Qt and openCV: Segmenting leaf by openCV2--00 : By threshold and findContours

These series of post will try to cut the leaf from the images(graph_00~graph_02) with different solutions.The first solution is based on threshold and contours.

graph_00

graph_01

graph_02

Step 0

read the image and covert it to gray image

cv::Mat color_image = cv::imread(Folder + "leaf" + num + ".png");
if(color_image.empty()){
   std::cerr<<"input is empty"<<std::endl;
   return -1;
}

cv::Mat image;
cv::cvtColor(color_image, image, CV_BGR2GRAY);

Step 1

Apply threshold.

cv::Mat binary;
cv::threshold(image, binary, 140, 255, cv::THRESH_BINARY);

graph_03

graph_04

graph_05

Step 2

Use morphology operations(erosion and close) to remove discontinuous region(erosion) and close the holes(close).

auto const structure = cv::getStructuringElement(cv::MORPH_ELLIPSE, 
                                                 cv::Size(5, 5));
cv::erode(binary, binary, structure);
cv::morphologyEx(binary, binary, cv::MORPH_CLOSE, structure);

graph_06

graph_07

graph_08

Step 3

find contours of the images.

std::vector<std::vector<cv::Point>> contours;
cv::findContours(binary, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);

graph_09

graph_10

graph_11

Step 4

From step 3, we could see that graph_09~graph_11 still contains a lot of undesired blobs, we could remove them by the area of contours(the pixels in the contours).

void remove_contours(std::vector<std::vector<cv::Point>>&contours, 
                     double cmin, double cmax)
{
    auto it = std::partition(std::begin(contours), std::end(contours), 
    [=](std::vector const &data)
    {
       auto const size = cv::contourArea(data);
       return !(size < cmin || size > cmax);
    });
    contours.erase(it, std::end(contours));
}

After that, we invert the mask(change 0 to 255, 255 to 0).