Book Image

The Computer Vision Workshop

By : Hafsa Asad, Vishwesh Ravi Shrimali, Nikhil Singh
Book Image

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.

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Table of Contents (10 chapters)
Preface
About the Book
1
1. Basics of Image Processing
1. Basics of Image Processing
Introduction
NumPy Arrays
Pixels in Images
Introduction to OpenCV
Summary
Free Chapter
2
2. Common Operations When Working with Images
2. Common Operations When Working with Images
Introduction
Geometric Transformations
Image Arithmetic
Binary Images
Summary
3
3. Working with Histograms
3. Working with Histograms
Introduction
Introduction to Matplotlib
Histograms with OpenCV
Summary
4
4. Working with contours
4. Working with contours
Introduction
Contours – Basic Detection and Plotting
Hierarchy
Contour Matching
Summary
5
5. Face Processing in Image and Video
5. Face Processing in Image and Video
Introduction
Introduction to Haar Cascades
GrabCut Technique
Summary
6
6. Object Tracking
6. Object Tracking
Introduction
Summary
7
7. Object Detection and Face Recognition
7. Object Detection and Face Recognition
Introduction
Face Recognition
Object Detection
Summary
8
8. OpenVINO with OpenCV
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

1. Basics of Image Processing

Activity 1.01: Mirror Effect with a Twist

Solution:

  1. Let's start by importing the necessary libraries:
    # Load modules
import cv2
import numpy as np
import matplotlib.pyplot as plt
  2. Next, let's specify the magic command for displaying images in a notebook:
    %matplotlib inline
  3. Now, we can load the image and display it using Matplotlib.

    Note

    Before proceeding, ensure that you can change the path to the images (highlighted) based on where the image is saved in your system.

    The code is as follows:

    # Load image
img = cv2.imread("../data/lion.jpg")
plt.imshow(img[:,:,::-1])
plt.show()

    The output is as follows:

    Figure 1.42: Image that we are going to use in this activity

  4. Next, let's display the image's shape:
    # Get image shape
img.shape

    The output is (407, 640, 3).

  5. Convert the image into HSV:
    # Convert image to HSV
imgHSV = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
plt.imshow(imgHSV[:,:,::-1])
plt.show()

    The output is as follows...

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