How to Compute the Extent of an Object in an Image using OpenCV Python?

OpenCV is an open-source computer vision and machine learning software library. It has many tools and functions that can help in image processing, computer vision, and machine learning. In this article, we will explore the process of computing the extent of an object in an image using OpenCV Python.

What is Extent?

Extent is one of the features used to describe an object in an image. It is the ratio of the area occupied by the object to the area of the bounding box. In simpler terms, extent is the percentage of the total area of the bounding box that is occupied by the object.

Steps to Compute Extent

The following steps can be followed to compute the extent of an object in an image.

1. Read the input image

import cv2

image = cv2.imread('object.jpg', cv2.IMREAD_GRAYSCALE)
cv2.imshow("Input Image", image)
cv2.waitKey(0)
cv2.destroyAllWindows()

In this code snippet, we first import the required libraries. Then we read the input image using the cv2.imread() function. The second parameter specifies the color space of the input image. In this case, we read the image as grayscale. Then, we display the input image in a window using the cv2.imshow() function. The cv2.waitKey(0) function waits for a keyboard event to close the window. Finally, we close all the windows opened using the cv2.destroyAllWindows() function.

2. Threshold the Image

_, threshold = cv2.threshold(image, 128, 255, cv2.THRESH_BINARY)
cv2.imshow("Threshold Image", threshold)
cv2.waitKey(0)
cv2.destroyAllWindows()

In this code snippet, we threshold the input image using the cv2.threshold() function. The first parameter is the input image, and the second parameter is the threshold value. Any pixel value below this threshold value is set to 0, and any pixel value above this threshold value is set to 255. The third parameter is the maximum value that can be assigned to a pixel, and the fourth parameter is the type of thresholding operation. In this case, we use binary thresholding. Then, we display the thresholded image in a window using the cv2.imshow() function. The cv2.waitKey(0) function waits for a keyboard event to close the window. Finally, we close all the windows opened using the cv2.destroyAllWindows() function.

3. Find the Contours

contours, _ = cv2.findContours(threshold, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

In this code snippet, we use the cv2.findContours() function to find the contours in the thresholded image. The first parameter is the input image, and the second parameter is the contour retrieval mode. The third parameter is the contour approximation method. In this case, we use the cv2.RETR_TREE mode for contour retrieval and the cv2.CHAIN_APPROX_SIMPLE method for contour approximation. The findContours() function returns the contours in the image and the hierarchy of contours. We are interested only in the contours, so we ignore the hierarchy by assigning the second return value to an underscore.

4. Compute the Extent

for cnt in contours:
    area = cv2.contourArea(cnt)
    x, y, w, h = cv2.boundingRect(cnt)

    rect_area = w * h
    if rect_area > 0:
        extent = float(area) / rect_area
        print(extent)

In this code snippet, we loop through all the contours found in the previous step using a for loop. For each contour, we compute its area using the cv2.contourArea() function. We then compute the bounding box of the contour using the cv2.boundingRect() function. This function returns the (x, y) coordinates of the top-left corner of the bounding box, its width, and height.

We then compute the area of the bounding box as rect_area = w * h. If the area of the bounding box is greater than 0, we compute the extent of the contour as extent = float(area) / rect_area. We print the extent for each contour in the image using the print() function.

Conclusion

In this article, we have learned how to compute the extent of an object in an image using OpenCV Python. We have explored the steps involved in computing the extent and written code snippets for each step. With this knowledge, you can now compute the extent of any object in an image using OpenCV Python.