Image Processing And Acquisition Using Python

By successkpk

Mastering Image Processing with Python: A Comprehensive Guide" is an indispensable resource for anyone looking to delve into the world of digital image manipulation using the Python programming language. This comprehensive book provides a deep dive into the various techniques, libraries, and applications of image processing, equipping readers with the skills to handle a wide range of image-related tasks.

Table of Contents:

1. Image Processing Examples

Explore a wide range of practical image processing examples, from basic adjustments to advanced techniques.

2. Brightness/Contrast

Learn how to enhance the brightness and contrast of images for better visual appeal.

3. Levels Adjustment

Discover techniques for adjusting image levels to improve overall quality.

4. Curves Adjustment

Dive into curve adjustments to fine-tune image tone and contrast.

5. Exposure Adjustment

Master exposure adjustments to correct overexposed or underexposed images.

6. Vibrance and Saturation Adjustment

Explore techniques to boost or reduce color vibrancy and saturation.

7. Hue/Saturation Adjustment

Learn how to manipulate hue and saturation for creative effects.

8. Color Balance Adjustments (Enhanced)

Gain insights into advanced color balance adjustments for precise color correction.

9. Photo Filter Adjustment

Discover how to apply filters to images to achieve specific visual effects.

10. Convert images in a folder to black and white - Automate the process of converting a batch of images to black and white.

11. Convert images in a folder to threshold - Learn how to convert images to binary format with thresholding.

12. Create an inverted image (negative) - Explore techniques to create negative or inverted versions of images.

13. Posterize adjustment - Discover the posterize effect for a unique artistic style.

14. Gradient Map Adjustments - Explore gradient mapping for creative image enhancements.

15. Selective Colors Adjustment - Learn how to selectively adjust and manipulate colors in your images.

16. Denoise Image Adjustment - Discover techniques for reducing noise and enhancing image clarity.

17. Two-Image Directory Blend: Merging Images with the Same Name from Different Directories - Explore advanced image merging and blending techniques.

18. Remove Black Areas and Convert to Grayscale Script - Automate the process of removing black areas and converting images to grayscale.

19. Image Embossing Automation with Python - Learn how to create embossed images programmatically.

20. Neon Glow Image Filter Application with Tkinter and PIL - Build an interactive neon glow filter using Python's Tkinter and PIL libraries.

21. Interactive Wave Filter Image Processor with Tkinter and PIL - Create an interactive wave filter for image processing using Tkinter and PIL.

22. Enhance Images with Adaptive Adjustments using OpenCV and Python - Explore adaptive image enhancement techniques with OpenCV and Python.

23. Download all the code at the link below.

Introduction to Image Processing with Python - Get started with the basics of image processing, digital images, and Python libraries.

What are Digital Images? - Understand the fundamental concepts of digital images.

Introduction to OpenCV, Pillow, and scikit-image - Explore key image processing libraries in Python.

And much more...

• Language: English
• Publisher: successkpk
• Release date: Jan 25, 2024
• ISBN: 9798224091904

Book preview

Table of Contents

Image Processing Examples

1.  Brightness/Contrast

2.  Levels Adjustment

3.  Curves Adjustment

4.  Exposure Adjustment

5.  Vibrance and Saturation Adjustment

6.  Hue/Saturation Adjustment

7.  Color Balance Adjustments (Enhanced)

8.  Photo Filter Adjustment

9.  Convert images in a folder to black and white.

10.  Convert images in a folder to threshold

11.  Create an inverted image (negative)

12.  Posterize adjustment

13.  Gradient Map Adjustments

14.  Selective Colors Adjustment

15.  Denoise Image Adjustment

16.  Two-Image Directory Blend: Merging Images with the Same Name from Different Directories

17.  Remove Black Areas and Convert to Grayscale Script

18.  Image Embossing Automation with Python

19.  Neon Glow Image Filter Application with Tkinter and PI

20.  Interactive Wave Filter Image Processor with Tkinter and PIL

21.  Enhance Images with Adaptive Adjustments using OpenCV and Python

22.  Download all the code at the link below.

Introduction to Image Processing with Python

What are Digital Images?

Introduction to OpenCV, Pillow, and scikit-image

Installation and Setup

Loading Images from Files

Displaying Images in Windows

Dimensions and Color Channels

Conclusion of Part 1

Cropping and Resizing Images

Flipping and Rotating Images

Changing Color Channels and Pixel Values

Saving Images Back to Files

Choosing Appropriate Image Formats

Understanding Image Filtering

Edge Detection

Enhancing Image Quality

Understanding Color Spaces

Adjusting Color Balance

Thresholding and Binary Images

Contour Detection

Image Classification with Convolutional Neural Networks (CNNs)

Object Detection with YOLO (You Only Look Once)

Image Restoration and Denoising

Panorama Stitching

Medical Imaging and Analysis

Style Transfer

Blurring (Smoothing)

Sharpening

Edge Detection

Noise Reduction

Color Spaces: RGB, HSV, and CMYK

Changing Saturation:

Changing Brightness and Contrast:

Color Balancing:

Color Conversion

Thresholding

Contour Detection

Region-Based Segmentation

Object Detection

Image Segmentation and Object Detection in Real-World Applications

1. Rotation

2. Scaling

3. Translation

4. Affine Transformations

 Augmentation in Deep Learning

 Image Registration

 Computer Graphics

Deep Learning for Image Processing:

1. Concept of Using Neural Networks for Image Processing:

2. Applications:

3. Deep Learning Frameworks:

Advantages of Deep Learning for Image Processing:

1. Face Detection:

2. Face Recognition:

Difference between Face Detection and Recognition:

Practical Use Cases:

OpenCV Face Detection:

Image Stitching and Panorama Creation:

Image Restoration Techniques:

Denoising Techniques:

Practical Applications:

Challenges and Considerations:

Importance of Image Processing in Medical Applications:

Demonstration: Image Segmentation and Tumor Detection

1. Loading and Preprocessing the Image:

2. Image Segmentation:

3. Tumor Detection:

Conclusion:

Image Processing in Art and Creative Projects: Where Technology Meets Creativity

Generative Art:

Augmented Reality (AR) Art:

Digital Painting and Mixed Media:

Collage and Montage:

Conclusion: Blending Technology and Creativity

Image Processing in Industry and Automation: Enhancing Precision and Efficiency

Real-World Industrial Applications:

Advantages:

Challenges and Considerations:

Conclusion: Bridging the Gap Between Precision and Automation

Comparison of Different Python Image Processing Libraries: OpenCV, Pillow, and scikit-image

1. OpenCV:

2. Pillow:

3. scikit-image:

Performance Optimization in Image Processing: Maximizing Efficiency and Speed

1. Vectorization:

2. Parallel Processing:

3. Memory Management:

4. Algorithmic Improvements:

5. Profiling:

6. Use Compiled Libraries:

7. GPU Acceleration:

8. Batch Processing:

9. Preprocessing and Postprocessing:

10. Caching and Memoization:

11. Data Types and Precision:

Conclusion: Balancing Speed and Efficiency

Image Processing Examples

1.  Brightness/Contrast

A screenshot of a person's face Description automatically generated

import tkinter as tk

from tkinter import ttk

from PIL import Image, ImageTk, ImageEnhance

class ImageAdjustmentApp:

def __init__(self, root):

self.root = root

self.root.title(Image Adjustment)

self.image = Image.open(rE:\Python Code\Images\myimage.jpg)  # Load your image

self.original_image = self.image.copy()

self.brightness_value = tk.DoubleVar()

self.contrast_value = tk.DoubleVar()

self.preview_var = tk.BooleanVar()

self.use_legacy_var = tk.BooleanVar()

self.create_widgets()

def create_widgets(self):

# Create labels, scrollbars, checkboxes, and buttons

ttk.Label(self.root, text=Brightness).grid(row=0, column=0)

self.brightness_scroll = ttk.Scale(self.root, from_=-150, to=150, variable=self.brightness_value)

self.brightness_scroll.grid(row=0, column=1)

ttk.Label(self.root, text=Contrast).grid(row=1, column=0)

self.contrast_scroll = ttk.Scale(self.root, from_=-100, to=100, variable=self.contrast_value)

self.contrast_scroll.grid(row=1, column=1)

ttk.Checkbutton(self.root, text=Preview, variable=self.preview_var).grid(row=2, column=0, columnspan=2)

ttk.Checkbutton(self.root, text=Use Legacy, variable=self.use_legacy_var).grid(row=3, column=0, columnspan=2)

ttk.Button(self.root, text=OK, command=self.apply_settings).grid(row=4, column=0)

ttk.Button(self.root, text=Reset, command=self.reset_values).grid(row=4, column=1)

self.brightness_label = ttk.Label(self.root, text=Brightness:)

self.brightness_label.grid(row=0, column=2)

self.brightness_value_label = ttk.Label(self.root, text=Brightness: 0.0)

self.brightness_value_label.grid(row=0, column=3)

self.contrast_label = ttk.Label(self.root, text=Contrast:)

self.contrast_label.grid(row=1, column=2)

self.contrast_value_label = ttk.Label(self.root, text=Contrast: 0.0)

self.contrast_value_label.grid(row=1, column=3)

self.original_image_label = ttk.Label(self.root)

self.original_image_label.grid(row=5, column=0)

self.adjusted_image_label = ttk.Label(self.root)

self.adjusted_image_label.grid(row=5, column=1)

self.brightness_value.trace(w, self.update_image)

self.contrast_value.trace(w, self.update_image)

self.preview_var.trace(w, self.update_image)

self.use_legacy_var.trace(w, self.update_image)

self.update_image()

def update_image(self, *args):

brightness = max(self.brightness_value.get(), -150)

contrast = max(self.contrast_value.get(), -100)

preview = self.preview_var.get()

use_legacy = self.use_legacy_var.get()

if not use_legacy:

enhancer = ImageEnhance.Brightness(self.original_image)

self.image = enhancer.enhance(1 + brightness / 100)

enhancer = ImageEnhance.Contrast(self.image)

self.image = enhancer.enhance(1 + contrast / 100)

else:

self.image = self.original_image.copy()

self.image = self.image.point(lambda p: p + brightness)

self.image = self.image.point(lambda p: p * (1 + contrast / 100))

if preview:

img_tk = ImageTk.PhotoImage(self.image)

self.adjusted_image_label.configure(image=img_tk)

self.adjusted_image_label.image = img_tk

else:

self.adjusted_image_label.configure(image=None)

formatted_brightness = Brightness: {:.1f}.format(brightness)

formatted_contrast = Contrast: {:.1f}.format(contrast)

self.brightness_value_label.configure(text=formatted_brightness)

self.contrast_value_label.configure(text=formatted_contrast)

self.original_img_tk = ImageTk.PhotoImage(self.original_image)

self.original_image_label.configure(image=self.original_img_tk)

self.original_image_label.image = self.original_img_tk

def apply_settings(self):

self.original_image = self.image.copy()

self.update_image()

def reset_values(self):

self.brightness_value.set(0)

self.contrast_value.set(0)

self.preview_var.set(False)

self.use_legacy_var.set(False)

self.original_image = Image.open(rE:\Python Code\Images\myimage.jpg)  # Reload the original image

self.update_image()

if __name__ == __main__:

root = tk.Tk()

app = ImageAdjustmentApp(root)

root.mainloop()

THIS CODE CREATES A GUI application for adjusting the brightness and contrast of an image using sliders. Here's a breakdown of the key sections:

1.  Importing Libraries: Import necessary libraries including tkinter, PIL for image processing, and ttk for themed GUI widgets.

2.  ImageAdjustmentApp Class: This class initializes the main application. It loads the original image, sets up variables to store adjustments and settings, and defines the GUI layout.

3.  create_widgets(): This method creates all the GUI elements like labels, sliders, checkboxes, and buttons. It also attaches event handlers for variable changes using the trace method.

4.  update_image(): This method is triggered whenever there's a change in any of the adjustment variables or settings. It calculates the new image based on the adjustments, updates the displayed image, and updates labels to show the values.

5.  apply_settings(): This method applies the current adjustments to the original image and updates the displayed image accordingly.

6.  reset_values(): This method resets all adjustments and settings to their default values and reloads the original image.

7.  Main Code Block: Creates the main application window, initializes ImageAdjustmentApp, and starts the main event loop with root.mainloop().

The code creates a GUI where you can adjust the brightness and contrast of an image in real-time. You can preview the changes and reset to the original image. When you adjust the sliders, the image updates in real-time based on the adjustments you've made. The preview checkbox allows you to toggle the real-time preview. If you check Use Legacy, the adjustments are applied directly to the pixel values of the image, similar to your original requirement.

2.  Levels Adjustment

A screenshot of a computer Description automatically generated

import tkinter as tk

from tkinter import ttk

from PIL import Image, ImageTk

import numpy as np

class LevelsAdjustmentApp:

def __init__(self, root):

self.root = root

self.root.title(Levels Adjustment)

self.image = Image.open(rE:\Python Code\Images\ImagesForSpeedDrawing\myimage1.jpg)

self.original_image = self.image.copy()

self.black_value = tk.DoubleVar(value=0)

self.white_value = tk.DoubleVar(value=255)

self.create_widgets()

def create_widgets(self):

# Create labels, scrollbars, and image labels

ttk.Label(self.root, text=Black Level).grid(row=0, column=0)

self.black_scroll = ttk.Scale(self.root, from_=0, to=255, variable=self.black_value, orient=horizontal)

self.black_scroll.grid(row=0, column=1, padx=10)

self.black_scroll.bind(, self.update_image)

ttk.Label(self.root, text=White Level).grid(row=1, column=0)

self.white_scroll = ttk.Scale(self.root, from_=0, to=255, variable=self.white_value, orient=horizontal)

self.white_scroll.grid(row=1, column=1, padx=10)

self.white_scroll.bind(, self.update_image)

self.original_image_label = ttk.Label(self.root)

self.original_image_label.grid(row=2, column=0)

self.adjusted_image_label = ttk.Label(self.root)

self.adjusted_image_label.grid(row=2, column=1)

ttk.Button(self.root, text=Reset, command=self.reset_values).grid(row=3, column=1, padx=10, pady=10)

self.update_image()

def update_image(self, event=None):

black_level = int(self.black_value.get())

white_level = int(self.white_value.get())

if black_level == white_level:

white_level += 1  # Avoid division by zero

# Convert PIL image to numpy array

image_array = np.array(self.original_image)

# Apply levels adjustment

adjusted_image_array = np.clip((image_array - black_level) * (255.0 / (white_level - black_level)), 0, 255)

# Convert numpy array back to PIL image

self.image = Image.fromarray(np.uint8(adjusted_image_array))

# Display both original and adjusted images

original_img_tk = ImageTk.PhotoImage(self.original_image)

adjusted_img_tk = ImageTk.PhotoImage(self.image)

self.original_image_label.configure(image=original_img_tk)

self.adjusted_image_label.configure(image=adjusted_img_tk)

self.original_image_label.image = original_img_tk

self.adjusted_image_label.image = adjusted_img_tk

def reset_values(self):

self.black_value.set(0)

self.white_value.set(255)

self.update_image()

if __name__ == __main__:

root = tk.Tk()

app = LevelsAdjustmentApp(root)

root.mainloop()

1.  Importing Libraries:

tkinter provides the tools to create GUI applications.

PIL (Python Imaging Library) is used for image manipulation.

numpy is used for numerical operations.

2.  Creating the App Class:

The LevelsAdjustmentApp class encapsulates the entire application.

3.  Initialization (__init__ Method):

The __init__ method sets up the initial state of the application.

The main window is created with the title Levels Adjustment.

An image is loaded from a file and a copy is created for manipulation.

DoubleVar variables black_value and white_value are initialized to control black and white levels.

4.  Creating Widgets (create_widgets Method):

This method creates the user interface elements.

Labels and scales for adjusting black and white levels are added.

Labels for displaying the original and adjusted images are added.

Scales' motion event is bound to the update_image method.

5.  Updating the Image (update_image Method):

This method is called when the scales are moved (due to the motion event binding) or explicitly by the OK button.

It calculates the new black and white levels from the scales.

To avoid division by zero, if black and white levels are equal, white level is incremented.

The original image is converted to a numpy array for manipulation.

The levels adjustment is applied using numpy operations to the numpy array.

Adjusted numpy array is converted back to a PIL image.

Both original and adjusted images are converted to ImageTk format for display.

Labels are updated with the new images and their references are stored to prevent garbage collection.

6.  Resetting Values (reset_values Method):

This method resets the black and white levels to their default values.

It then calls the update_image method to apply the reset values.

7.  Main Execution (__name__ == __main__) Block:

A Tkinter main window is created and the LevelsAdjustmentApp class is instantiated.

The