Android JNI開發 通過C++實現眼瞼標註
阿新 • • 發佈：2018-12-14
C++程式碼：
#include <jni.h> #include <string> #include <opencv2/opencv.hpp> #include <android/log.h> #include <iostream> #include <stdio.h> #define LOGD(...) __android_log_print(ANDROID_LOG_DEBUG, "keymatch", __VA_ARGS__) extern "C"{ using namespace cv; using namespace std; void printMAtMessage(Mat &mat); JNIEXPORT jintArray JNICALL Java_com_findai_xkk_myopencv_MainActivity_stringFromJNI( JNIEnv *env, jobject /* this */,jintArray pixels_ , jint w , jint h) { jint* pixels = env->GetIntArrayElements(pixels_, NULL); if (pixels==NULL){ return 0; } //圖片一進來時是ARGB 通過mat轉換BGRA Mat img(h,w,CV_8UC4,(uchar *)pixels); //pixels 操作的是同一份資料 h = h/2,w = w/2; resize(img,img,Size(w,h)); printMAtMessage(img); Mat temp; //轉化為單通道灰度圖，並列印資訊 cvtColor(img,temp,COLOR_RGBA2GRAY); // printMAtMessage(temp); int summat[h][w] ; // Mat cal_mat = temp.clone(); int max_cha = 0; // LOGD("rows: %d",temp.rows); // LOGD("cols: %d",temp.cols); // LOGD("w: %d",w); // LOGD("h: %d",h); // int kk=0; for(int i =6 ;i<h-6;i++){ // int sum_d = 0; for(int j =6;j<w-6;j++){ // LOGD("DDD %d",temp.at<uchar >(i,j)); int sum_s = temp.at<uchar >(i-1,j)+temp.at<uchar >(i-2,j)+temp.at<uchar >(i-3,j)+temp.at<uchar >(i-4,j)+temp.at<uchar >(i-5,j); // LOGD("------================211111111111111111111111111===------------ %d %s",i,j); int sum_x = temp.at<uchar >(i+1,j)+temp.at<uchar >(i+2,j)+temp.at<uchar >(i+3,j)+temp.at<uchar >(i+4,j)+temp.at<uchar >(i+5,j); // LOGD("------==============22222222222222222222=====------------"); int cha = sum_s - sum_x; // LOGD("------===============3333333333333333333333333====------------"); summat[i][j] = abs(cha); // LOGD("------===============33333333333 %d 33333333333333====------------",summat[i][j]); // temp.at<uchar >(i,j)=255; // kk++; // LOGD("i: %d",i); // LOGD("j: %d",j); // LOGD("------==============444444444444444444444444=====------------"); if(max_cha<cha){ max_cha = cha; // LOGD("max %d",max_cha); // LOGD("------============44666666666666666666666666666666666=======------------"); } // sum_d = sum_d + ; } } int std_max = max_cha*0.4; // LOGD("------=========== %d ========------------",max_cha); for(int i =6 ;i<h-6;i++){ // int max_col = 0; // int max_i = 0; // int max_j = 0; for(int j =6;j<w-6;j++){ // LOGD("DDD %d",temp.at<uchar >(i,j)); // int sum_s = temp.at<uchar >(i-1,j)+temp.at<uchar >(i-2,j)+temp.at<uchar >(i-3,j)+temp.at<uchar >(i-4,j)+temp.at<uchar >(i-5,j); // int sum_x = temp.at<uchar >(i+1,j)+temp.at<uchar >(i+2,j)+temp.at<uchar >(i+3,j)+temp.at<uchar >(i+4,j)+temp.at<uchar >(i+5,j); // int cha = sum_s - sum_x; // int cur_gray = summat[i][j]; // LOGD("------=======cur_gray==== %d ========------------",cur_gray); if(summat[i][j] > std_max){ // LOGD("------=======cur_gray==== %d ========------------",cal_mat.at<int>(i,j)); temp.at<uchar>(i,j)=255; } // if(max_cha<cha){ // max_cha = cha; // } // sum_d = sum_d + ; } } //轉化為四通道。特別注意：在呼叫ov影象處理函式時，一定要好好考慮一下圖片的位數和通道.否則可能出現各種問題. cvtColor(temp,temp,COLOR_GRAY2BGRA); // printMAtMessage(temp); uchar* tempData = temp.data; //對應資料指標 int size = w*h; jintArray result = env->NewIntArray(size); //env->SetIntArrayRegion(result,0,size,pixels); env->SetIntArrayRegion(result, 0, size, (const jint *) tempData); env->ReleaseIntArrayElements(pixels_, pixels, 0); return result; } void printMAtMessage(Mat &mat) { LOGD("***************************Mat資訊開始************************"); LOGD("mat.rows %d",mat.rows); LOGD("mat.cols %d",mat.cols); LOGD("mat.total %d",mat.total()); LOGD("mat.channels %d",mat.channels()); LOGD("mat.depth %d",mat.depth()); LOGD("mat.type %d",mat.type()); LOGD("mat.flags %d",mat.flags); LOGD("mat.elemSize %d",mat.elemSize()); LOGD("mat.elemSize1 %d",mat.elemSize1()); LOGD("***************************Mat資訊結束************************"); } }
JAVA程式碼：
package com.findai.xkk.myopencv; import android.graphics.Bitmap; import android.graphics.BitmapFactory; import android.support.v7.app.AppCompatActivity; import android.os.Bundle; import android.util.Log; import android.widget.ImageView; import android.widget.TextView; import org.opencv.android.OpenCVLoader; import org.opencv.android.Utils; import org.opencv.core.Mat; public class MainActivity extends AppCompatActivity { // Used to load the 'native-lib' library on application startup. static { System.loadLibrary("native-lib"); } @Override protected void onCreate(Bundle savedInstanceState) { super.onCreate(savedInstanceState); //非常重要，啟動opencv，必須寫在最前面 OpenCVLoader.initDebug(); setContentView(R.layout.activity_main); Bitmap bmp = BitmapFactory.decodeResource(getResources(),R.mipmap.eye); ImageView imageView = findViewById(R.id.iv_pro_img); int w = bmp.getWidth(); int h = bmp.getHeight(); // System.out.println(w); // System.out.println(h); int[] pixels = new int[w*h]; bmp.getPixels(pixels, 0, w, 0, 0, w, h); long startTime = System.currentTimeMillis(); int[] resultInt = stringFromJNI(pixels, w, h); long endTime = System.currentTimeMillis(); Log.e("JNITime",""+(endTime-startTime)); Bitmap resultImg = Bitmap.createBitmap(w/2,h/2, Bitmap.Config.ARGB_8888); //(@ColorInt int[] pixels, int offset, int stride,int x, int y, int width, int height) // System.out.println(w/2); // System.out.println(h/2); resultImg.setPixels(resultInt, 0, w/2, 0, 0, w/2,h/2); imageView.setImageBitmap(resultImg); } /** * A native method that is implemented by the 'native-lib' native library, * which is packaged with this application. */ public native int[] stringFromJNI(int[] pixels, int w, int h); }
核心思路：
眼瞼處，灰度變化最大，所以上下采集一定空間範圍的灰度值進行計算，同時減少噪聲的干擾。演算法實時性較高，能達到100FPS。
之前嘗試過深度學習，MRCNN等方法，效果是不錯，但是實時性跟不上，不適合在移動端部署。故採用傳統影象處理方法，嘗試過Sobel的梯度變化等等方法，最終本演算法 簡單有效實時性高。
效果圖：
GitHUB：
https://github.com/xkkjiayou/MYOPencv_real
JNI C++參考：
https://blog.csdn.net/wulafly/article/details/71076594
https://blog.csdn.net/qq_29540745/article/details/52487832