My code is the same as this tutorial.
When I see the result image after using cv::watershed(), there is a object(upper-right) that I want to find out, but it's missing.
There are indeed six marks in image after using cv::drawContours().
Is this normal because the inaccuracy of the watershed algorithm exist?
Here is part of my code:
Mat src = imread("result01.png");
Mat gray;
cvtColor(src, gray, COLOR_BGR2GRAY);
Mat thresh;
threshold(gray, thresh, 0, 255, THRESH_BINARY | THRESH_OTSU);
// noise removal
Mat kernel = Mat::ones(3, 3, CV_8UC1);
Mat opening;
morphologyEx(thresh, opening, MORPH_OPEN, kernel, Point(-1, -1), 2);
// Perform the distance transform algorithm
Mat dist_transform;
distanceTransform(opening, dist_transform, CV_DIST_L2, 5);
// Normalize the distance image for range = {0.0, 1.0}
// so we can visualize and threshold it
normalize(dist_transform, dist_transform, 0, 1., NORM_MINMAX);
// Threshold to obtain the peaks
// This will be the markers for the foreground objects
Mat dist_thresh;
threshold(dist_transform, dist_thresh, 0.5, 1., CV_THRESH_BINARY);
Mat dist_8u;
dist_thresh.convertTo(dist_8u, CV_8U);
// Find total markers
vector<vector<Point> > contours;
findContours(dist_8u, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
// Create the marker image for the watershed algorithm
Mat markers = Mat::zeros(dist_thresh.size(), CV_32SC1);
// Draw the foreground markers
for (size_t i = 0; i < contours.size(); i++)
drawContours(markers, contours, static_cast<int>(i), Scalar::all(static_cast<int>(i)+1), -1);
// Perform the watershed algorithm
watershed(src, markers);
Original image:
Result after watershed:
You can find original, intermediate and result image here:
Result images after specific process
In your example, what you consider background is given the same label (5) as the "missing" object.
You can easily adjust this by setting a label (>0) to background, too.
You can find what is for sure background dilating and negating the thresh image.
Then, when creating a marker, you define the labels as:
0: unknown
1: background
>1 : your objects
In your output image, markers will have:
-1 : the edges between objects
0: the background (as intended by watershed)
1: the background (as you defined)
>1 : your objects.
This code should help:
#include <opencv2\opencv.hpp>
#include <vector>
using namespace std;
using namespace cv;
int main()
{
Mat3b src = imread("path_to_image");
Mat1b gray;
cvtColor(src, gray, COLOR_BGR2GRAY);
Mat1b thresh;
threshold(gray, thresh, 0, 255, THRESH_BINARY | THRESH_OTSU);
// noise removal
Mat1b kernel = getStructuringElement(MORPH_RECT, Size(3,3));
Mat1b opening;
morphologyEx(thresh, opening, MORPH_OPEN, kernel, Point(-1, -1), 2);
Mat1b kernelb = getStructuringElement(MORPH_RECT, Size(21, 21));
Mat1b background;
morphologyEx(thresh, background, MORPH_DILATE, kernelb);
background = ~background;
// Perform the distance transform algorithm
Mat1f dist_transform;
distanceTransform(opening, dist_transform, CV_DIST_L2, 5);
// Normalize the distance image for range = {0.0, 1.0}
// so we can visualize and threshold it
normalize(dist_transform, dist_transform, 0, 1., NORM_MINMAX);
// Threshold to obtain the peaks
// This will be the markers for the foreground objects
Mat1f dist_thresh;
threshold(dist_transform, dist_thresh, 0.5, 1., CV_THRESH_BINARY);
Mat1b dist_8u;
dist_thresh.convertTo(dist_8u, CV_8U);
// Find total markers
vector<vector<Point> > contours;
findContours(dist_8u, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
// Create the marker image for the watershed algorithm
Mat1i markers(dist_thresh.rows, dist_thresh.cols, int(0));
// Background as 1
Mat1i one(markers.rows, markers.cols, int(1));
bitwise_or(one, markers, markers, background);
// Draw the foreground markers (from 2 up)
for (int i = 0; i < int(contours.size()); i++)
drawContours(markers, contours, i, Scalar(i+2), -1);
// Perform the watershed algorithm
Mat3b dbg;
cvtColor(opening, dbg, COLOR_GRAY2BGR);
watershed(dbg, markers);
Mat res;
markers.convertTo(res, CV_8U);
normalize(res, res, 0, 255, NORM_MINMAX);
return 0;
}
Result:
there is very little emgu cv data on watershed.
here is my translation to this problem using c#. i know it is not the right forum but this answer kips popping up so to all the searchers:
//Mat3b src = imread("path_to_image");
//cvtColor(src, gray, COLOR_BGR2GRAY);
Image<Gray, byte> gray = smallImage.Convert<Gray, byte>();
//threshold(gray, thresh, 0, 255, THRESH_BINARY | THRESH_OTSU);
Image<Gray, byte> thresh = gray.ThresholdBinaryInv(new Gray(55), new Gray(255));
// noise removal
Mat kernel = CvInvoke.GetStructuringElement(ElementShape.Rectangle, new Size(3, 3), new Point(-1, -1));
//Mat1b opening;
//morphologyEx(thresh, opening, MORPH_OPEN, kernel, Point(-1, -1), 2);
Image<Gray, byte> opening = thresh.MorphologyEx(MorphOp.Open, kernel, new Point(-1, -1), 2, BorderType.Default, new MCvScalar(255));
//Mat1b kernelb = getStructuringElement(MORPH_RECT, Size(21, 21));
Mat kernel1 = CvInvoke.GetStructuringElement(ElementShape.Rectangle, new Size(3, 3), new Point(-1, -1));
//Mat1b background;
//morphologyEx(thresh, background, MORPH_DILATE, kernelb);
Image<Gray, byte> background = thresh.MorphologyEx(MorphOp.Dilate, kernel, new Point(-1, -1), 2, BorderType.Default, new MCvScalar(255));
background = ~background;
//// Perform the distance transform algorithm
//Mat1f dist_transform;
//distanceTransform(opening, dist_transform, CV_DIST_L2, 5);
Image<Gray, float> dist_transform = new Image<Gray, float>(opening.Width, opening.Height);
CvInvoke.DistanceTransform(opening, dist_transform, null, DistType.L2, 5);
//// Normalize the distance image for range = {0.0, 1.0}
//// so we can visualize and threshold it
//normalize(dist_transform, dist_transform, 0, 1., NORM_MINMAX);
CvInvoke.Normalize(dist_transform, dist_transform, 0, 1.0, NormType.MinMax, DepthType.Default);
//// Threshold to obtain the peaks
//// This will be the markers for the foreground objects
//Mat1f dist_thresh;
//threshold(dist_transform, dist_thresh, 0.5, 1., CV_THRESH_BINARY);
Image<Gray, float> dist_thresh = new Image<Gray, float>(opening.Width, opening.Height);
CvInvoke.Threshold(dist_transform, dist_thresh, 0.5, 1.0, ThresholdType.Binary);
//Mat1b dist_8u;
//dist_thresh.convertTo(dist_8u, CV_8U);
Image<Gray, Byte> dist_8u = dist_thresh.Convert<Gray, Byte>();
//// Find total markers
//vector<vector<Point>> contours;
//findContours(dist_8u, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint();
CvInvoke.FindContours(dist_8u, contours, null, RetrType.External, ChainApproxMethod.ChainApproxSimple);
//// Create the marker image for the watershed algorithm
//Mat1i markers(dist_thresh.rows, dist_thresh.cols, int(0));
Image<Gray, int> markers = new Image<Gray, int>(dist_thresh.Width, dist_thresh.Height, new Gray(0));
//// Background as 1
//Mat1i one(markers.rows, markers.cols, int(1));
//bitwise_or(one, markers, markers, background);
Image<Gray, int> one = new Image<Gray, int>(markers.Cols, markers.Rows, new Gray(1));
CvInvoke.BitwiseOr(one, markers, markers, background);
//// Draw the foreground markers (from 2 up)
for (int i = 0; i < contours.Size; i++)
// drawContours(markers, contours, i, Scalar(i + 2), -1);
CvInvoke.DrawContours(markers, contours, i, new MCvScalar(i + 2));
//// Perform the watershed algorithm
//Mat3b dbg;
//cvtColor(opening, dbg, COLOR_GRAY2BGR);
//watershed(dbg, markers);
Image<Bgr, byte> dbg = new Image<Bgr, byte>(markers.Cols, markers.Rows);
CvInvoke.CvtColor(opening, dbg, ColorConversion.Gray2Bgr);
CvInvoke.Watershed(dbg, markers);
//Mat res;
//markers.convertTo(res, CV_8U);
//normalize(res, res, 0, 255, NORM_MINMAX);
CvInvoke.Normalize(markers, markers, 0, 255, NormType.MinMax);
//return 0;
to find light objects on dark background replace ThresholdBinaryInv with ThresholdBinary
Related
I have this image the vascular bundle
My work is to find the centroid of the blood vessels ,
I tried Image moments but I have this error message error
My code is here:
int main() {
cv::Mat img = imread("C:\\Users\\ASUS\\Desktop\\fond1.png ", CV_LOAD_IMAGE_COLOR);
Mat blue, green, red, step1, otsu, step11, green1, blue1;
Mat bgr[3]; //destination array
split(img, bgr);//split source
red.push_back(bgr[2]);
Moments mu = moments(red,true);
Point center;
center.x = mu.m10 / mu.m00;
center.y = mu.m01 / mu.m00;
circle(red, center, 2, Scalar(0, 0, 255));
imshow("Result",red);
Mat mask(red.size(), CV_8UC1, Scalar::all(0));
// Create Polygon from vertices
vector<Point> ROI_Vertices(3);
ROI_Vertices.push_back(Point(0,0 ));
ROI_Vertices.push_back(Point(center.x, center.y));
ROI_Vertices.push_back(Point(0,red.rows -1));
vector<Point> ROI_Poly;
approxPolyDP(ROI_Vertices, ROI_Poly, 1.0, true);
// Fill polygon white
fillConvexPoly(mask, &ROI_Poly[0], ROI_Poly.size(), 255, 8, 0);
Mat hide(red.size(), CV_8UC3);
red.copyTo(hide, mask);
imshow("mask", hide);
Mat blackhat,tophat,dst;
Mat element = getStructuringElement(MORPH_ELLIPSE, Size(6,6));
morphologyEx(hide, blackhat, MORPH_BLACKHAT, element);
imshow("step1", blackhat);
morphologyEx(blackhat, tophat, MORPH_TOPHAT, element);
imshow("step2", tophat);
cv::Mat r1 = cv::Mat::zeros(dst.rows, dst.cols, CV_8UC1);
tophat.copyTo(r1);
imshow("vessel", r1);
threshold(r1, dst, 9, 255, THRESH_BINARY);
// Find contours
vector<vector<Point>> contours;
vector<Vec4i> hierarchy;
///Get the moments
Mat canny_output;
// detect edges using canny
Canny(dst, canny_output, 50, 150, 3);
// find contours
findContours(canny_output, contours, hierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE, Point(0, 0));
// get the moments
vector<Moments> mu(contours.size());
for (int i = 0; i<contours.size(); i++)
{
mu[i] = moments(contours[i], false);
}
// get the centroid of figures.
vector<Point2f> mc(contours.size());
for (int i = 0; i<contours.size(); i++)
{
mc[i] = Point2f(mu[i].m10 / mu[i].m00, mu[i].m01 / mu[i].m00);
}
I am working on reading container code using an IP Camera and I came across #dhanushka's gradient based method for text detection and I've been successful with it as you can see below...
bool debugging = true;
Mat rgb = imread("/home/brian/qt/ANPR/images/0.jpg", 0);
if(debugging) { imshow("Original", rgb); }
Mat grad;
Mat morphKernel = getStructuringElement(MORPH_ELLIPSE, Size(3, 3));
morphologyEx(rgb, grad, MORPH_GRADIENT, morphKernel);
if(debugging) { imshow("gradient morph", grad); }
// binarize
Mat bw;
threshold(grad, bw, 0.0, 255.0, THRESH_BINARY | THRESH_OTSU);
if(debugging) { imshow("threshold", bw); }
// connect horizontally oriented regions
Mat connected;
morphKernel = getStructuringElement(MORPH_RECT, Size(10, 1));
morphologyEx(bw, connected, MORPH_CLOSE, morphKernel);
if(debugging) { imshow("horizontal regions morph", connected); }
// find contours
Mat mask = Mat::zeros(bw.size(), CV_8UC1);
vector<vector<Point> > contours2;
vector<Vec4i> hierarchy;
vector<Rect> txtRect;
vector<vector<Point> > txtContour;
findContours(connected, contours2, hierarchy, CV_RETR_CCOMP,
CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
// filter contours
for(int i = 0; i >= 0; i = hierarchy[i][0]) {
Rect rect = boundingRect(contours2[i]);
Mat maskROI(mask, rect);
maskROI = Scalar(0, 0, 0);
// fill the contour
drawContours(mask, contours2, i, Scalar(255, 255, 255), CV_FILLED);
// ratio of non-zero pixels in the filled region
double r = (double)countNonZero(maskROI)/(rect.width*rect.height);
/* assume at least 45% of the area is filled if it contains text */
if (r > .45 && (rect.height > 10 && rect.width > 10)) {
rectangle(rgb, rect, Scalar(0, 255, 0), 2);
txtRect.push_back(rect);
txtContour.push_back(contours2[i]);
}
}
if(debugging) { imshow("Characters", rgb); }
Mat text(rgb.size(), CV_8U, Scalar(255));
drawContours(text, txtContour, -1, Scalar(0), FILLED, 4);
if(debugging) { imshow("Detected Text", text); }
Step 0: Original Image
Step 1: Morphological gradient
Step 2: Binarized image
Step 3: Horizontally oriented regions
Step 4: Detected Text
Step 5: Extracted Text
The problem is that I have failed to properly extract the detected text so that I can use it in OCR to get the result BSIU225378.
The text I managed to extract is coming from the horizontal connected regions and its unusable for orc, is there a way I can extract text say from the binarized (threshold) image using the contours I found in the horizontal connected regions?
I am new to OpenCV and I am using this code to bound the text area in image. After that I am filtering contours and putting the bounded rectangle to a vector<Rect> to copy these to new image.
Mat large = img1;
Mat rgb;
// downsample and use it for processing
pyrUp(large, rgb);
Mat small;
cvtColor(rgb, small, CV_BGR2GRAY);
// morphological gradient
Mat grad;
Mat morphKernel = getStructuringElement(MORPH_ELLIPSE, Size(2, 2));
morphologyEx(small, grad, MORPH_GRADIENT, morphKernel);
// binarize
Mat bw;
threshold(grad, bw, 0.0, 255.0, THRESH_BINARY | THRESH_OTSU);
// connect horizontally oriented regions
Mat connected;
//morphKernel = getStructuringElement(MORPH_RECT, Size(7, 1));
//morphologyEx(bw, connected, MORPH_CLOSE, morphKernel);
// find contours
connected = bw;
Mat mask = Mat::zeros(bw.size(), CV_8UC1);
Mat mask2;
Mat mask3;
vector<vector<Point>> contours;
vector<Vec4i> hierarchy;
findContours(connected, contours, hierarchy, CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
/*drawContours(mask2, contours, -1, Scalar(255), CV_FILLED);
Mat Crop(img1.rows, img1.cols, CV_8UC3);
Crop.setTo(Scalar(0, 255, 0));
img1.copyTo(Crop, mask2);
normalize(mask2.clone(), mask2, 0.0, 255.0, CV_MINMAX, CV_8UC1);
*/
vector<Rect> rect1;
int i = 0;
//filter contours
for (int idx = 0; idx >= 0; idx = hierarchy[idx][0])
{
Rect rect = boundingRect(contours[idx]);
Mat maskROI(mask, rect);
maskROI = Scalar(0, 0, 0);
// fill the contour
drawContours(mask, contours, idx, Scalar(255, 255, 255), CV_FILLED);
// ratio of non-zero pixels in the filled region
double r = (double)countNonZero(maskROI) / (rect.width*rect.height);
if (r > .45 /* assume at least 45% of the area is filled if it contains text */
&&
(rect.height > 10 && rect.width > 10 && rect.height<150 && rect.width<150) /* constraints on region size */
/* these two conditions alone are not very robust. better to use something
like the number of significant peaks in a horizontal projection as a third condition */
)
{
//making rectangles on bounded area
rectangle(rgb, rect, Scalar(0, 255, 0), 2);
//pushing bounding rectangles in vector for new mask
rect1.push_back(rect);
}
}
Input output I am getting after bounded text ares is:
After that I am using this code to copy the bounded area only to new mask
//copying bounded rectangles area from small to new mask2
for (int i = 0; i < rect1.size(); i++){
mask2 = rgb(rect1[i]);
}
but by using this I only get this last bounded text area:
How can I get or update the mask2 rows or cols to get all the mapping of bounded text areas from rgb to mask2.
That's because mask2 will be equal to the last rgb(rect1[i]) called.
You can easily solve this in two ways (using copyTo):
Create a mask (black initialized, same size as input image), where you draw (white) rectangles. Then you copy the original image to a black initialized image of the same size, using the obtained mask.
Copy each sub-image directly to a black initialized image.
Starting from this image, where the red rectangles will be your detected rectangles:
With first approach you'll get a mask like:
and, for both approaches, the final result will be:
Code for first approach:
#include <opencv2/opencv.hpp>
#include <vector>
using namespace std;
using namespace cv;
int main()
{
// Your image
Mat3b img = imread("path_to_image");
// Your rectangles
vector<Rect> rects{Rect(100, 100, 100, 200), Rect(300, 200, 200, 100), Rect(500, 400, 80, 130)};
// Mask for rectangles (black initializeds)
Mat1b mask(img.rows, img.cols, uchar(0));
Mat3b dbgRects = img.clone();
for (int i = 0; i < rects.size(); ++i)
{
// Draw white rectangles on mask
rectangle(mask, rects[i], Scalar(255), CV_FILLED);
// Show rectangles
rectangle(dbgRects, rects[i], Scalar(0, 0, 255), 2);
}
// Black initizlied result
Mat3b result(img.rows, img.cols, Vec3b(0,0,0));
img.copyTo(result, mask);
imshow("Rectangles", dbgRects);
imshow("Result", result);
waitKey();
return 0;
}
Code for second approach:
#include <opencv2/opencv.hpp>
#include <vector>
using namespace std;
using namespace cv;
int main()
{
// Your image
Mat3b img = imread("path_to_image");
// Your rectangles
vector<Rect> rects{Rect(100, 100, 100, 200), Rect(300, 200, 200, 100), Rect(500, 400, 80, 130)};
// Black initizlied result
Mat3b result(img.rows, img.cols, Vec3b(0, 0, 0));
Mat3b dbgRects = img.clone();
for (int i = 0; i < rects.size(); ++i)
{
img(rects[i]).copyTo(result(rects[i]));
// Show rectangles
rectangle(dbgRects, rects[i], Scalar(0, 0, 255), 2);
}
imshow("Rectangles", dbgRects);
imshow("Result", result);
waitKey();
return 0;
}
I apply watershed segmentation to detect touching objects and it works okay doing that. Now, I would like to draw contours of each object, so I can get their length, area, moments etc.. But the objects in the result of the segmentation are still touching. So, I fail to draw contours of each one. How can I draw contours of each object?
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <iostream>
using namespace std;
using namespace cv;
int main()
{
Mat src = imread("source.png");
// Create binary image from source image
Mat srcGray;
cvtColor(src, srcGray, CV_BGR2GRAY);
Mat srcThresh;
threshold(srcGray, srcThresh, 0, 255, CV_THRESH_BINARY | CV_THRESH_OTSU);
// Perform the distance transform algorithm
Mat dist;
distanceTransform(srcThresh, dist, CV_DIST_L2, 3);
// Normalize the distance image for range = {0.0, 1.0}
normalize(dist, dist, 0, 1., NORM_MINMAX);
// Threshold to obtain the peaks
threshold(dist, dist, 0.1, 3.5, CV_THRESH_BINARY);
// Create the CV_8U version of the distance image
Mat dist_8u;
dist.convertTo(dist_8u, CV_8U);
// Find total markers
std::vector<std::vector<Point> > contours;
findContours(dist_8u, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
int ncomp = contours.size();
// Create the marker image for the watershed algorithm
Mat markers = Mat::zeros(dist.size(), CV_32SC1);
// Draw the foreground markers
for (int i = 0; i < ncomp; i++)
drawContours(markers, contours, i, Scalar::all(i + 1), -1);
// Draw the background marker
circle(markers, Point(5, 5), 3, CV_RGB(255, 255, 255), -1);
// Perform the watershed algorithm
watershed(src, markers);
Mat wgResult = (markers.clone()) * 10000;
imshow("Watershed", wgResult);
waitKey(0);
return 0;
}
Source image:
Watershed Result:
The markers matrix returned by watershed contains the indices of the segmented regions, according to the seed. So each component will have the same seed value. You can then create a binary matrix for each seed like:
Mat1b mask = (markers == seed);
Once you have the binary mask for each component, you can easily compute its area, moments, etc...
Code:
#include <opencv2/opencv.hpp>
#include <iostream>
using namespace std;
using namespace cv;
int main()
{
Mat src = imread("D:\\SO\\img\\postit.png");
// Create binary image from source image
Mat srcGray;
cvtColor(src, srcGray, CV_BGR2GRAY);
Mat srcThresh;
threshold(srcGray, srcThresh, 0, 255, CV_THRESH_BINARY | CV_THRESH_OTSU);
// Perform the distance transform algorithm
Mat dist;
distanceTransform(srcThresh, dist, CV_DIST_L2, 3);
// Normalize the distance image for range = {0.0, 1.0}
normalize(dist, dist, 0, 1., NORM_MINMAX);
// Threshold to obtain the peaks
threshold(dist, dist, 0.1, 3.5, CV_THRESH_BINARY);
// Create the CV_8U version of the distance image
Mat dist_8u;
dist.convertTo(dist_8u, CV_8U);
// Find total markers
std::vector<std::vector<Point> > contours;
findContours(dist_8u, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
int ncomp = contours.size();
// Create the marker image for the watershed algorithm
Mat markers = Mat::zeros(dist.size(), CV_32SC1);
// Draw the foreground markers
for (int i = 0; i < ncomp; i++)
drawContours(markers, contours, i, Scalar::all(i + 1), -1);
// Draw the background marker
circle(markers, Point(5, 5), 3, CV_RGB(255, 255, 255), -1);
// Perform the watershed algorithm
watershed(src, markers);
for (int seed = 1; seed <= ncomp; ++seed)
{
Mat1b mask = (markers == seed);
// Now you have the mask, you can compute your statistics
imshow("Mask", mask);
waitKey();
}
return 0;
}
There are many way to do this. Depending on the current image that have been shown, you can simply do erosion and dilation operation on order to separate them. However this will not work if the elapse area is bigger.
You need a closing operation:
http://docs.opencv.org/2.4/doc/tutorials/imgproc/opening_closing_hats/opening_closing_hats.html
threshold it.
apply closing operation.
get contours
I want to combine multiple threshold for detecting different type of red colour. I tried to make 4 type of thresholding and then I combine the result of 4 image into 1. but the result is always pitch black. is there any other way to do it? I guess the way I'm combining the 2 image into 1 is not correct using the addWeighted
Mat img = imread (nameImg);
cvtColor(img , hsv, CV_BGR2HSV);
Mat bw,bw2,bw3,bw4;
inRange(hsv, Scalar(0,28,192), Scalar(4,67,219),bw); // detecting acne 1
inRange(hsv, Scalar(0,40,152), Scalar(8,85,243),bw2); // acne 2
inRange(hsv, Scalar(0,85,202), Scalar(6,146,247),bw3); // acne 3
inRange(hsv, Scalar(156,93,176), Scalar(82,130,255),bw4); // acne 4
vector<vector<Point> > contours;
vector<vector<Point> > contours2;
vector<vector<Point> > contours3;
vector<vector<Point> > contours4;
findContours(bw.clone(), contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
findContours(bw2.clone(), contours2, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
findContours(bw3.clone(), contours3, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
findContours(bw4.clone(), contours4, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
Mat dst = Mat::zeros(img.size(), img.type());
drawContours(dst, contours, -1, Scalar::all(255), CV_FILLED);
Mat dst2 = Mat::zeros(img.size(), img.type());
drawContours(dst2, contours2, -1, Scalar::all(255), CV_FILLED);
Mat dst3 = Mat::zeros(img.size(), img.type());
drawContours(dst3, contours4, -1, Scalar::all(255), CV_FILLED);
Mat dst4 = Mat::zeros(img.size(), img.type());
drawContours(dst4, contours4, -1, Scalar::all(255), CV_FILLED);
Mat dst5, dst6;
imshow("dst",dst);
imshow("dst2",dst2);
imshow("dst3",dst3);
imshow("dst4",dst4);
addWeighted(dst, 0.5, dst2, 0.5, 0,dst5);//combine acne 1 with 2
addWeighted(dst4, 0.5, dst3, 0.5, 0,dst6); //combine acne 3 with 4
addWeighted(dst5, 0.5, dst6, 0.5, 0,out);
imshow("result",out); //the result always black