Summary  
This chapter explains how to detect key features in images by identifying corners (points of sharp intensity change) and blobs (regions of similar intensity) using algorithms like Harris, Shi-Tomasi, and SimpleBlobDetector.  

General domain of usage  
Image processing

## Corner Detection
Corner detection is used to identify **sharp changes in intensity** where two edges meet. It helps in feature matching, object tracking, and structure recognition.

### Popular Methods:

- **Harris corner detector** (`cv2.cornerHarris`): detects corners based on gradient changes;


- **Shi-Tomasi corner detector** (`cv2.goodFeaturesToTrack`): selects the strongest corners in an image;

## Blob Detection
Blob detection finds **regions of similar intensity** in an image, useful for object detection and tracking.

One of the popular methods for blob detection is **SimpleBlobDetector**

- `cv2.SimpleBlobDetector`: detects keypoints representing blobs based on size, shape, and intensity.


import unittest
import numpy as np
import importlib
import cv2

def _dynamic_test(test_case, condition, success_msg, failure_msg):
    if condition:
        test_case._testMethodName = success_msg
        test_case.assertTrue(True, success_msg)
    else:
        test_case._testMethodName = failure_msg
        test_case.fail(failure_msg)

class TestFeatureDetection(unittest.TestCase):

    @classmethod
    def setUpClass(cls):
        global user_code
        user_code = importlib.import_module("user_code")

    def test_image_type(self):
        """Check if grayscale images are NumPy arrays."""
        images = [
            ("gray_factory", getattr(user_code, "gray_factory", None)),
            ("gray_sunflowers", getattr(user_code, "gray_sunflowers", None))
        ]
        all_arrays = all(isinstance(img, np.ndarray) for _, img in images)
        _dynamic_test(
            self,
            all_arrays,
            "All grayscale images are NumPy arrays.",
            "Grayscale images gray_factory and gray_sunflowers must be NumPy arrays."
        )

    def test_harris_response_validity(self):
        harris = getattr(user_code, "harris_corners", None)
        valid = isinstance(harris, np.ndarray) and np.max(harris) > 0
        _dynamic_test(
            self,
            valid,
            "Harris corner response is valid with nonzero values.",
            "Harris corner response must be a NumPy array with nonzero values."
        )

    def test_shi_tomasi_output(self):
        shi_tomasi_corners = getattr(user_code, "shi_tomasi_corners", None)
        valid = isinstance(shi_tomasi_corners, np.ndarray) and shi_tomasi_corners.shape[1:] == (1, 2) and len(shi_tomasi_corners) > 0
        _dynamic_test(
            self,
            valid,
            "Shi-Tomasi detects corners with shape (N, 1, 2).",
            "Shi-Tomasi corners must be a non-empty NumPy array of shape (N, 1, 2)."
        )

    def test_blob_keypoints_type(self):
        keypoints = getattr(user_code, "keypoints", None)
        correct_type = isinstance(keypoints, tuple)
        contains_keypoints = len(keypoints) > 0 and isinstance(keypoints[0], cv2.KeyPoint) if keypoints else False
        _dynamic_test(
            self,
            correct_type and contains_keypoints,
            "Blob keypoints are a tuple of cv2.KeyPoint objects.",
            "Blob keypoints must be a tuple containing cv2.KeyPoint objects."
        )

    def test_blob_keypoints_presence(self):
        keypoints = getattr(user_code, "keypoints", ())
        _dynamic_test(
            self,
            len(keypoints) > 0,
            "At least one blob keypoint detected.",
            "Blob detection must yield at least one keypoint."
        )

if __name__ == "__main__":
    unittest.main()

test_code.py

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Corner and Blob Detection

Corner Detection

Popular Methods:

Blob Detection

Solution

Awesome!

Corner and Blob Detection

Corner Detection

Popular Methods:

Blob Detection

Solution