libtuning: Copy files from raspberrypi

Copy ctt_{awb,ccm,colors,ransac} from the raspberrypi tuning scripts as
basis for the libcamera implementation. color.py was renamed to
ctt_colors.py to better express the origin.

The files were taken from commit 66479605baca ("utils: raspberrypi: ctt:
Improve the Macbeth Chart search reliability").

Signed-off-by: Stefan Klug <stefan.klug@ideasonboard.com>
Acked-by: Kieran Bingham <kieran.bingham@ideasonboard.com>
Acked-by: Paul Elder <paul.elder@ideasonboard.com>
Acked-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com>

1 files changed, 71 insertions, 0 deletions

diff --git a/utils/tuning/libtuning/ctt_ransac.py b/utils/tuning/libtuning/ctt_ransac.py
new file mode 100644
index 00000000..01bba302
--- /dev/null
+++ b/utils/tuning/libtuning/ctt_ransac.py
@@ -0,0 +1,71 @@
+# 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)