import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid


def modcrop(img, scale):
    # img_in: BCHW or CHW or HW
    #img = np.copy(img_in)
    if img.ndim == 2:
        H, W = img.shape
        H_r, W_r = H % scale, W % scale
        img = img[:H - H_r, :W - W_r]
    elif img.ndim == 3:
        C, H, W = img.shape
        H_r, W_r = H % scale, W % scale
        img = img[:, :H - H_r, :W - W_r]
    elif img.ndim == 4:
        B, C, H, W = img.shape
        H_r, W_r = H % scale, W % scale
        img = img[:, :, :H - H_r, :W - W_r]
    else:
        raise ValueError('Wrong img ndim: [{:d}].'.format(img.ndim))
    return img

def modcrop_pil(image, modulo):
    w = image.width - image.width % modulo
    h = image.height - image.height % modulo
    return image.crop((0, 0, w, h))

def crop_center(pil_img, crop_width, crop_height):
    # Perform center crop on a PIL image
    img_width, img_height = pil_img.size
    return pil_img.crop(((img_width - crop_width) // 2,
                         (img_height - crop_height) // 2,
                         (img_width + crop_width) // 2,
                         (img_height + crop_height) // 2))


'''
# --------------------------------------------
# matlab's bicubic imresize (numpy and torch) [0, 1]
# --------------------------------------------
# from https://github.com/cszn/KAIR/blob/master/utils/utils_image.py
'''


# matlab 'imresize' function, now only support 'bicubic'
def cubic(x):
    absx = torch.abs(x)
    absx2 = absx**2
    absx3 = absx**3
    return (1.5*absx3 - 2.5*absx2 + 1) * ((absx <= 1).type_as(absx)) + \
        (-0.5*absx3 + 2.5*absx2 - 4*absx + 2) * (((absx > 1)*(absx <= 2)).type_as(absx))


def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing):
    if (scale < 1) and (antialiasing):
        # Use a modified kernel to simultaneously interpolate and antialias- larger kernel width
        kernel_width = kernel_width / scale

    # Output-space coordinates
    x = torch.linspace(1, out_length, out_length)

    # Input-space coordinates. Calculate the inverse mapping such that 0.5
    # in output space maps to 0.5 in input space, and 0.5+scale in output
    # space maps to 1.5 in input space.
    u = x / scale + 0.5 * (1 - 1 / scale)

    # What is the left-most pixel that can be involved in the computation?
    left = torch.floor(u - kernel_width / 2)

    # What is the maximum number of pixels that can be involved in the
    # computation?  Note: it's OK to use an extra pixel here; if the
    # corresponding weights are all zero, it will be eliminated at the end
    # of this function.
    P = math.ceil(kernel_width) + 2

    # The indices of the input pixels involved in computing the k-th output
    # pixel are in row k of the indices matrix.
    indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(0, P - 1, P).view(
        1, P).expand(out_length, P)

    # The weights used to compute the k-th output pixel are in row k of the
    # weights matrix.
    distance_to_center = u.view(out_length, 1).expand(out_length, P) - indices
    # apply cubic kernel
    if (scale < 1) and (antialiasing):
        weights = scale * cubic(distance_to_center * scale)
    else:
        weights = cubic(distance_to_center)
    # Normalize the weights matrix so that each row sums to 1.
    weights_sum = torch.sum(weights, 1).view(out_length, 1)
    weights = weights / weights_sum.expand(out_length, P)

    # If a column in weights is all zero, get rid of it. only consider the first and last column.
    weights_zero_tmp = torch.sum((weights == 0), 0)
    if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6):
        indices = indices.narrow(1, 1, P - 2)
        weights = weights.narrow(1, 1, P - 2)
    if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6):
        indices = indices.narrow(1, 0, P - 2)
        weights = weights.narrow(1, 0, P - 2)
    weights = weights.contiguous()
    indices = indices.contiguous()
    sym_len_s = -indices.min() + 1
    sym_len_e = indices.max() - in_length
    indices = indices + sym_len_s - 1
    return weights, indices, int(sym_len_s), int(sym_len_e)


# --------------------------------------------
# imresize for tensor image [0, 1]
# --------------------------------------------
def imresize(img, scale, antialiasing=True):
    # Now the scale should be the same for H and W
    # input: img: pytorch tensor, CHW or HW [0,1]
    # output: CHW or HW [0,1] w/o round
    need_squeeze = True if img.dim() == 2 else False
    if need_squeeze:
        img.unsqueeze_(0)
    in_C, in_H, in_W = img.size()
    out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
    kernel_width = 4
    kernel = 'cubic'

    # Return the desired dimension order for performing the resize.  The
    # strategy is to perform the resize first along the dimension with the
    # smallest scale factor.
    # Now we do not support this.

    # get weights and indices
    weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
        in_H, out_H, scale, kernel, kernel_width, antialiasing)
    weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
        in_W, out_W, scale, kernel, kernel_width, antialiasing)
    # process H dimension
    # symmetric copying
    img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W)
    img_aug.narrow(1, sym_len_Hs, in_H).copy_(img)

    sym_patch = img[:, :sym_len_Hs, :]
    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
    sym_patch_inv = sym_patch.index_select(1, inv_idx)
    img_aug.narrow(1, 0, sym_len_Hs).copy_(sym_patch_inv)

    sym_patch = img[:, -sym_len_He:, :]
    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
    sym_patch_inv = sym_patch.index_select(1, inv_idx)
    img_aug.narrow(1, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)

    out_1 = torch.FloatTensor(in_C, out_H, in_W)
    kernel_width = weights_H.size(1)
    for i in range(out_H):
        idx = int(indices_H[i][0])
        for j in range(out_C):
            out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_H[i])

    # process W dimension
    # symmetric copying
    out_1_aug = torch.FloatTensor(in_C, out_H, in_W + sym_len_Ws + sym_len_We)
    out_1_aug.narrow(2, sym_len_Ws, in_W).copy_(out_1)

    sym_patch = out_1[:, :, :sym_len_Ws]
    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
    sym_patch_inv = sym_patch.index_select(2, inv_idx)
    out_1_aug.narrow(2, 0, sym_len_Ws).copy_(sym_patch_inv)

    sym_patch = out_1[:, :, -sym_len_We:]
    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
    sym_patch_inv = sym_patch.index_select(2, inv_idx)
    out_1_aug.narrow(2, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)

    out_2 = torch.FloatTensor(in_C, out_H, out_W)
    kernel_width = weights_W.size(1)
    for i in range(out_W):
        idx = int(indices_W[i][0])
        for j in range(out_C):
            out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_W[i])
    if need_squeeze:
        out_2.squeeze_()
    return out_2


# --------------------------------------------
# imresize for numpy image [0, 1]
# --------------------------------------------
def imresize_np(img, scale, antialiasing=True):
    # Now the scale should be the same for H and W
    # input: img: Numpy, HWC or HW [0,1]
    # output: HWC or HW [0,1] w/o round
    img = torch.from_numpy(img)
    need_squeeze = True if img.dim() == 2 else False
    if need_squeeze:
        img.unsqueeze_(2)

    in_H, in_W, in_C = img.size()
    out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
    kernel_width = 4
    kernel = 'cubic'

    # Return the desired dimension order for performing the resize.  The
    # strategy is to perform the resize first along the dimension with the
    # smallest scale factor.
    # Now we do not support this.

    # get weights and indices
    weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
        in_H, out_H, scale, kernel, kernel_width, antialiasing)
    weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
        in_W, out_W, scale, kernel, kernel_width, antialiasing)
    # process H dimension
    # symmetric copying
    img_aug = torch.FloatTensor(in_H + sym_len_Hs + sym_len_He, in_W, in_C)
    img_aug.narrow(0, sym_len_Hs, in_H).copy_(img)

    sym_patch = img[:sym_len_Hs, :, :]
    inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
    sym_patch_inv = sym_patch.index_select(0, inv_idx)
    img_aug.narrow(0, 0, sym_len_Hs).copy_(sym_patch_inv)

    sym_patch = img[-sym_len_He:, :, :]
    inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
    sym_patch_inv = sym_patch.index_select(0, inv_idx)
    img_aug.narrow(0, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)

    out_1 = torch.FloatTensor(out_H, in_W, in_C)
    kernel_width = weights_H.size(1)
    for i in range(out_H):
        idx = int(indices_H[i][0])
        for j in range(out_C):
            out_1[i, :, j] = img_aug[idx:idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i])

    # process W dimension
    # symmetric copying
    out_1_aug = torch.FloatTensor(out_H, in_W + sym_len_Ws + sym_len_We, in_C)
    out_1_aug.narrow(1, sym_len_Ws, in_W).copy_(out_1)

    sym_patch = out_1[:, :sym_len_Ws, :]
    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
    sym_patch_inv = sym_patch.index_select(1, inv_idx)
    out_1_aug.narrow(1, 0, sym_len_Ws).copy_(sym_patch_inv)

    sym_patch = out_1[:, -sym_len_We:, :]
    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
    sym_patch_inv = sym_patch.index_select(1, inv_idx)
    out_1_aug.narrow(1, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)

    out_2 = torch.FloatTensor(out_H, out_W, in_C)
    kernel_width = weights_W.size(1)
    for i in range(out_W):
        idx = int(indices_W[i][0])
        for j in range(out_C):
            out_2[:, i, j] = out_1_aug[:, idx:idx + kernel_width, j].mv(weights_W[i])
    if need_squeeze:
        out_2.squeeze_()

    return out_2.numpy()
    
    
    