# Copyright 2019-present NAVER Corp.
# CC BY-NC-SA 3.0
# Available only for non-commercial use

import pdb
import numpy as np
import matplotlib.pyplot as pl


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
    According to the C++ source code of Daniel Scharstein
    According to the Matlab source code of Deqing Sun

    Copied from https://github.com/tomrunia/OpticalFlow_Visualization/blob/master/flow_vis.py
    Copyright (c) 2018 Tom Runia
    """

    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_compute_color(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
    :param u: np.ndarray, input horizontal flow
    :param v: np.ndarray, input vertical flow
    :param convert_to_bgr: bool, whether to change ordering and output BGR instead of RGB
    :return:

    Copied from https://github.com/tomrunia/OpticalFlow_Visualization/blob/master/flow_vis.py
    Copyright (c) 2018 Tom Runia
    """

    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_color(flow_uv, clip_flow=None, convert_to_bgr=False):
    """
    Expects a two dimensional flow image of shape [H,W,2]
    According to the C++ source code of Daniel Scharstein
    According to the Matlab source code of Deqing Sun
    :param flow_uv: np.ndarray of shape [H,W,2]
    :param clip_flow: float, maximum clipping value for flow
    :return:

    Copied from https://github.com/tomrunia/OpticalFlow_Visualization/blob/master/flow_vis.py
    Copyright (c) 2018 Tom Runia
    """

    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_compute_color(u, v, convert_to_bgr)


def show_flow(img0, img1, flow, mask=None):
    img0 = np.asarray(img0)
    img1 = np.asarray(img1)
    if mask is None:
        mask = 1
    mask = np.asarray(mask)
    if mask.ndim == 2:
        mask = mask[:, :, None]
    assert flow.ndim == 3
    assert flow.shape[:2] == img0.shape[:2] and flow.shape[2] == 2

    def noticks():
        pl.xticks([])
        pl.yticks([])

    fig = pl.figure("showing correspondences")
    ax1 = pl.subplot(221)
    ax1.numaxis = 0
    pl.imshow(img0 * mask)
    noticks()
    ax2 = pl.subplot(222)
    ax2.numaxis = 1
    pl.imshow(img1)
    noticks()

    ax = pl.subplot(212)
    ax.numaxis = 0
    flow_img = flow_to_color(np.where(np.isnan(flow), 0, flow))
    pl.imshow(flow_img * mask)
    noticks()

    pl.subplots_adjust(0.01, 0.01, 0.99, 0.99, wspace=0.02, hspace=0.02)

    def motion_notify_callback(event):
        if event.inaxes is None:
            return
        x, y = event.xdata, event.ydata
        ax1.lines = []
        ax2.lines = []
        try:
            x, y = int(x + 0.5), int(y + 0.5)
            ax1.plot(x, y, "+", ms=10, mew=2, color="blue", scalex=False, scaley=False)
            x, y = flow[y, x] + (x, y)
            ax2.plot(x, y, "+", ms=10, mew=2, color="red", scalex=False, scaley=False)
            # we redraw only the concerned axes
            renderer = fig.canvas.get_renderer()
            ax1.draw(renderer)
            ax2.draw(renderer)
            fig.canvas.blit(ax1.bbox)
            fig.canvas.blit(ax2.bbox)
        except IndexError:
            return

    cid_move = fig.canvas.mpl_connect("motion_notify_event", motion_notify_callback)
    print("Move your mouse over the images to show matches (ctrl-C to quit)")
    pl.show()
