# 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)