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[英]Object Serialization in C++ for storing the OpenCV's SVM object [OpenCV 3.1.0]
[英]SVM OpenCV c++ Predict returning nothing but 1's
我相信我已经成功地训练了一个SVM
,但是当我尝试用它进行预测时,输出完全是1。
我的培训代码如下:
for(size_t i = 0; i < (testPosArraySize); i++){
testGivenImg = imread(imagePosDir[i]);
detector->detect(testGivenImg, testKeypointsPos);
bowDE.compute(testGivenImg, testKeypointsPos, testFeaturesPos);
testFeaturesPos.reshape(1, 1);
testFeaturesVec.push_back(testFeaturesPos);
}
for(size_t i = 0; i < (testNegaArraySize); i++){
testGivenImg = imread(image[i]);
detector->detect(testGivenImg, testKeypointsNega);
bowDE.compute(testGivenImg, testKeypointsNega, testFeaturesNega);
testFeaturesNega.reshape(1, 1);
testFeaturesVec.push_back(testFeaturesNega);
}
Mat labels(numSamples, 1, CV_32F);
labels.rowRange(0, testPosArraySize).setTo(1);
labels.rowRange(testPosArraySize + 1, numSamples).setTo(-1);
SVM.model.train(fileTestFeat, labels, Mat(), Mat(), SVMParams());
我的预测代码如下所示:
vector<Mat> predictMatVec(predictArraySize); // -- amount of testing images
for(size_t i = 0; i < (predictArraySize); i++){
predictImg = imread(imageNegaDir[i]);
detector->detect(predictImg, predictKeypoints);
bowDE.compute(predictImg, predictKeypoints, predictFeatures);
predictFeatures.reshape(1, 1);
predictMatVec[i].push_back(predictFeatures);
Mat predictMat = Mat(predictMatVec);
float* predictFloat1D = (float*)predictMat.data;
Mat predictMat1D(1, fileTestFeat.cols, CV_32FC1, predictFloat1D);
float predictFloat = model.predict(predictMat1D);
cout << " -- SVM output: " << predictFloat << endl;
}
但它只返回1而已。
这有什么问题?
所以,词汇表已经创建(例如由BOWKMeansTrainer
),你开始训练你的SVM分类器,对吧?
此时,您有一个特征检测器,提取器,匹配器和一个BOW图像描述符提取器(使用视觉词袋计算图像描述符),例如:
cv::Ptr<cv::FeatureDetector> detector = cv::FeatureDetector::create("SURF");
cv::Ptr<cv::DescriptorExtractor> extractor = cv::DescriptorExtractor::create("SURF");
cv::Ptr<cv::DescriptorMatcher> matcher = cv::DescriptorMatcher::create("BruteForce ");
cv::BOWImgDescriptorExtractor bowide(extractor, matcher);
bowide->setVocabulary(vocabulary);
首先,我们需要搜索直方图的训练集:
cv::Mat samples;
cv::Mat labels(0, 1, CV_32FC1);
for(auto& it : imagePosDir)
{
cv::Mat image = cv::imread(it);
std::vector<cv::KeyPoint> keypoints;
detector->detect(image, keypoints);
if(keypoints.empty()) continue;
// Responses to the vocabulary
cv::Mat imgDescriptor;
bowide.compute(image, keypoints, imgDescriptor);
if(imgDescriptor.empty()) continue;
if(samples.empty())
{
samples.create(0, imgDescriptor.cols, imgDescriptor.type());
}
// Copy class samples and labels
std::cout << "Adding " << imgDescriptor.rows << " positive sample." << std::endl;
samples.push_back(imgDescriptor);
cv::Mat classLabels = cv::Mat::ones(imgDescriptor.rows, 1, CV_32FC1);
labels.push_back(classLabels);
}
对imagePosNeg
执行相同操作,但classLabels
将具有零值,例如:
...
cv::Mat classLabels = cv::Mat::zeros(imgDescriptor.rows, 1, CV_32FC1);
labels.push_back(classLabels);
...
注意我是如何构建样本和标签的,我标记了带有标签'1'的正样本,然后是标记为'0'的否定样本。 因此,我们为samples
每个类(此处为正数和负数)提供了训练数据。 让我们接受培训:
cv::Mat samples_32f;
samples.convertTo(samples_32f, CV_32F);
CvSVM svm;
svm.train(samples_32f, labels);
// Do something with the classifier, like saving it to file
然后测试让我们测试分类器:
for(auto& it : testDir)
{
cv::Mat image = cv::imread(it);
std::vector<cv::KeyPoint> keypoints;
detector->detect(image, keypoints);
if(keypoints.empty()) continue;
// Responses to the vocabulary
cv::Mat imgDescriptor;
bowide.compute(image, keypoints, imgDescriptor);
if(imgDescriptor.empty()) continue;
float res = svm.predict(imgDescriptor, true);
std::cout << "- Result of prediction: " << res << std::endl;
}
它有效吗?
更新#1:
这里我在OpenCV 3.0下做了一个关于BOW + SVM的简单例子: https : //github.com/bkornel/OpenCV_BOW_SVM/blob/master/main.cpp
这对我可口可乐/百事可乐瓶的分类很有帮助。 我还发布了二进制文件,因此您可以尝试使用数据库。 希望它有效:)
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