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libcamera: utils: Raspberry Pi Camera Tuning Tool

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Initial implementation of the Raspberry Pi (BCM2835) Camera Tuning Tool.

All code is licensed under the BSD-2-Clause terms.
Copyright (c) 2019-2020 Raspberry Pi Trading Ltd.

Signed-off-by: Naushir Patuck <naush@raspberrypi.com>
Acked-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com>
Signed-off-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com>
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Naushir Patuck 2020-05-03 16:49:53 +01:00 • committed by Laurent Pinchart
parent 0db2c8dc75
commit c01cfe14f5
14 changed files with 3552 additions and 0 deletions
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utils/raspberrypi/ctt/ctt_ransac.py Normal file
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# 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)