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