# -----------------------------------------------------------------------------------
# https://github.com/JingyunLiang/SwinIR/blob/main/utils/util_calculate_psnr_ssim.py
# -----------------------------------------------------------------------------------

import cv2
import torch
import numpy as np

def calculate_psnr(img1, img2, crop_border, input_order='HWC', test_y_channel=False):
    """Calculate PSNR (Peak Signal-to-Noise Ratio).
    Ref: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio
    Args:
        img1 (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 PSNR 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 img1.shape == img2.shape, (f'Image shapes are differnet: {img1.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"')
    img1 = reorder_image(img1, input_order=input_order)
    img2 = reorder_image(img2, input_order=input_order)
    img1 = img1.astype(np.float64)
    img2 = img2.astype(np.float64)

    if crop_border != 0:
        img1 = img1[crop_border:-crop_border, crop_border:-crop_border, ...]
        img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...]

    if test_y_channel:
        img1 = to_y_channel(img1)
        img2 = to_y_channel(img2)

    mse = np.mean((img1 - img2) ** 2)
    if mse == 0:
        return float('inf')
    return 20. * np.log10(255. / np.sqrt(mse))


def _ssim(img1, img2):
    """Calculate SSIM (structural similarity) for one channel images.
    It is called by func:`calculate_ssim`.
    Args:
        img1 (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

    img1 = img1.astype(np.float64)
    img2 = img2.astype(np.float64)
    kernel = cv2.getGaussianKernel(11, 1.5)
    window = np.outer(kernel, kernel.transpose())

    mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5]
    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(img1 ** 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(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2

    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
    return ssim_map.mean()


def calculate_ssim(img1, img2, crop_border, input_order='HWC', test_y_channel=False):
    """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:
        img1 (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 SSIM 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 img1.shape == img2.shape, (f'Image shapes are differnet: {img1.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"')
    img1 = reorder_image(img1, input_order=input_order)
    img2 = reorder_image(img2, input_order=input_order)
    img1 = img1.astype(np.float64)
    img2 = img2.astype(np.float64)

    if crop_border != 0:
        img1 = img1[crop_border:-crop_border, crop_border:-crop_border, ...]
        img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...]

    if test_y_channel:
        img1 = to_y_channel(img1)
        img2 = to_y_channel(img2)

    ssims = []
    for i in range(img1.shape[2]):
        ssims.append(_ssim(img1[..., i], img2[..., i]))
    return np.array(ssims).mean()


def _blocking_effect_factor(im):
    block_size = 8

    block_horizontal_positions = torch.arange(7, im.shape[3] - 1, 8)
    block_vertical_positions = torch.arange(7, im.shape[2] - 1, 8)

    horizontal_block_difference = (
                (im[:, :, :, block_horizontal_positions] - im[:, :, :, block_horizontal_positions + 1]) ** 2).sum(
        3).sum(2).sum(1)
    vertical_block_difference = (
                (im[:, :, block_vertical_positions, :] - im[:, :, block_vertical_positions + 1, :]) ** 2).sum(3).sum(
        2).sum(1)

    nonblock_horizontal_positions = np.setdiff1d(torch.arange(0, im.shape[3] - 1), block_horizontal_positions)
    nonblock_vertical_positions = np.setdiff1d(torch.arange(0, im.shape[2] - 1), block_vertical_positions)

    horizontal_nonblock_difference = (
                (im[:, :, :, nonblock_horizontal_positions] - im[:, :, :, nonblock_horizontal_positions + 1]) ** 2).sum(
        3).sum(2).sum(1)
    vertical_nonblock_difference = (
                (im[:, :, nonblock_vertical_positions, :] - im[:, :, nonblock_vertical_positions + 1, :]) ** 2).sum(
        3).sum(2).sum(1)

    n_boundary_horiz = im.shape[2] * (im.shape[3] // block_size - 1)
    n_boundary_vert = im.shape[3] * (im.shape[2] // block_size - 1)
    boundary_difference = (horizontal_block_difference + vertical_block_difference) / (
                n_boundary_horiz + n_boundary_vert)

    n_nonboundary_horiz = im.shape[2] * (im.shape[3] - 1) - n_boundary_horiz
    n_nonboundary_vert = im.shape[3] * (im.shape[2] - 1) - n_boundary_vert
    nonboundary_difference = (horizontal_nonblock_difference + vertical_nonblock_difference) / (
                n_nonboundary_horiz + n_nonboundary_vert)

    scaler = np.log2(block_size) / np.log2(min([im.shape[2], im.shape[3]]))
    bef = scaler * (boundary_difference - nonboundary_difference)

    bef[boundary_difference <= nonboundary_difference] = 0
    return bef


def calculate_psnrb(img1, img2, crop_border, input_order='HWC', test_y_channel=False):
    """Calculate PSNR-B (Peak Signal-to-Noise Ratio).
    Ref: Quality assessment of deblocked images, for JPEG image deblocking evaluation
    # https://gitlab.com/Queuecumber/quantization-guided-ac/-/blob/master/metrics/psnrb.py
    Args:
        img1 (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 PSNR 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 img1.shape == img2.shape, (f'Image shapes are differnet: {img1.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"')
    img1 = reorder_image(img1, input_order=input_order)
    img2 = reorder_image(img2, input_order=input_order)
    img1 = img1.astype(np.float64)
    img2 = img2.astype(np.float64)

    if crop_border != 0:
        img1 = img1[crop_border:-crop_border, crop_border:-crop_border, ...]
        img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...]

    if test_y_channel:
        img1 = to_y_channel(img1)
        img2 = to_y_channel(img2)

    # follow https://gitlab.com/Queuecumber/quantization-guided-ac/-/blob/master/metrics/psnrb.py
    img1 = torch.from_numpy(img1).permute(2, 0, 1).unsqueeze(0) / 255.
    img2 = torch.from_numpy(img2).permute(2, 0, 1).unsqueeze(0) / 255.

    total = 0
    for c in range(img1.shape[1]):
        mse = torch.nn.functional.mse_loss(img1[:, c:c + 1, :, :], img2[:, c:c + 1, :, :], reduction='none')
        bef = _blocking_effect_factor(img1[:, c:c + 1, :, :])

        mse = mse.view(mse.shape[0], -1).mean(1)
        total += 10 * torch.log10(1 / (mse + bef))

    return float(total) / img1.shape[1]


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 _convert_input_type_range(img):
    """Convert the type and range of the input image.
    It converts the input image to np.float32 type and range of [0, 1].
    It is mainly used for pre-processing the input image in colorspace
    convertion functions such as rgb2ycbcr and ycbcr2rgb.
    Args:
        img (ndarray): The input image. It accepts:
            1. np.uint8 type with range [0, 255];
            2. np.float32 type with range [0, 1].
    Returns:
        (ndarray): The converted image with type of np.float32 and range of
            [0, 1].
    """
    img_type = img.dtype
    img = img.astype(np.float32)
    if img_type == np.float32:
        pass
    elif img_type == np.uint8:
        img /= 255.
    else:
        raise TypeError('The img type should be np.float32 or np.uint8, ' f'but got {img_type}')
    return img


def _convert_output_type_range(img, dst_type):
    """Convert the type and range of the image according to dst_type.
    It converts the image to desired type and range. If `dst_type` is np.uint8,
    images will be converted to np.uint8 type with range [0, 255]. If
    `dst_type` is np.float32, it converts the image to np.float32 type with
    range [0, 1].
    It is mainly used for post-processing images in colorspace convertion
    functions such as rgb2ycbcr and ycbcr2rgb.
    Args:
        img (ndarray): The image to be converted with np.float32 type and
            range [0, 255].
        dst_type (np.uint8 | np.float32): If dst_type is np.uint8, it
            converts the image to np.uint8 type with range [0, 255]. If
            dst_type is np.float32, it converts the image to np.float32 type
            with range [0, 1].
    Returns:
        (ndarray): The converted image with desired type and range.
    """
    if dst_type not in (np.uint8, np.float32):
        raise TypeError('The dst_type should be np.float32 or np.uint8, ' f'but got {dst_type}')
    if dst_type == np.uint8:
        img = img.round()
    else:
        img /= 255.
    return img.astype(dst_type)


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