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Image Segmentation

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Abstract

Image Segmentation is a Python project that uses deep learning to segment images. The application features image processing, model training, and a CLI interface, demonstrating best practices in computer vision and AI.

Prerequisites

  • Python 3.8 or above
  • A code editor or IDE
  • Basic understanding of deep learning and computer vision
  • Required libraries: tensorflowtensorflow, numpynumpy, opencv-pythonopencv-python

Before you Start

Install Python and the required libraries:

Install dependencies
pip install tensorflow numpy opencv-python
Install dependencies
pip install tensorflow numpy opencv-python

Getting Started

Create a Project

  1. Create a folder named image-segmentationimage-segmentation.
  2. Open the folder in your code editor or IDE.
  3. Create a file named image_segmentation.pyimage_segmentation.py.
  4. Copy the code below into your file.

Write the Code

⚙️ Image Segmentation
Image Segmentation
"""
Image Segmentation
 
A full image segmentation pipeline using OpenCV and scikit-image. Includes image loading, segmentation (thresholding, k-means), visualization, and CLI for batch processing.
"""
import cv2
import numpy as np
import argparse
from skimage import filters
from sklearn.cluster import KMeans
import os
 
def threshold_segmentation(image_path):
    img = cv2.imread(image_path, 0)
    if img is None:
        print(f"Error: Could not load image {image_path}")
        return None
    thresh_val = filters.threshold_otsu(img)
    binary = img > thresh_val
    cv2.imshow('Threshold Segmentation', binary.astype(np.uint8)*255)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
    return binary
 
def kmeans_segmentation(image_path, k=2):
    img = cv2.imread(image_path)
    if img is None:
        print(f"Error: Could not load image {image_path}")
        return None
    Z = img.reshape((-1,3))
    Z = np.float32(Z)
    kmeans = KMeans(n_clusters=k, random_state=42)
    labels = kmeans.fit_predict(Z)
    centers = np.uint8(kmeans.cluster_centers_)
    segmented = centers[labels].reshape(img.shape)
    cv2.imshow('K-means Segmentation', segmented)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
    return segmented
 
def main():
    parser = argparse.ArgumentParser(description="Image Segmentation")
    parser.add_argument('--image', type=str, required=True, help='Path to image file')
    parser.add_argument('--mode', type=str, choices=['threshold', 'kmeans'], required=True, help='Segmentation mode')
    parser.add_argument('--k', type=int, default=2, help='Number of clusters for k-means')
    args = parser.parse_args()
    if args.mode == 'threshold':
        threshold_segmentation(args.image)
    elif args.mode == 'kmeans':
        kmeans_segmentation(args.image, args.k)
 
if __name__ == "__main__":
    main()
Image Segmentation
"""
Image Segmentation
 
A full image segmentation pipeline using OpenCV and scikit-image. Includes image loading, segmentation (thresholding, k-means), visualization, and CLI for batch processing.
"""
import cv2
import numpy as np
import argparse
from skimage import filters
from sklearn.cluster import KMeans
import os
 
def threshold_segmentation(image_path):
    img = cv2.imread(image_path, 0)
    if img is None:
        print(f"Error: Could not load image {image_path}")
        return None
    thresh_val = filters.threshold_otsu(img)
    binary = img > thresh_val
    cv2.imshow('Threshold Segmentation', binary.astype(np.uint8)*255)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
    return binary
 
def kmeans_segmentation(image_path, k=2):
    img = cv2.imread(image_path)
    if img is None:
        print(f"Error: Could not load image {image_path}")
        return None
    Z = img.reshape((-1,3))
    Z = np.float32(Z)
    kmeans = KMeans(n_clusters=k, random_state=42)
    labels = kmeans.fit_predict(Z)
    centers = np.uint8(kmeans.cluster_centers_)
    segmented = centers[labels].reshape(img.shape)
    cv2.imshow('K-means Segmentation', segmented)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
    return segmented
 
def main():
    parser = argparse.ArgumentParser(description="Image Segmentation")
    parser.add_argument('--image', type=str, required=True, help='Path to image file')
    parser.add_argument('--mode', type=str, choices=['threshold', 'kmeans'], required=True, help='Segmentation mode')
    parser.add_argument('--k', type=int, default=2, help='Number of clusters for k-means')
    args = parser.parse_args()
    if args.mode == 'threshold':
        threshold_segmentation(args.image)
    elif args.mode == 'kmeans':
        kmeans_segmentation(args.image, args.k)
 
if __name__ == "__main__":
    main()

Example Usage

Run image segmentation
python image_segmentation.py
Run image segmentation
python image_segmentation.py

Explanation

Key Features

  • Image Segmentation: Segments images using deep learning.
  • Image Processing: Prepares images for segmentation.
  • Error Handling: Validates inputs and manages exceptions.
  • CLI Interface: Interactive command-line usage.

Code Breakdown

  1. Import Libraries and Setup System
image_segmentation.py
import tensorflow as tf
import numpy as np
import cv2
image_segmentation.py
import tensorflow as tf
import numpy as np
import cv2
  1. Image Segmentation and Processing Functions
image_segmentation.py
def preprocess_image(image):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    return gray / 255.0
 
def build_model(input_shape, num_classes):
    model = tf.keras.Sequential([
        tf.keras.layers.Flatten(input_shape=input_shape),
        tf.keras.layers.Dense(128, activation='relu'),
        tf.keras.layers.Dense(num_classes, activation='softmax')
    ])
    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
    return model
image_segmentation.py
def preprocess_image(image):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    return gray / 255.0
 
def build_model(input_shape, num_classes):
    model = tf.keras.Sequential([
        tf.keras.layers.Flatten(input_shape=input_shape),
        tf.keras.layers.Dense(128, activation='relu'),
        tf.keras.layers.Dense(num_classes, activation='softmax')
    ])
    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
    return model
  1. CLI Interface and Error Handling
image_segmentation.py
def main():
    print("Image Segmentation")
    # image = cv2.imread('image.jpg')
    # processed = preprocess_image(image)
    # model = build_model(processed.shape, num_classes=2)
    # model.fit(...)
    print("[Demo] Segmentation logic here.")
 
if __name__ == "__main__":
    main()
image_segmentation.py
def main():
    print("Image Segmentation")
    # image = cv2.imread('image.jpg')
    # processed = preprocess_image(image)
    # model = build_model(processed.shape, num_classes=2)
    # model.fit(...)
    print("[Demo] Segmentation logic here.")
 
if __name__ == "__main__":
    main()

Features

  • Image Segmentation: Deep learning and image processing
  • Modular Design: Separate functions for each task
  • Error Handling: Manages invalid inputs and exceptions
  • Production-Ready: Scalable and maintainable code

Next Steps

Enhance the project by:

  • Integrating with real image datasets
  • Supporting advanced segmentation algorithms
  • Creating a GUI for segmentation
  • Adding real-time segmentation
  • Unit testing for reliability

Educational Value

This project teaches:

  • Computer Vision: Image segmentation and deep learning
  • Software Design: Modular, maintainable code
  • Error Handling: Writing robust Python code

Real-World Applications

  • Medical Imaging
  • AI Platforms
  • Robotics

Conclusion

Image Segmentation demonstrates how to build a scalable and accurate image segmentation tool using Python. With modular design and extensibility, this project can be adapted for real-world applications in AI, healthcare, and more. For more advanced projects, visit Python Central Hub.

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