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-rw-r--r--utils/tuning/libtuning/ctt_ransac.py71
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diff --git a/utils/tuning/libtuning/ctt_ransac.py b/utils/tuning/libtuning/ctt_ransac.py
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+# SPDX-License-Identifier: BSD-2-Clause
+#
+# Copyright (C) 2019, Raspberry Pi Ltd
+#
+# 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)