# The code is modified based on BasicSR metrics:
# https://github.com/XPixelGroup/BasicSR/tree/master/basicsr/metrics

from typing import Dict

import cv2
import numpy as np

from modelscope.metainfo import Metrics
from modelscope.utils.registry import default_group
from .base import Metric
from .builder import METRICS, MetricKeys


def bgr2ycbcr(img, y_only=False):
    """Convert a BGR image to YCbCr image.

    The bgr version of rgb2ycbcr.
    It implements the ITU-R BT.601 conversion for standard-definition
    television. See more details in
    https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.

    It differs from a similar function in cv2.cvtColor: `BGR <-> YCrCb`.
    In OpenCV, it implements a JPEG conversion. See more details in
    https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.

    Args:
        img (ndarray): The input image. It accepts:
            1. np.uint8 type with range [0, 255];
            2. np.float32 type with range [0, 1].
        y_only (bool): Whether to only return Y channel. Default: False.

    Returns:
        ndarray: The converted YCbCr image. The output image has the same type
            and range as input image.
    """
    img_type = img.dtype
    img = _convert_input_type_range(img)
    if y_only:
        out_img = np.dot(img, [24.966, 128.553, 65.481]) + 16.0
    else:
        out_img = np.matmul(
            img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786],
                  [65.481, -37.797, 112.0]]) + [16, 128, 128]
    out_img = _convert_output_type_range(out_img, img_type)
    return out_img


def reorder_image(img, input_order='HWC'):
    """Reorder images to 'HWC' order.

    If the input_order is (h, w), return (h, w, 1);
    If the input_order is (c, h, w), return (h, w, c);
    If the input_order is (h, w, c), return as it is.

    Args:
        img (ndarray): Input image.
        input_order (str): Whether the input order is 'HWC' or 'CHW'.
            If the input image shape is (h, w), input_order will not have
            effects. Default: 'HWC'.

    Returns:
        ndarray: reordered image.
    """

    if input_order not in ['HWC', 'CHW']:
        raise ValueError(
            f"Wrong input_order {input_order}. Supported input_orders are 'HWC' and 'CHW'"
        )
    if len(img.shape) == 2:
        img = img[..., None]
    if input_order == 'CHW':
        img = img.transpose(1, 2, 0)
    return img


def to_y_channel(img):
    """Change to Y channel of YCbCr.

    Args:
        img (ndarray): Images with range [0, 255].

    Returns:
        (ndarray): Images with range [0, 255] (float type) without round.
    """
    img = img.astype(np.float32) / 255.
    if img.ndim == 3 and img.shape[2] == 3:
        img = bgr2ycbcr(img, y_only=True)
        img = img[..., None]
    return img * 255.


def _ssim(img, img2):
    """Calculate SSIM (structural similarity) for one channel images.

    It is called by func:`calculate_ssim`.

    Args:
        img (ndarray): Images with range [0, 255] with order 'HWC'.
        img2 (ndarray): Images with range [0, 255] with order 'HWC'.

    Returns:
        float: SSIM result.
    """

    c1 = (0.01 * 255)**2
    c2 = (0.03 * 255)**2
    kernel = cv2.getGaussianKernel(11, 1.5)
    window = np.outer(kernel, kernel.transpose())

    mu1 = cv2.filter2D(img, -1, window)[5:-5,
                                        5:-5]  # valid mode for window size 11
    mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
    mu1_sq = mu1**2
    mu2_sq = mu2**2
    mu1_mu2 = mu1 * mu2
    sigma1_sq = cv2.filter2D(img**2, -1, window)[5:-5, 5:-5] - mu1_sq
    sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
    sigma12 = cv2.filter2D(img * img2, -1, window)[5:-5, 5:-5] - mu1_mu2

    tmp1 = (2 * mu1_mu2 + c1) * (2 * sigma12 + c2)
    tmp2 = (mu1_sq + mu2_sq + c1) * (sigma1_sq + sigma2_sq + c2)
    ssim_map = tmp1 / tmp2

    return ssim_map.mean()


def calculate_psnr(img,
                   img2,
                   crop_border,
                   input_order='HWC',
                   test_y_channel=False,
                   **kwargs):
    """Calculate PSNR (Peak Signal-to-Noise Ratio).

    Ref: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio

    Args:
        img (ndarray): Images with range [0, 255].
        img2 (ndarray): Images with range [0, 255].
        crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the calculation.
        input_order (str): Whether the input order is 'HWC' or 'CHW'. Default: 'HWC'.
        test_y_channel (bool): Test on Y channel of YCbCr. Default: False.

    Returns:
        float: PSNR result.
    """

    assert img.shape == img2.shape, (
        f'Image shapes are different: {img.shape}, {img2.shape}.')
    if input_order not in ['HWC', 'CHW']:
        raise ValueError(
            f'Wrong input_order {input_order}. Supported input_orders are "HWC" and "CHW"'
        )
    img = reorder_image(img, input_order=input_order)
    img2 = reorder_image(img2, input_order=input_order)

    if crop_border != 0:
        img = img[crop_border:-crop_border, crop_border:-crop_border, ...]
        img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...]

    if test_y_channel:
        img = to_y_channel(img)
        img2 = to_y_channel(img2)

    img = img.astype(np.float64)
    img2 = img2.astype(np.float64)

    mse = np.mean((img - img2)**2)
    if mse == 0:
        return float('inf')
    return 10. * np.log10(255. * 255. / mse)


def calculate_ssim(img,
                   img2,
                   crop_border,
                   input_order='HWC',
                   test_y_channel=False,
                   **kwargs):
    """Calculate SSIM (structural similarity).

    Ref:
    Image quality assessment: From error visibility to structural similarity

    The results are the same as that of the official released MATLAB code in
    https://ece.uwaterloo.ca/~z70wang/research/ssim/.

    For three-channel images, SSIM is calculated for each channel and then
    averaged.

    Args:
        img (ndarray): Images with range [0, 255].
        img2 (ndarray): Images with range [0, 255].
        crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the calculation.
        input_order (str): Whether the input order is 'HWC' or 'CHW'.
            Default: 'HWC'.
        test_y_channel (bool): Test on Y channel of YCbCr. Default: False.

    Returns:
        float: SSIM result.
    """

    assert img.shape == img2.shape, (
        f'Image shapes are different: {img.shape}, {img2.shape}.')
    if input_order not in ['HWC', 'CHW']:
        raise ValueError(
            f'Wrong input_order {input_order}. Supported input_orders are "HWC" and "CHW"'
        )
    img = reorder_image(img, input_order=input_order)
    img2 = reorder_image(img2, input_order=input_order)

    if crop_border != 0:
        img = img[crop_border:-crop_border, crop_border:-crop_border, ...]
        img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...]

    if test_y_channel:
        img = to_y_channel(img)
        img2 = to_y_channel(img2)

    img = img.astype(np.float64)
    img2 = img2.astype(np.float64)

    ssims = []
    for i in range(img.shape[2]):
        ssims.append(_ssim(img[..., i], img2[..., i]))
    return np.array(ssims).mean()


@METRICS.register_module(
    group_key=default_group, module_name=Metrics.image_color_enhance_metric)
class ImageColorEnhanceMetric(Metric):
    """The metric computation class for image color enhance classes.
    """

    def __init__(self):
        self.preds = []
        self.targets = []

    def add(self, outputs: Dict, inputs: Dict):
        ground_truths = outputs['target']
        eval_results = outputs['pred']
        self.preds.extend(eval_results)
        self.targets.extend(ground_truths)

    def evaluate(self):
        psnrs = [
            calculate_psnr(pred, target, 2, test_y_channel=False)
            for pred, target in zip(self.preds, self.targets)
        ]
        ssims = [
            calculate_ssim(pred, target, 2, test_y_channel=False)
            for pred, target in zip(self.preds, self.targets)
        ]
        return {
            MetricKeys.PSNR: sum(psnrs) / len(psnrs),
            MetricKeys.SSIM: sum(ssims) / len(ssims)
        }
