| OpenCV 3.4.3 Open Source Computer Vision

Finding contours in your image

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## Goal

In this tutorial you will learn how to:

- Use the OpenCV function cv::findContours
- Use the OpenCV function cv::drawContours

## Theory

## Code

C++

This tutorial code's is shown lines below. You can also download it from here

#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include <iostream>
using namespace cv;
using namespace std;
Mat src_gray;
int thresh = 100;
RNG rng(12345);
void thresh_callback(int, void* );
int main( int argc, char** argv )
{
CommandLineParser parser( argc, argv, "{@input | ../data/HappyFish.jpg | input image}" );
Mat src = imread( parser.get<String>( "@input" ) );
if( src.empty() )
{
cout << "Could not open or find the image!\n" << endl;
cout << "Usage: " << argv[0] << " <Input image>" << endl;
return -1;
}
cvtColor( src, src_gray, COLOR_BGR2GRAY );
blur( src_gray, src_gray, Size(3,3) );
const char* source_window = "Source";
namedWindow( source_window );
imshow( source_window, src );
const int max_thresh = 255;
createTrackbar( "Canny thresh:", source_window, &thresh, max_thresh, thresh_callback );
thresh_callback( 0, 0 );
waitKey();
return 0;
}
void thresh_callback(int, void* )
{
Mat canny_output;
Canny( src_gray, canny_output, thresh, thresh*2 );
vector<vector<Point> > contours;
vector<Vec4i> hierarchy;
findContours( canny_output, contours, hierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE );
Mat drawing = Mat::zeros( canny_output.size(), CV_8UC3 );
for( size_t i = 0; i< contours.size(); i++ )
{
Scalar color = Scalar( rng.uniform(0, 256), rng.uniform(0,256), rng.uniform(0,256) );
drawContours( drawing, contours, (int)i, color, 2, LINE_8, hierarchy, 0 );
}
imshow( "Contours", drawing );
}
Java

This tutorial code's is shown lines below. You can also download it from here

import java.awt.BorderLayout;
import java.awt.Container;
import java.awt.Image;
import java.util.ArrayList;
import java.util.List;
import java.util.Random;
import javax.swing.BoxLayout;
import javax.swing.ImageIcon;
import javax.swing.JFrame;
import javax.swing.JLabel;
import javax.swing.JPanel;
import javax.swing.JSlider;
import javax.swing.event.ChangeEvent;
import javax.swing.event.ChangeListener;
import org.opencv.core.Core;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.core.MatOfPoint;
import org.opencv.core.Point;
import org.opencv.core.Scalar;
import org.opencv.core.Size;
import org.opencv.highgui.HighGui;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
class FindContours {
private Mat srcGray = new Mat();
private JFrame frame;
private JLabel imgSrcLabel;
private JLabel imgContoursLabel;
private static final int MAX_THRESHOLD = 255;
private int threshold = 100;
private Random rng = new Random(12345);
public FindContours(String[] args) {
String filename = args.length > 0 ? args[0] : "../data/HappyFish.jpg";
Mat src = Imgcodecs.imread(filename);
if (src.empty()) {
System.err.println("Cannot read image: " + filename);
System.exit(0);
}
Imgproc.cvtColor(src, srcGray, Imgproc.COLOR_BGR2GRAY);
Imgproc.blur(srcGray, srcGray, new Size(3, 3));
// Create and set up the window.
frame = new JFrame("Finding contours in your image demo");
frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
// Set up the content pane.
Image img = HighGui.toBufferedImage(src);
addComponentsToPane(frame.getContentPane(), img);
// Use the content pane's default BorderLayout. No need for
// setLayout(new BorderLayout());
// Display the window.
frame.pack();
frame.setVisible(true);
update();
}
private void addComponentsToPane(Container pane, Image img) {
if (!(pane.getLayout() instanceof BorderLayout)) {
pane.add(new JLabel("Container doesn't use BorderLayout!"));
return;
}
JPanel sliderPanel = new JPanel();
sliderPanel.setLayout(new BoxLayout(sliderPanel, BoxLayout.PAGE_AXIS));
sliderPanel.add(new JLabel("Canny threshold: "));
JSlider slider = new JSlider(0, MAX_THRESHOLD, threshold);
slider.setMajorTickSpacing(20);
slider.setMinorTickSpacing(10);
slider.setPaintTicks(true);
slider.setPaintLabels(true);
slider.addChangeListener(new ChangeListener() {
@Override
public void stateChanged(ChangeEvent e) {
JSlider source = (JSlider) e.getSource();
threshold = source.getValue();
update();
}
});
sliderPanel.add(slider);
pane.add(sliderPanel, BorderLayout.PAGE_START);
JPanel imgPanel = new JPanel();
imgSrcLabel = new JLabel(new ImageIcon(img));
imgPanel.add(imgSrcLabel);
Mat blackImg = Mat.zeros(srcGray.size(), CvType.CV_8U);
imgContoursLabel = new JLabel(new ImageIcon(HighGui.toBufferedImage(blackImg)));
imgPanel.add(imgContoursLabel);
pane.add(imgPanel, BorderLayout.CENTER);
}
private void update() {
Mat cannyOutput = new Mat();
Imgproc.Canny(srcGray, cannyOutput, threshold, threshold * 2);
List<MatOfPoint> contours = new ArrayList<>();
Mat hierarchy = new Mat();
Imgproc.findContours(cannyOutput, contours, hierarchy, Imgproc.RETR_TREE, Imgproc.CHAIN_APPROX_SIMPLE);
Mat drawing = Mat.zeros(cannyOutput.size(), CvType.CV_8UC3);
for (int i = 0; i < contours.size(); i++) {
Scalar color = new Scalar(rng.nextInt(256), rng.nextInt(256), rng.nextInt(256));
Imgproc.drawContours(drawing, contours, i, color, 2, Core.LINE_8, hierarchy, 0, new Point());
}
imgContoursLabel.setIcon(new ImageIcon(HighGui.toBufferedImage(drawing)));
frame.repaint();
}
}
public class FindContoursDemo {
public static void main(String[] args) {
// Load the native OpenCV library
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
// Schedule a job for the event dispatch thread:
// creating and showing this application's GUI.
javax.swing.SwingUtilities.invokeLater(new Runnable() {
@Override
public void run() {
new FindContours(args);
}
});
}
}
Python

This tutorial code's is shown lines below. You can also download it from here

from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
import random as rng
rng.seed(12345)
def thresh_callback(val):
threshold = val
# Detect edges using Canny
canny_output = cv.Canny(src_gray, threshold, threshold * 2)
# Find contours
_, contours, hierarchy = cv.findContours(canny_output, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
# Draw contours
drawing = np.zeros((canny_output.shape[0], canny_output.shape[1], 3), dtype=np.uint8)
for i in range(len(contours)):
color = (rng.randint(0,256), rng.randint(0,256), rng.randint(0,256))
cv.drawContours(drawing, contours, i, color, 2, cv.LINE_8, hierarchy, 0)
# Show in a window
cv.imshow('Contours', drawing)
# Load source image
parser = argparse.ArgumentParser(description='Code for Finding contours in your image tutorial.')
parser.add_argument('--input', help='Path to input image.', default='../data/HappyFish.jpg')
args = parser.parse_args()
src = cv.imread(args.input)
if src is None:
print('Could not open or find the image:', args.input)
exit(0)
# Convert image to gray and blur it
src_gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
src_gray = cv.blur(src_gray, (3,3))
# Create Window
source_window = 'Source'
cv.namedWindow(source_window)
cv.imshow(source_window, src)
max_thresh = 255
thresh = 100 # initial threshold
cv.createTrackbar('Canny Thresh:', source_window, thresh, max_thresh, thresh_callback)
thresh_callback(thresh)
cv.waitKey()

## Explanation

## Result

Here it is: