c01cfe14f5
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>
69 lines
2.1 KiB
Python
69 lines
2.1 KiB
Python
# SPDX-License-Identifier: BSD-2-Clause
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#
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# Copyright (C) 2019, Raspberry Pi (Trading) Limited
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#
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# ctt_ransac.py - camera tuning tool RANSAC selector for Macbeth chart locator
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import numpy as np
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scale = 2
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"""
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constructs normalised macbeth chart corners for ransac algorithm
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"""
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def get_square_verts(c_err = 0.05,scale = scale):
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"""
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define macbeth chart corners
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"""
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b_bord_x,b_bord_y = scale*8.5,scale*13
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s_bord = 6*scale
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side = 41*scale
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x_max = side*6 + 5*s_bord + 2*b_bord_x
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y_max = side*4 + 3*s_bord + 2*b_bord_y
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c1 = (0,0)
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c2 = (0,y_max)
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c3 = (x_max,y_max)
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c4 = (x_max,0)
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mac_norm = np.array((c1,c2,c3,c4),np.float32)
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mac_norm = np.array([ mac_norm ])
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square_verts = []
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square_0 = np.array(((0,0),(0,side),
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(side,side),(side,0)),np.float32)
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offset_0 = np.array((b_bord_x,b_bord_y),np.float32)
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c_off = side * c_err
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offset_cont = np.array(((c_off,c_off),(c_off,-c_off),
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(-c_off,-c_off),(-c_off,c_off)),np.float32)
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square_0 += offset_0
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square_0 += offset_cont
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"""
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define macbeth square corners
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"""
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for i in range(6):
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shift_i = np.array(((i*side,0),(i*side,0),
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(i*side,0),(i*side,0)),np.float32)
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shift_bord =np.array(((i*s_bord,0),(i*s_bord,0),
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(i*s_bord,0),(i*s_bord,0)),np.float32)
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square_i = square_0 + shift_i + shift_bord
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for j in range(4):
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shift_j = np.array(((0,j*side),(0,j*side),
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(0,j*side),(0,j*side)),np.float32)
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shift_bord = np.array(((0,j*s_bord),
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(0,j*s_bord),(0,j*s_bord),
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(0,j*s_bord)),np.float32)
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square_j = square_i + shift_j + shift_bord
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square_verts.append(square_j)
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# print('square_verts')
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# print(square_verts)
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return np.array(square_verts,np.float32),mac_norm
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def get_square_centres(c_err = 0.05,scale=scale):
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"""
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define macbeth square centres
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"""
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verts,mac_norm = get_square_verts(c_err,scale=scale)
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centres = np.mean(verts,axis = 1)
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# print('centres')
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# print(centres)
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return np.array(centres,np.float32)
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