import numpy as np
import torch
import torch.nn as nn

# https://zhuanlan.zhihu.com/p/112030273
def warp_optical_flow(batch_x, batch_flow):
    """
    Modified from https://github.com/NVlabs/PWC-Net/blob/fc6ebf9a70a7387164df09a3a2070ba16f9c1ede/PyTorch/models/PWCNet.py  # NOQA
    warp an im2 back to im1, according to the optical flow
    x: [B, L, C, H, W] (im2)
    flo: [B, L, 2, H, W] flow
    """
    B, L, C, H, W = batch_x.shape
    B = B * L
    x = batch_x.contiguous().view(-1, C, H, W)
    flo = batch_flow.view(-1, 2, H, W)
    # mesh grid
    xx = torch.arange(0, W).view(1, -1).repeat(H, 1)
    yy = torch.arange(0, H).view(-1, 1).repeat(1, W)
    xx = xx.view(1, 1, H, W).repeat(B, 1, 1, 1)
    yy = yy.view(1, 1, H, W).repeat(B, 1, 1, 1)
    grid = torch.cat((xx, yy), 1).float()

    if x.is_cuda:
        grid = grid.cuda()
    vgrid = grid + flo

    # scale grid to [-1, 1]
    vgrid[:, 0, :, :] = 2.0 * vgrid[:, 0, :, :] / max(W - 1, 1) - 1.0
    vgrid[:, 1, :, :] = 2.0 * vgrid[:, 1, :, :] / max(H - 1, 1) - 1.0

    vgrid = vgrid.permute(0, 2, 3, 1)  # B, H, W, 2(compatible with API)
    output = nn.functional.grid_sample(x, vgrid)  # 按照vgrid将x warp到output张量上
    mask = torch.autograd.Variable(torch.ones(x.size())).cuda()
    mask = nn.functional.grid_sample(mask, vgrid)  # 这个我觉得没有太大意义，因为warp之后还是1（mask默认全是1）

    mask[mask < 0.9999] = 0
    mask[mask > 0] = 1  # 仍然全是1

    result = output * mask
    return result.view(-1, L, C, H, W)


UNKNOWN_FLOW_THRESH = 1e7


def flow_to_image(flow):
    """
    Convert flow into middlebury color code image
    :param flow: optical flow map
    :return: optical flow image in middlebury color
    """
    u = flow[:, :, 0]
    v = flow[:, :, 1]

    maxu = -999.
    maxv = -999.
    minu = 999.
    minv = 999.

    idxUnknow = (abs(u) > UNKNOWN_FLOW_THRESH) | (abs(v) > UNKNOWN_FLOW_THRESH)
    u[idxUnknow] = 0
    v[idxUnknow] = 0

    maxu = max(maxu, np.max(u))
    minu = min(minu, np.min(u))

    maxv = max(maxv, np.max(v))
    minv = min(minv, np.min(v))

    rad = np.sqrt(u ** 2 + v ** 2)
    maxrad = max(-1, np.max(rad))

    u = u / (maxrad + np.finfo(float).eps)
    v = v / (maxrad + np.finfo(float).eps)

    img = compute_color(u, v)

    idx = np.repeat(idxUnknow[:, :, np.newaxis], 3, axis=2)
    img[idx] = 0

    return np.uint8(img)


def compute_color(u, v):
    """
    compute optical flow color map
    :param u: optical flow horizontal map
    :param v: optical flow vertical map
    :return: optical flow in color code
    """
    [h, w] = u.shape
    img = np.zeros([h, w, 3])
    nanIdx = np.isnan(u) | np.isnan(v)
    u[nanIdx] = 0
    v[nanIdx] = 0

    colorwheel = make_color_wheel()
    ncols = np.size(colorwheel, 0)

    rad = np.sqrt(u ** 2 + v ** 2)

    a = np.arctan2(-v, -u) / np.pi

    fk = (a + 1) / 2 * (ncols - 1) + 1

    k0 = np.floor(fk).astype(int)

    k1 = k0 + 1
    k1[k1 == ncols + 1] = 1
    f = fk - k0

    for i in range(0, np.size(colorwheel, 1)):
        tmp = colorwheel[:, i]
        col0 = tmp[k0 - 1] / 255
        col1 = tmp[k1 - 1] / 255
        col = (1 - f) * col0 + f * col1

        idx = rad <= 1
        col[idx] = 1 - rad[idx] * (1 - col[idx])
        notidx = np.logical_not(idx)

        col[notidx] *= 0.75
        img[:, :, i] = np.uint8(np.floor(255 * col * (1 - nanIdx)))

    return img


def make_color_wheel():
    """
    Generate color wheel according Middlebury color code
    :return: Color wheel
    """
    RY = 15
    YG = 6
    GC = 4
    CB = 11
    BM = 13
    MR = 6

    ncols = RY + YG + GC + CB + BM + MR

    colorwheel = np.zeros([ncols, 3])

    col = 0

    # RY
    colorwheel[0:RY, 0] = 255
    colorwheel[0:RY, 1] = np.transpose(np.floor(255 * np.arange(0, RY) / RY))
    col += RY

    # YG
    colorwheel[col:col + YG, 0] = 255 - np.transpose(np.floor(255 * np.arange(0, YG) / YG))
    colorwheel[col:col + YG, 1] = 255
    col += YG

    # GC
    colorwheel[col:col + GC, 1] = 255
    colorwheel[col:col + GC, 2] = np.transpose(np.floor(255 * np.arange(0, GC) / GC))
    col += GC

    # CB
    colorwheel[col:col + CB, 1] = 255 - np.transpose(np.floor(255 * np.arange(0, CB) / CB))
    colorwheel[col:col + CB, 2] = 255
    col += CB

    # BM
    colorwheel[col:col + BM, 2] = 255
    colorwheel[col:col + BM, 0] = np.transpose(np.floor(255 * np.arange(0, BM) / BM))
    col += + BM

    # MR
    colorwheel[col:col + MR, 2] = 255 - np.transpose(np.floor(255 * np.arange(0, MR) / MR))
    colorwheel[col:col + MR, 0] = 255

    return colorwheel