如何在小图像上使用Opencv FeatureDetecter

发布时间：2020-12-15 03:05:18 所属栏目：Java 来源：网络整理

导读：我在 Java中使用Opencv 3,我试图在其他图像上找到小图像(如25×25像素).但FeatureDetector检测(0,0)大小Mat在小图像上. Mat smallImage = ... FeatureDetector detector = FeatureDetector.create(FeatureDetector.ORB); DescriptorExtractor descriptor = D

我在 Java中使用Opencv 3,我试图在其他图像上找到小图像(如25×25像素).但FeatureDetector检测(0,0)大小Mat在小图像上.

Mat smallImage = ...

    FeatureDetector detector = FeatureDetector.create(FeatureDetector.ORB);
    DescriptorExtractor descriptor = DescriptorExtractor.create(DescriptorExtractor.ORB);
    DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMING);

    Mat descriptorsSmall = new Mat();
    MatOfKeyPoint keyPointsSmall = new MatOfKeyPoint();

    detector.detect(smallImage,keyPointsSmall);
    descriptor.compute(smallImage,keyPointsSmall,descriptorsSmall);

在这里,我得到keyPointsSmall和descriptorsSmall大小为零,并确保检测不起作用.

但是,如果我在150×150像素的较大图像上尝试此功能,那就可以了.
有什么建议？谢谢.

我在这里添加样品.
我们有这个源图像：

并且让它说我们有P字母的模板,所以我们需要在源图像上检测这个P.

好吧,将图像缩放到更高的分辨率对我来说不起作用.那将耗费时间和资源.
理想情况下,它应该是旋转尺度不变的.但是没有旋转和缩放的简单解决方案也可以.

除OpenCv之外的其他解决方案对我来说是不可接受的. (例如使用Tesseract)

解决方法

用于文本识别的关键点检测不是最佳解决方案,因为您将获得许多看起来相似的功能,并且如果模板非常小,则滑动窗口将不会产生足够的检测到的功能.

幸运的是,OpenCV 3在contrib存储库中包含一个文本检测/识别模块：link,其中一个示例取自here,还有许多其他模块可以找到here：

/*
 * cropped_word_recognition.cpp
 *
 * A demo program of text recognition in a given cropped word.
 * Shows the use of the OCRBeamSearchDecoder class API using the provided default classifier.
 *
 * Created on: Jul 9,2015
 *     Author: Lluis Gomez i Bigorda <lgomez AT cvc.uab.es>
 */

#include "opencv2/text.hpp"
#include "opencv2/core/utility.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"

#include <iostream>

using namespace std;
using namespace cv;
using namespace cv::text;

int main(int argc,char* argv[])
{

    cout << endl << argv[0] << endl << endl;
    cout << "A demo program of Scene Text Character Recognition: " << endl;
    cout << "Shows the use of the OCRBeamSearchDecoder::ClassifierCallback class using the Single Layer CNN character classifier described in:" << endl;
    cout << "Coates,Adam,et al. "Text detection and character recognition in scene images with unsupervised feature learning." ICDAR 2011." << endl << endl;

    Mat image;
    if(argc>1)
        image  = imread(argv[1]);
    else
    {
        cout << "    Usage: " << argv[0] << " <input_image>" << endl;
        cout << "           the input image must contain a single character (e.g. scenetext_char01.jpg)." << endl << endl;
        return(0);
    }

    string vocabulary = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789"; // must have the same order as the clasifier output classes

    Ptr<OCRHMMDecoder::ClassifierCallback> ocr = loadOCRHMMClassifierCNN("OCRBeamSearch_CNN_model_data.xml.gz");

    double t_r = (double)getTickCount();
    vector<int> out_classes;
    vector<double> out_confidences;

    ocr->eval(image,out_classes,out_confidences);

    cout << "OCR output = "" << vocabulary[out_classes[0]] << "" with confidence "
         << out_confidences[0] << ". Evaluated in "
         << ((double)getTickCount() - t_r)*1000/getTickFrequency() << " ms." << endl << endl;

    return 0;
}

（编辑：李大同）

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