Image Processing with Histogram Techniques

Overview

This project implements various image processing techniques, focusing on grayscale and color image histogram operations. The code uses Python and popular libraries like OpenCV, Matplotlib, NumPy, and Scikit-image to compute histograms, perform histogram equalization, and apply histogram matching for both grayscale and colored images.

Features

• Histogram Computation: Calculate a 64-bin grayscale histogram for an image.
• Histogram Equalization: Perform histogram equalization to enhance contrast in grayscale images.
• Histogram Comparison: Compare histograms of two images using the Bhattacharyya Coefficient, a similarity measure.
• Histogram Matching: Match histograms between two grayscale or colored images.

Prerequisites

To run this project, you'll need Python 3.x installed along with the following Python libraries:

• opencv-python
• scikit-image
• numpy
• matplotlib

You can install the required dependencies by running the following command:

pip install opencv-python scikit-image numpy matplotlib

Usage

Part 1: Histogram Computation

Compute a 64-bin grayscale histogram of an image (test.jpg) and compare it with the histogram computed using NumPy's built-in function.

Part 2: Histogram Equalization

Apply histogram equalization to a grayscale image (test.jpg), enhancing its contrast by redistributing pixel intensities.

Part 3: Histogram Comparison

Compare the histograms of two grayscale images (day.jpg and night.jpg) by calculating the Bhattacharyya Coefficient to measure the similarity between them.

Part 4: Histogram Matching

Match histograms between two grayscale images (day.jpg and night.jpg) and between two colored images by handling each RGB channel independently.

Running the Program

Run the main script to execute all the functions:

Functions

• BinArrGenerate(n, start, end): Generates an array consisting of n + 1 elements with equal differences from start to end.
• BinCompute(bin_arr, pixel): Computes the appropriate bin for a given pixel based on the bin array.
• image_histogram(bin, imageArr): Calculates the histogram of an image using the specified number of bins.
• part1_histogram_compute(): Calculates and plots the 64-bin grayscale histogram of an image using both custom logic and NumPy's built-in histogram function.
• part2_histogram_equalization(): Performs histogram equalization on a grayscale image to enhance its contrast.
• part3_histogram_comparing(): Compares histograms of two images using the Bhattacharyya Coefficient and outputs the result.
• histogram_matching_algorithm(I1, I2): Implements histogram matching between two grayscale images, adjusting the pixel values of the first image to match the histogram of the second.
• histogram_matching_colored(): Implements histogram matching for colored images by applying histogram matching independently to the red, green, and blue channels.
• part4_histogram_matching(): Displays the results of histogram matching for both grayscale and colored images.