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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
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)
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