The Computer Vision Workshop

By : Hafsa Asad, Vishwesh Ravi Shrimali, Nikhil Singh

The Computer Vision Workshop

By: Hafsa Asad, Vishwesh Ravi Shrimali, Nikhil Singh

Overview of this book

Computer Vision (CV) has become an important aspect of AI technology. From driverless cars to medical diagnostics and monitoring the health of crops to fraud detection in banking, computer vision is used across all domains to automate tasks. The Computer Vision Workshop will help you understand how computers master the art of processing digital images and videos to mimic human activities. Starting with an introduction to the OpenCV library, you'll learn how to write your first script using basic image processing operations. You'll then get to grips with essential image and video processing techniques such as histograms, contours, and face processing. As you progress, you'll become familiar with advanced computer vision and deep learning concepts, such as object detection, tracking, and recognition, and finally shift your focus from 2D to 3D visualization. This CV course will enable you to experiment with camera calibration and explore both passive and active canonical 3D reconstruction methods. By the end of this book, you'll have developed the practical skills necessary for building powerful applications to solve computer vision problems.

Preface

About the Book

1. Basics of Image Processing

Introduction

NumPy Arrays

Pixels in Images

Introduction to OpenCV

Summary

Free Chapter

2. Common Operations When Working with Images

Introduction

Geometric Transformations

Image Arithmetic

Binary Images

Summary

3. Working with Histograms

Introduction

Introduction to Matplotlib

Histograms with OpenCV

Summary

4. Working with contours

Introduction

Contours – Basic Detection and Plotting

Hierarchy

Contour Matching

Summary

5. Face Processing in Image and Video

Introduction

Introduction to Haar Cascades

GrabCut Technique

Summary

6. Object Tracking

Introduction

Summary

7. Object Detection and Face Recognition

Introduction

Face Recognition

Object Detection

Summary

8. OpenVINO with OpenCV

Introduction

Exploring the OpenVINO Toolkit

Model Conversion Using Model Optimizer

Summary

Appendix

1. Basics of Image Processing

2. Common Operations When Working with Images

3. Working with Histograms

4. Working with contours

5. Face Processing in Image and Video

6. Object Tracking

7. Object Detection and Face Recognition

8. OpenVINO with OpenCV

Customer Reviews

5 star

4 star

3 star

2 star

1 star

3. Working with Histograms

Activity 3.01: Enhancing Images Using Histogram Equalization and CLAHE

Solution:

Open a new file in a Jupyter notebook in the folder you want to work on. Remember that your relevant finger veins image file must also be in the same folder so that when reading the image, you won't need to give a path along with the filename.
Import the OpenCV library and the pyplot module from the Matplotlib library:
```
import cv2
import matplotlib.pyplot as plt
```
Read the image as grayscale:
```
img= cv2.imread('fingervein.bmp', 0)
```
Display the image. Let's use Matplotlib to display the images in this example. You can also use OpenCV's cv2.imshow() if you want:
```
imgplot = plt.imshow(img , cmap="gray")
plt.title('Original image')
plt.show()
```
The plot looks as follows:
Figure 3.59: Original finger vein image
The values on the X and Y axes show the width and height of the displayed image.
Plot the histogram:
```
plt.hist(img...
```

The Computer Vision Workshop

By : Hafsa Asad, Vishwesh Ravi Shrimali, Nikhil Singh

The Computer Vision Workshop

By: Hafsa Asad, Vishwesh Ravi Shrimali, Nikhil Singh

Overview of this book

Related Content you might be interested in

Current Title:

The Computer Vision Workshop

Mastering OpenCV 4 with Python

OpenCV 3 Computer Vision with Python Cookbook

Computer Vision with Python 3

3. Working with Histograms

Activity 3.01: Enhancing Images Using Histogram Equalization and CLAHE