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"""
crop
for torch tensor
Given image, bbox(center, bboxsize)
return: cropped image, tform(used for transform the keypoint accordingly)
only support crop to squared images
"""
import torch
from kornia.geometry.transform.imgwarp import (
get_perspective_transform,
warp_affine,
warp_perspective,
)
def points2bbox(points, points_scale=None):
if points_scale:
assert points_scale[0] == points_scale[1]
points = points.clone()
points[:, :, :2] = (points[:, :, :2] * 0.5 + 0.5) * points_scale[0]
min_coords, _ = torch.min(points, dim=1)
xmin, ymin = min_coords[:, 0], min_coords[:, 1]
max_coords, _ = torch.max(points, dim=1)
xmax, ymax = max_coords[:, 0], max_coords[:, 1]
center = torch.stack([xmax + xmin, ymax + ymin], dim=-1) * 0.5
width = xmax - xmin
height = ymax - ymin
# Convert the bounding box to a square box
size = torch.max(width, height).unsqueeze(-1)
return center, size
def augment_bbox(center, bbox_size, scale=[1.0, 1.0], trans_scale=0.0):
batch_size = center.shape[0]
trans_scale = (torch.rand([batch_size, 2], device=center.device) * 2.0 - 1.0) * trans_scale
center = center + trans_scale * bbox_size # 0.5
scale = (torch.rand([batch_size, 1], device=center.device) * (scale[1] - scale[0]) + scale[0])
size = bbox_size * scale
return center, size
def crop_tensor(image, center, bbox_size, crop_size, interpolation="bilinear", align_corners=False):
"""for batch image
Args:
image (torch.Tensor): the reference tensor of shape BXHxWXC.
center: [bz, 2]
bboxsize: [bz, 1]
crop_size;
interpolation (str): Interpolation flag. Default: 'bilinear'.
align_corners (bool): mode for grid_generation. Default: False. See
https://pytorch.org/docs/stable/nn.functional.html#torch.nn.functional.interpolate for details
Returns:
cropped_image
tform
"""
dtype = image.dtype
device = image.device
batch_size = image.shape[0]
# points: top-left, top-right, bottom-right, bottom-left
src_pts = (
torch.zeros([4, 2], dtype=dtype, device=device).unsqueeze(0).expand(batch_size, -1,
-1).contiguous()
)
src_pts[:, 0, :] = center - bbox_size * 0.5 # / (self.crop_size - 1)
src_pts[:, 1, 0] = center[:, 0] + bbox_size[:, 0] * 0.5
src_pts[:, 1, 1] = center[:, 1] - bbox_size[:, 0] * 0.5
src_pts[:, 2, :] = center + bbox_size * 0.5
src_pts[:, 3, 0] = center[:, 0] - bbox_size[:, 0] * 0.5
src_pts[:, 3, 1] = center[:, 1] + bbox_size[:, 0] * 0.5
DST_PTS = torch.tensor(
[[
[0, 0],
[crop_size - 1, 0],
[crop_size - 1, crop_size - 1],
[0, crop_size - 1],
]],
dtype=dtype,
device=device,
).expand(batch_size, -1, -1)
# estimate transformation between points
dst_trans_src = get_perspective_transform(src_pts, DST_PTS)
# simulate broadcasting
# dst_trans_src = dst_trans_src.expand(batch_size, -1, -1)
# warp images
cropped_image = warp_affine(
image,
dst_trans_src[:, :2, :],
(crop_size, crop_size),
mode=interpolation,
align_corners=align_corners,
)
tform = torch.transpose(dst_trans_src, 2, 1)
# tform = torch.inverse(dst_trans_src)
return cropped_image, tform
class Cropper(object):
def __init__(self, crop_size, scale=[1, 1], trans_scale=0.0):
self.crop_size = crop_size
self.scale = scale
self.trans_scale = trans_scale
def crop(self, image, points, points_scale=None):
# points to bbox
center, bbox_size = points2bbox(points.clone(), points_scale)
center, bbox_size = augment_bbox(
center, bbox_size, scale=self.scale, trans_scale=self.trans_scale
)
# crop
cropped_image, tform = crop_tensor(image, center, bbox_size, self.crop_size)
return cropped_image, tform
def transform_points(self, points, tform, points_scale=None, normalize=True):
points_2d = points[:, :, :2]
#'input points must use original range'
if points_scale:
assert points_scale[0] == points_scale[1]
points_2d = (points_2d * 0.5 + 0.5) * points_scale[0]
batch_size, n_points, _ = points.shape
trans_points_2d = torch.bmm(
torch.cat(
[
points_2d,
torch.ones(
[batch_size, n_points, 1],
device=points.device,
dtype=points.dtype,
),
],
dim=-1,
),
tform,
)
trans_points = torch.cat([trans_points_2d[:, :, :2], points[:, :, 2:]], dim=-1)
if normalize:
trans_points[:, :, :2] = trans_points[:, :, :2] / self.crop_size * 2 - 1
return trans_points
def transform_points(points, tform, points_scale=None):
points_2d = points[:, :, :2]
#'input points must use original range'
if points_scale:
assert points_scale[0] == points_scale[1]
points_2d = (points_2d * 0.5 + 0.5) * points_scale[0]
batch_size, n_points, _ = points.shape
trans_points_2d = torch.bmm(
torch.cat(
[
points_2d,
torch.ones([batch_size, n_points, 1], device=points.device, dtype=points.dtype),
],
dim=-1,
),
tform,
)
trans_points = torch.cat([trans_points_2d[:, :, :2], points[:, :, 2:]], dim=-1)
return trans_points
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