3. How to detect a Christmas Tree using C++?

How to detect a Christmas Tree using C++?

October 8, 2017 C++ OpenCV Tutorials 1 Comment

Hello Friends,

As a follow-up to our previous post on Detecting Christmas trees using Python, in this tutorial, we’ll detect the Christmas tress using C++.
Background: For our new project, let’s try to recognize a Christmas Tree, remember, this will stay true for all the tree detection.
Let’s consider the following images…

  

  

 

 

Here is the source code and below it, its explanation.

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//Christmas Tree Detection
//For Ubuntu use: //g++ -Wall -pedantic -ansi -O2 -pipe -s -o christmas_tree christmas_tree.cpp `pkg-config --cflags --libs opencv`
 
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <iostream>
 
using namespace cv;
using namespace std;
 
int main(int argc,char *argv[])
{
    Mat original,tmp,tmp1;
    vector <vector<Point> > contours;
    Moments m;
    Rect boundrect;
    Point2f center;
    double radius, max_area=0,tmp_area=0;
    unsigned int j, k;
    int i;
 
    for(i = 1; i < argc; ++i)
    {
        original = imread(argv[i]);
        if(original.empty())
        {
            cerr << "Error"<<endl;
            return -1;
        }
 
        GaussianBlur(original, tmp, Size(3, 3), 0, 0, BORDER_DEFAULT);
        erode(tmp, tmp, Mat(), Point(-1, -1), 10);
        cvtColor(tmp, tmp, CV_BGR2HSV);
        inRange(tmp, Scalar(0, 0, 0), Scalar(180, 255, 200), tmp);
 
        dilate(original, tmp1, Mat(), Point(-1, -1), 15);
        cvtColor(tmp1, tmp1, CV_BGR2HLS);
        inRange(tmp1, Scalar(0, 185, 0), Scalar(180, 255, 255), tmp1);
        dilate(tmp1, tmp1, Mat(), Point(-1, -1), 10);
 
        bitwise_and(tmp, tmp1, tmp1);
 
        findContours(tmp1, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
        max_area = 0;
        j = 0;
        for(k = 0; k < contours.size(); k++)
        {
            tmp_area = contourArea(contours[k]);
            if(tmp_area > max_area)
            {
                max_area = tmp_area;
                j = k;
            }
        }
        tmp1 = Mat::zeros(original.size(),CV_8U);
        approxPolyDP(contours[j], contours[j], 30, true);
        drawContours(tmp1, contours, j, Scalar(255,255,255), CV_FILLED);
 
        m = moments(contours[j]);
        boundrect = boundingRect(contours[j]);
        center = Point2f(m.m10/m.m00, m.m01/m.m00);
        radius = (center.y - (boundrect.tl().y))/4.0*3.0;
        Rect heightrect(center.x-original.cols/5, boundrect.tl().y, original.cols/5*2, boundrect.size().height);
 
        tmp = Mat::zeros(original.size(), CV_8U);
        rectangle(tmp, heightrect, Scalar(255, 255, 255), -1);
        circle(tmp, center, radius, Scalar(255, 255, 255), -1);
 
        bitwise_and(tmp, tmp1, tmp1);
 
        findContours(tmp1, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
        max_area = 0;
        j = 0;
        for(k = 0; k < contours.size(); k++)
        {
            tmp_area = contourArea(contours[k]);
            if(tmp_area > max_area)
            {
                max_area = tmp_area;
                j = k;
            }
        }
 
        approxPolyDP(contours[j], contours[j], 30, true);
        convexHull(contours[j], contours[j]);
 
        drawContours(original, contours, j, Scalar(0, 0, 255), 3);
 
        namedWindow(argv[i], CV_WINDOW_NORMAL|CV_WINDOW_KEEPRATIO|CV_GUI_EXPANDED);
        imshow(argv[i], original);
 
        waitKey(0);
        destroyWindow(argv[i]);
    }
 
    return 0;
}

Explanation:

The first step is to detect the most bright pixels in the picture, but we have to do a distinction between the tree itself and the snow which reflect its light. Here we try to exclude the snow appling a really simple filter on the color codes:

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GaussianBlur(original, tmp, Size(3, 3), 0, 0, BORDER_DEFAULT);
erode(tmp, tmp, Mat(), Point(-1, -1), 10);
cvtColor(tmp, tmp, CV_BGR2HSV);
inRange(tmp, Scalar(0, 0, 0), Scalar(180, 255, 200), tmp);

Then we find every “bright” pixel:

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dilate(original, tmp1, Mat(), Point(-1, -1), 15);
cvtColor(tmp1, tmp1, CV_BGR2HLS);
inRange(tmp1, Scalar(0, 185, 0), Scalar(180, 255, 255), tmp1);
dilate(tmp1, tmp1, Mat(), Point(-1, -1), 10);

Finally we join the two results:

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bitwise_and(tmp, tmp1, tmp1);

Now we look for the biggest bright object:

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findContours(tmp1, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
max_area = 0;
j = 0;
for(k = 0; k < contours.size(); k++)
{
    tmp_area = contourArea(contours[k]);
    if(tmp_area > max_area)
    {
        max_area = tmp_area;
        j = k;
    }
}
tmp1 = Mat::zeros(original.size(),CV_8U);
approxPolyDP(contours[j], contours[j], 30, true);
drawContours(tmp1, contours, j, Scalar(255,255,255), CV_FILLED);

Now we have almost done, but there are still some imperfection due to the snow. To cut them off we’ll build a mask using a circle and a rectangle to approximate the shape of a tree to delete unwanted pieces:

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m = moments(contours[j]);
boundrect = boundingRect(contours[j]);
center = Point2f(m.m10/m.m00, m.m01/m.m00);
radius = (center.y - (boundrect.tl().y))/4.0*3.0;
Rect heightrect(center.x-original.cols/5, boundrect.tl().y, original.cols/5*2, boundrect.size().height);
 
tmp = Mat::zeros(original.size(), CV_8U);
rectangle(tmp, heightrect, Scalar(255, 255, 255), -1);
circle(tmp, center, radius, Scalar(255, 255, 255), -1);
 
bitwise_and(tmp, tmp1, tmp1);

The last step is to find the contour of our tree and draw it on the original picture.

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findContours(tmp1, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
max_area = 0;
j = 0;
for(k = 0; k < contours.size(); k++)
{
    tmp_area = contourArea(contours[k]);
    if(tmp_area > max_area)
    {
        max_area = tmp_area;
        j = k;
    }
}
 
approxPolyDP(contours[j], contours[j], 30, true);
convexHull(contours[j], contours[j]);
 
drawContours(original, contours, j, Scalar(0, 0, 255), 3);

Here some pictures of the final output:

     

Usage of "Assert" in OpenCV World
Recognizing day to day objects using Object Recognition in OpenCV (C++)
Artcile Views: 2,435

1 thought on “How to detect a Christmas Tree using C++?”

  1. cvgflmtrg says:
    October 29, 2017 at 3:31 am

    I just love your blog. Thanks for all the efforts that you put in.

    Reply

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