load the dataset
if epoch_size is not None:
resampled = True
#corrupt_filter = lambda sample: (sample['__key__'] not in CORRUPT_KEYS)
import webdataset as wds
dataset = wds.WebDataset(expand_urls(urls),
nodesplitter=wds.split_by_node,
shardshuffle=True,
resampled=resampled,
cache_dir=cache_dir,
) #.select(corrupt_filter)
if train:
dataset = dataset.shuffle(100)
dataset = dataset.decode('rgb8').rename(jpg='jpg;jpeg;png')
# Process the dataset
dataset = dataset.compose(split_data)
# Filter/clean the dataset
SUPPRESS_KP_CONF_THRESH = cfg.DATASETS.get('SUPPRESS_KP_CONF_THRESH', 0.0)
SUPPRESS_BETAS_THRESH = cfg.DATASETS.get('SUPPRESS_BETAS_THRESH', 0.0)
SUPPRESS_BAD_POSES = cfg.DATASETS.get('SUPPRESS_BAD_POSES', False)
POSES_BETAS_SIMULTANEOUS = cfg.DATASETS.get('POSES_BETAS_SIMULTANEOUS', False)
BETAS_REG = cfg.DATASETS.get('BETAS_REG', False)
FILTER_NO_POSES = cfg.DATASETS.get('FILTER_NO_POSES', False)
FILTER_NUM_KP = cfg.DATASETS.get('FILTER_NUM_KP', 4)
FILTER_NUM_KP_THRESH = cfg.DATASETS.get('FILTER_NUM_KP_THRESH', 0.0)
FILTER_REPROJ_THRESH = cfg.DATASETS.get('FILTER_REPROJ_THRESH', 0.0)
FILTER_MIN_BBOX_SIZE = cfg.DATASETS.get('FILTER_MIN_BBOX_SIZE', 0.0)
if SUPPRESS_KP_CONF_THRESH > 0:
dataset = dataset.map(lambda x: suppress_bad_kps(x, thresh=SUPPRESS_KP_CONF_THRESH))
if SUPPRESS_BETAS_THRESH > 0:
dataset = dataset.map(lambda x: supress_bad_betas(x, thresh=SUPPRESS_BETAS_THRESH))
if SUPPRESS_BAD_POSES:
dataset = dataset.map(lambda x: supress_bad_poses(x))
if POSES_BETAS_SIMULTANEOUS:
dataset = dataset.map(lambda x: poses_betas_simultaneous(x))
if FILTER_NO_POSES:
dataset = dataset.select(lambda x: filter_no_poses(x))
if FILTER_NUM_KP > 0:
dataset = dataset.select(lambda x: filter_numkp(x, numkp=FILTER_NUM_KP, thresh=FILTER_NUM_KP_THRESH))
if FILTER_REPROJ_THRESH > 0:
dataset = dataset.select(lambda x: filter_reproj_error(x, thresh=FILTER_REPROJ_THRESH))
if FILTER_MIN_BBOX_SIZE > 0:
dataset = dataset.select(lambda x: filter_bbox_size(x, thresh=FILTER_MIN_BBOX_SIZE))
if BETAS_REG:
dataset = dataset.map(lambda x: set_betas_for_reg(x)) # NOTE: Must be at the end
use_skimage_antialias = cfg.DATASETS.get('USE_SKIMAGE_ANTIALIAS', False)
border_mode = {
'constant': cv2.BORDER_CONSTANT,
'replicate': cv2.BORDER_REPLICATE,
}[cfg.DATASETS.get('BORDER_MODE', 'constant')]
# Process the dataset further
dataset = dataset.map(lambda x: ImageDataset.process_webdataset_tar_item(x, train,
augm_config=cfg.DATASETS.CONFIG,
MEAN=MEAN, STD=STD, IMG_SIZE=IMG_SIZE,
BBOX_SHAPE=BBOX_SHAPE,
use_skimage_antialias=use_skimage_antialias,
border_mode=border_mode,
))
if epoch_size is not None:
dataset = dataset.with_epoch(epoch_size)
return dataset
@staticmethod
def process_webdataset_tar_item(item, train,
augm_config=None,
MEAN=DEFAULT_MEAN,
STD=DEFAULT_STD,
IMG_SIZE=DEFAULT_IMG_SIZE,
BBOX_SHAPE=None,
use_skimage_antialias=False,
border_mode=cv2.BORDER_CONSTANT,
):
# Read data from item
key = item['__key__']
image = item['jpg']
data = item['data.pyd']
mask = item['mask']
keypoints_2d = data['keypoints_2d']
keypoints_3d = data['keypoints_3d']
center = data['center']
scale = data['scale']
hand_pose = data['hand_pose']
betas = data['betas']
right = data['right']
has_hand_pose = data['has_hand_pose']
has_betas = data['has_betas']
# image_file = data['image_file']
# Process data
orig_keypoints_2d = keypoints_2d.copy()
center_x = center[0]
center_y = center[1]
bbox_size = expand_to_aspect_ratio(scale*200, target_aspect_ratio=BBOX_SHAPE).max()
if bbox_size < 1:
breakpoint()
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 = copy.deepcopy(augm_config)
# Crop image and (possibly) perform data augmentation
img_rgba = np.concatenate([image, mask.astype(np.uint8)[:,:,None]*255], axis=2)
img_patch_rgba, keypoints_2d, keypoints_3d, mano_params, has_mano_params, img_size, trans = get_example(img_rgba,
center_x, center_y,
bbox_size, bbox_size,
keypoints_2d, keypoints_3d,
mano_params, has_mano_params,
FLIP_KEYPOINT_PERMUTATION,
IMG_SIZE, IMG_SIZE,
MEAN, STD, train, right, augm_config,
is_bgr=False, return_trans=True,
use_skimage_antialias=use_skimage_antialias,
border_mode=border_mode,
)
img_patch = img_patch_rgba[:3,:,:]
mask_patch = (img_patch_rgba[3,:,:] / 255.0).clip(0,1)
if (mask_patch < 0.5).all():
mask_patch = np.ones_like(mask_patch)
item = {}
item['img'] = img_patch
item['mask'] = mask_patch
# item['img_og'] = image
# item['mask_og'] = mask
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'] = center.copy()
item['box_size'] = bbox_size
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['_scale'] = scale
item['_trans'] = trans
item['imgname'] = key
# item['idx'] = idx
return item
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