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私はfindHomography（）を使用して2つのイメージを比較しています。私はサーフとふるいにかけるアルゴリズムを使用すると、最新のAndroidのアーキテクチャ用にコンパイルするOpenCVの3.1.0にopencv_contribから余分なモジュールを追加しました。 ndk-buildを使用してライブラリを正常にコンパイルできます。warpPerspectiveでのエラー
問題：シーンとコンピューティングwarpPerspectiveで物体を検出しながら
、次の例外がいくつかの画像にスローされます。
11-10 20:47:30.990 10503-11056/ E/cv::error(): OpenCV Error: Assertion failed ((M0.type() == CV_32F || M0.type() == CV_64F) && M0.rows == 3 && M0.cols == 3) in void cv::warpPerspective(cv::InputArray, cv::OutputArray, cv::InputArray, cv::Size, int, int, const Scalar&), file /Volumes/Linux/builds/master_pack-android/opencv/modules/imgproc/src/imgwarp.cpp, line 6120
--------- beginning of crash
11-10 20:47:31.020 10503-11056/ A/libc: Fatal signal 6 (SIGABRT), code -6 in tid 11056 (Thread-31509)
11-10 20:47:31.122 200-200/? A/DEBUG: *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** ***
11-10 20:47:31.122 200-200/? A/DEBUG: Build fingerprint: 'google/hammerhead/hammerhead:6.0.1/M4B30X/3237893:user/release-keys'
11-10 20:47:31.122 200-200/? A/DEBUG: Revision: '11'
11-10 20:47:31.122 200-200/? A/DEBUG: ABI: 'arm'
11-10 20:47:31.122 200-200/? A/DEBUG: pid: 10503, tid: 11056, name: Thread-31509 >>> <<<
11-10 20:47:31.122 200-200/? A/DEBUG: signal 6 (SIGABRT), code -6 (SI_TKILL), fault addr --------
11-10 20:47:31.149 200-200/? A/DEBUG: r0 00000000 r1 00002b30 r2 00000006 r3 926e0978
11-10 20:47:31.149 200-200/? A/DEBUG: r4 926e0980 r5 926e0930 r6 00000000 r7 0000010c
11-10 20:47:31.149 200-200/? A/DEBUG: r8 00000047 r9 00000001 sl 00000050 fp 00000001
11-10 20:47:31.149 200-200/? A/DEBUG: ip 00000006 sp 926cfc48 lr b6d4fb61 pc b6d51f50 cpsr 400f0010
11-10 20:47:31.174 200-200/? A/DEBUG: backtrace:
11-10 20:47:31.174 200-200/? A/DEBUG: #00 pc 00041f50 /system/lib/libc.so (tgkill+12)
11-10 20:47:31.174 200-200/? A/DEBUG: #01 pc 0003fb5d /system/lib/libc.so (pthread_kill+32)
11-10 20:47:31.174 200-200/? A/DEBUG: #02 pc 0001c30f /system/lib/libc.so (raise+10)
11-10 20:47:31.174 200-200/? A/DEBUG: #03 pc 000194c1 /system/lib/libc.so (__libc_android_abort+34)
11-10 20:47:31.174 200-200/? A/DEBUG: #04 pc 000174ac /system/lib/libc.so (abort+4)
11-10 20:47:31.174 200-200/? A/DEBUG: #05 pc 00666958 /data/app/-2/lib/arm/libopencv_java3.so (_ZN9__gnu_cxx27__verbose_terminate_handlerEv+344)
11-10 20:47:31.175 200-200/? A/DEBUG: #06 pc 0063d7b0 /data/app/-2/lib/arm/libopencv_java3.so (_ZN10__cxxabiv111__terminateEPFvvE+4)
11-10 20:47:31.175 200-200/? A/DEBUG: #07 pc 0063d7f0 /data/app/-2/lib/arm/libopencv_java3.so (_ZSt9terminatev+16)
11-10 20:47:31.175 200-200/? A/DEBUG: #08 pc 0063d1cc /data/app/-2/lib/arm/libopencv_java3.so (__cxa_throw+168)
11-10 20:47:31.175 200-200/? A/DEBUG: #09 pc 001e477d /data/app/-2/lib/arm/libopencv_java3.so (_ZN2cv5errorERKNS_9ExceptionE+244)
11-10 20:47:31.175 200-200/? A/DEBUG: #10 pc 001e48bd /data/app/-2/lib/arm/libopencv_java3.so (_ZN2cv5errorEiRKNS_6StringEPKcS4_i+108)
11-10 20:47:31.175 200-200/? A/DEBUG: #11 pc 002ca5fd /data/app/-2/lib/arm/libopencv_java3.so (_ZN2cv15warpPerspectiveERKNS_11_InputArrayERKNS_12_OutputArrayES2_NS_5Size_IiEEiiRKNS_7Scalar_IdEE+356)
11-10 20:47:31.175 200-200/? A/DEBUG: #12 pc 00007375 /data/app/-2/lib/arm/libnonfree.so (_Z15detect_featuresP7_JNIEnvP8_jstringS2_i+2844)
11-10 20:47:31.175 200-200/? A/DEBUG: #13 pc 022bfd23 /data/app/-2/oat/arm/base.odex (offset 0x13ce000) (boolean .NonfreeJNILib.detectFeatures(java.lang.String, java.lang.String, int)+126)
11-10 20:47:31.176 200-200/? A/DEBUG: #14 pc 0258c149 /data/app/-2/oat/arm/base.odex (offset 0x13ce000) (void .tasks.AdDetectionAsyncTask$1.run()+292)
11-10 20:47:31.176 200-200/? A/DEBUG: #15 pc 71c99c67 /data/dalvik-cache/arm/[email protected]@boot.oat (offset 0x1ed6000)
コード：
#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;
}
ことができるもの問題？