Create the Feature Detector Module

Welcome back! In the previous unit, we explored Harris corners and used them as a visual diagnostic for image texture. While that was a great starting point, real-world panoramas require algorithms that are stronger and more reliable when handling scale, rotation, and complex textured scenes.

In this lesson, we will upgrade our toolkit. We are going to build a clean, reusable feature detection module named features.py. By storing our detection logic in a single reusable file, we will not have to rewrite it every time we want to inspect or match image features. Think of it as organizing tools into a toolbox: whenever you need a specific detector, it is ready and waiting.

As a quick reminder, in our CodeSignal environment, powerful libraries like OpenCV (cv2) are already pre-installed for you. You will not need to worry about setting up your environment or installing libraries — we can dive straight into writing code!

Each feature method has unique strengths and produces a different type of descriptor, which is the numeric "fingerprint" around a keypoint.

Here is how to interpret the table:

• SIFT is the heavy lifter. It is usually the strongest default for panorama-style matching because it handles scale and rotation well.
• ORB is the speedster. It uses compact binary descriptors, so it is fast and memory efficient.
• AKAZE is a modern middle ground. It often performs well on textured scenes with strong edges.

Separating this comparison from the code keeps the lesson focused: first we choose the tool conceptually, then we implement a factory that creates it.

Our first reusable function is a detector factory. It returns the appropriate OpenCV detector based on the method argument.

import cv2

def create_detector(method="sift", nfeatures=2000):
    if method == "sift":
        if not hasattr(cv2, "SIFT_create"):
            raise ValueError("SIFT is not available in this OpenCV build")
        return cv2.SIFT_create(nfeatures=nfeatures)

    if method == "orb":
        return cv2.ORB_create(nfeatures=nfeatures)

    if method == "akaze":
        return cv2.AKAZE_create()

    raise ValueError(f"Unknown feature method: {method}")

The SIFT branch includes an availability check because older OpenCV builds sometimes placed SIFT in an extra module or did not include it at all. Modern OpenCV builds usually include it, but the explicit check gives learners a clear error message if it is missing.

We do not add the same availability check for ORB and AKAZE here because they are standard OpenCV feature detectors in the environment used for this course. If a very unusual OpenCV build were missing one of them, the detector creation call would fail immediately. For our course pipeline, the SIFT check is the most helpful one because it is the detector with the most common version-related history.

Notice that AKAZE_create() does not receive nfeatures. OpenCV's AKAZE constructor does not use the same simple maximum-feature argument that SIFT and ORB do, so we call it without that option.