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Mastering OpenCV with Practical Computer Vision Projects

Jese Leos
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Mastering OpenCV with Practical Computer Vision Projects
Mastering OpenCV with Practical Computer Vision Projects
by Daniel L茅lis Baggio

4.4 out of 5

Language : English
File size : 13765 KB
Text-to-Speech : Enabled
Screen Reader : Supported
Enhanced typesetting : Enabled
Print length : 342 pages

OpenCV (Open Source Computer Vision Library) is a powerful open-source library for computer vision, machine learning, and image processing. It provides a comprehensive set of algorithms and tools for image processing, video analysis, and real-time computer vision applications.

This article provides a comprehensive guide to mastering OpenCV, with a focus on practical computer vision projects. We will cover the fundamentals of OpenCV, including image processing, object detection, tracking, face recognition, and augmented reality. We will also provide hands-on examples and code snippets to help you get started with your own computer vision projects.

Getting Started with OpenCV

To get started with OpenCV, you will need to install the library on your computer. OpenCV is available for Windows, macOS, and Linux. You can download the latest version of OpenCV from the official website: https://opencv.org/

Once you have installed OpenCV, you can create a new project in your preferred programming language. OpenCV supports C++, Python, Java, and other popular programming languages. In this article, we will use Python to demonstrate the concepts of OpenCV.

Image Processing with OpenCV

Image processing is one of the core components of computer vision. OpenCV provides a wide range of image processing functions, including:

  • Image filtering
  • Color space conversion
  • Geometric transformations
  • Image segmentation
  • Feature extraction

Image processing is used in a variety of applications, such as image enhancement, noise removal, object detection, and face recognition. In this section, we will provide some examples of how to perform basic image processing tasks with OpenCV.

Image Filtering

Image filtering is used to remove noise from images and enhance the features of interest. OpenCV provides a variety of filters, including:

  • Gaussian blur
  • Median blur
  • Bilateral filter
  • Canny edge detection
  • Sobel edge detection

In the following example, we will use the Gaussian blur filter to smooth an image:

python import cv2

# Read the input image image = cv2.imread('input.jpg')

# Apply Gaussian blur blurred_image = cv2.GaussianBlur(image, (5, 5),0)

# Display the blurred image cv2.imshow('Blurred Image', blurred_image) cv2.waitKey(0) cv2.destroyAllWindows()

Color Space Conversion

Color space conversion is used to convert images from one color space to another. OpenCV provides a variety of color space conversion functions, including:

  • BGR to grayscale
  • BGR to HSV
  • BGR to YCrCb
  • HSV to BGR
  • YCrCb to BGR

In the following example, we will convert an image from the BGR color space to the grayscale color space:

python import cv2

# Read the input image image = cv2.imread('input.jpg')

# Convert the image to grayscale gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Display the grayscale image cv2.imshow('Grayscale Image', gray_image) cv2.waitKey(0) cv2.destroyAllWindows()

Geometric Transformations

Geometric transformations are used to change the size, shape, and orientation of images. OpenCV provides a variety of geometric transformation functions, including:

  • Scaling
  • Rotation
  • Translation
  • Affine transformation
  • Perspective transformation

In the following example, we will scale an image to half of its original size:

python import cv2

# Read the input image image = cv2.imread('input.jpg')

# Scale the image to half of its original size scaled_image = cv2.resize(image, (0, 0),fx=0.5, fy=0.5)

# Display the scaled image cv2.imshow('Scaled Image', scaled_image) cv2.waitKey(0) cv2.destroyAllWindows()

Image Segmentation

Image segmentation is used to divide an image into different regions or segments. OpenCV provides a variety of image segmentation algorithms, including:

  • Thresholding
  • Clustering
  • Graph-based segmentation
  • Region growing
  • Watershed segmentation

In the following example, we will use the thresholding algorithm to segment an image into two regions:

python import cv2

# Read the input image image = cv2.imread('input.jpg')

# Convert the image to grayscale gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Apply thresholding thresh, binary_image = cv2.threshold(gray_image, 128, 255, cv2.THRESH_BINARY)

# Display the binary image cv2.imshow('Binary Image', binary_image) cv2.waitKey(0) cv2.destroyAllWindows()

Feature Extraction

Feature extraction is used to extract the most important features from an image. OpenCV provides a variety of feature extraction algorithms, including:

  • Histogram of oriented gradients (HOG)
  • Scale-invariant feature transform (SIFT)
  • Speeded-up robust features (SURF)
  • Oriented FAST and rotated BRIEF (ORB)
  • Features from accelerated segment test (FAST)

In the following example, we will use the HOG algorithm to extract features from an image:

python import cv2

# Read the input image image = cv2.imread('input.jpg')

# Convert the image to grayscale gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Extract HOG features hog = cv2.HOGDescriptor() features = hog.compute(gray_image)

# Print the HOG features print(features)

Object Detection with OpenCV

Object detection is one of the most important applications of computer vision. OpenCV provides a variety of object detection algorithms, including:

  • Haar cascades
  • Histogram of oriented gradients (HOG) + linear SVM
  • Convolutional neural networks (CNNs)
  • You Only Look Once (YOLO)
  • Single Shot Detector (SSD)

In this section, we will provide some examples of how to perform object detection with OpenCV.

Haar Cascades

Haar cascades are a classic object detection algorithm that uses a cascade of boosted classifiers to detect objects in an image. OpenCV provides pre-trained Haar cascades for a variety of objects, including faces, eyes, noses, and mouths.

In the following example, we will use the Haar cascades algorithm to detect faces in an image:

python import cv2

# Read the input image image = cv2.imread('input.jpg')

# Convert the image to grayscale gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Create a Haar cascade classifier for face detection face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

# Detect faces in the image faces = face_cascade.detectMultiScale(gray_image, 1.1, 4)

# Draw bounding boxes around the faces for (x, y, w,

Mastering OpenCV with Practical Computer Vision Projects
Mastering OpenCV with Practical Computer Vision Projects
by Daniel L茅lis Baggio

4.4 out of 5

Language : English
File size : 13765 KB
Text-to-Speech : Enabled
Screen Reader : Supported
Enhanced typesetting : Enabled
Print length : 342 pages
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Mastering OpenCV with Practical Computer Vision Projects
Mastering OpenCV with Practical Computer Vision Projects
by Daniel L茅lis Baggio

4.4 out of 5

Language : English
File size : 13765 KB
Text-to-Speech : Enabled
Screen Reader : Supported
Enhanced typesetting : Enabled
Print length : 342 pages
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