Build a Matching Report

Welcome to the final unit of the course! You have done a fantastic job designing the architecture, building the feature detector, extracting descriptors, and matching them between two views. Now, we are ready to build an essential safeguard for our feature matching process: an early warning system.

Seeing a visual representation of matched points is useful, but before a program attempts more complex geometry, it needs hard numbers. If an image lacks texture or if the images do not overlap enough, the subsequent step, homography estimation, can fail or create a heavily distorted image.

In previous lessons, we built powerful tools in our custom features.py and cvkit.py modules. We will use those same helpers here. Instead of passing their results blindly to the next stage, we are going to build a repeatable diagnostic report to analyze keypoint counts and match counts.

To create our warning system, we will write a function named diagnose_matching(). This function reads a report dictionary and returns a short message telling the user whether the pipeline looks healthy.

The code keys use underscores because they are dictionary keys. When thinking about the report, read them as normal labels such as "left keypoints", "right keypoints", and "good matches."

def diagnose_matching(report):
    if report["left_keypoints"] < 100 or report["right_keypoints"] < 100:
        return "diagnosis: few keypoints; inspect blur, texture, contrast, and overlap"

    if report["good_matches"] < 20:
        return "diagnosis: weak matching; try more overlap, more texture, SIFT, or a lower ratio threshold"

    return "diagnosis: enough matches to try homography estimation next"

These thresholds are beginner-friendly warning signs, not universal laws:

• Fewer than about 100 keypoints in either image can mean the image is blurry, low texture, low contrast, or poorly suited for feature matching.
• Fewer than about 20 good matches can make homography estimation fragile.
• Even with enough matches, geometry can still fail if matches are concentrated in one small region or come from repeated patterns.

The order of checks matters. If either image has too few keypoints, that warning appears first because weak matching may simply be a symptom of poor feature detection.