import numpy as np
import torch
import torch.nn.functional as F
from PIL import ImageFile

ImageFile.LOAD_TRUNCATED_IMAGES = True


def warp(img, flow):
    B, _, H, W = flow.shape
    xx = torch.linspace(-1.0, 1.0, W).view(1, 1, 1, W).expand(B, -1, H, -1)
    yy = torch.linspace(-1.0, 1.0, H).view(1, 1, H, 1).expand(B, -1, -1, W)
    grid = torch.cat([xx, yy], 1).to(img)
    flow_ = torch.cat([flow[:, 0:1, :, :] / ((W - 1.0) / 2.0), flow[:, 1:2, :, :] / ((H - 1.0) / 2.0)], 1)
    grid_ = (grid + flow_).permute(0, 2, 3, 1)
    output = F.grid_sample(input=img, grid=grid_, mode="bilinear", padding_mode="border", align_corners=True)
    return output


def make_colorwheel():
    """
    Generates a color wheel for optical flow visualization as presented in:
        Baker et al. "A Database and Evaluation Methodology for Optical Flow" (ICCV, 2007)
        URL: http://vision.middlebury.edu/flow/flowEval-iccv07.pdf
    Code follows the original C++ source code of Daniel Scharstein.
    Code follows the Matlab source code of Deqing Sun.
    Returns:
        np.ndarray: 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.floor(255 * np.arange(0, RY) / RY)
    col = col + RY
    # YG
    colorwheel[col : col + YG, 0] = 255 - np.floor(255 * np.arange(0, YG) / YG)
    colorwheel[col : col + YG, 1] = 255
    col = col + YG
    # GC
    colorwheel[col : col + GC, 1] = 255
    colorwheel[col : col + GC, 2] = np.floor(255 * np.arange(0, GC) / GC)
    col = col + GC
    # CB
    colorwheel[col : col + CB, 1] = 255 - np.floor(255 * np.arange(CB) / CB)
    colorwheel[col : col + CB, 2] = 255
    col = col + CB
    # BM
    colorwheel[col : col + BM, 2] = 255
    colorwheel[col : col + BM, 0] = np.floor(255 * np.arange(0, BM) / BM)
    col = col + BM
    # MR
    colorwheel[col : col + MR, 2] = 255 - np.floor(255 * np.arange(MR) / MR)
    colorwheel[col : col + MR, 0] = 255
    return colorwheel


def flow_uv_to_colors(u, v, convert_to_bgr=False):
    """
    Applies the flow color wheel to (possibly clipped) flow components u and v.
    According to the C++ source code of Daniel Scharstein
    According to the Matlab source code of Deqing Sun
    Args:
        u (np.ndarray): Input horizontal flow of shape [H,W]
        v (np.ndarray): Input vertical flow of shape [H,W]
        convert_to_bgr (bool, optional): Convert output image to BGR. Defaults to False.
    Returns:
        np.ndarray: Flow visualization image of shape [H,W,3]
    """
    flow_image = np.zeros((u.shape[0], u.shape[1], 3), np.uint8)
    colorwheel = make_colorwheel()  # shape [55x3]
    ncols = colorwheel.shape[0]
    rad = np.sqrt(np.square(u) + np.square(v))
    a = np.arctan2(-v, -u) / np.pi
    fk = (a + 1) / 2 * (ncols - 1)
    k0 = np.floor(fk).astype(np.int32)
    k1 = k0 + 1
    k1[k1 == ncols] = 0
    f = fk - k0
    for i in range(colorwheel.shape[1]):
        tmp = colorwheel[:, i]
        col0 = tmp[k0] / 255.0
        col1 = tmp[k1] / 255.0
        col = (1 - f) * col0 + f * col1
        idx = rad <= 1
        col[idx] = 1 - rad[idx] * (1 - col[idx])
        col[~idx] = col[~idx] * 0.75  # out of range
        # Note the 2-i => BGR instead of RGB
        ch_idx = 2 - i if convert_to_bgr else i
        flow_image[:, :, ch_idx] = np.floor(255 * col)
    return flow_image


def flow_to_image(flow_uv, clip_flow=None, convert_to_bgr=False):
    """
    Expects a two dimensional flow image of shape.
    Args:
        flow_uv (np.ndarray): Flow UV image of shape [H,W,2]
        clip_flow (float, optional): Clip maximum of flow values. Defaults to None.
        convert_to_bgr (bool, optional): Convert output image to BGR. Defaults to False.
    Returns:
        np.ndarray: Flow visualization image of shape [H,W,3]
    """
    assert flow_uv.ndim == 3, "input flow must have three dimensions"
    assert flow_uv.shape[2] == 2, "input flow must have shape [H,W,2]"
    if clip_flow is not None:
        flow_uv = np.clip(flow_uv, 0, clip_flow)
    u = flow_uv[:, :, 0]
    v = flow_uv[:, :, 1]
    rad = np.sqrt(np.square(u) + np.square(v))
    rad_max = np.max(rad)
    epsilon = 1e-5
    u = u / (rad_max + epsilon)
    v = v / (rad_max + epsilon)
    return flow_uv_to_colors(u, v, convert_to_bgr)