问题描述
我是OpenCV的新手，正在尝试计算图像中的对象数量.在使用MATLAB Image Processing Toolbox之前，我已经做到了这一点，并且在OpenCV(Android)中也采用了相同的方法.
I am new to OpenCV and am trying to count the number of objects in an image. I have done this before using MATLAB Image Processing Toolbox and adapted the same approach in OpenCV (Android) also.
第一步是将图像转换为灰度.然后将其阈值化，然后计算斑点的数量.在Matlab中，有一个命令-"bwlabel"，它给出了斑点的数量.我在OpenCV中找不到这样的东西(再次，我在OpenCV和Android中都是菜鸟.)
The first step was to convert an image to gray scale. Then to threshold it and then counting the number of blobs. In Matlab there is a command - "bwlabel", which gives the number of blobs. I couldn't find such thing in OpenCV (again, I am a noob in OpenCV as well as Android).
这是我的代码，
//JPG to Bitmap to MAT
Bitmap i = BitmapFactory.decodeFile(imgPath + "mms.jpg");
Bitmap bmpImg = i.copy(Bitmap.Config.ARGB_8888, false);
Mat srcMat = new Mat ( bmpImg.getHeight(), bmpImg.getWidth(), CvType.CV_8UC3);
Utils.bitmapToMat(bmpImg, srcMat);
//convert to gray scale and save image
Mat gray = new Mat(srcMat.size(), CvType.CV_8UC1);
Imgproc.cvtColor(srcMat, gray, Imgproc.COLOR_RGB2GRAY,4);
//write bitmap
Boolean bool = Highgui.imwrite(imgPath + "gray.jpg", gray);
//thresholding
Mat threshed = new Mat(bmpImg.getWidth(),bmpImg.getHeight(), CvType.CV_8UC1);
Imgproc.adaptiveThreshold(gray, threshed, 255, Imgproc.ADAPTIVE_THRESH_MEAN_C, Imgproc.THRESH_BINARY, 75, 5);//15, 8 were original tests. Casey was 75,10
Core.bitwise_not(threshed, threshed);
Utils.matToBitmap(threshed, bmpImg);
//write bitmap
bool = Highgui.imwrite(imgPath + "threshed.jpg", threshed);
Toast.makeText(this, "Thresholded image saved!", Toast.LENGTH_SHORT).show();
在下一步中，我尝试使用膨胀和侵蚀填充填充孔和字母，但斑点彼此附着，最终将产生错误的计数.在调整膨胀和腐蚀参数时，要在填充孔和使斑点彼此附着之间进行权衡.
In the next step, I tried to fill the holes and letters using dilation followed by an erosion but the blobs gets attached to each other which will ultimately give a wrong count. There is a tradeoff between filling holes and getting the blobs attached to each other on tuning the parameters for dilation and erosion.
这是代码，
//morphological operations
//dilation
Mat dilated = new Mat(bmpImg.getWidth(),bmpImg.getHeight(), CvType.CV_8UC1);
Imgproc.dilate(threshed, dilated, Imgproc.getStructuringElement(Imgproc.MORPH_ELLIPSE, new org.opencv.core.Size (16, 16)));
Utils.matToBitmap(dilated, bmpImg);
//write bitmap
bool = Highgui.imwrite(imgPath + "dilated.jpg", dilated);
Toast.makeText(this, "Dilated image saved!", Toast.LENGTH_SHORT).show();
//erosion
Mat eroded = new Mat(bmpImg.getWidth(),bmpImg.getHeight(), CvType.CV_8UC1);
Imgproc.erode(dilated, eroded, Imgproc.getStructuringElement(Imgproc.MORPH_ELLIPSE, new org.opencv.core.Size(15, 15)));
Utils.matToBitmap(eroded, bmpImg);
//write bitmap
bool = Highgui.imwrite(imgPath + "eroded.jpg", eroded);
Toast.makeText(this, "Eroded image saved!", Toast.LENGTH_SHORT).show();
因为有时我的M& M可能彼此相邻！ ;)
Because sometimes my M&Ms might be just next to each other! ;)
我也尝试使用Hough Circles，但结果非常不可靠(已测试硬币图像和真实硬币)
I also tried to use Hough Circles but the result is very unreliable (tested with coin images as well as real coins)
这是代码，
//hough circles
Mat circles = new Mat();
// parameters
int iCannyUpperThreshold = 100;
int iMinRadius = 20;
int iMaxRadius = 400;
int iAccumulator = 100;
Imgproc.HoughCircles(gray, circles, Imgproc.CV_HOUGH_GRADIENT,
1.0, gray.rows() / 8, iCannyUpperThreshold, iAccumulator,
iMinRadius, iMaxRadius);
// draw
if (circles.cols() > 0)
{
Toast.makeText(this, "Coins : " +circles.cols() , Toast.LENGTH_LONG).show();
}
else
{
Toast.makeText(this, "No coins found", Toast.LENGTH_LONG).show();
}
此方法的问题在于该算法仅限于完美圆(AFAIK).因此，当我尝试扫描和计数躺在我桌上的M& Ms或硬币时，它不能很好地工作(因为设备的角度发生了变化).使用这种方法，有时我会得到更少的反对.的硬币数量有时甚至更多(我不知道为什么还要更多?).
The problem with this approach is that the algorithm is limited to perfect circles only (AFAIK). So, it doesn't work well when I try to scan and count M&Ms or coins lying on my desk (because angle of the device changes). With this approach, sometimes I get less no. of coins detected and sometimes more (I don't get it why more??).
在扫描此图像时，该应用有时显示19个硬币，有时计数38个硬币...我知道还有其他特征可能被检测为圆圈，但我完全不明白为什么是38 ..?
On scanning this image the app sometimes shows 19 coins and sometimes 38 coins counted...I know there are other features which may be detected as circles but I totally don't get it why 38..?
所以我的问题...
- 有没有更好的方法来填充孔而不连接相邻的斑点?
- 如何准确计算对象数?我不想将我的应用程序限制为仅使用HoughCircles方法计数圆.
仅供参考:OpenCV-2.4.9-android-sdk.请记住，我也是OpenCV和Android的新手.
FYI : OpenCV-2.4.9-android-sdk. Kindly keep in mind that I am a newbie in OpenCV and Android too.
非常感谢您的帮助.
感谢&干杯！
Jainam
推荐答案
因此，我们将您生成的阈值图像作为输入，然后对其进行进一步修改.目前的代码是C ++，但我想您可以轻松将其转换为android平台
So to proceed we take your threshold image which you have generated as input and further modify it. The present code is in C++ but I guess you can easily convert it into android platform
现在，您可以尝试进行泛洪填充，而不必进行扩散或模糊处理
Now instead of dilation or blurring you can try flood fill
结果
现在终于应用轮廓检测算法算法，我们得到
Finally now applying the contour detection algorithm algorithm we get
上面的代码是
Mat dst = imread($path to the threshold image); // image should be single channel black and white image
imshow("dst",dst);
cv::Mat mask = cv::Mat::zeros(dst.rows + 2, dst.cols + 2, CV_8U);
// A image with size greater than the present object is created
cv::floodFill(dst, mask, cv::Point(0,0), 255, 0, cv::Scalar(), cv::Scalar(), 4 + (255 << 8) + cv::FLOODFILL_MASK_ONLY);
erode(mask,mask,Mat());
// Now to remove the outer boundary
rectangle(mask,Rect(0,0,mask.cols,mask.rows), Scalar(255,255,255),2,8,0);
imshow("Mask",mask);
Mat copy;
mask.copyTo(copy);
vector<vector<Point> > contours;
vector<Vec4i> hierarchy;
findContours( copy, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0) );
vector<vector<Point> > contours_poly( contours.size() );
vector<Rect> boundRect( contours.size() );
vector<Point2f>center( contours.size() );
vector<float>Distance( contours.size() );
vector<float>radius( contours.size() );
Mat drawing = cv::Mat::zeros(mask.rows, mask.cols, CV_8U);
int num_object = 0;
for( int i = 0; i < contours.size(); i++ ){
approxPolyDP( Mat(contours[i]), contours_poly[i], 3, true );
// To get rid of the smaller object and the outer rectangle created
//because of the additional mask image we enforce a lower limit on area
//to remove noise and an upper limit to remove the outer border.
if (contourArea(contours_poly[i])>(mask.rows*mask.cols/10000) && contourArea(contours_poly[i])<mask.rows*mask.cols*0.9){
boundRect[i] = boundingRect( Mat(contours_poly[i]) );
minEnclosingCircle( (Mat)contours_poly[i], center[i], radius[i] );
circle(drawing,center[i], (int)radius[i], Scalar(255,255,255), 2, 8, 0);
rectangle(drawing,boundRect[i], Scalar(255,255,255),2,8,0);
num_object++;
}
}
cout <<"No. of object detected =" <<num_object<<endl;
imshow("drawing",drawing);
waitKey(2);
char key = (char) waitKey(20);
if(key == 32){
// You can save your images here using a space
}
我希望这可以帮助您解决问题
I hope this helps you in solving your problem
这篇关于计数对象填充孔的更好方法的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！