Summary  
This chapter shows how to implement code for histogram equalization to enhance image contrast by redistributing pixel intensities globally and locally using adaptive (CLAHE) methods to limit noise amplification.

General domain of usage  
Digital image processing

## シンプルなヒストグラム平坦化

ヒストグラム平坦化は、画像の**全体的なコントラストを強調**するための手法。画素の強度値を再分布し、可能な範囲全体（8ビット画像では0から255）に広げることで実現。特に暗すぎる、または明るすぎる画像に有効で、画素強度のヒストグラムを均等化することで特徴をより見やすくする。

```
equalized = cv2.equalizeHist(image)
```

- `cv2.equalizeHist(image)`  
  - `image`: 入力グレースケール画像（単一チャンネルである必要あり）;
  - ヒストグラムを引き伸ばし平坦化することでコントラストを強調した新しい画像を返す。

この手法はグローバル処理であり、画像全体のコントラストを均一に強調する。そのため、一部の領域で過度な強調や他の領域でディテールの損失が生じる場合がある。

注意


## **適応型ヒストグラム平坦化（CLAHE）**

CLAHE（コントラスト制限付き適応型ヒストグラム平坦化）は、画像全体ではなく小さな領域（タイル）ごとに処理を行う高度なヒストグラム平坦化手法。
**局所コントラスト**を強調し、各タイル内でヒストグラムのコントラストを制限することでノイズの過度な増幅を防止。

```
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
clahe_equalized = clahe.apply(image)
```

- `cv2.createCLAHE(...)` は以下のパラメータでCLAHEオブジェクトを作成：
  - `clipLimit`: コントラスト制限の閾値（値が大きいほどコントラストが強くなる）
  - `tileGridSize`: 画像を分割するグリッドのサイズ（例：8x8）
- `clahe.apply(image)` は入力画像にCLAHEを適用

CLAHEは、照明条件が異なる画像や、医用画像、低照度写真など局所的なディテールの保持が重要な場合に特に有効。

Note

import unittest
import numpy as np
import importlib

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 TestHistogramEqualization(unittest.TestCase):

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

    def test_equalized_shape_and_type(self):
        original_shape = user_code.image.shape
        arr = getattr(user_code, "equalized", None)
        _dynamic_test(
            self,
            isinstance(arr, np.ndarray) and arr.shape == original_shape,
            "Equalized image is a NumPy array with correct shape.",
            f"The variable `equalized` should be a NumPy array with shape {original_shape}."
        )

    def test_clahe_equalized_shape_and_type(self):
        original_shape = user_code.image.shape
        arr = getattr(user_code, "clahe_equalized", None)
        _dynamic_test(
            self,
            isinstance(arr, np.ndarray) and arr.shape == original_shape,
            "CLAHE equalized image is a NumPy array with correct shape.",
            f"The variable `clahe_equalized` should be a NumPy array with shape {original_shape}."
        )

    def test_equalized_histogram_change(self):
        original_hist = np.histogram(user_code.image.ravel(), bins=256)[0]
        equalized = getattr(user_code, "equalized", None)
        eq_hist = np.histogram(equalized.ravel(), bins=256)[0] if isinstance(equalized, np.ndarray) else original_hist
        _dynamic_test(
            self,
            isinstance(equalized, np.ndarray) and not np.array_equal(original_hist, eq_hist),
            "Equalized image histogram differs from original.",
            "The variable `equalized` should be a NumPy array with a changed histogram."
        )

    def test_clahe_histogram_change(self):
        original_hist = np.histogram(user_code.image.ravel(), bins=256)[0]
        clahe = getattr(user_code, "clahe_equalized", None)
        clahe_hist = np.histogram(clahe.ravel(), bins=256)[0] if isinstance(clahe, np.ndarray) else original_hist
        _dynamic_test(
            self,
            isinstance(clahe, np.ndarray) and not np.array_equal(original_hist, clahe_hist),
            "CLAHE equalized image histogram differs from original.",
            "The variable `clahe_equalized` should be a NumPy array with a changed histogram."
        )

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


test_code.py

コンピュータビジョンの包括的な入門講座であり、機械による視覚データの認識と解釈に焦点を当てています。画像の前処理、特徴抽出、物体検出、現代のビジョンシステムで使用されるディープラーニング技術を網羅します。

Computer vision enables machines to interpret and analyze visual data, mimicking human perception. This section covers the basics of image representation, color models, and mathematical foundations essential for understanding how computers process images. You'll explore real-world applications, from autonomous vehicles to medical imaging, and learn how Computer vision integrates with AI and machine learning. 

OpenCV is a powerful library for image manipulation and computer vision tasks. This section covers essential techniques like image filtering, transformations, edge detection, and segmentation. You'll learn how to perform blurring, thresholding, contour detection, and feature extraction to enhance and analyze images efficiently.

CNNs process visual data using convolution, pooling, and activation layers to extract features for tasks like image classification and object detection. Key components include padding, convolution for feature extraction, pooling for complexity reduction, and activation for non-linearity. Popular architectures like AlexNet, VGG, and ResNet power AI in healthcare, autonomy, and security.

Object detection is a fundamental task in computer vision that involves identifying and localizing objects within an image. Unlike image classification, which assigns a single label to an entire image, object detection not only classifies objects but also determines their positions using bounding boxes. This section covers key techniques and algorithms used in object detection, ranging from traditional methods to deep learning-based approaches like YOLO and U-Net.

Computer vision has significantly advanced over the years, shifting from basic image processing methods to complex deep learning techniques. This section delves into the latest innovations in computer vision, focusing on transfer learning, facial recognition, and image generation. We will explore the benefits of pre-trained models on performance, the principles of facial recognition technology, and the way AI creates images through deep learning.

ヒストグラム平坦化

シンプルなヒストグラム平坦化

適応型ヒストグラム平坦化（CLAHE）

解答