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| # -*- coding: utf-8 -*- | |
| # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved | |
| # Part of the code is from https://github.com/rwightman/efficientdet-pytorch/blob/master/effdet/data/transforms.py | |
| # Modified by Xingyi Zhou | |
| # The original code is under Apache-2.0 License | |
| import numpy as np | |
| import sys | |
| from fvcore.transforms.transform import ( | |
| BlendTransform, | |
| CropTransform, | |
| HFlipTransform, | |
| NoOpTransform, | |
| Transform, | |
| VFlipTransform, | |
| ) | |
| from PIL import Image | |
| from detectron2.data.transforms.augmentation import Augmentation | |
| from .custom_transform import EfficientDetResizeCropTransform | |
| __all__ = [ | |
| "EfficientDetResizeCrop", | |
| ] | |
| class EfficientDetResizeCrop(Augmentation): | |
| """ | |
| Scale the shorter edge to the given size, with a limit of `max_size` on the longer edge. | |
| If `max_size` is reached, then downscale so that the longer edge does not exceed max_size. | |
| """ | |
| def __init__( | |
| self, size, scale, interp=Image.BILINEAR | |
| ): | |
| """ | |
| """ | |
| super().__init__() | |
| self.target_size = (size, size) | |
| self.scale = scale | |
| self.interp = interp | |
| def get_transform(self, img): | |
| # Select a random scale factor. | |
| scale_factor = np.random.uniform(*self.scale) | |
| scaled_target_height = scale_factor * self.target_size[0] | |
| scaled_target_width = scale_factor * self.target_size[1] | |
| # Recompute the accurate scale_factor using rounded scaled image size. | |
| width, height = img.shape[1], img.shape[0] | |
| img_scale_y = scaled_target_height / height | |
| img_scale_x = scaled_target_width / width | |
| img_scale = min(img_scale_y, img_scale_x) | |
| # Select non-zero random offset (x, y) if scaled image is larger than target size | |
| scaled_h = int(height * img_scale) | |
| scaled_w = int(width * img_scale) | |
| offset_y = scaled_h - self.target_size[0] | |
| offset_x = scaled_w - self.target_size[1] | |
| offset_y = int(max(0.0, float(offset_y)) * np.random.uniform(0, 1)) | |
| offset_x = int(max(0.0, float(offset_x)) * np.random.uniform(0, 1)) | |
| return EfficientDetResizeCropTransform( | |
| scaled_h, scaled_w, offset_y, offset_x, img_scale, self.target_size, self.interp) | |