简介

在linuxt系统下使用OpenCV2.3 + NDK R6编译 OpenCV人脸检测应用

准备

注:http://code.google.com/p/android-opencv/网站上说要使用crystax ndk r4代替NDK。估计可能是对于较旧的Android版本需要这样。如果NDK无法编译,请尝试使用crystax ndk r4编译。

OpenCV设置

从网站上下载OpenCV 2.3.0 for Android 后,解压到某个目录,如~/目录下
设置OPENCV_PACKAGE_DIR环境变量
$ export OPENCV_PACKAGE_DIR=~/enCV-2.3.0/

步骤

新建一个Android工程

在eclipse中新建一个android 工程如study.opencv,并且在工程根目录下新建一个名为jni的目录。将下载的android-ndk-r6解压到某个目录下,如~/
从~/android-ndk-r6/sample下某个sample中拷贝Android.mk, Application.mk到study.opencv/jni目录

设置编译脚本

在Android.mk中,include $(CLEAR_VARS)后面,加入下行
include $(OPENCV_PACKAGE_DIR)/$(TARGET_ARCH_ABI)/share/opencv/OpenCV.mk
如果应用支持ARM NEON那么还需要加入以下行

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include $(OPENCV_PACKAGE_DIR)/armeabi-v7a-neon/share/opencv/OpenCV.mk
LOCAL_ARM_NEON := true

在Application.mk中加入以下行

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APP_STL := gnustl_static
APP_CPPFLAGS := -frtti -fexceptions

注:关于Android.mk与Application.mk的详细说明,请参考ndk/docs下Android-mk.html和Application-mk.html。

Java层定义native接口

新建study.opencv.FaceRec类,定义一个人脸检测的本地接口

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/**
 * detect front face from image.
 * 
 * @param xml
 *            opencv haarcascade xml file path
 * @param infile
 *            input image file path
 * @param outfile
 *            output image file path
 */
public native void detect(String xml, String infile, String outfile);

生成jni头文件

使用javah命令生成jni头文件

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$ cd ~/workspace/study.opencv/bin
$ javah study.opencv.FaceRec

会在bin目录生成一个study_opencv_FaceRec.h文件。将此文件拷贝到../jni目录中

注:如果接口有变更,请先手动删除生成的.h文件。以防止一些意外的错误。

在c层实现图像人脸检测

在jni目录中使用文本编辑器新建一个facedetect.cpp,实现图像人脸检测

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#include "cv.h"
#include "highgui.h"

#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <assert.h>
#include <math.h>
#include <float.h>
#include <limits.h>
#include <time.h>
#include <ctype.h>

#include <android/log.h>
#include <study_opencv_FaceRec.h>
#include <jni.h>

#define  LOG_TAG    "opencv_face_detect"
#define  LOGI(...)  __android_log_print(ANDROID_LOG_INFO,LOG_TAG,__VA_ARGS__)
#define  LOGE(...)  __android_log_print(ANDROID_LOG_ERROR,LOG_TAG,__VA_ARGS__)

static CvMemStorage* storage = 0;
static CvHaarClassifierCascade* cascade = 0;
void detect_and_draw( IplImage* image );
const char* cascade_name =
    "haarcascade_frontalface_alt.xml";
/*    "haarcascade_profileface.xml";*/
/*int captureFromImage(char* xml, char* filename);*/
char* jstring2String(JNIEnv*, jstring);
int captureFromImage(char* xml, char* filename, char* outfile)
{
    LOGI("begin: ");
    // we just detect image
    // CvCapture* capture = 0;
    IplImage *frame, *frame_copy = 0;
    const char* input_name = "lina.png";
    if(xml != NULL)
    {
        cascade_name = xml;    
    }
    if(filename != NULL)
    {
        input_name = filename;
    }
    LOGI("xml=%s,filename=%s", cascade_name, input_name);
    // load xml 
    cascade = (CvHaarClassifierCascade*)cvLoad( cascade_name, 0, 0, 0 );
    LOGI("load cascade ok ? %d", cascade != NULL ? 1 : 0);
    if( !cascade )
    {
        LOGI("ERROR: Could not load classifier cascade\n" );
        // I just won't write long full file path, to instead of relative path, but I failed.
        FILE * fp = fopen(input_name,"w");
        if(fp == NULL){
            LOGE("create failed");
        }
        return -1;
    }
    storage = cvCreateMemStorage(0);
    // cvNamedWindow( "result", 1 );
    IplImage* image = cvLoadImage( input_name, 1 );
    if( image )
    {
        LOGI("load image successfully");
        detect_and_draw( image );
        // cvWaitKey(0);
        if(outfile != NULL)
        {
            LOGI("after detected save image file");
            cvSaveImage(outfile, image);//把图像写入文件
        }
        cvReleaseImage( &image );
    }
    else
    {
        LOGE("can't load image from : %s ", input_name);
    }
}
void detect_and_draw( IplImage* img )
{
    static CvScalar colors[] = 
    {
        {{0,0,255}},
        {{0,128,255}},
        {{0,255,255}},
        {{0,255,0}},
        {{255,128,0}},
        {{255,255,0}},
        {{255,0,0}},
        {{255,0,255}}
    };
    double scale = 1.3;
    IplImage* gray = cvCreateImage( cvSize(img->width,img->height), 8, 1 );
    IplImage* small_img = cvCreateImage( cvSize( cvRound (img->width/scale),
                         cvRound (img->height/scale)),
                     8, 1 );
    int i;
    cvCvtColor( img, gray, CV_BGR2GRAY );
    cvResize( gray, small_img, CV_INTER_LINEAR );
    cvEqualizeHist( small_img, small_img );
    cvClearMemStorage( storage );
    if( cascade )
    {
        double t = (double)cvGetTickCount();
        CvSeq* faces = cvHaarDetectObjects( small_img, cascade, storage,
                                            1.1, 2, 0/*CV_HAAR_DO_CANNY_PRUNING*/,
                                            cvSize(30, 30) );
        t = (double)cvGetTickCount() - t;
        LOGI( "detection time = %gms\n", t/((double)cvGetTickFrequency()*1000.) );
        for( i = 0; i < (faces ? faces->total : 0); i++ )
        {
            CvRect* r = (CvRect*)cvGetSeqElem( faces, i );
            CvPoint center;
            int radius;
            center.x = cvRound((r->x + r->width*0.5)*scale);
            center.y = cvRound((r->y + r->height*0.5)*scale);
            radius = cvRound((r->width + r->height)*0.25*scale);
            cvCircle( img, center, radius, colors[i%8], 3, 8, 0 );
        }
    }
    // cvShowImage( "result", img );
    cvReleaseImage( &gray );
    cvReleaseImage( &small_img );
}

JNIEXPORT void JNICALL Java_study_opencv_FaceRec_detect
  (JNIEnv * env, jobject obj, jstring xml, jstring filename, jstring outfile)
{
    LOGI("top method invoked! ");/*LOGI("1");
    char * c_xml = (char *)env->GetStringUTFChars(xml, JNI_FALSE);
    LOGI("char * = %s", c_xml);
    if(c_xml == NULL)
    {
        LOGI("error in get char*");
        return;
    }
    char * c_file = env->GetStringCritical(env, filename, 0);
    if(c_xml == NULL)
    {
        LOGI("error in get char*");
        return;
    }
    captureFromImage(c_xml, c_file);
    env->ReleaseStringCritical(env, xml, c_xml);
    env->ReleaseStringCritical(env, file_name, c_file);
    */
    captureFromImage(jstring2String(env,xml), jstring2String(env,filename), jstring2String(env,outfile));

}

//jstring to char*

char* jstring2String(JNIEnv* env, jstring jstr)
{
    if(jstr == NULL)
    {
        LOGI("NullPointerException!");
        return NULL;
    }
    char* rtn = NULL;
    jclass clsstring = env->FindClass("java/lang/String");
    jstring strencode = env->NewStringUTF("utf-8");
    jmethodID mid = env->GetMethodID(clsstring, "getBytes", "(Ljava/lang/String;)[B");
    jbyteArray barr= (jbyteArray)env->CallObjectMethod(jstr, mid, strencode);
    jsize alen = env->GetArrayLength(barr);
    jbyte* ba = env->GetByteArrayElements(barr, JNI_FALSE);
    if (alen > 0)
    {
        rtn = (char*)malloc(alen + 1);
        memcpy(rtn, ba, alen);
        rtn[alen] = 0;
    }
    env->ReleaseByteArrayElements(barr, ba, 0);
    LOGI("char*=%s",rtn);
    return rtn;
}

Android.mk:

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LOCAL_PATH:= $(call my-dir)

include $(CLEAR_VARS)
include $(OPENCV_PACKAGE_DIR)/$(TARGET_ARCH_ABI)/share/opencv/OpenCV.mk

LOCAL_MODULE    := facedetect
LOCAL_CFLAGS    := -Werror
LOCAL_SRC_FILES := \
    facedetect.cpp \

LOCAL_LDLIBS    := -llog

include $(BUILD_SHARED_LIBRARY)

Application.mk:

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APP_ABI := armeabi armeabi-v7a
APP_PLATFORM := android-10
APP_STL := gnustl_static
APP_CPPFLAGS := -frtti -fexceptions

使用NDK进行编译

在工程jni目录下执行ndk-build

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$ cd ~/workspace/study.opencv/jni
$ ~/android-ndk-r6/ndk-build.

如果编译成功,则会在工程下面生成libs/armeabi/facedetect.so库了.
如有编译失败,请根据提示修改错误

调用JNI接口

将opencv人脸检测要用到的xml文件(位于OpenCV-2.3.0/armeabi/share/opencv/haarcascades/目录下)及图像文件使用DDMS push到data/data/study.opencv/files目录中。

在activity中新建一个线程,调用FaceRec#detect方法。

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@Override
    public void onCreate(Bundle savedInstanceState) {
        super.onCreate(savedInstanceState);
        setContentView(R.layout.main);

        final FaceRec face = new FaceRec();
        new Thread() {
            @Override
            public void run() {
                face.detect(
                        "/data/data/study.opencv/files/haarcascade_frontalface_alt2.xml",
                        "/data/data/study.opencv/files/wqw1.jpg",
                        "/data/data/study.opencv/files/wqw1_detected.jpg");
            }
        }.start();

    }

运行结果

经测试,对png,jpg,bmp图片可以正确识别,不过就是速度有点慢。

参考