# Source code for torchreid.data.transforms

    from __future__ import absolute_import
    from __future__ import division
    from __future__ import print_function

    from PIL import Image
    import random
    import numpy as np
    import math

    import torch
    from torchvision.transforms import *

    class Random2DTranslation(object):
        """Randomly translates the input image with a probability.

        Specifically, given a predefined shape (height, width), the input is first
        resized with a factor of 1.25, leading to (height*1.25, width*1.25), then
        a random crop is performed. Such operation is done with a probability.

        Args:
            height (int): target image height.
            width (int): target image width.
            p (float, optional): probability that this operation takes place.
                Default is 0.5.
            interpolation (int, optional): desired interpolation. Default is
                ``PIL.Image.BILINEAR``
        """

        def __init__(self, height, width, p=0.5, interpolation=Image.BILINEAR):
            self.height = height
            self.width = width
            self.p = p
            self.interpolation = interpolation

        def __call__(self, img):
            if random.uniform(0, 1) > self.p:
                return img.resize((self.width, self.height), self.interpolation)

            new_width, new_height = int(round(self.width * 1.125)), int(round(self.height * 1.125))
            resized_img = img.resize((new_width, new_height), self.interpolation)
            x_maxrange = new_width - self.width
            y_maxrange = new_height - self.height
            x1 = int(round(random.uniform(0, x_maxrange)))
            y1 = int(round(random.uniform(0, y_maxrange)))
            croped_img = resized_img.crop((x1, y1, x1 + self.width, y1 + self.height))
            return croped_img

    class RandomErasing(object):
        """Randomly erases an image patch.

        Origin: `<https://github.com/zhunzhong07/Random-Erasing>`_

        Reference:
            Zhong et al. Random Erasing Data Augmentation.

        Args:
            probability (float, optional): probability that this operation takes place.
                Default is 0.5.
            sl (float, optional): min erasing area.
            sh (float, optional): max erasing area.
            r1 (float, optional): min aspect ratio.
            mean (list, optional): erasing value.
        """

        def __init__(self, probability=0.5, sl=0.02, sh=0.4, r1=0.3, mean=[0.4914, 0.4822, 0.4465]):
            self.probability = probability
            self.mean = mean
            self.sl = sl
            self.sh = sh
            self.r1 = r1

        def __call__(self, img):
            if random.uniform(0, 1) > self.probability:
                return img

            for attempt in range(100):
                area = img.size()[1] * img.size()[2]

                target_area = random.uniform(self.sl, self.sh) * area
                aspect_ratio = random.uniform(self.r1, 1/self.r1)

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

                if w < img.size()[2] and h < img.size()[1]:
                    x1 = random.randint(0, img.size()[1] - h)
                    y1 = random.randint(0, img.size()[2] - w)
                    if img.size()[0] == 3:
                        img[0, x1:x1+h, y1:y1+w] = self.mean[0]
                        img[1, x1:x1+h, y1:y1+w] = self.mean[1]
                        img[2, x1:x1+h, y1:y1+w] = self.mean[2]
                    else:
                        img[0, x1:x1+h, y1:y1+w] = self.mean[0]
                    return img

            return img

    class ColorAugmentation(object):
        """Randomly alters the intensities of RGB channels.

        Reference:
            Krizhevsky et al. ImageNet Classification with Deep ConvolutionalNeural
            Networks. NIPS 2012.

        Args:
            p (float, optional): probability that this operation takes place.
                Default is 0.5.
        """

        def __init__(self, p=0.5):
            self.p = p
            self.eig_vec = torch.Tensor([
                [0.4009, 0.7192, -0.5675],
                [-0.8140, -0.0045, -0.5808],
                [0.4203, -0.6948, -0.5836],
            ])
            self.eig_val = torch.Tensor([[0.2175, 0.0188, 0.0045]])

        def _check_input(self, tensor):
            assert tensor.dim() == 3 and tensor.size(0) == 3

        def __call__(self, tensor):
            if random.uniform(0, 1) > self.p:
                return tensor
            alpha = torch.normal(mean=torch.zeros_like(self.eig_val)) * 0.1
            quatity = torch.mm(self.eig_val * alpha, self.eig_vec)
            tensor = tensor + quatity.view(3, 1, 1)
            return tensor

    def build_transforms(height, width, random_erase=False, color_jitter=False,
                         color_aug=False, norm_mean=[0.485, 0.456, 0.406],
                         norm_std=[0.229, 0.224, 0.225], **kwargs):
        """Builds train and test transform functions

        Args:
            height (int): target image height.
            width (int): target image width.
            random_erase (bool, optional): use random erasing. Default is False.
            color_jitter (bool, optional): use color jittering. Default is False.
            color_aug (bool, optional): use color augmentation. Default is False.
            norm_mean (list): normalization mean values. Default is ImageNet means.
            norm_std (list): normalization standard deviation values. Default is
                ImageNet standard deviation values.
        """
        normalize = Normalize(mean=norm_mean, std=norm_std)

        # build train transformations
        transform_tr = []
        transform_tr += [Random2DTranslation(height, width)]
        transform_tr += [RandomHorizontalFlip()]
        if color_jitter:
            transform_tr += [ColorJitter(brightness=0.2, contrast=0.15, saturation=0, hue=0)]
        transform_tr += [ToTensor()]
        if color_aug:
            transform_tr += [ColorAugmentation()]
        transform_tr += [normalize]
        if random_erase:
            transform_tr += [RandomErasing()]
        transform_tr = Compose(transform_tr)

        # build test transformations
        transform_te = Compose([
            Resize((height, width)),
            ToTensor(),
            normalize,
        ])

        return transform_tr, transform_te