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from copy import deepcopy
from typing import Tuple
import numpy as np
import torch
from torch.nn import functional as F
from torchvision.transforms.functional import resize
from torchvision.transforms.functional import to_pil_image
class ResizeLongestSide:
"""
Resizes images to the longest side 'target_length', as well as provides
methods for resizing coordinates and boxes. Provides methods for
transforming both numpy array and batched torch tensors.
"""
def __init__(self, target_length: int) -> None:
self.target_length = target_length
def apply_image(self, image: np.ndarray) -> np.ndarray:
"""
Expects a numpy array with shape HxWxC in uint8 format.
"""
target_size = self.get_preprocess_shape(
image.shape[0], image.shape[1], self.target_length
)
return np.array(resize(to_pil_image(image), target_size))
def apply_coords(
self, coords: np.ndarray, original_size: Tuple[int, ...]
) -> np.ndarray:
"""
Expects a numpy array of length 2 in the final dimension. Requires the
original image size in (H, W) format.
"""
old_h, old_w = original_size
new_h, new_w = self.get_preprocess_shape(
original_size[0], original_size[1], self.target_length
)
coords = deepcopy(coords).astype(float)
coords[..., 0] = coords[..., 0] * (new_w / old_w)
coords[..., 1] = coords[..., 1] * (new_h / old_h)
return coords
def apply_boxes(
self, boxes: np.ndarray, original_size: Tuple[int, ...]
) -> np.ndarray:
"""
Expects a numpy array shape Bx4. Requires the original image size
in (H, W) format.
"""
boxes = self.apply_coords(boxes.reshape(-1, 2, 2), original_size)
return boxes.reshape(-1, 4)
def apply_image_torch(self, image: torch.Tensor) -> torch.Tensor:
"""
Expects batched images with shape BxCxHxW and float format. This
transformation may not exactly match apply_image. apply_image is
the transformation expected by the model.
"""
target_size = self.get_preprocess_shape(
image.shape[0], image.shape[1], self.target_length
)
return F.interpolate(
image, target_size, mode="bilinear", align_corners=False, antialias=True
)
def apply_coords_torch(
self, coords: torch.Tensor, original_size: Tuple[int, ...]
) -> torch.Tensor:
"""
Expects a torch tensor with length 2 in the last dimension. Requires the
original image size in (H, W) format.
"""
old_h, old_w = original_size
new_h, new_w = self.get_preprocess_shape(
original_size[0], original_size[1], self.target_length
)
coords = deepcopy(coords).to(torch.float)
coords[..., 0] = coords[..., 0] * (new_w / old_w)
coords[..., 1] = coords[..., 1] * (new_h / old_h)
return coords
def apply_boxes_torch(
self, boxes: torch.Tensor, original_size: Tuple[int, ...]
) -> torch.Tensor:
"""
Expects a torch tensor with shape Bx4. Requires the original image
size in (H, W) format.
"""
boxes = self.apply_coords_torch(boxes.reshape(-1, 2, 2), original_size)
return boxes.reshape(-1, 4)
@staticmethod
def get_preprocess_shape(
oldh: int, oldw: int, long_side_length: int
) -> Tuple[int, int]:
"""
Compute the output size given input size and target long side length.
"""
scale = long_side_length * 1.0 / max(oldh, oldw)
newh, neww = oldh * scale, oldw * scale
neww = int(neww + 0.5)
newh = int(newh + 0.5)
return (newh, neww)
class ResizeShortestSide:
"""
Resizes images to the longest side 'target_length', as well as provides
methods for resizing coordinates and boxes. Provides methods for
transforming both numpy array and batched torch tensors.
"""
def __init__(self, target_length: int) -> None:
self.target_length = target_length
def apply_image(self, image: np.ndarray) -> np.ndarray:
"""
Expects a numpy array with shape HxWxC in uint8 format.
"""
target_size = self.get_preprocess_shape(
image.shape[0], image.shape[1], self.target_length
)
return np.array(resize(to_pil_image(image), target_size))
def apply_coords(
self, coords: np.ndarray, original_size: Tuple[int, ...]
) -> np.ndarray:
"""
Expects a numpy array of length 2 in the final dimension. Requires the
original image size in (H, W) format.
"""
old_h, old_w = original_size
new_h, new_w = self.get_preprocess_shape(
original_size[0], original_size[1], self.target_length
)
coords = deepcopy(coords).astype(float)
coords[..., 0] = coords[..., 0] * (new_w / old_w)
coords[..., 1] = coords[..., 1] * (new_h / old_h)
return coords
def apply_boxes(
self, boxes: np.ndarray, original_size: Tuple[int, ...]
) -> np.ndarray:
"""
Expects a numpy array shape Bx4. Requires the original image size
in (H, W) format.
"""
boxes = self.apply_coords(boxes.reshape(-1, 2, 2), original_size)
return boxes.reshape(-1, 4)
def apply_image_torch(self, image: torch.Tensor) -> torch.Tensor:
"""
Expects batched images with shape BxCxHxW and float format. This
transformation may not exactly match apply_image. apply_image is
the transformation expected by the model.
"""
target_size = self.get_preprocess_shape(
image.shape[0], image.shape[1], self.target_length
)
return F.interpolate(
image, target_size, mode="bilinear", align_corners=False, antialias=True
)
def apply_coords_torch(
self, coords: torch.Tensor, original_size: Tuple[int, ...]
) -> torch.Tensor:
"""
Expects a torch tensor with length 2 in the last dimension. Requires the
original image size in (H, W) format.
"""
old_h, old_w = original_size
new_h, new_w = self.get_preprocess_shape(
original_size[0], original_size[1], self.target_length
)
coords = deepcopy(coords).to(torch.float)
coords[..., 0] = coords[..., 0] * (new_w / old_w)
coords[..., 1] = coords[..., 1] * (new_h / old_h)
return coords
def apply_boxes_torch(
self, boxes: torch.Tensor, original_size: Tuple[int, ...]
) -> torch.Tensor:
"""
Expects a torch tensor with shape Bx4. Requires the original image
size in (H, W) format.
"""
boxes = self.apply_coords_torch(boxes.reshape(-1, 2, 2), original_size)
return boxes.reshape(-1, 4)
@staticmethod
def get_preprocess_shape(
oldh: int, oldw: int, short_side_length: int
) -> Tuple[int, int]:
"""
Compute the output size given input size and target long side length.
"""
scale = short_side_length * 1.0 / min(oldh, oldw)
newh, neww = oldh * scale, oldw * scale
neww = int(neww + 0.5)
newh = int(newh + 0.5)
return (newh, neww)
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