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# Copyright (c) Meta Platforms, Inc. and affiliates. | |
# All rights reserved. | |
# This source code is licensed under the license found in the | |
# LICENSE file in the root directory of this source tree. | |
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 # type: ignore | |
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. | |
""" | |
# Expects an image in BCHW format. May not exactly match apply_image. | |
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) | |
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) | |