So what type of shape descriptors does OpenCV provide?
The most notable are Hu Moments which are can be used to describe, characterize, and quantify the shape of an object in an image.
Hu Moments are normally extracted from the silhouette or outline of an object in an image. By describing the silhouette or outline of an object, we are able to extract a shape feature vector (i.e. a list of numbers) to represent the shape of the object.
We can then compare two feature vectors using a similarity metric or distance function to determine how “similar” the shapes are.
In this blog post I’ll show you how to extract the Hu Moments shape descriptor using Python and OpenCV.
OpenCV and Python versions:
This example will run on Python 2.7/Python 3.4+ and OpenCV 2.4.X/OpenCV 3.0+.
OpenCV Shape Descriptor: Hu Moments Example
As I mentioned, Hu Moments are used to characterize the outline or “silhouette” of an object in an image.
Normally, we obtain this shape after applying some sort of segmentation (i.e. setting the background pixels to black and the foreground pixels to white). Thresholding is the most common approach to obtain our segmentation.
After we have performed thresholding we have the silhouette of the object in the image.
We could also find the contours of the silhouette and draw them, thus creating an outline of the object.
Regardless of which method we choose, we can still apply the Hu Moments shape descriptors provided that we obtain consistent representations across all images.
For example, it wouldn’t make sense to extract Hu Moments shape features from the silhouette of one set of images and then extract Hu Moments shape descriptors from the outline of another set of images if our intention is to compare the shape features in some way.
Anyway, let’s get started and extract our OpenCV shape descriptors.
First, we’ll need an image,
This image is of a diamond, where the black pixels correspond to the background of the image and the white pixels correspond to the foreground. This is an example of a silhouette of an object in an image. If we had just the border of the diamond, it would be the outline of the object.
Regardless, it is important to note that our Hu Moments shape descriptor will only be computed over the white pixels.
Now, let’s extract our shape descriptors:
>>> import cv2 >>> image = cv2.imread("diamond.png") >>> image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
The first thing we need to do is import our
cv2 package which provides us with our OpenCV bindings.
Then, we load our diamond image off disk using the
cv2.imread method and convert it to grayscale.
We convert our image to grayscale because Hu Moments requires a single channel image — the shape quantification is only carried out among the white pixels.
From here, we can compute our Hu Moments shape descriptor using OpenCV:
>>> cv2.HuMoments(cv2.moments(image)).flatten() array([ 6.53608067e-04, 6.07480284e-16, 9.67218398e-18, 1.40311655e-19, -1.18450102e-37, 8.60883492e-28, -1.12639633e-37])
In order to compute our Hu Moments, we first need to compute the original 24 moments associated with the image using
From there, we pass these moments into
cv2.HuMoments, which calculates Hu’s seven invariant moments.
Finally, we flatten our array to form our shape feature vector.
This feature vector can be used to quantify and represent the shape of an object in an image.
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In this blog post I showed you how to use the Hu Moments OpenCV shape descriptor.
In future blog posts I will show you how to compare Hu Moments feature vectors for similarity.
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