問題描述
我有一個(gè)像:
我想刪除數(shù)字周圍的黑色行和列.所以我希望結(jié)果是:
i want to remove the black rows and cols round the number. So i want that the result is:
我試試這個(gè):
void findX(IplImage* imgSrc,int* min, int* max){
int i;
int minFound=0;
CvMat data;
CvScalar maxVal=cvRealScalar(imgSrc->width * 255);
CvScalar val=cvRealScalar(0);
//For each col sum, if sum < width*255 then we find the min
//then continue to end to search the max, if sum< width*255 then is new max
for (i=0; i< imgSrc->width; i++){
cvGetCol(imgSrc, &data, i);
val= cvSum(&data);
if(val.val[0] < maxVal.val[0]){
*max= i;
if(!minFound){
*min= i;
minFound= 1;
}
}
}
}
void findY(IplImage* imgSrc,int* min, int* max){
int i;
int minFound=0;
CvMat data;
CvScalar maxVal=cvRealScalar(imgSrc->width * 255);
CvScalar val=cvRealScalar(0);
//For each col sum, if sum < width*255 then we find the min
//then continue to end to search the max, if sum< width*255 then is new max
for (i=0; i< imgSrc->height; i++){
cvGetRow(imgSrc, &data, i);
val= cvSum(&data);
if(val.val[0] < maxVal.val[0]){
*max=i;
if(!minFound){
*min= i;
minFound= 1;
}
}
}
}
CvRect findBB(IplImage* imgSrc){
CvRect aux;
int xmin, xmax, ymin, ymax;
xmin=xmax=ymin=ymax=0;
findX(imgSrc, &xmin, &xmax);
findY(imgSrc, &ymin, &ymax);
aux=cvRect(xmin, ymin, xmax-xmin, ymax-ymin);
//printf("BB: %d,%d - %d,%d
", aux.x, aux.y, aux.width, aux.height);
return aux;
}
所以我使用:
IplImage *my_image = cvLoad....
CvRect bb = findBB(my_image);
IplImage *new_image = cvCreateImage(cvSize(bb.width,bb.height), my_image->depth, 1);
cvShowImage("test",new_image);
效果不佳，因?yàn)槲覈L試檢查新圖像中是否存在黑色行或列.我能做什么?有人能幫我嗎?(對(duì)不起我的英語！)
it doesn't work good, cause i try to check if in new image there are black rows or cols and they are present. what can i do? can someone help me? (sorry for my english!)
推薦答案
一種方法是簡單地執(zhí)行 邊界框技術(shù)檢測數(shù)字，如下圖所示:
One way to do it is to simply execute the bounding box technique to detect the digit, as illustrated by the image below:
由于您的圖像已經(jīng)過處理，因此我使用的邊界框技術(shù)要簡單得多.
Since your image is already processed the bounding box technique I use is a lot simpler.
在該過程之后，您真正需要做的就是將原始圖像的 ROI(感興趣區(qū)域)設(shè)置為框定義的區(qū)域，以實(shí)現(xiàn) 裁剪 效果并隔離對(duì)象:
After that procedure, all you really need to do is set the ROI (Region of Interest) of the original image to the area defined by the box to achieve the crop effect and isolate the object:
請(qǐng)注意，在生成的圖像中，邊框中有一個(gè)額外的非白色行/列像素.好吧，他們也不是黑人.那是因?yàn)槲覜]有執(zhí)行任何閾值方法將圖像二值化為黑白.下面的代碼演示了在圖像的灰度版本上執(zhí)行的邊界框技術(shù).
Notice that in the resulting image there is one extra row/column of pixels in the border that are not white. Well, they are not black either. That's because I didn't performed any threshold method to binarize the image to black and white. The code below demonstrates the bounding box technique being executed on a grayscale version of the image.
這幾乎是實(shí)現(xiàn)您想要的目標(biāo)的路線圖.出于教育目的，我將分享我使用 OpenCV 的 C++ 接口編寫的代碼.我相信你有能力將它轉(zhuǎn)換成 C 接口.
This is pretty much the roadmap to achieve what you want. For educational purposes I'm sharing the code I wrote using the C++ interface of OpenCV. I'm sure you are capable of converting it to the C interface.
#include <cv.h>
#include <highgui.h>
#include <vector>
int main(int argc, char* argv[])
{
cv::Mat img = cv::imread(argv[1]);
// Convert RGB Mat to GRAY
cv::Mat gray;
cv::cvtColor(img, gray, CV_BGR2GRAY);
// Store the set of points in the image before assembling the bounding box
std::vector<cv::Point> points;
cv::Mat_<uchar>::iterator it = gray.begin<uchar>();
cv::Mat_<uchar>::iterator end = gray.end<uchar>();
for (; it != end; ++it)
{
if (*it) points.push_back(it.pos());
}
// Compute minimal bounding box
cv::RotatedRect box = cv::minAreaRect(cv::Mat(points));
// Draw bounding box in the original image (debug purposes)
//cv::Point2f vertices[4];
//box.points(vertices);
//for (int i = 0; i < 4; ++i)
//{
//cv::line(img, vertices[i], vertices[(i + 1) % 4], cv::Scalar(0, 255, 0), 1, CV_AA);
/
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