我正在使用findHomography()比较两个图像.我在OpenCV 3.1.0中添加了从opencv_contrib开始的额外模块,以使用Surf和Sift算法并针对最新的Android体系结构进行编译.我可以使用ndk-build成功编译这些库.

问题：
在LG Nexus 5上运行该应用程序时,我可以使用imread读取图像,但是在LG Nexus 5X上运行相同的应用程序时,imread不会读取图像.我已经在Samsung S6和OnePlus X上进行了测试,并且存在相同的问题.下面是我的本机方法：

#include <jni.h>
#include <string.h>
#include <stdio.h>
#include <android/log.h>

#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/xfeatures2d/nonfree.hpp"
#include "opencv2/opencv.hpp"

using namespace std;
using namespace cv;

#define  LOG_TAG    "nonfree_jni"
#define  LOGI(...)  __android_log_print(ANDROID_LOG_INFO,LOG_TAG,__VA_ARGS__)

jboolean detect_features(JNIEnv * env, jstring scenePath, jstring objectPath) {

    const char *nativeScenePath = (env)->GetStringUTFChars(scenePath, NULL);
    const char *nativeObjectPath = (env)->GetStringUTFChars(objectPath, NULL);

    nativeScenePath = env->GetStringUTFChars(scenePath, 0);
    nativeObjectPath = env->GetStringUTFChars(objectPath, 0);

    (env)->ReleaseStringUTFChars(scenePath, nativeScenePath);
    (env)->ReleaseStringUTFChars(objectPath, nativeObjectPath);

    __android_log_print(ANDROID_LOG_VERBOSE, LOG_TAG, "Object path: ----- %s \n", nativeObjectPath);
    __android_log_print(ANDROID_LOG_VERBOSE, LOG_TAG, "Scene path: ----- %s \n", nativeScenePath);

    Mat img_object = imread( nativeObjectPath, CV_LOAD_IMAGE_GRAYSCALE );
    Mat img_scene = imread( nativeScenePath, CV_LOAD_IMAGE_GRAYSCALE );


    if( !img_object.data || !img_scene.data){
        LOGI(" --(!) Error reading images ");
        return false;
    }

        //-- Step 1: Detect the keypoints using SURF Detector
        int minHessian = 400;

    __android_log_print(ANDROID_LOG_VERBOSE, LOG_TAG, "Image comparison rows: ----- %d \n", img_object.rows);
    __android_log_print(ANDROID_LOG_VERBOSE, LOG_TAG, "Image comparison colums: ----- %d \n", img_object.cols);

//        cv::xfeatures2d::SurfFeatureDetector detector( minHessian );
        Ptr<cv::xfeatures2d::SurfFeatureDetector> detector = cv::xfeatures2d::SurfFeatureDetector::create(minHessian);

        std::vector<KeyPoint> keypoints_object, keypoints_scene;
        detector->detect( img_object, keypoints_object );
        detector->detect( img_scene, keypoints_scene );

        //-- Step 2: Calculate descriptors (feature vectors)
//        cv::xfeatures2d::SurfDescriptorExtractor extractor;
        Ptr<cv::xfeatures2d::SurfDescriptorExtractor> extractor = cv::xfeatures2d::SurfDescriptorExtractor::create();

        Mat descriptors_object, descriptors_scene;

        extractor->compute( img_object, keypoints_object, descriptors_object );
        extractor->compute( img_scene, keypoints_scene, descriptors_scene );

        //-- Step 3: Matching descriptor vectors using FLANN matcher
        FlannBasedMatcher matcher;
        std::vector< DMatch > matches;
        matcher.match( descriptors_object, descriptors_scene, matches );

        double max_dist = 0; double min_dist = 100;

        //-- Quick calculation of max and min distances between keypoints
        for( int i = 0; i < descriptors_object.rows; i++ )
        {
            double dist = matches[i].distance;
            if (dist == 0) continue;
            if( dist < min_dist ) min_dist = dist;
            if( dist > max_dist ) max_dist = dist;
        }

        __android_log_print(ANDROID_LOG_VERBOSE, LOG_TAG, "-- Max dist : %f \n", max_dist);
        __android_log_print(ANDROID_LOG_VERBOSE, LOG_TAG, "-- Min dist : %f \n", min_dist);

        //-- Draw only "good" matches (i.e. whose distance is less than 3*min_dist )
        std::vector< DMatch > good_matches;

        for( int i = 0; i < descriptors_object.rows; i++ )
        {
            if( matches[i].distance <= 0.1 ) //3*min_dist
            {
                good_matches.push_back( matches[i]);
            }
        }

        __android_log_print(ANDROID_LOG_VERBOSE, LOG_TAG, "FLANN total matches -----: %zu \n", matches.size());
        __android_log_print(ANDROID_LOG_VERBOSE, LOG_TAG, "FLANN good matches -----: %zu \n", good_matches.size());

        Mat img_matches;
        drawMatches( img_object, keypoints_object, img_scene, keypoints_scene,
                    good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
                    vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );

        //-- Localize the object
        std::vector<Point2f> obj;
        std::vector<Point2f> scene;

        for( int i = 0; i < good_matches.size(); i++ )
        {
            //-- Get the keypoints from the good matches
            obj.push_back( keypoints_object[ good_matches[i].queryIdx ].pt );
            scene.push_back( keypoints_scene[ good_matches[i].trainIdx ].pt );
        }

        if (good_matches.size() >= 5)
        {
            Mat H = findHomography( obj, scene, CV_RANSAC );

            //-- Get the corners from the image_1 ( the object to be "detected" )
            std::vector<Point2f> obj_corners(4);
            obj_corners[0] = cvPoint(0,0); obj_corners[1] = cvPoint( img_object.cols, 0 );
            obj_corners[2] = cvPoint( img_object.cols, img_object.rows ); obj_corners[3] = cvPoint( 0, img_object.rows );
            std::vector<Point2f> scene_corners(4);

            Mat output, matrix;

            warpPerspective(img_object, output, H, { img_scene.cols, img_scene.rows });

            ////////////////////////////////////////////////////////////////////////////////

            detector->detect( output, keypoints_object );

            //-- Step 2: Calculate descriptors (feature vectors)
            //cv::xfeatures2d::SurfDescriptorExtractor extractor;
            Ptr<cv::xfeatures2d::SurfDescriptorExtractor> extractor = cv::xfeatures2d::SurfDescriptorExtractor::create();

            extractor->compute( output, keypoints_object, descriptors_object );
            extractor->compute( img_scene, keypoints_scene, descriptors_scene );

            std::vector<std::vector<cv::DMatch>> matches2;
            BFMatcher matcher;
            matcher.knnMatch(descriptors_object, descriptors_scene, matches2, 2);
            vector<cv::DMatch> good_matches2;

            for (int i = 0; i < matches2.size(); ++i)
            {
                const float ratio = 0.8; // As in Lowe's paper; can be tuned
                if (matches2[i][0].distance < ratio * matches2[i][1].distance)
                {
                    good_matches2.push_back(matches2[i][0]);
                }
            }

            if (matches2.size() == 0 || good_matches2.size() == 0) {
            LOGI( "End run!\n");
                return false;
            }

            double ratioOfSimilarity =  static_cast<double>(good_matches2.size()) / static_cast<double>(matches2.size());

            __android_log_print(ANDROID_LOG_VERBOSE, LOG_TAG, "Bruteforce total matches -----: %zu \n", matches2.size());
            __android_log_print(ANDROID_LOG_VERBOSE, LOG_TAG, "Bruteforce good matches -----: %zu \n", good_matches2.size());
            __android_log_print(ANDROID_LOG_VERBOSE, LOG_TAG, "Bruteforce similarity ratio -----: %f \n", ratioOfSimilarity);

            if(ratioOfSimilarity >= 0.3) {
            LOGI( "End run!\n");
                return true;
            }

            LOGI( "End run!\n");
            return false;

        }
        LOGI( "End run!\n");
        return false;
}

该方法在这一行中断：

if( !img_object.data || !img_scene.data){
        LOGI(" --(!) Error reading images ");
        return false;
}

解决方法:

我在Nexus 5x android 7.0设备上测试了您的未读问题,
所以我只在我的android项目中使用了imread命令.

我的opencv库是OpenCV 3.1.0预先构建的库.

经过一些测试,我只能以5x的像素读取图像：

> / sdcard确定
> / storage / emulated / 0 /失败

我认为实际上是相同的路径,但它不会使用第二个选项加载图像.

Mat flag=imread("/sdcard/Pictures/mytest.jpg", CV_LOAD_IMAGE_GRAYSCALE);

在我的开发经验中,我遇到了外部存储路径的问题,因为某些设备模拟了外部存储,而另一些则没有.

因此,通常为避免此问题,我会在执行时将资源复制到内部.APK.

我将资源存储在res.raw文件夹中,并获得了内部路径

config_path = m_context.getApplicationContext().getFilesDir().toString();

希望我的测试有助于解决您的问题.

干杯.

乌奈