### Simple Histogram Equalization ###
Histogram equalization **improves image contrast** by spreading out intensity values, making dark regions brighter and light regions darker.


**Adaptive Histogram Equalization (CLAHE)**

Unlike traditional histogram equalization, **CLAHE (Contrast Limited Adaptive Histogram Equalization)** improves local contrast while preventing over-brightening in specific areas.

import unittest
import numpy as np
import importlib

class TestHistogramEqualization(unittest.TestCase):
    
    @classmethod
    def setUpClass(cls):
        global user_code
        user_code = importlib.import_module("user_code")
    
    def test_image_shape(self):
        """Check if all processed images have the same shape as the original."""
        self.assertEqual(user_code.equalized.shape, user_code.image.shape, "Equalized image should have the same shape as the original.")
        self.assertEqual(user_code.clahe_equalized.shape, user_code.image.shape, "CLAHE equalized image should have the same shape as the original.")
    
    def test_image_type(self):
        """Ensure output images are NumPy arrays."""
        self.assertIsInstance(user_code.equalized, np.ndarray, "Equalized image should be a NumPy array.")
        self.assertIsInstance(user_code.clahe_equalized, np.ndarray, "CLAHE equalized image should be a NumPy array.")
    
    def test_histogram_change(self):
        """Ensure histogram equalization modifies the image histogram."""
        self.assertFalse(np.array_equal(np.histogram(user_code.image.ravel(), bins=256), np.histogram(user_code.equalized.ravel(), bins=256)), "Histogram of equalized image should be different from the original.")
        self.assertFalse(np.array_equal(np.histogram(user_code.image.ravel(), bins=256), np.histogram(user_code.clahe_equalized.ravel(), bins=256)), "Histogram of CLAHE equalized image should be different from the original.")
    
if __name__ == "__main__":
    unittest.main()

test_code.py

This course provides a comprehensive introduction to Computer Vision, covering both fundamental and advanced topics. You'll learn how machines process, analyze, and interpret visual data through various techniques such as image processing, feature extraction, object detection, and deep learning models.

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.

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Computer Vision Course Outline

Histogram Equalization

Simple Histogram Equalization

Solução

Histogram Equalization

Simple Histogram Equalization

Solução