transforms.py

import numpy as np
import random

import torch
from torchvision import transforms as T
from torchvision.transforms import functional as F


def pad_if_smaller(img, size, fill=0):
    # 如果图像最小边长小于给定size，则用数值fill进行padding
    min_size = min(img.size)
    if min_size < size:
        ow, oh = img.size
        padh = size - oh if oh < size else 0
        padw = size - ow if ow < size else 0
        img = F.pad(img, (0, 0, padw, padh), fill=fill)
    return img


class Compose(object):
    def __init__(self, transforms):
        self.transforms = transforms

    def __call__(self, image, target):
        for t in self.transforms:
            image, target = t(image, target)
        return image, target


class RandomResize(object):
    def __init__(self, min_size, max_size=None):
        self.min_size = min_size
        if max_size is None:
            max_size = min_size
        self.max_size = max_size

    def __call__(self, image, target):
        size = random.randint(self.min_size, self.max_size)
        # 这里size传入的是int类型，所以是将图像的最小边长缩放到size大小
        image = F.resize(image, [size,size])
        # 这里的interpolation注意下，在torchvision(0.9.0)以后才有InterpolationMode.NEAREST
        # 如果是之前的版本需要使用PIL.Image.NEAREST
        target = F.resize(target, [size,size], interpolation=T.InterpolationMode.NEAREST)
        return image, target


class RandomHorizontalFlip(object):
    def __init__(self, flip_prob):
        self.flip_prob = flip_prob

    def __call__(self, image, target):
        if random.random() < self.flip_prob:
            image = F.hflip(image)
            target = F.hflip(target)
            # w, _ = image.size
            # boxes = boxes.clone()
            # boxes[:, [0, 2]] = w - boxes[:, [2, 0]]  # x_min, x_max = w - x_max, w - x_min
        return image, target


class RandomCrop(object):
    def __init__(self, size):
        self.size = size

    def __call__(self, image, target):
        image = pad_if_smaller(image, self.size)
        target = pad_if_smaller(target, self.size, fill=255)
        crop_params = T.RandomCrop.get_params(image, (self.size, self.size))
        image = F.crop(image, *crop_params)
        target = F.crop(target, *crop_params)
        return image, target


class CenterCrop(object):
    def __init__(self, size):
        # size定义了剪裁的高和宽
        self.size = size

    def __call__(self, image, target):
        # 如果图像或目标小于指定的尺寸，则进行填充
        image = pad_if_smaller(image, self.size)
        target = pad_if_smaller(target, self.size, fill=255)

        # 计算中心剪裁的参数
        image_width, image_height = image.size
        crop_height, crop_width = self.size, self.size

        # 计算中心点
        center_x, center_y = image_width // 2, image_height // 2

        # 计算剪裁的左上角坐标
        top = center_y - crop_height // 2
        left = center_x - crop_width // 2

        # 使用中心点坐标进行剪裁
        image = F.crop(image, top, left, crop_height, crop_width)
        target = F.crop(target, top, left, crop_height, crop_width)

        return image, target


# 如果图像或目标小于指定尺寸的辅助函数，你需要根据实际情况来实现这个函数
def pad_if_smaller(img, size, fill=0):
    min_size = min(img.size)
    if min_size < size:
        ow, oh = img.size
        padh = size - oh if oh < size else 0
        padw = size - ow if ow < size else 0
        img = F.pad(img, (0, 0, padw, padh), fill=fill)
    return img

class CenterCrop(object):
    def __init__(self, size):
        self.size = size

    def __call__(self, image, target):
        image = F.center_crop(image, self.size)
        target = F.center_crop(target, self.size)
        return image, target


class ToTensor(object):
    def __call__(self, image, target):
        image = F.to_tensor(image)
        target = torch.as_tensor(np.array(target), dtype=torch.int64)
        return image, target


class Normalize(object):
    def __init__(self, mean, std):
        self.mean = mean
        self.std = std

    def __call__(self, image, target):
        image = F.normalize(image, mean=self.mean, std=self.std)
        return image, target