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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 | |
| import random | |
| class RandomScale: | |
| """ | |
| 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, max_length: int, min_length: int) -> None: | |
| self.max_length = max_length | |
| self.min_length = min_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.max_length, self.min_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.max_length, self.min_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.max_length, self.min_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.max_length, self.min_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, max_length: int, min_length: int | |
| ) -> Tuple[int, int]: | |
| """ | |
| Compute the output size given input size and target long side length. | |
| """ | |
| max_scale = max_length * 1.0 / max(oldh, oldw) | |
| min_scale = min_length * 1.0 / max(oldh, oldw) | |
| scale = min_scale + random.random() * (max_scale-min_scale) | |
| newh, neww = oldh * scale, oldw * scale | |
| neww = int(neww + 0.5) | |
| newh = int(newh + 0.5) | |
| return (newh, neww) | |