想用opencv识别图像中特定物体的个数，怎么做到

将字符定位，然后进行分割成一个一个的独立字符。可以准备一下字符样本，丢进opencv中的bp神经网络训练分类器，用训练好的分类器去对切分的字符进行识别。一个基本的思路，网上应该很多。

OpenCV中获取图像某一像素值

This is a basic example for the OpenCV

First we must know the structure of IplImage:

IPL image:

IplImage

|-- int nChannels; // Number of color channels (1,2,3,4)

|-- int depth; // Pixel depth in bits:

| // IPL_DEPTH_8U, IPL_DEPTH_8S,

| // IPL_DEPTH_16U,IPL_DEPTH_16S,

| // IPL_DEPTH_32S,IPL_DEPTH_32F,

| // IPL_DEPTH_64F

|-- int width; // image width in pixels

|-- int height; // image height in pixels

|-- char imageData; // pointer to aligned image data

| // Note that color images are stored in BGR order

|-- int dataOrder; // 0 - interleaved color channels,

| // 1 - separate color channels

| // cvCreateImage can only create interleaved images

|-- int origin; // 0 - top-left origin,

| // 1 - bottom-left origin (Windows bitmaps style)

|-- int widthStep; // size of aligned image row in bytes

|-- int imageSize; // image data size in bytes = heightwidthStep

|-- struct _IplROI roi;// image ROI when not NULL specifies image

| // region to be processed

|-- char imageDataOrigin; // pointer to the unaligned origin of image data

| // (needed for correct image deallocation)

|

|-- int align; // Alignment of image rows: 4 or 8 byte alignment

| // OpenCV ignores this and uses widthStep instead

|-- char colorModel[4]; // Color model - ignored by OpenCV

//------------------------------------------------------------------------------int main(int argc, char argv[])

{

IplImage img=cvLoadImage("c://fruitfsbmp",1);

CvScalar s;

for(int i=0;i<img->height;i++){

for(int j=0;j<img->width;j++){

s=cvGet2D(img,i,j); // get the (i,j) pixel value

printf("B=%f, G=%f, R=%f ",sval[0],sval[1],sval[2]);

sval[0]=111;

sval[1]=111;

sval[2]=111;

cvSet2D(img,i,j,s);//set the (i,j) pixel value

}

}

cvNamedWindow("Image",1);

cvShowImage("Image",img);

cvWaitKey(0); //等待按键

cvDestroyWindow( "Image" );//销毁窗口

cvReleaseImage( &img ); //释放图像

return 0;

}

其实还有更好的方法，例如将其封装成类，调用起来更加方便，效率也很高。

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