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.

Now that we have our rules, we need to gather the data to feed into them. The pipeline loads both images, preprocesses them, extracts features, and matches descriptors.

left = read_color(args.left)
right = read_color(args.right)

left_gray = preprocess_for_features(left)
right_gray = preprocess_for_features(right)

kp1, des1 = detect_and_compute(left_gray, method=args.method)
kp2, des2 = detect_and_compute(right_gray, method=args.method)
matches = match_descriptors(des1, des2, ratio=args.ratio)

The keypoints and matches are then summarized in a dictionary:

report = {
    "method": args.method,
    "ratio": args.ratio,
    "left_keypoints": len(kp1),
    "right_keypoints": len(kp2),
    "good_matches": len(matches),
}

for key, value in report.items():
    print(f"{key}: {value}")

print(diagnose_matching(report))

A healthy terminal report might look like this:

method: sift
ratio: 0.75
left_keypoints: 1250
right_keypoints: 1180
good_matches: 145
diagnosis: enough matches to try homography estimation next

A weaker pair might show this instead:

method: sift
ratio: 0.75
left_keypoints: 340
right_keypoints: 290
good_matches: 8
diagnosis: weak matching; try more overlap, more texture, SIFT, or a lower ratio threshold

The terminal report is just as important as the preview image. It gives you repeatable numbers that are easier to compare between image pairs, methods, and ratio thresholds.

Text reports are excellent for automated decision-making, but a visual sanity check is also important. We can use OpenCV's cv2.drawMatches() function to connect the matched points across both images.

preview = cv2.drawMatches(
    left,
    kp1,
    right,
    kp2,
    matches[:80],
    None,
    flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS,
)

We draw only the top 80 matches because hundreds of lines can become unreadable. Our matching helper already sorts matches by distance, so the first 80 are the strongest ones according to descriptor distance.

The flag cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS keeps the preview clean by hiding keypoints that did not match.

Finally, we save the preview and display it:

cv2.imwrite(args.out, preview)
cv2.imshow("matching report", preview)
cv2.waitKey(0)
cv2.destroyAllWindows()

The cv2.waitKey(0) command pauses the script until you press a key, allowing you to inspect the visual report as long as you need.

Congratulations on making it to the final unit of the course! Building a robust feature matching and reporting stage is no small feat.

In this lesson, we established a crucial safety checkpoint for the image stitching pipeline. By building the diagnostic function, we learned how to evaluate raw counts of keypoints and matches against practical thresholds. This helps catch problems like blur, low texture, poor overlap, or overly strict matching before a program attempts more fragile geometry.

You also learned how to package these statistics into a clean dictionary and visualize the top 80 matches to verify your work with your own eyes.

Now, it is time for you to take the wheel. In the upcoming practice exercises, you will write these diagnostic rules, assemble the report dictionary, and generate the visual output yourself. These final exercises will solidify everything you have learned and complete your local feature matching toolkit!