How to find the solidity and equivalent diameter of an object in an image using OpenCV Python?

Are you curious about how to find the solidity and equivalent diameter of an object in an image using OpenCV Python? Well, you’re in the right place. In this tutorial, we’re going to explore how to use OpenCV Python to find the solidity and equivalent diameter of an object in an image.

First, let’s understand the concept of solidity and equivalent diameter:

Understanding Solidity and Equivalent Diameter

Solidity is the measure of how compact an object is. In other words, it is the ratio of the area of the object to the area of its convex hull. If the object is completely compact and has no holes, its solidity value will be 1. On the other hand, if the object has holes or is not compact, its solidity value will be less than 1.

Equivalent Diameter is the diameter of a circle having the same area as the object in the image. It is a useful parameter to characterize the size of an object in an image.

Now that we understand the concepts of solidity and equivalent diameter, let’s dive into the code. We will be using OpenCV and Python to implement this.

Setting Up the Environment

First, we need to make sure that we have the necessary libraries installed. We will be using OpenCV and NumPy for this tutorial.

import cv2 
import numpy as np 

Loading the Image

Next, let’s load the image we want to find the solidity and equivalent diameter for.

img = cv2.imread('image_name.jpg')

Pre-Processing the Image

Before we can find the solidity and equivalent diameter of an object in the image, we need to pre-process the image. This involves converting the image to grayscale, applying a threshold, and finding contours.

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY_INV)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

Finding the Contours

Now that we have pre-processed the image, we can find the contours using the cv2.findContours() function.

contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

Finding the Solidity

To find the solidity of an object, we need to find the area of the object and the area of its convex hull. We can do this using the cv2.contourArea() and cv2.convexHull() functions.

for cnt in contours:
    area = cv2.contourArea(cnt)
    hull = cv2.convexHull(cnt)
    hull_area = cv2.contourArea(hull)
    solidity = float(area)/hull_area
    print("Solidity:", solidity)

Finding the Equivalent Diameter

To find the equivalent diameter of an object, we need to find the area of the object and calculate the diameter of a circle with the same area.

for cnt in contours:
    area = cv2.contourArea(cnt)
    equi_diameter = np.sqrt(4*area/np.pi)
    print("Equivalent Diameter:", equi_diameter)

That’s it! We have successfully found the solidity and equivalent diameter of an object in an image using OpenCV Python.

Conclusion

In this tutorial, we have explored how to use OpenCV Python to find the solidity and equivalent diameter of an object in an image. We have learned that solidity is the measure of how compact an object is, and equivalent diameter is the diameter of a circle having the same area as the object in the image. We have also learned how to pre-process the image, find the contours, and calculate the solidity and equivalent diameter. With these techniques in our toolkit, we can analyze and characterize objects in images with confidence.