android中opencv操作图片像素----之图像灰度处理

灰度图 以数组存储每个像素的数据，每个数据叫做一个灰度值
彩色图像灰度处理公式：R*0.299+G*0.587+B*0.114
方式一：指针操作。

extern "C" {
JNIEXPORT jintArray JNICALL
Java_com_xy_opencv_ndk_1opencv002_MainActivity_grayPixels(JNIEnv *env, jclass type,                                                          jintArray pixels_, jint w, jint h) {
 jint *pixels = env->GetIntArrayElements(pixels_, NULL);
    if (pixels == NULL) {
        return 0;
    }
    //图片一进来时是ARGB  通过mat转换BGRA
    Mat img(h, w, CV_8UC4, pixels);
    //读取返回一个uchar数组
    uchar* ptr = img.ptr(0);
    //获取当前CPU的钟摆时间
    double time0 = static_cast<double>(getTickCount());
    /**
     * 指针处理
     */
    //灰度处理图片
    for(int i = 0; i < w * h; i++){
        //R*0.299 + G * 0.587 + B * 0.114
        uchar  grayPixel = (uchar)(ptr[4*i+2]*0.299+ptr[4*i+1]*0.587+ptr[4*i+0]*0.114);
        ptr[4*i+0] = grayPixel;
        ptr[4*i+1] = grayPixel;
        ptr[4*i+2] = grayPixel;
    }

   //计算运行时间
    time0 = ((double)getTickCount() - time0)/getTickFrequency();
    __android_log_print(ANDROID_LOG_INFO,"JNI","%ld",time0);

    int size = w * h;
    jintArray result = env->NewIntArray(size);
    env->SetIntArrayRegion(result, 0, size, pixels);
    env->ReleaseIntArrayElements(pixels_,pixels,0);
    env->ReleaseIntArrayElements(pixels_, pixels, 0);
    return result;
        }
    }

方式二：迭代器处理。

extern "C" {
JNIEXPORT jintArray JNICALL
Java_com_xy_opencv_ndk_1opencv002_MainActivity_grayPixels(JNIEnv *env, jclass type,                                                          jintArray pixels_, jint w, jint h) {
 jint *pixels = env->GetIntArrayElements(pixels_, NULL);
    if (pixels == NULL) {
        return 0;
    }
    //图片一进来时是ARGB  通过mat转换BGRA
    Mat img(h, w, CV_8UC4, pixels);
       //获取当前CPU的钟摆时间
    double time0 = static_cast<double>(getTickCount());
    /**
     * 迭代器处理
     */
    //起始位置的迭代器
   // Vec3b是向量
    Mat_<Vec3b>::iterator it = img.begin<Vec3b>();
    //结束为止的迭代器
    Mat_<Vec3b>::iterator itEnd = img.end<Vec3b>();

    for (; it!= itEnd; ++it) {
        uchar temp = (uchar)((*it)[2]*0.299+(*it)[1]*0.587+(*it)[0]*0.114);
        (*it)[0] = temp;
        (*it)[1] = temp;
        (*it)[2] = temp;
    }
     //计算运行时间
    time0 = ((double)getTickCount() - time0)/getTickFrequency();
    __android_log_print(ANDROID_LOG_INFO,"JNI","%ld",time0);

    int size = w * h;
    jintArray result = env->NewIntArray(size);
    env->SetIntArrayRegion(result, 0, size, pixels);
    env->ReleaseIntArrayElements(pixels_,pixels,0);
    env->ReleaseIntArrayElements(pixels_, pixels, 0);
    return result;
        }
    }

方式三：动态地址计算。

extern "C" {
JNIEXPORT jintArray JNICALL
Java_com_xy_opencv_ndk_1opencv002_MainActivity_grayPixels(JNIEnv *env, jclass type,                                                          jintArray pixels_, jint w, jint h) {
 jint *pixels = env->GetIntArrayElements(pixels_, NULL);
    if (pixels == NULL) {
        return 0;
    }
    //图片一进来时是ARGB  通过mat转换BGRA
    Mat img(h, w, CV_8UC4, pixels);
 /**
     * 动态地址计算
     * 得用Vec4b
     */
    int row = img.rows;
    int col = img.cols;
    for (int i = 0; i < row; i++) {
        for (int j = 0; j < col; j++) {
            uchar temp = (uchar)(img.at<Vec4b>(i,j)[2]*0.299
                         +img.at<Vec4b>(i,j)[1]*0.587
                         +img.at<Vec4b>(i,j)[0]*0.114);
            img.at<Vec4b>(i,j)[0]=temp;
            img.at<Vec4b>(i,j)[1]=temp;
            img.at<Vec4b>(i,j)[2]=temp;
        }
    }

    //计算运行时间
    time0 = ((double)getTickCount() - time0)/getTickFrequency();
    __android_log_print(ANDROID_LOG_INFO,"JNI","%ld",time0);
    int size = w * h;
    jintArray result = env->NewIntArray(size);
    env->SetIntArrayRegion(result, 0, size, pixels);
    env->ReleaseIntArrayElements(pixels_,pixels,0);
    env->ReleaseIntArrayElements(pixels_, pixels, 0);
    return result;
        }
    }

这三种方式中，性能最好的是第一种，耗时少，第二种和第三种耗时差不多，但第二种使用了迭代器，可以保证不会越界，安全性高，第三种方式使用了Mat中的方法动态操作地址中的数据，便于理解。