编写一段C++程序基于OPENCV实现工业表面缺陷的分类检测
时间: 2023-07-11 21:04:26 浏览: 120
这是一个简单的基于OpenCV的工业表面缺陷分类检测程序,它使用了SVM分类器来识别两种不同类型的缺陷:裂缝和凸起。
首先,我们需要导入必要的OpenCV库和头文件:
```c++
#include <opencv2/opencv.hpp>
#include <opencv2/ml.hpp>
using namespace cv;
using namespace cv::ml;
using namespace std;
```
然后,定义一些常量和变量:
```c++
const int IMAGE_WIDTH = 64;
const int IMAGE_HEIGHT = 64;
const int NUM_TRAINING_SAMPLES = 40;
const int NUM_TESTING_SAMPLES = 10;
const int NUM_FEATURES = IMAGE_WIDTH * IMAGE_HEIGHT;
const int NUM_CLASSES = 2;
const string TRAINING_DATA_PATH = "training_data/";
const string TESTING_DATA_PATH = "testing_data/";
const string SVM_MODEL_PATH = "svm_model.xml";
Mat trainingData;
Mat trainingLabels;
Mat testingData;
Mat testingLabels;
Ptr<SVM> svm;
```
接下来,我们定义一个函数,用于将图像转换为特征向量。
```c++
Mat imageToFeatureVector(Mat image) {
Mat grayImage;
cvtColor(image, grayImage, COLOR_BGR2GRAY);
resize(grayImage, grayImage, Size(IMAGE_WIDTH, IMAGE_HEIGHT));
Mat featureVector = grayImage.reshape(1, 1);
return featureVector;
}
```
然后,我们定义一个函数,用于加载训练和测试数据。
```c++
void loadData() {
for (int i = 1; i <= NUM_TRAINING_SAMPLES; i++) {
string imagePath = TRAINING_DATA_PATH + "crack_" + to_string(i) + ".jpg";
Mat image = imread(imagePath);
Mat featureVector = imageToFeatureVector(image);
trainingData.push_back(featureVector);
trainingLabels.push_back(0);
}
for (int i = 1; i <= NUM_TRAINING_SAMPLES; i++) {
string imagePath = TRAINING_DATA_PATH + "bump_" + to_string(i) + ".jpg";
Mat image = imread(imagePath);
Mat featureVector = imageToFeatureVector(image);
trainingData.push_back(featureVector);
trainingLabels.push_back(1);
}
for (int i = 1; i <= NUM_TESTING_SAMPLES; i++) {
string imagePath = TESTING_DATA_PATH + "crack_" + to_string(i) + ".jpg";
Mat image = imread(imagePath);
Mat featureVector = imageToFeatureVector(image);
testingData.push_back(featureVector);
testingLabels.push_back(0);
}
for (int i = 1; i <= NUM_TESTING_SAMPLES; i++) {
string imagePath = TESTING_DATA_PATH + "bump_" + to_string(i) + ".jpg";
Mat image = imread(imagePath);
Mat featureVector = imageToFeatureVector(image);
testingData.push_back(featureVector);
testingLabels.push_back(1);
}
}
```
然后,我们定义一个函数,用于训练SVM分类器。
```c++
void trainSVM() {
svm = SVM::create();
svm->setType(SVM::C_SVC);
svm->setKernel(SVM::LINEAR);
svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 100, 1e-6));
Ptr<TrainData> trainData = TrainData::create(trainingData, ROW_SAMPLE, trainingLabels);
svm->train(trainData);
svm->save(SVM_MODEL_PATH);
}
```
最后,我们定义一个函数,用于测试SVM分类器。
```c++
void testSVM() {
int numCorrect = 0;
for (int i = 0; i < testingData.rows; i++) {
Mat featureVector = testingData.row(i);
int trueLabel = testingLabels.at<int>(i);
int predictedLabel = svm->predict(featureVector);
if (predictedLabel == trueLabel) {
numCorrect++;
}
}
float accuracy = (float)numCorrect / testingData.rows;
cout << "Accuracy: " << accuracy << endl;
}
```
现在,我们可以在main函数中调用这些函数来训练和测试SVM分类器。
```c++
int main() {
loadData();
trainSVM();
testSVM();
return 0;
}
```
这是一个简单的工业表面缺陷分类检测程序,你可以使用更多的训练数据和更复杂的特征来提高分类器的准确性。
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