我正在尝试实施汽车牌照检测器。
我设法找到轮廓并在IplImage上绘制它们，然后我解剖了盘子中的数字。我将解剖后的图像存储在一个名为numbers的数组中，我试图将它们与存储在digits
现在，当我尝试使用cvMatchTemplate时，我收到类型错误：cvResize中的断言失败（src.type（）== dst.type（））
我发布的代码非常冗长，但错误是当我尝试调整大小时..
public static String recognize(IplImage a){
String plate = "";
cvSaveImage("platebig.jpg",a);
CvRect r = new CvRect();
r.x(0);
r.y(0);
r.width(a.width()/2+50);
r.height(a.height()/2+30);
cvSetImageROI(a, r);
IplImage cropped = cvCreateImage(cvGetSize(a), a.depth(), a.nChannels());
cvCopy(a, cropped);
IplImage tmp = cvCreateImage(cvGetSize(cropped), IPL_DEPTH_8U, 1);
cvCvtColor(cropped, tmp, CV_BGR2GRAY);
cvSmooth(tmp, tmp, CV_GAUSSIAN, 11, 11, 0.2f, 0.1f);
cvEqualizeHist(tmp, tmp);
cvThreshold(tmp, tmp, 128, 255, CV_THRESH_BINARY_INV);
cvDilate(tmp,tmp,null,2);
//cvCanny(tmp, tmp, 100, 50, 3);
IplImage [] numbers = new IplImage[7];
int i = 0;
CvMemStorage storage = cvCreateMemStorage(0);
CvSeq contour = new CvSeq(null);
CvMemStorage storage2 = cvCreateMemStorage(0);
CvSeq contour2 = new CvSeq(null);
cvFindContours( tmp, storage, contour, Loader.sizeof(CvContour.class),
CV_RETR_TREE, CV_CHAIN_APPROX_NONE, cvPoint(0, 0) );
int [] sorter = new int[7];
CvSeq contourLow=cvApproxPoly(contour, Loader.sizeof(CvContour.class), storage,CV_POLY_APPROX_DP,1,1);
for( ; contourLow != null; contourLow = contourLow.h_next() ){
CvRect rect;
rect=cvBoundingRect(contourLow);
if(i<7&&rect.width()>30&&rect.height()>30)
{
numbers[i] = IplImage.create(rect.width(),
rect.height(), cropped.depth(), cropped.nChannels());
cvSetImageROI(cropped, rect);
cvCopy(cropped, numbers[i]);
IplImage batata = cvCreateImage(cvGetSize(numbers[i]), IPL_DEPTH_8U, 1);
cvCvtColor(numbers[i], batata, CV_BGR2GRAY);
cvSmooth(batata, batata, CV_GAUSSIAN, 11, 11, 0.2f, 0.1f);
cvEqualizeHist(batata, batata);
cvThreshold(batata, batata, 128, 255, CV_THRESH_BINARY_INV);
cvDilate(batata,batata,null,2);
cvCanny(numbers[i],batata,10,100,3);
cvFindContours( batata, storage2, contour2, Loader.sizeof(CvContour.class),
CV_RETR_TREE, CV_CHAIN_APPROX_NONE, cvPoint(0, 0) );
CvSeq contourLow1=cvApproxPoly(contour2, Loader.sizeof(CvContour.class), storage2,CV_POLY_APPROX_DP,1,1);
for( ; contourLow1 != null; contourLow1 = contourLow1.h_next())
{
CvScalar color = CV_RGB( 255,0,0);
cvSetImageROI(cropped, rect);
cvDrawContours(cropped, contourLow1, color, CV_RGB(255,0,0), 127,1,8);
}
r.x(8);
r.y(62);
r.width(52);
r.height(126-62);
numbers[0] = cvCreateImage(cvSize(r.width(), r.height()), cropped.depth(), cropped.nChannels());
cvSetImageROI(cropped, r);
cvCopy(cropped, numbers[0]);
r.x(79);
r.y(20);
r.width(146-79);
r.height(126-20);
numbers[1] = cvCreateImage(cvSize(r.width(), r.height()), cropped.depth(), cropped.nChannels());
cvSetImageROI(cropped, r);
cvCopy(cropped, numbers[1]);
r.x(161);
r.y(20);
r.width(224-161);
r.height(126-20);
numbers[2] = cvCreateImage(cvSize(r.width(), r.height()), cropped.depth(), cropped.nChannels());
cvSetImageROI(cropped, r);
cvCopy(cropped, numbers[2]);
r.x(237);
r.y(20);
r.width(306-237);
r.height(126-20);
numbers[3] = cvCreateImage(cvSize(r.width(), r.height()), cropped.depth(), cropped.nChannels());
cvSetImageROI(cropped, r);
cvCopy(cropped, numbers[3]);
r.x(316);
r.y(20);
r.width(385-316);
r.height(126-20);
numbers[4] = cvCreateImage(cvSize(r.width(), r.height()), cropped.depth(), cropped.nChannels());
cvSetImageROI(cropped, r);
cvCopy(cropped, numbers[4]);
r.x(395);
r.y(20);
r.width(464-395);
r.height(126-20);
numbers[5] = cvCreateImage(cvSize(r.width(), r.height()), cropped.depth(), cropped.nChannels());
cvSetImageROI(cropped, r);
cvCopy(cropped, numbers[5]);
r.x(470);
r.y(20);
r.width(544-470);
r.height(126-20);
numbers[6] = cvCreateImage(cvSize(r.width(), r.height()), cropped.depth(), cropped.nChannels());
cvSetImageROI(cropped, r);
cvCopy(cropped, numbers[6]);
sorter[i] = rect.x();
i++;
}
}
IplImage [] digits = new IplImage[11];
for(int j = 0;j<10;j++){
String str = j +".jpg";
IplImage temp = cvLoadImage(str,CV_LOAD_IMAGE_COLOR);
digits[j] = IplImage.create(temp.width(),
temp.height(), IPL_DEPTH_32F, 3);
cvConvertScale(temp, digits[j]);
}
digits[10] = cvLoadImage("o.jpg");
for(int k =1;k<7;k++){
double max = 0;
int index = 0;
for(int w = 0;w<10;w++){
IplImage temp = IplImage.create(1,1, IPL_DEPTH_32F, 3);
cvZero(temp);
IplImage res = IplImage.create(digits[w].width(),digits[w].height(), digits[w].depth(), 3);
cvResize(numbers[k], res);
cvMatchTemplate(digits[w], res, temp, CV_TM_CCOEFF);
答案 0 :(得分：0)
Resize（）可能会抛出错误，因为源和目标的bit_type不相同。 查看两者的图像通道/深度类型，比较它们，它可能会导致解决您的问题。
答案 1 :(得分：0)
使用cvConvert将有助于同时转换图像深度和n通道。这将解决cvResize()的问题，因为两个图像必须具有相同的深度和通道数。
IplImage a = IplImage.create(img.width(), img.height(), img.depth(), img.nchannels());
cvConvert(img,a);