joint_transforms.py

import math
import numbers
import random

import torch
from PIL import Image, ImageOps
import numpy as np
import torchvision.transforms.functional as F


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

    def __call__(self, img, mask):
        assert img.size == mask.size
        for t in self.transforms:
            img, mask = t(img, mask)
        return img, mask


class RandomCrop(object):
    def __init__(self, size, padding=0):
        if isinstance(size, numbers.Number):
            self.size = (int(size), int(size))
        else:
            self.size = size
        self.padding = padding

    def __call__(self, img, mask):
        if self.padding > 0:
            img = ImageOps.expand(img, border=self.padding, fill=0)
            mask = ImageOps.expand(mask, border=self.padding, fill=0)

        assert img.size == mask.size
        w, h = img.size
        th, tw = self.size
        if w == tw and h == th:
            return img, mask
        if w < tw or h < th:
            return img.resize((tw, th), Image.BILINEAR), mask.resize((tw, th), Image.NEAREST)

        x1 = random.randint(0, w - tw)
        y1 = random.randint(0, h - th)
        return img.crop((x1, y1, x1 + tw, y1 + th)), mask.crop((x1, y1, x1 + tw, y1 + th))


class CenterCrop(object):
    def __init__(self, size):
        if isinstance(size, numbers.Number):
            self.size = (int(size), int(size))
        else:
            self.size = size

    def __call__(self, img, mask):
        assert img.size == mask.size
        w, h = img.size
        th, tw = self.size
        x1 = int(round((w - tw) / 2.))
        y1 = int(round((h - th) / 2.))
        return img.crop((x1, y1, x1 + tw, y1 + th)), mask.crop((x1, y1, x1 + tw, y1 + th))

class RandomHorizontallyFlip(object):
    def __init__(self, p=0.5):
        self.p = p

    def __call__(self, img, mask):

        if torch.rand(1) < self.p:
            return F.hflip(img), F.hflip(mask)
        return img, mask



class FreeScale(object):
    def __init__(self, size):
        self.size = tuple(reversed(size))  # size: (h, w)

    def __call__(self, img, mask):
        assert img.size == mask.size
        return img.resize(self.size, Image.BILINEAR), mask.resize(self.size, Image.NEAREST)


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

    def __call__(self, img, mask):
        assert img.size == mask.size
        w, h = img.size
        if (w >= h and w == self.size) or (h >= w and h == self.size):
            return img, mask
        if w > h:
            ow = self.size
            oh = int(self.size * h / w)
            return img.resize((ow, oh), Image.BILINEAR), mask.resize((ow, oh), Image.NEAREST)
        else:
            oh = self.size
            ow = int(self.size * w / h)
            return img.resize((ow, oh), Image.BILINEAR), mask.resize((ow, oh), Image.NEAREST)


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

    def __call__(self, img, mask):
        assert img.size == mask.size
        for attempt in range(10):
            area = img.size[0] * img.size[1]
            target_area = random.uniform(0.45, 1.0) * area
            aspect_ratio = random.uniform(0.5, 2)

            w = int(round(math.sqrt(target_area * aspect_ratio)))
            h = int(round(math.sqrt(target_area / aspect_ratio)))

            if random.random() < 0.5:
                w, h = h, w

            if w <= img.size[0] and h <= img.size[1]:
                x1 = random.randint(0, img.size[0] - w)
                y1 = random.randint(0, img.size[1] - h)

                img = img.crop((x1, y1, x1 + w, y1 + h))
                mask = mask.crop((x1, y1, x1 + w, y1 + h))
                assert (img.size == (w, h))

                return img.resize((self.size, self.size), Image.BILINEAR), mask.resize((self.size, self.size),
                                                                                       Image.NEAREST)

        # Fallback
        scale = Scale(self.size)
        crop = CenterCrop(self.size)
        return crop(*scale(img, mask))


class RandomRotate(object):
    def __init__(self, degree):
        self.degree = degree

    def __call__(self, img, mask):
        rotate_degree = random.random() * 2 * self.degree - self.degree
        return img.rotate(rotate_degree, Image.BILINEAR), mask.rotate(rotate_degree, Image.NEAREST)


class RandomSized(object):
    def __init__(self, size):
        self.size = size
        self.scale = Scale(self.size)
        self.crop = RandomCrop(self.size)

    def __call__(self, img, mask):
        assert img.size == mask.size

        w = int(random.uniform(0.5, 2) * img.size[0])
        h = int(random.uniform(0.5, 2) * img.size[1])

        img, mask = img.resize((w, h), Image.BILINEAR), mask.resize((w, h), Image.NEAREST)

        return self.crop(*self.scale(img, mask))


class SlidingCropOld(object):
    def __init__(self, crop_size, stride_rate, ignore_label):
        self.crop_size = crop_size
        self.stride_rate = stride_rate
        self.ignore_label = ignore_label

    def _pad(self, img, mask):
        h, w = img.shape[: 2]
        pad_h = max(self.crop_size - h, 0)
        pad_w = max(self.crop_size - w, 0)
        img = np.pad(img, ((0, pad_h), (0, pad_w), (0, 0)), 'constant')
        mask = np.pad(mask, ((0, pad_h), (0, pad_w)), 'constant', constant_values=self.ignore_label)
        return img, mask

    def __call__(self, img, mask):
        assert img.size == mask.size

        w, h = img.size
        long_size = max(h, w)

        img = np.array(img)
        mask = np.array(mask)

        if long_size > self.crop_size:
            stride = int(math.ceil(self.crop_size * self.stride_rate))
            h_step_num = int(math.ceil((h - self.crop_size) / float(stride))) + 1
            w_step_num = int(math.ceil((w - self.crop_size) / float(stride))) + 1
            img_sublist, mask_sublist = [], []
            for yy in range(h_step_num):
                for xx in range(w_step_num):
                    sy, sx = yy * stride, xx * stride
                    ey, ex = sy + self.crop_size, sx + self.crop_size
                    img_sub = img[sy: ey, sx: ex, :]
                    mask_sub = mask[sy: ey, sx: ex]
                    img_sub, mask_sub = self._pad(img_sub, mask_sub)
                    img_sublist.append(Image.fromarray(img_sub.astype(np.uint8)).convert('RGB'))
                    mask_sublist.append(Image.fromarray(mask_sub.astype(np.uint8)).convert('P'))
            return img_sublist, mask_sublist
        else:
            img, mask = self._pad(img, mask)
            img = Image.fromarray(img.astype(np.uint8)).convert('RGB')
            mask = Image.fromarray(mask.astype(np.uint8)).convert('P')
            return img, mask


class SlidingCrop(object):
    def __init__(self, crop_size, stride_rate, ignore_label):
        self.crop_size = crop_size
        self.stride_rate = stride_rate
        self.ignore_label = ignore_label

    def _pad(self, img, mask):
        h, w = img.shape[: 2]
        pad_h = max(self.crop_size - h, 0)
        pad_w = max(self.crop_size - w, 0)
        img = np.pad(img, ((0, pad_h), (0, pad_w), (0, 0)), 'constant')
        mask = np.pad(mask, ((0, pad_h), (0, pad_w)), 'constant', constant_values=self.ignore_label)
        return img, mask, h, w

    def __call__(self, img, mask):
        assert img.size == mask.size

        w, h = img.size
        long_size = max(h, w)

        img = np.array(img)
        mask = np.array(mask)

        if long_size > self.crop_size:
            stride = int(math.ceil(self.crop_size * self.stride_rate))
            h_step_num = int(math.ceil((h - self.crop_size) / float(stride))) + 1
            w_step_num = int(math.ceil((w - self.crop_size) / float(stride))) + 1
            img_slices, mask_slices, slices_info = [], [], []
            for yy in range(h_step_num):
                for xx in range(w_step_num):
                    sy, sx = yy * stride, xx * stride
                    ey, ex = sy + self.crop_size, sx + self.crop_size
                    img_sub = img[sy: ey, sx: ex, :]
                    mask_sub = mask[sy: ey, sx: ex]
                    img_sub, mask_sub, sub_h, sub_w = self._pad(img_sub, mask_sub)
                    img_slices.append(Image.fromarray(img_sub.astype(np.uint8)).convert('RGB'))
                    mask_slices.append(Image.fromarray(mask_sub.astype(np.uint8)).convert('P'))
                    slices_info.append([sy, ey, sx, ex, sub_h, sub_w])
            return img_slices, mask_slices, slices_info
        else:
            img, mask, sub_h, sub_w = self._pad(img, mask)
            img = Image.fromarray(img.astype(np.uint8)).convert('RGB')
            mask = Image.fromarray(mask.astype(np.uint8)).convert('P')
            return [img], [mask], [[0, sub_h, 0, sub_w, sub_h, sub_w]]

class Resize(object):

    def __init__(self, size):
        self.size = size

    def __call__(self, img, mask):

        img  = F.resize(img, self.size, F.InterpolationMode.BILINEAR)
        mask = F.resize(mask, self.size, F.InterpolationMode.NEAREST)

        return img, mask

class RandomVerticalFlip(object):
    def __init__(self, p=0.5):
        self.p = p


    def __call__(self, img, mask):
        if torch.rand(1) < self.p:
            return F.vflip(img), F.vflip(mask)
        return img, mask