Histogram For Image Processing

Objective

• Understand the concept of image histograms and master the method of drawing them.
• Learn the principle of histogram equalization and apply it to image processing.

Content

Convert a color image to a grayscale image, draw histograms (gray and equalized), and analyze the effect of histogram equalization.

Requirements

1. Image Selection: Choose a suitable color image and convert it to grayscale.
2. Histogram Drawing: Implement histogram drawing and histogram equalization using any programming language.
3. Completeness: Provide the complete project process, code, results, and analysis.

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Project Structure

• main.py: Main script to execute experiments.
• experiment_executor.py: Class to run experiments and process images.
• image_processor.py: Class containing image processing functions.
• result_saver.py: Class for saving experiment results.
• data/: Directory containing input images.
• all_results/: Directory to store experiment results.

Getting Started

1. Clone the repository: git clone https://github.com/Hetawk/histogram-python.git
2. Navigate to the project directory: cd histogram-python
3. Install dependencies: pip install -r requirements.txt
4. Run the experiments: python main.py

Experiment Results

• Each experiment result will be saved in the all_results directory.
• Results include output images and histograms.

Analysis

• Analyze the effect of histogram equalization on image contrast and brightness.
• Compare the original image, grayscale image, and equalized image.

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Contributors

• Enoch Kwateh Dongbo

Sample Results

Natural Scenery Image

Portrait Image

Another Portrait Image