Find and Draw Contours using OpenCV Python (original) (raw)

Last Updated : 07 May, 2025

**Contours are edges or outline of a objects in a image and is used in image processing to identify shapes, detect objects or measure their size. We use OpenCV’s **findContours() function that works best for binary images.

There are three important arguments of this function:

• **Source Image: This is the image from which we want to find the contours.
• **Contour Retrieval Mode: This determines how contours are retrieved.
• **Contour Approximation Method: This decides how much detail to keep when storing the contours.

The function gives us three outputs:

• **Image: The image with contours found in it.
• **Contours: A list of contours. Each contour is made up of the (x, y) coordinates that outline a shape in the image.
• **Hierarchy: This gives extra information about the contours like which ones are inside others.

Lets implement it in python.

1. Importing Necessary Libraries

First, we need to import libraries like numpy and OpenCV that help us process image.

Python `

import cv2 import numpy as np

`

2. Reading Image

Now, we load the image we want to work with. We use **cv2.imread() to read the image and cv2.waitKey(0) pauses the program until you press a key.

Python `

image = cv2.imread('./image.png') cv2.waitKey(0)

`

You can download the image we used in the code from **here.

3. Converting Image to GrayScale

To make it easier to process the image, we convert it from color (BGR) to grayscale. Grayscale images are simpler to work with for tasks like detecting edges.

Python `

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

`

4. Edge Detection Using Canny

Next, we apply **Canny edge detection which highlights the edges of objects in the image. This helps us find boundaries of shapes and objects easily.

Python `

edged = cv2.Canny(gray, 30, 200) cv2.waitKey(0)

`

5. Finding Contours

We then find the contours, which are the boundaries of objects in the image. This helps us detect the shapes in the image. We focus on the external contours.

Python `

contours, hierarchy = cv2.findContours(edged, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

`

6. Displaying Canny Edges After Contouring

Now, we show the edges that we found using Canny edge detection. This gives us a visual idea of where the edges of the objects are.

Python `

cv2.imshow('Canny Edges After Contouring', edged) cv2.waitKey(0)

`

**Output:

Canny Edges After Contouring

7. Printing Number of Contours Found

Python `

print("Number of Contours Found = " + str(len(contours)))

`

**Output:

**Number of Contours Found = 3

**8. Drawing Contours on the Original Image

Finally, we draw the contours on the original image to visualize the shapes we found. The contours are drawn in green, and we display the updated image.

Python `

cv2.drawContours(image, contours, -1, (0, 255, 0), 3) cv2.imshow('Contours', image) cv2.waitKey(0) cv2.destroyAllWindows()

`

**Output:

Contours on the Original Image

With the following steps we can find contours in a image that can be used for image segmentation and object detection.