How to find coordinates of contours in openCV

Contours in image processing refer to the curves or boundaries. These represent the continuous lines that define the object shapes within an image. 

These contours help with various tasks. It includes object detection, shape analysis, and image segmentation. We can define Contours as a list of coordinates (x, y) that describe the contour's shape.   

Contour Detection:   

Contour detection is typically performed on binary images. We can obtain it through thresholding techniques. It includes Binary Thresholding or by using the output of the Canny edge detector. The latter helps identify edges in grayscale images. We can achieve this by detecting areas with rapid changes in pixel values.   

Finding Contours:   

To find contours in an image, OpenCV provides the `findContours` function. This function returns a list of contours detected in the image. You can specify the contour retrieval mode and contour approximation method as parameters. The retrieval mode determines the hierarchy of contours (parent-child relationships). The approximation method simplifies the contours by reducing the number of points.   

Contour Hierarchy:   

Detected contours often have a hierarchical structure, which includes parent and child contours. We can nest the first child contour within the parent contour. This hierarchy information can be useful when you want to identify specific contours. It can also analyze the relationships between contours.   

Contour Coordinates:   

We can represent each contour as a list of coordinates (x, y) that outline the contour's shape. We can store these coordinates in a Numpy array or a Python list, making it easy to access and manipulate them.   

Contour Features:   

Contours can calculate various features of objects in an image. It includes contour area, perimeter, centroid, and more. These features are valuable for tasks like shape analysis and object recognition.   

Contour Visualization:   

To visualize contours, you can use the `drawContours` function in OpenCV. This function allows you to draw contours on a colored image. It makes it easier to see how contours outline specific objects.   

Applications:   

Contours are fundamental in image processing, computer vision, and artificial intelligence. They are widely used in object detection, image segmentation, and recognition tasks. For example, in object detection, you can identify objects. We can do it by detecting their contours and analyzing their shapes. Contours also play a crucial role in boundary extraction shape recognition.  

The cv2.findContours function is a fundamental image-processing operation. We can provide it using the OpenCV library. It helps detect and identify contours within binary or grayscale images. Contours are simply the boundaries of objects or regions in an image.   

Syntax   

contours, hierarchy = cv2.findContours(image, mode, method, offset=(0, 0))   

Here are some key points about the cv2.findContours function:   

Input Image: 

cv2.findContours typically works on binary or grayscale images. Here, we can represent the objects by contrasting pixel values against their background. The function detects and analyzes the shapes and boundaries of objects.   

Output Contours: 

The main output of this function is a list of contours. We can represent each contour as a list of points that make up the contour's boundary. These contours can help various purposes. It includes object recognition, measurement, or segmentation.   

Hierarchy:

The function can return information about the hierarchical relationships between contours. This is particularly useful when dealing with nested objects or holes within objects.   

Contour Retrieval Modes:    

The function allows you to specify the retrieval mode for contours. You can retrieve all contours. You can also retrieve only external contours or retrieve a hierarchy of contours. It depends on your specific needs.   

Contour Approximation:   

Sometimes, contours may have many points, making them computationally expensive to work with. cv2.findContours provides an option to approximate contours using methods. It includes the Douglas-Peucker algorithm. This reduces the number of points while maintaining the overall shape of the contour.   

Preview of the output that you will get on running this code from your IDE

Code

This code snippet converts an input image to grayscale, applies a binary threshold to create 'thresh.jpg', and finds and draws contours on the thresholded image, saving it as 'contour.jpg'. You can display the co-ordinates of the contour by referring to the instructions given below.

Follow the steps carefully to get the output easily.

• Download and install VS Code on your desktop.
• Open VS Code and create a new file in the editor.
• Copy the code snippet that you want to run, using the "Copy" button or by selecting the text and using the copy command (Ctrl+C on Windows/Linux or Cmd+C on Mac).,
• Paste the code into your file in VS Code, and save the file with a meaningful name and the appropriate file extension for Python use (.py).file extension.
• pip install opencv-python - Use this line in the command prompt to install OpenCV.
• Remove the last two lines.
• Add the following lines at the end -

contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
    for point in contour:
        x, y = point[0]  
        print(f"X: {x}, Y: {y}")

• Make sure you give the correct path of the image. Refer to the output image.
• To run the code, open the file in VS Code and click the "Run" button in the top menu, or use the keyboard shortcut Ctrl+Alt+N (on Windows and Linux) or Cmd+Alt+N (on Mac).

I hope you found this useful. I have added the dependencies and their version information below.

I found this code snippet by searching for "findcontour opencv" in kandi. You can try any such use case!

Dependencies

If you do not have Opencv that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the page in kandi.

You can search for any dependent library on kandi like Opencv.

Environment tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

1. The solution is created and tested using Vscode 1.77.2 version
2. This code was tested using Python version 3.8.0
3. This code was tested using opencv-python version 72

By using this technique, you can identify and extract contours from a binary image, returning a list of contours, where each contour is represented by a list of points. This process also facilitates an easy-to-use, hassle-free method to create a hands-on working version of code.

FAQ 

1. What is the approximation method used in openCV findcontour()?   

OpenCV's `findContours()` primarily uses the `cv2.CHAIN_APPROX_SIMPLE` approximation method. It helps in compressing contour segments into endpoints.   

2. How does threshold or canny edge detection work with openCV findcontour()?   

`findContours()` is commonly paired with thresholding or Canny edge detection. We can do it by preprocessing images before extracting contours.   

3. What are the different contour retrieval modes available in openCV findcontour()?   

`cv2.RETR_EXTERNAL`, `cv2.RETR_LIST`, `cv2.RETR_CCOMP`, and `cv2.RETR_TREE`.   

4. What is a child contour, and how does it relate to openCV findcontour()?   

In `findContours()`, a child contour is one contained within another contour. The hierarchy indicates parent-child relationships.   

5. How does the library of functions in openCV make using findcontour() easier?   

OpenCV's functions simplify `findContours()` usage by offering image preprocessing, thresholding, and edge detection. It also offers contour manipulation tools, streamlining advanced image analysis.  

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