# Author: Bingxin Ke
# Last modified: 2024-01-11

import numpy as np
import torch

def compute_scale_and_shift(prediction, target, mask):
    # system matrix: A = [[a_00, a_01], [a_10, a_11]]
    a_00 = torch.sum(mask * prediction * prediction, (1, 2))
    a_01 = torch.sum(mask * prediction, (1, 2))
    a_11 = torch.sum(mask, (1, 2))

    # right hand side: b = [b_0, b_1]
    b_0 = torch.sum(mask * prediction * target, (1, 2))
    b_1 = torch.sum(mask * target, (1, 2))

    # solution: x = A^-1 . b = [[a_11, -a_01], [-a_10, a_00]] / (a_00 * a_11 - a_01 * a_10) . b
    x_0 = torch.ones_like(b_0)
    x_1 = torch.zeros_like(b_1)

    det = a_00 * a_11 - a_01 * a_01
    valid = det.nonzero()

    x_0[valid] = (a_11[valid] * b_0[valid] - a_01[valid] * b_1[valid]) / det[valid]
    x_1[valid] = (-a_01[valid] * b_0[valid] + a_00[valid] * b_1[valid]) / det[valid]

    return x_0, x_1

def align_depth_least_square(
    gt_arr: np.ndarray,
    pred_arr: np.ndarray,
    valid_mask_arr: np.ndarray,
    return_scale_shift=True,
    max_resolution=None,
):
    ori_shape = pred_arr.shape  # input shape

    gt = gt_arr.squeeze()  # [H, W]
    pred = pred_arr.squeeze()
    valid_mask = valid_mask_arr.squeeze()

    # Downsample
    if max_resolution is not None:
        scale_factor = np.min(max_resolution / np.array(ori_shape[-2:]))
        if scale_factor < 1:
            downscaler = torch.nn.Upsample(scale_factor=scale_factor, mode="nearest")
            gt = downscaler(torch.as_tensor(gt).unsqueeze(0)).numpy()
            pred = downscaler(torch.as_tensor(pred).unsqueeze(0)).numpy()
            valid_mask = (
                downscaler(torch.as_tensor(valid_mask).unsqueeze(0).float())
                .bool()
                .numpy()
            )

    assert (
        gt.shape == pred.shape == valid_mask.shape
    ), f"{gt.shape}, {pred.shape}, {valid_mask.shape}"

    gt_masked = gt[valid_mask].reshape((-1, 1))
    pred_masked = pred[valid_mask].reshape((-1, 1))

    # numpy solver
    _ones = np.ones_like(pred_masked)
    A = np.concatenate([pred_masked, _ones], axis=-1)
    X = np.linalg.lstsq(A, gt_masked, rcond=None)[0]
    scale, shift = X

    aligned_pred = pred_arr * scale + shift

    # restore dimensions
    aligned_pred = aligned_pred.reshape(ori_shape)

    if return_scale_shift:
        return aligned_pred, scale, shift
    else:
        return aligned_pred


# ******************** disparity space ********************
def depth2disparity(depth, return_mask=False):
    if isinstance(depth, torch.Tensor):
        disparity = torch.zeros_like(depth)
    elif isinstance(depth, np.ndarray):
        disparity = np.zeros_like(depth)
    non_negtive_mask = depth > 0
    disparity[non_negtive_mask] = 1.0 / depth[non_negtive_mask]
    if return_mask:
        return disparity, non_negtive_mask
    else:
        return disparity


def disparity2depth(disparity, **kwargs):
    return depth2disparity(disparity, **kwargs)
