Health/Assets/OpenCVForUnity/org/opencv_contrib/xphoto/LearningBasedWB.cs

using OpenCVForUnity.CoreModule;
using OpenCVForUnity.UtilsModule;
using System;
using System.Collections.Generic;
using System.Runtime.InteropServices;

namespace OpenCVForUnity.XphotoModule
{

    // C++: class LearningBasedWB
    /**
     * More sophisticated learning-based automatic white balance algorithm.
     *
     * As REF: GrayworldWB, this algorithm works by applying different gains to the input
     * image channels, but their computation is a bit more involved compared to the
     * simple gray-world assumption. More details about the algorithm can be found in
     * CITE: Cheng2015 .
     *
     * To mask out saturated pixels this function uses only pixels that satisfy the
     * following condition:
     *
     * \( \frac{\textrm{max}(R,G,B)}{\texttt{range_max_val}} < \texttt{saturation_thresh} \)
     *
     * Currently supports images of type REF: CV_8UC3 and REF: CV_16UC3.
     */

    public class LearningBasedWB : WhiteBalancer
    {

        protected override void Dispose(bool disposing)
        {

            try
            {
                if (disposing)
                {
                }
                if (IsEnabledDispose)
                {
                    if (nativeObj != IntPtr.Zero)
                        xphoto_LearningBasedWB_delete(nativeObj);
                    nativeObj = IntPtr.Zero;
                }
            }
            finally
            {
                base.Dispose(disposing);
            }

        }

        protected internal LearningBasedWB(IntPtr addr) : base(addr) { }

        // internal usage only
        public static new LearningBasedWB __fromPtr__(IntPtr addr) { return new LearningBasedWB(addr); }

        //
        // C++:  void cv::xphoto::LearningBasedWB::extractSimpleFeatures(Mat src, Mat& dst)
        //

        /**
         * Implements the feature extraction part of the algorithm.
         *
         *     In accordance with CITE: Cheng2015 , computes the following features for the input image:
         *     1. Chromaticity of an average (R,G,B) tuple
         *     2. Chromaticity of the brightest (R,G,B) tuple (while ignoring saturated pixels)
         *     3. Chromaticity of the dominant (R,G,B) tuple (the one that has the highest value in the RGB histogram)
         *     4. Mode of the chromaticity palette, that is constructed by taking 300 most common colors according to
         *        the RGB histogram and projecting them on the chromaticity plane. Mode is the most high-density point
         *        of the palette, which is computed by a straightforward fixed-bandwidth kernel density estimator with
         *        a Epanechnikov kernel function.
         *
         *     param src Input three-channel image (BGR color space is assumed).
         *     param dst An array of four (r,g) chromaticity tuples corresponding to the features listed above.
         */
        public void extractSimpleFeatures(Mat src, Mat dst)
        {
            ThrowIfDisposed();
            if (src != null) src.ThrowIfDisposed();
            if (dst != null) dst.ThrowIfDisposed();

            xphoto_LearningBasedWB_extractSimpleFeatures_10(nativeObj, src.nativeObj, dst.nativeObj);


        }


        //
        // C++:  int cv::xphoto::LearningBasedWB::getRangeMaxVal()
        //

        /**
         * Maximum possible value of the input image (e.g. 255 for 8 bit images,
         *                4095 for 12 bit images)
         * SEE: setRangeMaxVal
         * return automatically generated
         */
        public int getRangeMaxVal()
        {
            ThrowIfDisposed();

            return xphoto_LearningBasedWB_getRangeMaxVal_10(nativeObj);


        }


        //
        // C++:  void cv::xphoto::LearningBasedWB::setRangeMaxVal(int val)
        //

        /**
         *  getRangeMaxVal SEE: getRangeMaxVal
         * param val automatically generated
         */
        public void setRangeMaxVal(int val)
        {
            ThrowIfDisposed();

            xphoto_LearningBasedWB_setRangeMaxVal_10(nativeObj, val);


        }


        //
        // C++:  float cv::xphoto::LearningBasedWB::getSaturationThreshold()
        //

        /**
         * Threshold that is used to determine saturated pixels, i.e. pixels where at least one of the
         *         channels exceeds \(\texttt{saturation_threshold}\times\texttt{range_max_val}\) are ignored.
         * SEE: setSaturationThreshold
         * return automatically generated
         */
        public float getSaturationThreshold()
        {
            ThrowIfDisposed();

            return xphoto_LearningBasedWB_getSaturationThreshold_10(nativeObj);


        }


        //
        // C++:  void cv::xphoto::LearningBasedWB::setSaturationThreshold(float val)
        //

        /**
         *  getSaturationThreshold SEE: getSaturationThreshold
         * param val automatically generated
         */
        public void setSaturationThreshold(float val)
        {
            ThrowIfDisposed();

            xphoto_LearningBasedWB_setSaturationThreshold_10(nativeObj, val);


        }


        //
        // C++:  int cv::xphoto::LearningBasedWB::getHistBinNum()
        //

        /**
         * Defines the size of one dimension of a three-dimensional RGB histogram that is used internally
         *         by the algorithm. It often makes sense to increase the number of bins for images with higher bit depth
         *         (e.g. 256 bins for a 12 bit image).
         * SEE: setHistBinNum
         * return automatically generated
         */
        public int getHistBinNum()
        {
            ThrowIfDisposed();

            return xphoto_LearningBasedWB_getHistBinNum_10(nativeObj);


        }


        //
        // C++:  void cv::xphoto::LearningBasedWB::setHistBinNum(int val)
        //

        /**
         *  getHistBinNum SEE: getHistBinNum
         * param val automatically generated
         */
        public void setHistBinNum(int val)
        {
            ThrowIfDisposed();

            xphoto_LearningBasedWB_setHistBinNum_10(nativeObj, val);


        }


#if (UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR
        const string LIBNAME = "__Internal";
#else
        const string LIBNAME = "opencvforunity";
#endif



        // C++:  void cv::xphoto::LearningBasedWB::extractSimpleFeatures(Mat src, Mat& dst)
        [DllImport(LIBNAME)]
        private static extern void xphoto_LearningBasedWB_extractSimpleFeatures_10(IntPtr nativeObj, IntPtr src_nativeObj, IntPtr dst_nativeObj);

        // C++:  int cv::xphoto::LearningBasedWB::getRangeMaxVal()
        [DllImport(LIBNAME)]
        private static extern int xphoto_LearningBasedWB_getRangeMaxVal_10(IntPtr nativeObj);

        // C++:  void cv::xphoto::LearningBasedWB::setRangeMaxVal(int val)
        [DllImport(LIBNAME)]
        private static extern void xphoto_LearningBasedWB_setRangeMaxVal_10(IntPtr nativeObj, int val);

        // C++:  float cv::xphoto::LearningBasedWB::getSaturationThreshold()
        [DllImport(LIBNAME)]
        private static extern float xphoto_LearningBasedWB_getSaturationThreshold_10(IntPtr nativeObj);

        // C++:  void cv::xphoto::LearningBasedWB::setSaturationThreshold(float val)
        [DllImport(LIBNAME)]
        private static extern void xphoto_LearningBasedWB_setSaturationThreshold_10(IntPtr nativeObj, float val);

        // C++:  int cv::xphoto::LearningBasedWB::getHistBinNum()
        [DllImport(LIBNAME)]
        private static extern int xphoto_LearningBasedWB_getHistBinNum_10(IntPtr nativeObj);

        // C++:  void cv::xphoto::LearningBasedWB::setHistBinNum(int val)
        [DllImport(LIBNAME)]
        private static extern void xphoto_LearningBasedWB_setHistBinNum_10(IntPtr nativeObj, int val);

        // native support for java finalize()
        [DllImport(LIBNAME)]
        private static extern void xphoto_LearningBasedWB_delete(IntPtr nativeObj);

    }
}