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from lib.kits.basic import *
from .data import to_tensor
def lurb_to_cwh(
lurb : Union[list, np.ndarray, torch.Tensor],
):
'''
Convert the left-upper-right-bottom format to the center-width-height format.
### Args
- lurb: Union[list, np.ndarray, torch.Tensor], (..., 4)
- The left-upper-right-bottom format bounding box.
### Returns
- Union[list, np.ndarray, torch.Tensor], (..., 4)
- The center-width-height format bounding box.
'''
lurb, recover_type_back = to_tensor(lurb, device=None, temporary=True)
assert lurb.shape[-1] == 4, f"Invalid shape: {lurb.shape}, should be (..., 4)"
c = (lurb[..., :2] + lurb[..., 2:]) / 2 # (..., 2)
wh = lurb[..., 2:] - lurb[..., :2] # (..., 2)
cwh = torch.cat([c, wh], dim=-1) # (..., 4)
return recover_type_back(cwh)
def cwh_to_lurb(
cwh : Union[list, np.ndarray, torch.Tensor],
):
'''
Convert the center-width-height format to the left-upper-right-bottom format.
### Args
- cwh: Union[list, np.ndarray, torch.Tensor], (..., 4)
- The center-width-height format bounding box.
### Returns
- Union[list, np.ndarray, torch.Tensor], (..., 4)
- The left-upper-right-bottom format bounding box.
'''
cwh, recover_type_back = to_tensor(cwh, device=None, temporary=True)
assert cwh.shape[-1] == 4, f"Invalid shape: {cwh.shape}, should be (..., 4)"
l = cwh[..., :2] - cwh[..., 2:] / 2 # (..., 2)
r = cwh[..., :2] + cwh[..., 2:] / 2 # (..., 2)
lurb = torch.cat([l, r], dim=-1) # (..., 4)
return recover_type_back(lurb)
def cwh_to_cs(
cwh : Union[list, np.ndarray, torch.Tensor],
reduce : Optional[str] = None,
):
'''
Convert the center-width-height format to the center-scale format.
*Only works when width and height are the same.*
### Args
- cwh: Union[list, np.ndarray, torch.Tensor], (..., 4)
- The center-width-height format bounding box.
- reduce: Optional[str], default None, valid values: None, 'max'
- Determine how to reduce the width and height to a single scale.
### Returns
- Union[list, np.ndarray, torch.Tensor], (..., 3)
- The center-scale format bounding box.
'''
cwh, recover_type_back = to_tensor(cwh, device=None, temporary=True)
assert cwh.shape[-1] == 4, f"Invalid shape: {cwh.shape}, should be (..., 4)"
if reduce is None:
if (cwh[..., 2] != cwh[..., 3]).any():
get_logger().warning(f"Width and height are supposed to be the same, but they're not. The larger one will be used.")
c = cwh[..., :2] # (..., 2)
s = cwh[..., 2:].max(dim=-1)[0] # (...,)
cs = torch.cat([c, s[..., None]], dim=-1) # (..., 3)
return recover_type_back(cs)
def cs_to_cwh(
cs : Union[list, np.ndarray, torch.Tensor],
):
'''
Convert the center-scale format to the center-width-height format.
### Args
- cs: Union[list, np.ndarray, torch.Tensor], (..., 3)
- The center-scale format bounding box.
### Returns
- Union[list, np.ndarray, torch.Tensor], (..., 4)
- The center-width-height format bounding box.
'''
cs, recover_type_back = to_tensor(cs, device=None, temporary=True)
assert cs.shape[-1] == 3, f"Invalid shape: {cs.shape}, should be (..., 3)"
c = cs[..., :2] # (..., 2)
s = cs[..., 2] # (...,)
cwh = torch.cat([c, s[..., None], s[..., None]], dim=-1) # (..., 4)
return recover_type_back(cwh)
def lurb_to_cs(
lurb : Union[list, np.ndarray, torch.Tensor],
):
'''
Convert the left-upper-right-bottom format to the center-scale format.
*Only works when width and height are the same.*
### Args
- lurb: Union[list, np.ndarray, torch.Tensor], (..., 4)
- The left-upper-right-bottom format bounding box.
### Returns
- Union[list, np.ndarray, torch.Tensor], (..., 3)
- The center-scale format bounding box.
'''
return cwh_to_cs(lurb_to_cwh(lurb), reduce='max')
def cs_to_lurb(
cs : Union[list, np.ndarray, torch.Tensor],
):
'''
Convert the center-scale format to the left-upper-right-bottom format.
### Args
- cs: Union[list, np.ndarray, torch.Tensor], (..., 3)
- The center-scale format bounding box.
### Returns
- Union[list, np.ndarray, torch.Tensor], (..., 4)
- The left-upper-right-bottom format bounding box.
'''
return cwh_to_lurb(cs_to_cwh(cs))
def lurb_to_luwh(
lurb : Union[list, np.ndarray, torch.Tensor],
):
'''
Convert the left-upper-right-bottom format to the left-upper-width-height format.
### Args
- lurb: Union[list, np.ndarray, torch.Tensor]
- The left-upper-right-bottom format bounding box.
### Returns
- Union[list, np.ndarray, torch.Tensor]
- The left-upper-width-height format bounding box.
'''
lurb, recover_type_back = to_tensor(lurb, device=None, temporary=True)
assert lurb.shape[-1] == 4, f"Invalid shape: {lurb.shape}, should be (..., 4)"
lu = lurb[..., :2] # (..., 2)
wh = lurb[..., 2:] - lurb[..., :2] # (..., 2)
luwh = torch.cat([lu, wh], dim=-1) # (..., 4)
return recover_type_back(luwh)
def luwh_to_lurb(
luwh : Union[list, np.ndarray, torch.Tensor],
):
'''
Convert the left-upper-width-height format to the left-upper-right-bottom format.
### Args
- luwh: Union[list, np.ndarray, torch.Tensor]
- The left-upper-width-height format bounding box.
### Returns
- Union[list, np.ndarray, torch.Tensor]
- The left-upper-right-bottom format bounding box.
'''
luwh, recover_type_back = to_tensor(luwh, device=None, temporary=True)
assert luwh.shape[-1] == 4, f"Invalid shape: {luwh.shape}, should be (..., 4)"
l = luwh[..., :2] # (..., 2)
r = luwh[..., :2] + luwh[..., 2:] # (..., 2)
lurb = torch.cat([l, r], dim=-1) # (..., 4)
return recover_type_back(lurb)
def crop_with_lurb(data, lurb, padding=0):
"""
Crop the img-like data according to the lurb bounding box.
### Args
- data: Union[np.ndarray, torch.Tensor], shape (H, W, C)
- Data like image.
- lurb: Union[list, np.ndarray, torch.Tensor], shape (4,)
- Bounding box with [left, upper, right, bottom] coordinates.
- padding: int, default 0
- Padding value for out-of-bound areas.
### Returns
- Union[np.ndarray, torch.Tensor], shape (H', W', C)
- Cropped image with padding if necessary.
"""
data, recover_type_back = to_tensor(data, device=None, temporary=True)
# Ensure lurb is in numpy array format for indexing
lurb = np.array(lurb).astype(np.int64)
l_, u_, r_, b_ = lurb
# Determine the shape of the data.
H_raw, W_raw, C_raw = data.size()
# Compute the cropped patch size.
H_patch = b_ - u_
W_patch = r_ - l_
# Create an output buffer of the crop size, initialized to padding
if isinstance(data, np.ndarray):
output = np.full((H_patch, W_patch, C_raw), padding, dtype=data.dtype)
else:
output = torch.full((H_patch, W_patch, C_raw), padding, dtype=data.dtype)
# Calculate the valid region in the original data
valid_l_ = max(0, l_)
valid_u_ = max(0, u_)
valid_r_ = min(W_raw, r_)
valid_b_ = min(H_raw, b_)
# Calculate the corresponding valid region in the output
target_l_ = valid_l_ - l_
target_u_ = valid_u_ - u_
target_r_ = target_l_ + (valid_r_ - valid_l_)
target_b_ = target_u_ + (valid_b_ - valid_u_)
# Copy the valid region into the output buffer
output[target_u_:target_b_, target_l_:target_r_, :] = data[valid_u_:valid_b_, valid_l_:valid_r_, :]
return recover_type_back(output)
def fit_bbox_to_aspect_ratio(
bbox : np.ndarray,
tgt_ratio : Optional[Tuple[int, int]] = None,
bbox_type : str = 'lurb'
):
'''
Fit a random bounding box to a target aspect ratio through enlarging the bounding box with least change.
### Args
- bbox: np.ndarray, shape is determined by `bbox_type`, e.g. for 'lurb', shape is (4,)
- The bounding box to be modified. The format is determined by `bbox_type`.
- tgt_ratio: Optional[Tuple[int, int]], default None
- The target aspect ratio to be matched.
- bbox_type: str, default 'lurb', valid values: 'lurb', 'cwh'.
### Returns
- np.ndarray, shape is determined by `bbox_type`, e.g. for 'lurb', shape is (4,)
- The modified bounding box.
'''
bbox = bbox.copy()
if bbox_type == 'lurb':
bbx_cwh = lurb_to_cwh(bbox)
bbx_wh = bbx_cwh[2:]
elif bbox_type == 'cwh':
bbx_wh = bbox[2:]
else:
raise ValueError(f"Unsupported bbox type: {bbox_type}")
new_bbx_wh = expand_wh_to_aspect_ratio(bbx_wh, tgt_ratio)
if bbox_type == 'lurb':
bbx_cwh[2:] = new_bbx_wh
new_bbox = cwh_to_lurb(bbx_cwh)
elif bbox_type == 'cwh':
new_bbox = np.concatenate([bbox[:2], new_bbx_wh])
else:
raise ValueError(f"Unsupported bbox type: {bbox_type}")
return new_bbox
def expand_wh_to_aspect_ratio(bbx_wh:np.ndarray, tgt_aspect_ratio:Optional[Tuple[int, int]]=None):
'''
Increase the size of the bounding box to match the target shape.
Modified from https://github.com/shubham-goel/4D-Humans/blob/6ec79656a23c33237c724742ca2a0ec00b398b53/hmr2/datasets/utils.py#L14-L33
'''
if tgt_aspect_ratio is None:
return bbx_wh
try:
bbx_w , bbx_h = bbx_wh
except (ValueError, TypeError):
get_logger().warning(f"Invalid bbox_wh content: {bbx_wh}")
return bbx_wh
tgt_w, tgt_h = tgt_aspect_ratio
if bbx_h / bbx_w < tgt_h / tgt_w:
new_h = max(bbx_w * tgt_h / tgt_w, bbx_h)
new_w = bbx_w
else:
new_h = bbx_h
new_w = max(bbx_h * tgt_w / tgt_h, bbx_w)
assert new_h >= bbx_h and new_w >= bbx_w
return to_numpy([new_w, new_h]) |