519 lines
20 KiB
C++
519 lines
20 KiB
C++
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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright(C) 2020, Huawei Technologies Co.,Ltd. All rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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//
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// Author: Longbu Wang <wanglongbu@huawei.com.com>
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// Jinheng Zhang <zhangjinheng1@huawei.com>
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// Chenqi Shan <shanchenqi@huawei.com>
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#ifndef __OPENCV_MCC_CCM_HPP__
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#define __OPENCV_MCC_CCM_HPP__
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#include <opencv2/core.hpp>
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#include <opencv2/imgproc.hpp>
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namespace cv
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{
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namespace ccm
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{
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/** @addtogroup color_correction
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@{
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Introduction
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------------
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The purpose of color correction is to adjust the color response of input
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and output devices to a known state. The device being calibrated is sometimes
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called the calibration source; the color space used as the standard is sometimes
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called the calibration target. Color calibration has been used in many industries,
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such as television production, games, photography, engineering, chemistry,
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medicine, etc. Due to the manufacturing process of the input and output equipment,
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the channel response has nonlinear distortion. In order to correct the picture output
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of the equipment, it is nessary to calibrate the captured color and the actual color.
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*/
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/** @brief Enum of the possible types of ccm.
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*/
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enum CCM_TYPE
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{
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CCM_3x3, ///< The CCM with the shape \f$3\times3\f$ performs linear transformation on color values.
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CCM_4x3, ///< The CCM with the shape \f$4\times3\f$ performs affine transformation.
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};
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/** @brief Enum of the possible types of initial method.
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*/
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enum INITIAL_METHOD_TYPE
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{
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INITIAL_METHOD_WHITE_BALANCE, ///< The white balance method. The initial value is:\n
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/// \f$
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/// M_{CCM}=
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/// \begin{bmatrix}
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/// k_R & 0 & 0\\
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/// 0 & k_G & 0\\
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/// 0 & 0 & k_B\\
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/// \end{bmatrix}
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/// \f$\n
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/// where\n
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/// \f$
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/// k_R=mean(R_{li}')/mean(R_{li})\\
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/// k_R=mean(G_{li}')/mean(G_{li})\\
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/// k_R=mean(B_{li}')/mean(B_{li})
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/// \f$
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INITIAL_METHOD_LEAST_SQUARE, ///<the least square method is an optimal solution under the linear RGB distance function
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};
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/** @brief Macbeth and Vinyl ColorChecker with 2deg D50
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*/
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enum CONST_COLOR {
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COLORCHECKER_Macbeth, ///< Macbeth ColorChecker
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COLORCHECKER_Vinyl, ///< DKK ColorChecker
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COLORCHECKER_DigitalSG, ///< DigitalSG ColorChecker with 140 squares
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};
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enum COLOR_SPACE {
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COLOR_SPACE_sRGB, ///< https://en.wikipedia.org/wiki/SRGB , RGB color space
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COLOR_SPACE_sRGBL, ///< https://en.wikipedia.org/wiki/SRGB , linear RGB color space
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COLOR_SPACE_AdobeRGB, ///< https://en.wikipedia.org/wiki/Adobe_RGB_color_space , RGB color space
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COLOR_SPACE_AdobeRGBL, ///< https://en.wikipedia.org/wiki/Adobe_RGB_color_space , linear RGB color space
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COLOR_SPACE_WideGamutRGB, ///< https://en.wikipedia.org/wiki/Wide-gamut_RGB_color_space , RGB color space
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COLOR_SPACE_WideGamutRGBL, ///< https://en.wikipedia.org/wiki/Wide-gamut_RGB_color_space , linear RGB color space
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COLOR_SPACE_ProPhotoRGB, ///< https://en.wikipedia.org/wiki/ProPhoto_RGB_color_space , RGB color space
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COLOR_SPACE_ProPhotoRGBL, ///< https://en.wikipedia.org/wiki/ProPhoto_RGB_color_space , linear RGB color space
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COLOR_SPACE_DCI_P3_RGB, ///< https://en.wikipedia.org/wiki/DCI-P3 , RGB color space
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COLOR_SPACE_DCI_P3_RGBL, ///< https://en.wikipedia.org/wiki/DCI-P3 , linear RGB color space
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COLOR_SPACE_AppleRGB, ///< https://en.wikipedia.org/wiki/RGB_color_space , RGB color space
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COLOR_SPACE_AppleRGBL, ///< https://en.wikipedia.org/wiki/RGB_color_space , linear RGB color space
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COLOR_SPACE_REC_709_RGB, ///< https://en.wikipedia.org/wiki/Rec._709 , RGB color space
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COLOR_SPACE_REC_709_RGBL, ///< https://en.wikipedia.org/wiki/Rec._709 , linear RGB color space
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COLOR_SPACE_REC_2020_RGB, ///< https://en.wikipedia.org/wiki/Rec._2020 , RGB color space
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COLOR_SPACE_REC_2020_RGBL, ///< https://en.wikipedia.org/wiki/Rec._2020 , linear RGB color space
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COLOR_SPACE_XYZ_D65_2, ///< https://en.wikipedia.org/wiki/CIE_1931_color_space , non-RGB color space
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COLOR_SPACE_XYZ_D65_10, ///< non-RGB color space
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COLOR_SPACE_XYZ_D50_2, ///< non-RGB color space
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COLOR_SPACE_XYZ_D50_10, ///< non-RGB color space
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COLOR_SPACE_XYZ_A_2, ///< non-RGB color space
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COLOR_SPACE_XYZ_A_10, ///< non-RGB color space
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COLOR_SPACE_XYZ_D55_2, ///< non-RGB color space
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COLOR_SPACE_XYZ_D55_10, ///< non-RGB color space
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COLOR_SPACE_XYZ_D75_2, ///< non-RGB color space
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COLOR_SPACE_XYZ_D75_10, ///< non-RGB color space
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COLOR_SPACE_XYZ_E_2, ///< non-RGB color space
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COLOR_SPACE_XYZ_E_10, ///< non-RGB color space
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COLOR_SPACE_Lab_D65_2, ///< https://en.wikipedia.org/wiki/CIELAB_color_space , non-RGB color space
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COLOR_SPACE_Lab_D65_10, ///< non-RGB color space
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COLOR_SPACE_Lab_D50_2, ///< non-RGB color space
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COLOR_SPACE_Lab_D50_10, ///< non-RGB color space
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COLOR_SPACE_Lab_A_2, ///< non-RGB color space
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COLOR_SPACE_Lab_A_10, ///< non-RGB color space
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COLOR_SPACE_Lab_D55_2, ///< non-RGB color space
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COLOR_SPACE_Lab_D55_10, ///< non-RGB color space
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COLOR_SPACE_Lab_D75_2, ///< non-RGB color space
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COLOR_SPACE_Lab_D75_10, ///< non-RGB color space
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COLOR_SPACE_Lab_E_2, ///< non-RGB color space
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COLOR_SPACE_Lab_E_10, ///< non-RGB color space
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};
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/** @brief Linearization transformation type
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The first step in color correction is to linearize the detected colors.
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Because the input color space has not been calibrated, we usually use some empirical methods to linearize.
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There are several common linearization methods.
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The first is identical transformation, the second is gamma correction, and the third is polynomial fitting.
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Linearization is generally an elementwise function. The mathematical symbols are as follows:
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\f$C\f$: any channel of a color, could be \f$R, G\f$ or \f$B\f$.
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\f$R, G, B\f$: \f$R, G, B\f$ channels respectively.
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\f$G\f$: grayscale;
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\f$s,sl\f$: subscript, which represents the detected data and its linearized value, the former is the input and the latter is the output;
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\f$d,dl\f$: subscript, which represents the reference data and its linearized value
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### Identical Transformation
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No change is made during the Identical transformation linearization, usually because the tristimulus values of the input RGB image is already proportional to the luminance.
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For example, if the input measurement data is in RAW format, the measurement data is already linear, so no linearization is required.
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The identity transformation formula is as follows:
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\f[
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C_{sl}=C_s
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\f]
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### Gamma Correction
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Gamma correction is a means of performing nonlinearity in RGB space, see the Color Space documentation for details.
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In the linearization part, the value of \f$\gamma\f$ is usually set to 2.2.
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You can also customize the value.
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The formula for gamma correction linearization is as follows:
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\f[
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C_{sl}=C_s^{\gamma},\qquad C_s\ge0\\
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C_{sl}=-(-C_s)^{\gamma},\qquad C_s<0\\\\
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\f]
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### Polynomial Fitting
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Polynomial fitting uses polynomials to linearize.
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Provided the polynomial is:
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\f[
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f(x)=a_nx^n+a_{n-1}x^{n-1}+... +a_0
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\f]
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Then:
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\f[
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C_{sl}=f(C_s)
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\f]
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In practice, \f$n\le3\f$ is used to prevent overfitting.
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There are many variants of polynomial fitting, the difference lies in the way of generating \f$f(x)\f$.
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It is usually necessary to use linearized reference colors and corresponding detected colors to calculate the polynomial parameters.
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However, not all colors can participate in the calculation. The saturation detected colors needs to be removed. See the algorithm introduction document for details.
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#### Fitting Channels Respectively
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Use three polynomials, \f$r(x), g(x), b(x)\f$, to linearize each channel of the RGB color space[1-3]:
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\f[
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R_{sl}=r(R_s)\\
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G_{sl}=g(G_s)\\
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B_{sl}=b(B_s)\\
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\f]
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The polynomial is generated by minimizing the residual sum of squares between the detected data and the linearized reference data.
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Take the R-channel as an example:
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\f[
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R=\arg min_{f}(\Sigma(R_{dl}-f(R_S)^2)
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\f]
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It's equivalent to finding the least square regression for below equations:
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\f[
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f(R_{s1})=R_{dl1}\\
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f(R_{s2})=R_{dl2}\\
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...
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\f]
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With a polynomial, the above equations becomes:
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\f[
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\begin{bmatrix}
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R_{s1}^{n} & R_{s1}^{n-1} & ... & 1\\
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R_{s2}^{n} & R_{s2}^{n-1} & ... & 1\\
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... & ... & ... & ...
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\end{bmatrix}
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\begin{bmatrix}
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a_{n}\\
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a_{n-1}\\
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... \\
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a_0
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\end{bmatrix}
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=
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\begin{bmatrix}
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R_{dl1}\\
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R_{dl2}\\
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...
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\end{bmatrix}
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\f]
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It can be expressed as a system of linear equations:
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\f[
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AX=B
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\f]
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When the number of reference colors is not less than the degree of the polynomial, the linear system has a least-squares solution:
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\f[
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X=(A^TA)^{-1}A^TB
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\f]
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Once we get the polynomial coefficients, we can get the polynomial r.
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This method of finding polynomial coefficients can be implemented by numpy.polyfit in numpy, expressed here as:
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\f[
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R=polyfit(R_S, R_{dl})
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\f]
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Note that, in general, the polynomial that we want to obtain is guaranteed to monotonically increase in the interval [0,1] ,
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but this means that nonlinear method is needed to generate the polynomials(see [4] for detail).
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This would greatly increases the complexity of the program.
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Considering that the monotonicity does not affect the correct operation of the color correction program, polyfit is still used to implement the program.
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Parameters for other channels can also be derived in a similar way.
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#### Grayscale Polynomial Fitting
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In this method[2], single polynomial is used for all channels.
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The polynomial is still a polyfit result from the detected colors to the linear reference colors.
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However, only the gray of the reference colors can participate in the calculation.
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Since the detected colors corresponding to the gray of reference colors is not necessarily gray, it needs to be grayed.
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Grayscale refers to the Y channel of the XYZ color space.
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The color space of the detected data is not determined and cannot be converted into the XYZ space.
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Therefore, the sRGB formula is used to approximate[5].
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\f[
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G_{s}=0.2126R_{s}+0.7152G_{s}+0.0722B_{s}
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\f]
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Then the polynomial parameters can be obtained by using the polyfit.
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\f[
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f=polyfit(G_{s}, G_{dl})
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\f]
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After \f$f\f$ is obtained, linearization can be performed.
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#### Logarithmic Polynomial Fitting
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For gamma correction formula, we take the logarithm:
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\f[
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ln(C_{sl})={\gamma}ln(C_s),\qquad C_s\ge0\
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\f]
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It can be seen that there is a linear relationship between \f$ln(C_s)\f$ and \f$ln(C_{sl})\f$. It can be considered that the formula is an approximation of a polynomial relationship, that is, there exists a polynomial \f$f\f$, which makes[2]:
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\f[
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ln(C_{sl})=f(ln(C_s)), \qquad C_s>0\\
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C_{sl}=0, \qquad C_s=0
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\f]
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Because \f$exp(ln(0))\to\infty \f$, the channel whose component is 0 is directly mapped to 0 in the formula above.
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For fitting channels respectively, we have:
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\f[
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r=polyfit(ln(R_s),ln(R_{dl}))\\
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g=polyfit(ln(G_s),ln(G_{dl}))\\
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b=polyfit(ln(B_s),ln(B_{dl}))\\
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\f]
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Note that the parameter of \f$ln(*) \f$ cannot be 0.
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Therefore, we need to delete the channels whose values are 0 from \f$R_s \f$ and \f$R_{dl} \f$, \f$G_s\f$ and \f$G_{dl}\f$, \f$B_s\f$ and \f$B_{dl}\f$.
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Therefore:
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\f[
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ln(R_{sl})=r(ln(R_s)), \qquad R_s>0\\
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R_{sl}=0, \qquad R_s=0\\
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ln(G_{sl})=g(ln(G_s)),\qquad G_s>0\\
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G_{sl}=0, \qquad G_s=0\\
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ln(B_{sl})=b(ln(B_s)),\qquad B_s>0\\
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B_{sl}=0, \qquad B_s=0\\
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\f]
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For grayscale polynomials, there are also:
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\f[
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f=polyfit(ln(G_{sl}),ln(G_{dl}))
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\f]
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and:
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\f[
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ln(C_{sl})=f(ln(C_s)), \qquad C_s>0\\
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C_sl=0, \qquad C_s=0
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\f]
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*/
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enum LINEAR_TYPE
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{
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LINEARIZATION_IDENTITY, ///<no change is made
|
||
|
|
LINEARIZATION_GAMMA, ///<gamma correction; Need assign a value to gamma simultaneously
|
||
|
|
LINEARIZATION_COLORPOLYFIT, ///<polynomial fitting channels respectively; Need assign a value to deg simultaneously
|
||
|
|
LINEARIZATION_COLORLOGPOLYFIT, ///<logarithmic polynomial fitting channels respectively; Need assign a value to deg simultaneously
|
||
|
|
LINEARIZATION_GRAYPOLYFIT, ///<grayscale polynomial fitting; Need assign a value to deg and dst_whites simultaneously
|
||
|
|
LINEARIZATION_GRAYLOGPOLYFIT ///<grayscale Logarithmic polynomial fitting; Need assign a value to deg and dst_whites simultaneously
|
||
|
|
};
|
||
|
|
|
||
|
|
/** @brief Enum of possible functions to calculate the distance between colors.
|
||
|
|
|
||
|
|
See https://en.wikipedia.org/wiki/Color_difference for details
|
||
|
|
*/
|
||
|
|
enum DISTANCE_TYPE
|
||
|
|
{
|
||
|
|
DISTANCE_CIE76, ///<The 1976 formula is the first formula that related a measured color difference to a known set of CIELAB coordinates.
|
||
|
|
DISTANCE_CIE94_GRAPHIC_ARTS, ///<The 1976 definition was extended to address perceptual non-uniformities.
|
||
|
|
DISTANCE_CIE94_TEXTILES,
|
||
|
|
DISTANCE_CIE2000,
|
||
|
|
DISTANCE_CMC_1TO1, ///<In 1984, the Colour Measurement Committee of the Society of Dyers and Colourists defined a difference measure, also based on the L*C*h color model.
|
||
|
|
DISTANCE_CMC_2TO1,
|
||
|
|
DISTANCE_RGB, ///<Euclidean distance of rgb color space
|
||
|
|
DISTANCE_RGBL ///<Euclidean distance of rgbl color space
|
||
|
|
};
|
||
|
|
|
||
|
|
/** @brief Core class of ccm model
|
||
|
|
|
||
|
|
Produce a ColorCorrectionModel instance for inference
|
||
|
|
*/
|
||
|
|
class CV_EXPORTS_W ColorCorrectionModel
|
||
|
|
{
|
||
|
|
public:
|
||
|
|
/** @brief Color Correction Model
|
||
|
|
|
||
|
|
Supported list of color cards:
|
||
|
|
- @ref COLORCHECKER_Macbeth, the Macbeth ColorChecker
|
||
|
|
- @ref COLORCHECKER_Vinyl, the DKK ColorChecker
|
||
|
|
- @ref COLORCHECKER_DigitalSG, the DigitalSG ColorChecker with 140 squares
|
||
|
|
|
||
|
|
@param src detected colors of ColorChecker patches;\n
|
||
|
|
the color type is RGB not BGR, and the color values are in [0, 1];
|
||
|
|
@param constcolor the Built-in color card
|
||
|
|
*/
|
||
|
|
CV_WRAP ColorCorrectionModel(const Mat& src, CONST_COLOR constcolor);
|
||
|
|
|
||
|
|
/** @brief Color Correction Model
|
||
|
|
@param src detected colors of ColorChecker patches;\n
|
||
|
|
the color type is RGB not BGR, and the color values are in [0, 1];
|
||
|
|
@param colors the reference color values, the color values are in [0, 1].\n
|
||
|
|
@param ref_cs the corresponding color space
|
||
|
|
If the color type is some RGB, the format is RGB not BGR;\n
|
||
|
|
*/
|
||
|
|
CV_WRAP ColorCorrectionModel(const Mat& src, Mat colors, COLOR_SPACE ref_cs);
|
||
|
|
|
||
|
|
/** @brief Color Correction Model
|
||
|
|
@param src detected colors of ColorChecker patches;\n
|
||
|
|
the color type is RGB not BGR, and the color values are in [0, 1];
|
||
|
|
@param colors the reference color values, the color values are in [0, 1].
|
||
|
|
@param ref_cs the corresponding color space
|
||
|
|
If the color type is some RGB, the format is RGB not BGR;
|
||
|
|
@param colored mask of colored color
|
||
|
|
*/
|
||
|
|
CV_WRAP ColorCorrectionModel(const Mat& src, Mat colors, COLOR_SPACE ref_cs, Mat colored);
|
||
|
|
|
||
|
|
/** @brief set ColorSpace
|
||
|
|
@note It should be some RGB color space;
|
||
|
|
Supported list of color cards:
|
||
|
|
- @ref COLOR_SPACE_sRGB
|
||
|
|
- @ref COLOR_SPACE_AdobeRGB
|
||
|
|
- @ref COLOR_SPACE_WideGamutRGB
|
||
|
|
- @ref COLOR_SPACE_ProPhotoRGB
|
||
|
|
- @ref COLOR_SPACE_DCI_P3_RGB
|
||
|
|
- @ref COLOR_SPACE_AppleRGB
|
||
|
|
- @ref COLOR_SPACE_REC_709_RGB
|
||
|
|
- @ref COLOR_SPACE_REC_2020_RGB
|
||
|
|
@param cs the absolute color space that detected colors convert to;\n
|
||
|
|
default: @ref COLOR_SPACE_sRGB
|
||
|
|
*/
|
||
|
|
CV_WRAP void setColorSpace(COLOR_SPACE cs);
|
||
|
|
|
||
|
|
/** @brief set ccm_type
|
||
|
|
@param ccm_type the shape of color correction matrix(CCM);\n
|
||
|
|
default: @ref CCM_3x3
|
||
|
|
*/
|
||
|
|
CV_WRAP void setCCM_TYPE(CCM_TYPE ccm_type);
|
||
|
|
|
||
|
|
/** @brief set Distance
|
||
|
|
@param distance the type of color distance;\n
|
||
|
|
default: @ref DISTANCE_CIE2000
|
||
|
|
*/
|
||
|
|
CV_WRAP void setDistance(DISTANCE_TYPE distance);
|
||
|
|
|
||
|
|
/** @brief set Linear
|
||
|
|
@param linear_type the method of linearization;\n
|
||
|
|
default: @ref LINEARIZATION_GAMMA
|
||
|
|
*/
|
||
|
|
CV_WRAP void setLinear(LINEAR_TYPE linear_type);
|
||
|
|
|
||
|
|
/** @brief set Gamma
|
||
|
|
|
||
|
|
@note only valid when linear is set to "gamma";\n
|
||
|
|
|
||
|
|
@param gamma the gamma value of gamma correction;\n
|
||
|
|
default: 2.2;
|
||
|
|
*/
|
||
|
|
CV_WRAP void setLinearGamma(const double& gamma);
|
||
|
|
|
||
|
|
/** @brief set degree
|
||
|
|
@note only valid when linear is set to
|
||
|
|
- @ref LINEARIZATION_COLORPOLYFIT
|
||
|
|
- @ref LINEARIZATION_GRAYPOLYFIT
|
||
|
|
- @ref LINEARIZATION_COLORLOGPOLYFIT
|
||
|
|
- @ref LINEARIZATION_GRAYLOGPOLYFIT
|
||
|
|
|
||
|
|
@param deg the degree of linearization polynomial;\n
|
||
|
|
default: 3
|
||
|
|
|
||
|
|
*/
|
||
|
|
CV_WRAP void setLinearDegree(const int& deg);
|
||
|
|
|
||
|
|
/** @brief set SaturatedThreshold.
|
||
|
|
The colors in the closed interval [lower, upper] are reserved to participate
|
||
|
|
in the calculation of the loss function and initialization parameters
|
||
|
|
@param lower the lower threshold to determine saturation;\n
|
||
|
|
default: 0;
|
||
|
|
@param upper the upper threshold to determine saturation;\n
|
||
|
|
default: 0
|
||
|
|
*/
|
||
|
|
CV_WRAP void setSaturatedThreshold(const double& lower, const double& upper);
|
||
|
|
|
||
|
|
/** @brief set WeightsList
|
||
|
|
@param weights_list the list of weight of each color;\n
|
||
|
|
default: empty array
|
||
|
|
*/
|
||
|
|
CV_WRAP void setWeightsList(const Mat& weights_list);
|
||
|
|
|
||
|
|
/** @brief set WeightCoeff
|
||
|
|
@param weights_coeff the exponent number of L* component of the reference color in CIE Lab color space;\n
|
||
|
|
default: 0
|
||
|
|
*/
|
||
|
|
CV_WRAP void setWeightCoeff(const double& weights_coeff);
|
||
|
|
|
||
|
|
/** @brief set InitialMethod
|
||
|
|
@param initial_method_type the method of calculating CCM initial value;\n
|
||
|
|
default: INITIAL_METHOD_LEAST_SQUARE
|
||
|
|
*/
|
||
|
|
CV_WRAP void setInitialMethod(INITIAL_METHOD_TYPE initial_method_type);
|
||
|
|
|
||
|
|
/** @brief set MaxCount
|
||
|
|
@param max_count used in MinProblemSolver-DownhillSolver;\n
|
||
|
|
Terminal criteria to the algorithm;\n
|
||
|
|
default: 5000;
|
||
|
|
*/
|
||
|
|
CV_WRAP void setMaxCount(const int& max_count);
|
||
|
|
|
||
|
|
/** @brief set Epsilon
|
||
|
|
@param epsilon used in MinProblemSolver-DownhillSolver;\n
|
||
|
|
Terminal criteria to the algorithm;\n
|
||
|
|
default: 1e-4;
|
||
|
|
*/
|
||
|
|
CV_WRAP void setEpsilon(const double& epsilon);
|
||
|
|
|
||
|
|
/** @brief make color correction */
|
||
|
|
CV_WRAP void run();
|
||
|
|
|
||
|
|
CV_WRAP Mat getCCM() const;
|
||
|
|
CV_WRAP double getLoss() const;
|
||
|
|
CV_WRAP Mat get_src_rgbl() const;
|
||
|
|
CV_WRAP Mat get_dst_rgbl() const;
|
||
|
|
CV_WRAP Mat getMask() const;
|
||
|
|
CV_WRAP Mat getWeights() const;
|
||
|
|
|
||
|
|
/** @brief Infer using fitting ccm.
|
||
|
|
@param img the input image.
|
||
|
|
@param islinear default false.
|
||
|
|
@return the output array.
|
||
|
|
*/
|
||
|
|
CV_WRAP Mat infer(const Mat& img, bool islinear = false);
|
||
|
|
|
||
|
|
class Impl;
|
||
|
|
private:
|
||
|
|
std::shared_ptr<Impl> p;
|
||
|
|
};
|
||
|
|
|
||
|
|
//! @} ccm
|
||
|
|
} // namespace ccm
|
||
|
|
} // namespace cv
|
||
|
|
|
||
|
|
#endif
|