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An example in C++ that can easily ported in python is the following:

#include <iostream>
#include <opencv2/opencv.hpp>

using namespace std;
using namespace cv;

int main()
{
    // Load source image
    string filename = "nodes.bmp";
    Mat src = imread(filename);

    // Check if image is loaded fine
    if(!src.data)
        cerr << "Problem loading image!!!" << endl;

    // Show source image
    imshow("src", src);

    // Transform source image to gray if it is not
    Mat gray;

    if (src.channels() == 3)
    {
        cvtColor(src, gray, CV_BGR2GRAY);
    }
    else
    {
        gray = src;
    }

    // Show gray image
    imshow("gray", gray);

    // Apply threshold to grayscale image
    Mat bw;
    threshold(gray, bw, 50, 255, THRESH_BINARY || THRESH_OTSU);

    // Show binary image
    imshow("binary", bw);

    // Create the images that will use to extract the horizonta and vertical lines
    Mat horizontal = bw.clone();
    Mat vertical = bw.clone();

    // Specify size on horizontal axis
    int horizontalsize = horizontal.cols / 10;

    // Create structure element for extracting horizontal lines through morphology operations
    Mat horizontalStructure = getStructuringElement(MORPH_RECT, Size(horizontalsize,1));

    // Apply morphology operations
    erode(horizontal, horizontal, horizontalStructure, Point(-1, -1));
    dilate(horizontal, horizontal, horizontalStructure, Point(-1, -1));
    dilate(horizontal, horizontal, horizontalStructure, Point(-1, -1)); // expand horizontal lines

    // Show extracted horizontal lines
    imshow("horizontal", horizontal);

    // Specify size on vertical axis
    int verticalsize = vertical.rows / 10;

    // Create structure element for extracting vertical lines through morphology operations
    Mat verticalStructure = getStructuringElement(MORPH_RECT, Size( 1,verticalsize));

    // Apply morphology operations
    erode(vertical, vertical, verticalStructure, Point(-1, -1));
    dilate(vertical, vertical, verticalStructure, Point(-1, -1));
    dilate(vertical, vertical, verticalStructure, Point(-1, -1)); // expand vertical lines

    // Show extracted vertical lines
    imshow("vertical", vertical);

    Mat joints;
    bitwise_and(horizontal, vertical, joints);
    imshow("joints", joints);

    vector<vector<Point> > contours;
    findContours(joints, contours, RETR_CCOMP, CHAIN_APPROX_SIMPLE);

    /// Get the moments
    vector<Moments> mu(contours.size() );
    for( size_t i = 0; i < contours.size(); i++ )
        { mu[i] = moments( contours[i], false ); }

    ///  Get the mass centers:
    vector<Point2f> mc( contours.size() );
    for( size_t i = 0; i < contours.size(); i++ )
        { mc[i] = Point2f( mu[i].m10/mu[i].m00 , mu[i].m01/mu[i].m00 ); }

    struct Less
    {
        bool operator () (const Point2f &a, const Point2f &b)
        {
            return (a.x + a.y*10000) < (b.x + b.y*10000);
        }
    };

    std::sort(mc.begin(), mc.end(), Less());

    for( size_t i = 0; i< contours.size(); i++ )
    {
        string text = to_string(static_cast<int>(i));
        Point origin = Point(mc[i].x + 1, mc[i].y + 20);

        if((mc[i].x + 20) > src.cols/* && mc[i].y < src.rows*/)
            origin = Point(mc[i].x - 25, mc[i].y + 22);
        if ((mc[i].y + 20) > src.rows/* && mc[i].x < src.cols*/)
            origin = Point(mc[i].x + 1, mc[i].y - 10);
        if (((mc[i].x + 20) > src.cols) && ((mc[i].y + 20) > src.rows))
            origin = Point(mc[i].x - 27, mc[i].y - 10);

        putText(src, text, origin, FONT_HERSHEY_COMPLEX_SMALL, 1, Scalar( 0, 255, 0 ));
        circle( src, mc[i], 3, Scalar( 0, 0, 255 ), -1, 8, 0 );
    }

    imshow("joint_v2", src);

    waitKey(0);

    return 0;
}

if you also want to find the joints in the spots where the horizontal and vertical lines are not cross each other then you can make a small trick by expanding the lines in the exported horizontal and vertical images, in such a way that they finally every vertical lines crosses the corresponding horizontal line. As I said it should be quite straight forward to port the above code to python. Moreover, some optimization might be needed. Enjoy!!!

An example in C++ that can easily ported in python is the following:following (bear in mind that there are quite some techniques that you could follow, such as to apply a skeletonize algorithm first, etc...):

#include <iostream>
#include <opencv2/opencv.hpp>

using namespace std;
using namespace cv;

int main()
{
    // Load source image
    string filename = "nodes.bmp";
    Mat src = imread(filename);

    // Check if image is loaded fine
    if(!src.data)
        cerr << "Problem loading image!!!" << endl;

    // Show source image
    imshow("src", src);

    // Transform source image to gray if it is not
    Mat gray;

    if (src.channels() == 3)
    {
        cvtColor(src, gray, CV_BGR2GRAY);
    }
    else
    {
        gray = src;
    }

    // Show gray image
    imshow("gray", gray);

    // Apply threshold to grayscale image
    Mat bw;
    threshold(gray, bw, 50, 255, THRESH_BINARY || THRESH_OTSU);

    // Show binary image
    imshow("binary", bw);

    // Create the images that will use to extract the horizonta and vertical lines
    Mat horizontal = bw.clone();
    Mat vertical = bw.clone();

    // Specify size on horizontal axis
    int horizontalsize = horizontal.cols / 10;

    // Create structure element for extracting horizontal lines through morphology operations
    Mat horizontalStructure = getStructuringElement(MORPH_RECT, Size(horizontalsize,1));

    // Apply morphology operations
    erode(horizontal, horizontal, horizontalStructure, Point(-1, -1));
    dilate(horizontal, horizontal, horizontalStructure, Point(-1, -1));
    dilate(horizontal, horizontal, horizontalStructure, Point(-1, -1)); // expand horizontal lines

    // Show extracted horizontal lines
    imshow("horizontal", horizontal);

    // Specify size on vertical axis
    int verticalsize = vertical.rows / 10;

    // Create structure element for extracting vertical lines through morphology operations
    Mat verticalStructure = getStructuringElement(MORPH_RECT, Size( 1,verticalsize));

    // Apply morphology operations
    erode(vertical, vertical, verticalStructure, Point(-1, -1));
    dilate(vertical, vertical, verticalStructure, Point(-1, -1));
    dilate(vertical, vertical, verticalStructure, Point(-1, -1)); // expand vertical lines

    // Show extracted vertical lines
    imshow("vertical", vertical);

    Mat joints;
    bitwise_and(horizontal, vertical, joints);
    imshow("joints", joints);

    vector<vector<Point> > contours;
    findContours(joints, contours, RETR_CCOMP, CHAIN_APPROX_SIMPLE);

    /// Get the moments
    vector<Moments> mu(contours.size() );
    for( size_t i = 0; i < contours.size(); i++ )
        { mu[i] = moments( contours[i], false ); }

    ///  Get the mass centers:
    vector<Point2f> mc( contours.size() );
    for( size_t i = 0; i < contours.size(); i++ )
        { mc[i] = Point2f( mu[i].m10/mu[i].m00 , mu[i].m01/mu[i].m00 ); }

    struct Less
    {
        bool operator () (const Point2f &a, const Point2f &b)
        {
            return (a.x + a.y*10000) < (b.x + b.y*10000);
        }
    };

    std::sort(mc.begin(), mc.end(), Less());

    for( size_t i = 0; i< contours.size(); i++ )
    {
        string text = to_string(static_cast<int>(i));
        Point origin = Point(mc[i].x + 1, mc[i].y + 20);

        if((mc[i].x + 20) > src.cols/* && mc[i].y < src.rows*/)
            origin = Point(mc[i].x - 25, mc[i].y + 22);
        if ((mc[i].y + 20) > src.rows/* && mc[i].x < src.cols*/)
            origin = Point(mc[i].x + 1, mc[i].y - 10);
        if (((mc[i].x + 20) > src.cols) && ((mc[i].y + 20) > src.rows))
            origin = Point(mc[i].x - 27, mc[i].y - 10);

        putText(src, text, origin, FONT_HERSHEY_COMPLEX_SMALL, 1, Scalar( 0, 255, 0 ));
        circle( src, mc[i], 3, Scalar( 0, 0, 255 ), -1, 8, 0 );
    }

    imshow("joint_v2", src);

    waitKey(0);

    return 0;
}

if you also want to find the joints in the spots where the horizontal and vertical lines are not cross each other then you can make a small trick by expanding the lines in the exported horizontal and vertical images, in such a way that they finally every vertical lines crosses the corresponding horizontal line. As I said it should be quite straight forward to port the above code to python. Moreover, some optimization might be needed. Enjoy!!!

An example in C++ that can easily ported in python is the following (bear in mind that there are quite some techniques that you could follow, such as to apply a skeletonize algorithm first, etc...):

#include <iostream>
#include <opencv2/opencv.hpp>

using namespace std;
using namespace cv;

int main()
{
    // Load source image
    string filename = "nodes.bmp";
    Mat src = imread(filename);

    // Check if image is loaded fine
    if(!src.data)
        cerr << "Problem loading image!!!" << endl;

    // Show source image
    imshow("src", src);

    // Transform source image to gray if it is not
    Mat gray;

    if (src.channels() == 3)
    {
        cvtColor(src, gray, CV_BGR2GRAY);
    }
    else
    {
        gray = src;
    }

    // Show gray image
    imshow("gray", gray);

    // Apply threshold to grayscale image
    Mat bw;
    threshold(gray, bw, 50, 255, THRESH_BINARY || THRESH_OTSU);

    // Show binary image
    imshow("binary", bw);

    // Create the images that will use to extract the horizonta and vertical lines
    Mat horizontal = bw.clone();
    Mat vertical = bw.clone();

    // Specify size on horizontal axis
    int horizontalsize = horizontal.cols / 10;

    // Create structure element for extracting horizontal lines through morphology operations
    Mat horizontalStructure = getStructuringElement(MORPH_RECT, Size(horizontalsize,1));

    // Apply morphology operations
    erode(horizontal, horizontal, horizontalStructure, Point(-1, -1));
    dilate(horizontal, horizontal, horizontalStructure, Point(-1, -1));
    horizontalStructure = getStructuringElement(MORPH_RECT, Size(500,1)); // 500 is a random value choose whatever you want here
    dilate(horizontal, horizontal, horizontalStructure, Point(-1, -1)); // expand horizontal lines

    // Show extracted horizontal lines
    imshow("horizontal", horizontal);

    // Specify size on vertical axis
    int verticalsize = vertical.rows / 10;

    // Create structure element for extracting vertical lines through morphology operations
    Mat verticalStructure = getStructuringElement(MORPH_RECT, Size( 1,verticalsize));

    // Apply morphology operations
    erode(vertical, vertical, verticalStructure, Point(-1, -1));
    dilate(vertical, vertical, verticalStructure, Point(-1, -1));
    verticalStructure = getStructuringElement(MORPH_RECT, Size( 1,500)); // same stand here as previous
    dilate(vertical, vertical, verticalStructure, Point(-1, -1)); // expand vertical lines

    // Show extracted vertical lines
    imshow("vertical", vertical);

    Mat joints;
    bitwise_and(horizontal, vertical, joints);
    imshow("joints", joints);

    vector<vector<Point> > contours;
    findContours(joints, contours, RETR_CCOMP, CHAIN_APPROX_SIMPLE);

    /// Get the moments
    vector<Moments> mu(contours.size() );
    for( size_t i = 0; i < contours.size(); i++ )
        { mu[i] = moments( contours[i], false ); }

    ///  Get the mass centers:
    vector<Point2f> mc( contours.size() );
    for( size_t i = 0; i < contours.size(); i++ )
        { mc[i] = Point2f( mu[i].m10/mu[i].m00 , mu[i].m01/mu[i].m00 ); }

    struct Less
    {
        bool operator () (const Point2f &a, const Point2f &b)
        {
            return (a.x + a.y*10000) < (b.x + b.y*10000);
        }
    };

    std::sort(mc.begin(), mc.end(), Less());

    for( size_t i = 0; i< contours.size(); i++ )
    {
        string text = to_string(static_cast<int>(i));
        Point origin = Point(mc[i].x + 1, mc[i].y + 20);

        if((mc[i].x + 20) > src.cols/* && mc[i].y < src.rows*/)
            origin = Point(mc[i].x - 25, mc[i].y + 22);
        if ((mc[i].y + 20) > src.rows/* && mc[i].x < src.cols*/)
            origin = Point(mc[i].x + 1, mc[i].y - 10);
        if (((mc[i].x + 20) > src.cols) && ((mc[i].y + 20) > src.rows))
            origin = Point(mc[i].x - 27, mc[i].y - 10);

        putText(src, text, origin, FONT_HERSHEY_COMPLEX_SMALL, 1, Scalar( 0, 255, 0 ));
        circle( src, mc[i], 3, Scalar( 0, 0, 255 ), -1, 8, 0 );
    }

    imshow("joint_v2", src);

    waitKey(0);

    return 0;
}

if you also want to find the joints in the spots where the horizontal and vertical lines are not cross each other then you can make a small trick by expanding the lines in the exported horizontal and vertical images, in such a way that they finally every vertical lines crosses the corresponding horizontal line. As I said it should be quite straight forward to port the above code to python. Moreover, some optimization might be needed. Enjoy!!!