hog feature extraction python code

histogram of oriented gradients (hog) is a feature descriptor widely used in computer vision and image processing for object detection. it captures the distribution of gradient orientations in an image, making it particularly useful for tasks like pedestrian detection, face detection, and more.
in this tutorial, we'll cover the basics of hog feature extraction using python with the scikit-image library, which provides a simple interface for extracting hog features.
ensure you have scikit-image installed. you can install it via pip:
let's dive into a simple example of extracting hog features from an image.
in this example:
in this tutorial, you've learned the basics of extracting hog features from images using python. hog features are widely used in various computer vision tasks such as object detection, tracking, and recognition. experiment with different parameters and explore how hog features can be utilized in your own projects.
chatgpt
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[belfonzus]

Wow. I came looking for tutorials on how to do hog because chat gpt wasn't getting it right. The last thing I need is something that claims to be a tutorial but is actually just a badly voiceovered version of what I was already trying to get away from. Don't bother posting videos of chat gpt telling you stuff, if people needed that, they'd just ask chat gpt themselves.