# SPDX-License-Identifier: BSD-2-Clause
#
# Copyright (C) 2019, Raspberry Pi (Trading) Limited
#
# ctt_ransac.py - camera tuning tool RANSAC selector for Macbeth chart locator

import numpy as np

scale = 2


"""
constructs normalised macbeth chart corners for ransac algorithm
"""
def get_square_verts(c_err=0.05, scale=scale):
    """
    define macbeth chart corners
    """
    b_bord_x, b_bord_y = scale*8.5, scale*13
    s_bord = 6*scale
    side = 41*scale
    x_max = side*6 + 5*s_bord + 2*b_bord_x
    y_max = side*4 + 3*s_bord + 2*b_bord_y
    c1 = (0, 0)
    c2 = (0, y_max)
    c3 = (x_max, y_max)
    c4 = (x_max, 0)
    mac_norm = np.array((c1, c2, c3, c4), np.float32)
    mac_norm = np.array([mac_norm])

    square_verts = []
    square_0 = np.array(((0, 0), (0, side),
                         (side, side), (side, 0)), np.float32)
    offset_0 = np.array((b_bord_x, b_bord_y), np.float32)
    c_off = side * c_err
    offset_cont = np.array(((c_off, c_off), (c_off, -c_off),
                            (-c_off, -c_off), (-c_off, c_off)), np.float32)
    square_0 += offset_0
    square_0 += offset_cont
    """
    define macbeth square corners
    """
    for i in range(6):
        shift_i = np.array(((i*side, 0), (i*side, 0),
                            (i*side, 0), (i*side, 0)), np.float32)
        shift_bord = np.array(((i*s_bord, 0), (i*s_bord, 0),
                               (i*s_bord, 0), (i*s_bord, 0)), np.float32)
        square_i = square_0 + shift_i + shift_bord
        for j in range(4):
            shift_j = np.array(((0, j*side), (0, j*side),
                                (0, j*side), (0, j*side)), np.float32)
            shift_bord = np.array(((0, j*s_bord),
                                   (0, j*s_bord), (0, j*s_bord),
                                   (0, j*s_bord)), np.float32)
            square_j = square_i + shift_j + shift_bord
            square_verts.append(square_j)
    # print('square_verts')
    # print(square_verts)
    return np.array(square_verts, np.float32), mac_norm


def get_square_centres(c_err=0.05, scale=scale):
    """
    define macbeth square centres
    """
    verts, mac_norm = get_square_verts(c_err, scale=scale)

    centres = np.mean(verts, axis=1)
    # print('centres')
    # print(centres)
    return np.array(centres, np.float32)