# coding: utf8
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import random
from typing import Callable

import cv2
import PIL
import numpy as np
import paddlehub.vision.functional as F


class Compose:
    def __init__(self, transforms, to_rgb=True, stay_rgb=False, is_permute=True):
        if not isinstance(transforms, list):
            raise TypeError('The transforms must be a list!')
        if len(transforms) < 1:
            raise ValueError('The length of transforms ' + \
                             'must be equal or larger than 1!')
        self.transforms = transforms
        self.to_rgb = to_rgb
        self.stay_rgb = stay_rgb
        self.is_permute = is_permute

    def __call__(self, im):
        if isinstance(im, str):
            im = cv2.imread(im).astype('float32')

        if im is None:
            raise ValueError('Can\'t read The image file {}!'.format(im))
        if self.to_rgb:
            im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)

        for op in self.transforms:
            im = op(im)

        if not self.stay_rgb:
            im = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)

        if self.is_permute:
            im = F.permute(im)

        return im


class RandomHorizontalFlip:
    def __init__(self, prob=0.5):
        self.prob = prob

    def __call__(self, im):
        if random.random() < self.prob:
            im = F.horizontal_flip(im)
        return im


class RandomVerticalFlip:
    def __init__(self, prob=0.1):
        self.prob = prob

    def __call__(self, im):
        if random.random() < self.prob:
            im = F.vertical_flip(im)
        return im


class Resize:
    # The interpolation mode
    interpolation_dict = {
        'NEAREST': cv2.INTER_NEAREST,
        'LINEAR': cv2.INTER_LINEAR,
        'CUBIC': cv2.INTER_CUBIC,
        'AREA': cv2.INTER_AREA,
        'LANCZOS4': cv2.INTER_LANCZOS4
    }

    def __init__(self, target_size=512, interpolation='LINEAR'):
        self.interpolation = interpolation
        if not (interpolation == "RANDOM" or interpolation in self.interpolation_dict):
            raise ValueError("interpolation should be one of {}".format(self.interpolation_dict.keys()))
        if isinstance(target_size, list) or isinstance(target_size, tuple):
            if len(target_size) != 2:
                raise TypeError(
                    'when target is list or tuple, it should include 2 elements, but it is {}'.format(target_size))
        elif not isinstance(target_size, int):
            raise TypeError("Type of target_size is invalid. Must be Integer or List or tuple, now is {}".format(
                type(target_size)))

        self.target_size = target_size

    def __call__(self, im):
        if self.interpolation == "RANDOM":
            interpolation = random.choice(list(self.interpolation_dict.keys()))
        else:
            interpolation = self.interpolation
        im = F.resize(im, self.target_size, self.interpolation_dict[interpolation])
        return im


class ResizeByLong:
    def __init__(self, long_size):
        self.long_size = long_size

    def __call__(self, im):
        im = F.resize_long(im, self.long_size)
        return im


class ResizeRangeScaling:
    def __init__(self, min_value=400, max_value=600):
        if min_value > max_value:
            raise ValueError('min_value must be less than max_value, '
                             'but they are {} and {}.'.format(min_value, max_value))
        self.min_value = min_value
        self.max_value = max_value

    def __call__(self, im):
        if self.min_value == self.max_value:
            random_size = self.max_value
        else:
            random_size = int(np.random.uniform(self.min_value, self.max_value) + 0.5)
        im = F.resize_long(im, random_size, cv2.INTER_LINEAR)
        return im


class ResizeStepScaling:
    def __init__(self, min_scale_factor=0.75, max_scale_factor=1.25, scale_step_size=0.25):
        if min_scale_factor > max_scale_factor:
            raise ValueError('min_scale_factor must be less than max_scale_factor, '
                             'but they are {} and {}.'.format(min_scale_factor, max_scale_factor))
        self.min_scale_factor = min_scale_factor
        self.max_scale_factor = max_scale_factor
        self.scale_step_size = scale_step_size

    def __call__(self, im):
        if self.min_scale_factor == self.max_scale_factor:
            scale_factor = self.min_scale_factor

        elif self.scale_step_size == 0:
            scale_factor = np.random.uniform(self.min_scale_factor, self.max_scale_factor)

        else:
            num_steps = int((self.max_scale_factor - self.min_scale_factor) / self.scale_step_size + 1)
            scale_factors = np.linspace(self.min_scale_factor, self.max_scale_factor, num_steps).tolist()
            np.random.shuffle(scale_factors)
            scale_factor = scale_factors[0]
        w = int(round(scale_factor * im.shape[1]))
        h = int(round(scale_factor * im.shape[0]))

        im = F.resize(im, (w, h), cv2.INTER_LINEAR)
        return im


class Normalize:
    def __init__(self, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]):
        self.mean = mean
        self.std = std
        if not (isinstance(self.mean, list) and isinstance(self.std, list)):
            raise ValueError("{}: input type is invalid.".format(self))
        from functools import reduce
        if reduce(lambda x, y: x * y, self.std) == 0:
            raise ValueError('{}: std is invalid!'.format(self))

    def __call__(self, im):
        mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
        std = np.array(self.std)[np.newaxis, np.newaxis, :]
        im = F.normalize(im, mean, std)
        return im


class Padding:
    def __init__(self, target_size, im_padding_value=[127.5, 127.5, 127.5]):
        if isinstance(target_size, list) or isinstance(target_size, tuple):
            if len(target_size) != 2:
                raise ValueError(
                    'when target is list or tuple, it should include 2 elements, but it is {}'.format(target_size))
        elif not isinstance(target_size, int):
            raise TypeError("Type of target_size is invalid. Must be Integer or List or tuple, now is {}".format(
                type(target_size)))
        self.target_size = target_size
        self.im_padding_value = im_padding_value

    def __call__(self, im):
        im_height, im_width = im.shape[0], im.shape[1]
        if isinstance(self.target_size, int):
            target_height = self.target_size
            target_width = self.target_size
        else:
            target_height = self.target_size[1]
            target_width = self.target_size[0]
        pad_height = target_height - im_height
        pad_width = target_width - im_width
        if pad_height < 0 or pad_width < 0:
            raise ValueError(
                'the size of image should be less than target_size, but the size of image ({}, {}), is larger than target_size ({}, {})'
                .format(im_width, im_height, target_width, target_height))
        else:
            im = cv2.copyMakeBorder(im, 0, pad_height, 0, pad_width, cv2.BORDER_CONSTANT, value=self.im_padding_value)

        return im


class RandomPaddingCrop:
    def __init__(self, crop_size=512, im_padding_value=[127.5, 127.5, 127.5]):
        if isinstance(crop_size, list) or isinstance(crop_size, tuple):
            if len(crop_size) != 2:
                raise ValueError(
                    'when crop_size is list or tuple, it should include 2 elements, but it is {}'.format(crop_size))
        elif not isinstance(crop_size, int):
            raise TypeError("Type of crop_size is invalid. Must be Integer or List or tuple, now is {}".format(
                type(crop_size)))
        self.crop_size = crop_size
        self.im_padding_value = im_padding_value

    def __call__(self, im):
        if isinstance(self.crop_size, int):
            crop_width = self.crop_size
            crop_height = self.crop_size
        else:
            crop_width = self.crop_size[0]
            crop_height = self.crop_size[1]

        img_height = im.shape[0]
        img_width = im.shape[1]

        if img_height == crop_height and img_width == crop_width:
            return im
        else:
            pad_height = max(crop_height - img_height, 0)
            pad_width = max(crop_width - img_width, 0)
            if (pad_height > 0 or pad_width > 0):
                im = cv2.copyMakeBorder(
                    im, 0, pad_height, 0, pad_width, cv2.BORDER_CONSTANT, value=self.im_padding_value)

            if crop_height > 0 and crop_width > 0:
                h_off = np.random.randint(img_height - crop_height + 1)
                w_off = np.random.randint(img_width - crop_width + 1)

                im = im[h_off:(crop_height + h_off), w_off:(w_off + crop_width), :]

            return im


class RandomBlur:
    def __init__(self, prob=0.1):
        self.prob = prob

    def __call__(self, im):
        if self.prob <= 0:
            n = 0
        elif self.prob >= 1:
            n = 1
        else:
            n = int(1.0 / self.prob)
        if n > 0:
            if np.random.randint(0, n) == 0:
                radius = np.random.randint(3, 10)
                if radius % 2 != 1:
                    radius = radius + 1
                if radius > 9:
                    radius = 9
                im = cv2.GaussianBlur(im, (radius, radius), 0, 0)

        return im


class RandomRotation:
    def __init__(self, max_rotation=15, im_padding_value=[127.5, 127.5, 127.5]):
        self.max_rotation = max_rotation
        self.im_padding_value = im_padding_value

    def __call__(self, im):
        if self.max_rotation > 0:
            (h, w) = im.shape[:2]
            do_rotation = np.random.uniform(-self.max_rotation, self.max_rotation)
            pc = (w // 2, h // 2)
            r = cv2.getRotationMatrix2D(pc, do_rotation, 1.0)
            cos = np.abs(r[0, 0])
            sin = np.abs(r[0, 1])

            nw = int((h * sin) + (w * cos))
            nh = int((h * cos) + (w * sin))

            (cx, cy) = pc
            r[0, 2] += (nw / 2) - cx
            r[1, 2] += (nh / 2) - cy
            dsize = (nw, nh)
            im = cv2.warpAffine(
                im,
                r,
                dsize=dsize,
                flags=cv2.INTER_LINEAR,
                borderMode=cv2.BORDER_CONSTANT,
                borderValue=self.im_padding_value)

        return im


class RandomScaleAspect:
    def __init__(self, min_scale=0.5, aspect_ratio=0.33):
        self.min_scale = min_scale
        self.aspect_ratio = aspect_ratio

    def __call__(self, im):
        if self.min_scale != 0 and self.aspect_ratio != 0:
            img_height = im.shape[0]
            img_width = im.shape[1]
            for i in range(0, 10):
                area = img_height * img_width
                target_area = area * np.random.uniform(self.min_scale, 1.0)
                aspectRatio = np.random.uniform(self.aspect_ratio, 1.0 / self.aspect_ratio)

                dw = int(np.sqrt(target_area * 1.0 * aspectRatio))
                dh = int(np.sqrt(target_area * 1.0 / aspectRatio))
                if (np.random.randint(10) < 5):
                    tmp = dw
                    dw = dh
                    dh = tmp

                if (dh < img_height and dw < img_width):
                    h1 = np.random.randint(0, img_height - dh)
                    w1 = np.random.randint(0, img_width - dw)

                    im = im[h1:(h1 + dh), w1:(w1 + dw), :]
                    im = cv2.resize(im, (img_width, img_height), interpolation=cv2.INTER_LINEAR)

        return im


class RandomDistort:
    def __init__(self,
                 brightness_range=0.5,
                 brightness_prob=0.5,
                 contrast_range=0.5,
                 contrast_prob=0.5,
                 saturation_range=0.5,
                 saturation_prob=0.5,
                 hue_range=18,
                 hue_prob=0.5):
        self.brightness_range = brightness_range
        self.brightness_prob = brightness_prob
        self.contrast_range = contrast_range
        self.contrast_prob = contrast_prob
        self.saturation_range = saturation_range
        self.saturation_prob = saturation_prob
        self.hue_range = hue_range
        self.hue_prob = hue_prob

    def __call__(self, im):
        brightness_lower = 1 - self.brightness_range
        brightness_upper = 1 + self.brightness_range
        contrast_lower = 1 - self.contrast_range
        contrast_upper = 1 + self.contrast_range
        saturation_lower = 1 - self.saturation_range
        saturation_upper = 1 + self.saturation_range
        hue_lower = -self.hue_range
        hue_upper = self.hue_range
        ops = ['brightness', 'contrast', 'saturation', 'hue']
        random.shuffle(ops)
        params_dict = {
            'brightness': {
                'brightness_lower': brightness_lower,
                'brightness_upper': brightness_upper
            },
            'contrast': {
                'contrast_lower': contrast_lower,
                'contrast_upper': contrast_upper
            },
            'saturation': {
                'saturation_lower': saturation_lower,
                'saturation_upper': saturation_upper
            },
            'hue': {
                'hue_lower': hue_lower,
                'hue_upper': hue_upper
            }
        }
        prob_dict = {
            'brightness': self.brightness_prob,
            'contrast': self.contrast_prob,
            'saturation': self.saturation_prob,
            'hue': self.hue_prob
        }
        im = im.astype('uint8')
        im = PIL.Image.fromarray(im)
        for id in range(4):
            params = params_dict[ops[id].__name__]
            prob = prob_dict[ops[id].__name__]
            params['im'] = im
            if np.random.uniform(0, 1) < prob:
                im = ops[id](**params)
        im = np.asarray(im).astype('float32')

        return im


class RGB2LAB:
    """
    Convert color space from RGB to LAB.
    """

    def rgb2xyz(self, rgb: np.ndarray) -> np.ndarray:
        """
        Convert color space from RGB to XYZ.

        Args:
           img(np.ndarray): Original RGB image.

        Return:
            img(np.ndarray): Converted XYZ image.
        """
        mask = (rgb > 0.04045)
        np.seterr(invalid='ignore')
        rgb = (((rgb + .055) / 1.055)**2.4) * mask + rgb / 12.92 * (1 - mask)
        rgb = np.nan_to_num(rgb)
        x = .412453 * rgb[0, :, :] + .357580 * rgb[1, :, :] + .180423 * rgb[2, :, :]
        y = .212671 * rgb[0, :, :] + .715160 * rgb[1, :, :] + .072169 * rgb[2, :, :]
        z = .019334 * rgb[0, :, :] + .119193 * rgb[1, :, :] + .950227 * rgb[2, :, :]
        out = np.concatenate((x[None, :, :], y[None, :, :], z[None, :, :]), axis=0)
        return out

    def xyz2lab(self, xyz: np.ndarray) -> np.ndarray:
        """
        Convert color space from XYZ to LAB.

        Args:
           img(np.ndarray): Original XYZ image.

        Return:
            img(np.ndarray): Converted LAB image.
        """
        sc = np.array((0.95047, 1., 1.08883))[:, None, None]
        xyz_scale = xyz / sc
        mask = (xyz_scale > .008856).astype(np.float32)
        xyz_int = np.cbrt(xyz_scale) * mask + (7.787 * xyz_scale + 16. / 116.) * (1 - mask)
        L = 116. * xyz_int[1, :, :] - 16.
        a = 500. * (xyz_int[0, :, :] - xyz_int[1, :, :])
        b = 200. * (xyz_int[1, :, :] - xyz_int[2, :, :])
        out = np.concatenate((L[None, :, :], a[None, :, :], b[None, :, :]), axis=0)
        return out

    def rgb2lab(self, rgb: np.ndarray) -> np.ndarray:
        """
        Convert color space from RGB to LAB.

        Args:
           img(np.ndarray): Original RGB image.

        Return:
            img(np.ndarray): Converted LAB image.
        """
        lab = self.xyz2lab(self.rgb2xyz(rgb))
        l_rs = (lab[[0], :, :] - 50) / 100
        ab_rs = lab[1:, :, :] / 110
        out = np.concatenate((l_rs, ab_rs), axis=0)
        return out

    def __call__(self, img: np.ndarray) -> np.ndarray:
        img = img / 255
        img = np.array(img).transpose(2, 0, 1)
        return self.rgb2lab(img)


class LAB2RGB:
    """
    Convert color space from LAB to RGB.
    """

    def __init__(self, mode: str = 'RGB2LAB'):
        self.mode = mode

    def xyz2rgb(self, xyz: np.ndarray) -> np.ndarray:
        """
        Convert color space from XYZ to RGB.

        Args:
           img(np.ndarray): Original XYZ image.

        Return:
            img(np.ndarray): Converted RGB image.
        """
        r = 3.24048134 * xyz[:, 0, :, :] - 1.53715152 * xyz[:, 1, :, :] - 0.49853633 * xyz[:, 2, :, :]
        g = -0.96925495 * xyz[:, 0, :, :] + 1.87599 * xyz[:, 1, :, :] + .04155593 * xyz[:, 2, :, :]
        b = .05564664 * xyz[:, 0, :, :] - .20404134 * xyz[:, 1, :, :] + 1.05731107 * xyz[:, 2, :, :]
        rgb = np.concatenate((r[:, None, :, :], g[:, None, :, :], b[:, None, :, :]), axis=1)
        rgb = np.maximum(rgb, 0)  # sometimes reaches a small negative number, which causes NaNs
        mask = (rgb > .0031308).astype(np.float32)
        np.seterr(invalid='ignore')
        out = (1.055 * (rgb**(1. / 2.4)) - 0.055) * mask + 12.92 * rgb * (1 - mask)
        out = np.nan_to_num(out)
        return out

    def lab2xyz(self, lab: np.ndarray) -> np.ndarray:
        """
        Convert color space from LAB to XYZ.

        Args:
           img(np.ndarray): Original LAB image.

        Return:
            img(np.ndarray): Converted XYZ image.
        """
        y_int = (lab[:, 0, :, :] + 16.) / 116.
        x_int = (lab[:, 1, :, :] / 500.) + y_int
        z_int = y_int - (lab[:, 2, :, :] / 200.)
        z_int = np.maximum(z_int, 0)
        out = np.concatenate((x_int[:, None, :, :], y_int[:, None, :, :], z_int[:, None, :, :]), axis=1)
        mask = (out > .2068966).astype(np.float32)
        np.seterr(invalid='ignore')
        out = (out**3.) * mask + (out - 16. / 116.) / 7.787 * (1 - mask)
        out = np.nan_to_num(out)
        sc = np.array((0.95047, 1., 1.08883))[None, :, None, None]
        out = out * sc
        return out

    def lab2rgb(self, lab_rs: np.ndarray) -> np.ndarray:
        """
        Convert color space from LAB to RGB.

        Args:
           img(np.ndarray): Original LAB image.

        Return:
            img(np.ndarray): Converted RGB image.
        """
        l = lab_rs[:, [0], :, :] * 100 + 50
        ab = lab_rs[:, 1:, :, :] * 110
        lab = np.concatenate((l, ab), axis=1)
        out = self.xyz2rgb(self.lab2xyz(lab))
        return out

    def __call__(self, img: np.ndarray) -> np.ndarray:
        return self.lab2rgb(img)


class ColorPostprocess:
    """
    Transform images from [0, 1] to [0, 255]

    Args:
       type(type): Type of Image value.

    Return:
        img(np.ndarray): Image in range of 0-255.
    """

    def __init__(self, type: type = np.uint8):
        self.type = type

    def __call__(self, img: np.ndarray):
        img = np.transpose(img, (1, 2, 0))
        img = np.clip(img, 0, 1) * 255
        img = img.astype(self.type)
        return img


class CenterCrop:
    """
        Crop the middle part of the image to the specified size.

        Args:
           crop_size(int): Crop size.

        Return:
            img(np.ndarray): Croped image.
    """

    def __init__(self, crop_size: int):
        self.crop_size = crop_size

    def __call__(self, img: np.ndarray):
        img_width, img_height, _ = img.shape
        crop_top = int((img_height - self.crop_size) / 2.)
        crop_left = int((img_width - self.crop_size) / 2.)
        return img[crop_left:crop_left + self.crop_size, crop_top:crop_top + self.crop_size, :]


class SetType:
    """
    Set image type.

    Args:
       type(type): Type of Image value.

    Return:
        img(np.ndarray): Transformed image.
    """

    def __init__(self, datatype: type = 'float32'):
        self.type = datatype

    def __call__(self, img: np.ndarray):
        img = img.astype(self.type)
        return img


class ResizeScaling:
    """Resize images by scaling method.

    Args:
        target(int): Target image size.
        interpolation(Callable): Interpolation method.
    """

    def __init__(self, target: int = 368, interpolation: Callable = cv2.INTER_CUBIC):
        self.target = target
        self.interpolation = interpolation

    def __call__(self, img, scale_search):
        scale = scale_search * self.target / img.shape[0]
        resize_img = cv2.resize(img, (0, 0), fx=scale, fy=scale, interpolation=self.interpolation)
        return resize_img