https://github.com/hemangsharma/image-processing-and-feature-extraction-notebooks
https://github.com/hemangsharma/image-processing-and-feature-extraction-notebooks
Last synced: 2 months ago
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- Host: GitHub
- URL: https://github.com/hemangsharma/image-processing-and-feature-extraction-notebooks
- Owner: hemangsharma
- Created: 2024-11-03T06:33:22.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-01-07T01:55:13.000Z (4 months ago)
- Last Synced: 2025-01-07T02:41:04.246Z (4 months ago)
- Language: Jupyter Notebook
- Size: 1.72 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Image Processing and Feature Extraction Notebooks
## Overview
This repository contains two Jupyter notebooks designed to demonstrate the fundamental steps of image preprocessing and feature extraction. These processes are essential for preparing images as input for machine learning models. Each notebook walks through a series of techniques to transform and analyze images, using Python libraries such as OpenCV, NumPy, Matplotlib, and scikit-image. Below is an outline of each notebook’s purpose and key steps.
---
### Notebook 1: Image Preprocessing for Machine Learning Input
#### Purpose
This notebook demonstrates how an image is processed to be ready as input for machine learning models. Each step shows how to simplify and structure image data to make it compatible with various algorithms.
#### Key Steps
1. **Load and Display the Image**
- Load an image from a file path (`sample.png` by default) using the `PIL` library.
- Display the original image using Matplotlib.
2. **Convert to Grayscale**
- Convert the original image to grayscale, reducing it to a single channel and simplifying data complexity.
3. **Resize the Image (Optional)**
- Resize the image to a standard 28x28 pixel format. This size is frequently used in image classification models to reduce the input data size.
4. **Flatten the Image**
- Convert the resized image to a NumPy array and flatten it into a one-dimensional array. This step reduces the 2D image data to a single vector of pixel values.
5. **Normalize Pixel Values**
- Normalize the pixel values to a range between 0 and 1. Normalization is essential for standardizing inputs, improving model training stability and performance.
6. **Visualize the Image Data as an Array**
- Visualize the pixel values of the resized image in its 28x28 format for a better understanding of the data.
7. **Reshape for Model Input**
- Reshape the normalized data to be compatible with machine learning models (typically a 1x784 vector for a 28x28 image).
---
### Notebook 2: Feature Extraction Techniques for Images
#### Purpose
This notebook provides an introduction to various feature extraction techniques commonly used in image processing. These features capture essential characteristics of images, such as edges, textures, and color distributions, which can be utilized for object detection, image classification, and other computer vision tasks.
#### Key Steps
1. **Load and Display the Original Image**
- Load the image and display it to observe the original content and structure.
2. **Convert to Grayscale**
- Convert the image to grayscale for simplified processing and improved computational efficiency.
3. **Edge Detection (Sobel and Canny)**
- **Sobel Edge Detection**: Applies a Sobel filter to detect edges by calculating intensity changes in the x and y directions.
- **Canny Edge Detection**: Uses a multi-stage process involving noise reduction, gradient calculation, and edge tracing to produce a detailed edge map.
4. **Histogram of Oriented Gradients (HOG)**
- HOG captures gradient orientation in localized portions of an image, describing the image's structure. It provides a feature vector that represents edge distributions and is widely used in object detection tasks.
5. **Color Histogram**
- A color histogram calculates the frequency of each color intensity in the image’s color channels (red, green, and blue). This technique is helpful for color-based image analysis, providing insights into the color distribution within the image.
6. **Local Binary Pattern (LBP)**
- LBP is a texture descriptor that labels pixels based on the relationships with neighboring pixels. This feature is used for texture classification and recognition.
---
### Getting Started
To run these notebooks, you will need to install the following Python libraries:
- `numpy`
- `matplotlib`
- `Pillow`
- `opencv-python`
- `scikit-image`
Install the dependencies with:
```bash
pip install numpy matplotlib pillow opencv-python scikit-image
```
---
### How to Use
1. **Run Notebook 1** to understand the process of image preprocessing, specifically transforming an image into a format suitable for machine learning models.
2. **Run Notebook 2** to explore feature extraction methods for images, gaining insights into different techniques for edge, texture, and color feature extraction.
### Example Images
The sample image path is set to `'sample.png'`. Replace this path with any image file you wish to use. Ensure the image is in the same directory as the notebooks, or provide an absolute path.
---
### Conclusion
These notebooks provide a practical introduction to the preprocessing and feature extraction steps essential in computer vision and machine learning tasks. By understanding these steps, you can better prepare images for advanced analysis and model training.