Android中值濾波、直方圖均衡化處理數(shù)組

前言：簡書菜鳥一枚，主要用于記錄。如有侵權(quán)，望告知，我會下架文章。

所遇場景：有設(shè)備采集到512個點位的壓力數(shù)據(jù)。要根據(jù)數(shù)據(jù)跑算法模型得出姿勢。跑算法前，要對數(shù)據(jù)先進行預(yù)處理，參考圖像預(yù)處理的中值濾波、直方圖均衡

一、找資源。未能找到直接可用的資源。參考資源：

1.java中值濾波_中值濾波 java實現(xiàn)
2.Java實現(xiàn)圖像中值濾波 - 優(yōu)化
3.直方圖均衡化原理
4.JAVA實現(xiàn)直方圖均衡化增強灰度和彩色圖像
算法與項目結(jié)合理解：

image.png

更多中值濾波、直方圖理解另找資料。我也是似懂非懂。

二、代碼實現(xiàn)

1.中值濾波：

    /**
     *
     * @param pixels 圖像像素點，二維數(shù)組
     * @param w 二維數(shù)組的列
     * @param h 二維數(shù)組的行
     */
    public static void medianFilter(int[][] pixels, int w, int h) {
        w = h = 4;
        pixels = new int[][]{
                {150, 145, 151, 50},
                {146, 250, 144, 50},
                {151, 148, 150, 50},
                {50, 50, 50, 50}};
//        w = h = 3;
//        pixels = new int[][]{{150, 145, 151}, {146, 250, 144}, {151, 148, 150}};
        // 初始實現(xiàn)，發(fā)現(xiàn)：冗余了，未去掉邊界
//        for (int y = 0; y < h; y++) {
//            StringBuffer buffer = new StringBuffer();
//            for (int x = 0; x < w; x++) {
//                int value = pixels[y][x];
//                buffer.append(" " + value);
//                int index = y * w + x;
//                int[] medians = new int[9]; // 3*3過濾
//                for (int i = 0; i < medians.length; i++) {
//                    int ux = i % 3 + x;
//                    int uy = y + i / 3;
//                    if (uy >= h) uy = h - 1;
//                    if (ux >= w) ux = w - 1;
//                    medians[i] = pixels[uy][ux];
//                }
//                MyLog.i("遍歷原數(shù)組00： " + buffer.toString() + ",  i " + index + ", medians =" + Arrays.toString(medians));
//            }
//            MyLog.i("遍歷原數(shù)組11： " + buffer.toString());
//        }

        StringBuffer buffer = new StringBuffer();
        for (int y = 1; y < h - 1; y++) {
            buffer.append("\n");
            for (int x = 1; x < w - 1; x++) {
                int value = pixels[y][x];
                buffer.append(" " + value);
                int[] filterArray = new int[9]; // 3*3過濾
                for (int i = 0; i < filterArray.length; i++) {
                    int ux = i % 3 + x;
                    int uy = y + i / 3;
//                    MyLog.i("遍歷原數(shù)組00： " + uy + ",  ux " + ux + ", filterArray =" + Arrays.toString(filterArray));
                    filterArray[i] = pixels[uy-1][ux-1];
                }
                Arrays.sort(filterArray);
                pixels[y][x] = filterArray[4]; // 中值替換
                MyLog.i("遍歷原數(shù)組中值下標y,x (" + y + "," + x +"), 變化" + value + "->" + filterArray[4] + ", filterArray =" + Arrays.toString(filterArray));
            }
            MyLog.i("遍歷原數(shù)組中值： " + buffer.toString());
        }
    }

中值濾波 優(yōu)化-減少循環(huán)

     /**
     * http://events.jianshu.io/p/964f68851abc
     *
     * @param pixel 一維數(shù)組
     * @param w     一維數(shù)組轉(zhuǎn)二維數(shù)組的列
     * @param h     一維數(shù)組轉(zhuǎn)二維數(shù)組的行
     */
    public static int[] medianFiltering(int[] pixel, int w, int h) {
        int[] newPixel = new int[w * h];
        int[] tempR = new int[9];
        for (int y = 0; y < h; y++) {
            for (int x = 0; x < w; x++) {
                if (x == 0 || x == w - 1 || y == 0 || y == h - 1) {
                    // 首行/列、尾行/列
                    newPixel[y * w + x] = pixel[y * w + x];
                    continue;
                }
                tempR[0] = pixel[x - 1 + (y - 1) * w];
                tempR[1] = pixel[x + (y - 1) * w];
                tempR[2] = pixel[x + 1 + (y - 1) * w];
                tempR[3] = pixel[x - 1 + y * w];
                tempR[4] = pixel[x + y * w];
                tempR[5] = pixel[x + 1 + y * w];
                tempR[6] = pixel[x - 1 + (y + 1) * w];
                tempR[7] = pixel[x + (y + 1) * w];
                tempR[8] = pixel[x + 1 + (y + 1) * w];
                // median value
                Arrays.sort(tempR);
                newPixel[y * w + x] = tempR[4];
            }
        }
        return newPixel;
    }

2.直方圖均衡化：

     /**
     * //blog.csdn.net/qq_41037012/article/details/104900944
     * https://www.freesion.com/article/1932342426/
     *
     * @return
     */
    public static void histogram2(int[][] pixels, int x, int y) {
        int width = 4;
        int height = 4;
        pixels = new int[][]{
                {255, 128, 200, 50},
                {50, 200, 255, 50},
                {255, 200, 128, 128},
                {200, 200, 255, 50}};
        // 記錄灰度值出現(xiàn)個數(shù)
        double[] count = new double[256];
        for (int k = 0; k < count.length; k++) {
            for (int i = 0; i < width; i++) {
                StringBuffer buffer = new StringBuffer();
                for (int j = 0; j < height; j++) {
                    buffer.append(" " + pixels[i][j]);
                    if (pixels[i][j] == k) {
                        // 累計灰度值個數(shù)
                        if (count[k] == 0) {
                            count[k] = 1;
                        } else {
                            count[k] += 1;
                        }
                    }
                }
                if (k == 0) MyLog.i("遍歷原數(shù)組： " + buffer.toString());
            }
            if (count[k] != 0) MyLog.i("灰度值： " + k + ", 個數(shù) " + count[k]);
        }
        // 計算各灰度值出現(xiàn)概率
        double[] Pr = new double[count.length];
        // 對應(yīng)灰度值概率累計; 第一個灰度值的是0.03 = 0 + 0.03; 第二個灰度值的 0.23 = 0.2 + 0.3; ...
        double[] sk = new double[Pr.length];
        for (int i = 0; i < Pr.length; i++) {
            Pr[i] = count[i] / (double) (width * height);
            for (int j = 0; j <= i; j++) {
                sk[i] += Pr[j];
            }
            if (count[i] != 0) {
                MyLog.i("計算各灰度值出現(xiàn)概率, 灰度值 i " + i + ", 次數(shù) " + count[i] + ", 概率 " + Pr[i] + ", 累計概率 " + sk[i]);
            }
        }
        int[][] newPixels = new int[width][height]; // 統(tǒng)計新圖的灰度值
        for (int i = 0; i < width; i++) {
            StringBuffer buffer = new StringBuffer();
            for (int j = 0; j < height; j++) {
                int h = pixels[i][j];
                newPixels[i][j] = (int) ((count.length - 1) * sk[h] + 0.5);
                buffer.append(" " + newPixels[i][j]);
            }
            MyLog.i("遍歷處理后的數(shù)組： " + buffer.toString());
        }
        MyLog.i("-----------------------------------");
    }

直方圖均衡化 優(yōu)化-減少循環(huán)

     /**
     * @param pixels 一維數(shù)組
     */
    public static void histogram(int[] pixels) {
        pixels = new int[]{255, 128, 200, 50, 50, 200, 255, 50, 255, 200, 128, 128, 200, 200, 255, 50};
        // 記錄灰度值出現(xiàn)個數(shù)
        double[] count = new double[256];
        StringBuffer buffer = new StringBuffer();
        for (int k = 0; k < count.length; k++) {
            for (int i = 0; i < pixels.length; i++) {
                if (k == 0) {
                    buffer.append(" " + pixels[i]);
                    if (i % 4 == 3) buffer.append("\n");
                }

                if (pixels[i] == k) {
                    // 累計灰度值個數(shù)
                    count[k] += 1;
                }

            }
            if (count[k] != 0) MyLog.i("灰度值： " + k + ", 個數(shù) " + count[k]);
            if (k == count.length - 1) MyLog.i("遍歷原數(shù)組： \n" + buffer.toString());
        }
        // 計算各灰度值出現(xiàn)概率
        double[] Pr = new double[count.length];
        // 對應(yīng)灰度值概率累計; 第一個灰度值的是0.03 = 0 + 0.03; 第二個灰度值的 0.23 = 0.2 + 0.3; ...
        double[] sk = new double[Pr.length];
        for (int i = 0; i < Pr.length; i++) {
            Pr[i] = count[i] / (double) pixels.length;
            for (int j = 0; j <= i; j++) {
                sk[i] += Pr[j];
            }
            if (count[i] != 0) {
                MyLog.i("計算各灰度值出現(xiàn)概率, 灰度值 i " + i + ", 次數(shù) " + count[i] + ", 概率 " + Pr[i] + ", 累計概率 " + sk[i]);
            }
        }
        StringBuffer buffer2 = new StringBuffer();
        for (int i = 0; i < pixels.length; i++) {
            pixels[i] = (int) ((count.length - 1) * sk[pixels[i]] + 0.5);
            buffer2.append(" " + pixels[i]);
            if (i % 4 == 3) buffer2.append("\n");
        }
        MyLog.i("遍歷處理后的數(shù)組： \n" + buffer2.toString());
        MyLog.i("-----------------------------------");
    }

log輸出

2023-02-12 15:52:40.695 MyLog: (BitmapUtil.java:41).histogram 灰度值： 50, 個數(shù) 4.0
2023-02-12 15:52:40.696 MyLog: (BitmapUtil.java:41).histogram 灰度值： 128, 個數(shù) 3.0
2023-02-12 15:52:40.696 MyLog: (BitmapUtil.java:41).histogram 灰度值： 200, 個數(shù) 5.0
2023-02-12 15:52:40.696 MyLog: (BitmapUtil.java:41).histogram 灰度值： 255, 個數(shù) 4.0
2023-02-12 15:52:40.697 MyLog: (BitmapUtil.java:42).histogram 遍歷原數(shù)組： 
     255 128 200 50
     50 200 255 50
     255 200 128 128
     200 200 255 50
2023-02-12 15:52:40.697 MyLog: (BitmapUtil.java:54).histogram 計算各灰度值出現(xiàn)概率, 灰度值 i 50, 次數(shù) 4.0, 概率 0.25, 累計概率 0.25
2023-02-12 15:52:40.698 MyLog: (BitmapUtil.java:54).histogram 計算各灰度值出現(xiàn)概率, 灰度值 i 128, 次數(shù) 3.0, 概率 0.1875, 累計概率 0.4375
2023-02-12 15:52:40.699 MyLog: (BitmapUtil.java:54).histogram 計算各灰度值出現(xiàn)概率, 灰度值 i 200, 次數(shù) 5.0, 概率 0.3125, 累計概率 0.75
2023-02-12 15:52:40.700 MyLog: (BitmapUtil.java:54).histogram 計算各灰度值出現(xiàn)概率, 灰度值 i 255, 次數(shù) 4.0, 概率 0.25, 累計概率 1.0
2023-02-12 15:52:40.701 MyLog: (BitmapUtil.java:63).histogram 遍歷處理后的數(shù)組： 
     255 112 191 64
     64 191 255 64
     255 191 112 112
     191 191 255 64
2023-02-12 15:52:40.701 MyLog: (BitmapUtil.java:64).histogram -----------------------------------
2023-02-12 15:52:40.702 MyLog: (BitmapUtil.java:177).medianFilter 遍歷原數(shù)組中值下標y,x (1,1), 變化250->150, filterArray =[144, 145, 146, 148, 150, 150, 151, 151, 250]
2023-02-12 15:52:40.702 MyLog: (BitmapUtil.java:177).medianFilter 遍歷原數(shù)組中值下標y,x (1,2), 變化144->145, filterArray =[50, 50, 50, 144, 145, 148, 150, 150, 151]
2023-02-12 15:52:40.703 MyLog: (BitmapUtil.java:179).medianFilter 遍歷原數(shù)組中值： 
     250 144            
2023-02-12 15:52:40.703 MyLog: (BitmapUtil.java:177).medianFilter 遍歷原數(shù)組中值下標y,x (2,1), 變化148->146, filterArray =[50, 50, 50, 145, 146, 148, 150, 150, 151]
2023-02-12 15:52:40.704 MyLog: (BitmapUtil.java:177).medianFilter 遍歷原數(shù)組中值下標y,x (2,2), 變化150->50, filterArray =[50, 50, 50, 50, 50, 145, 146, 150, 150]
2023-02-12 15:52:40.704 MyLog: (BitmapUtil.java:179).medianFilter 遍歷原數(shù)組中值： 
     250 144
     148 150
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