Book Image

OpenCV 3.x with Python By Example - Second Edition

By : Gabriel Garrido Calvo, Prateek Joshi
Book Image

OpenCV 3.x with Python By Example - Second Edition

By: Gabriel Garrido Calvo, Prateek Joshi

Overview of this book

Computer vision is found everywhere in modern technology. OpenCV for Python enables us to run computer vision algorithms in real time. With the advent of powerful machines, we have more processing power to work with. Using this technology, we can seamlessly integrate our computer vision applications into the cloud. Focusing on OpenCV 3.x and Python 3.6, this book will walk you through all the building blocks needed to build amazing computer vision applications with ease. We start off by manipulating images using simple filtering and geometric transformations. We then discuss affine and projective transformations and see how we can use them to apply cool advanced manipulations to your photos like resizing them while keeping the content intact or smoothly removing undesired elements. We will then cover techniques of object tracking, body part recognition, and object recognition using advanced techniques of machine learning such as artificial neural network. 3D reconstruction and augmented reality techniques are also included. The book covers popular OpenCV libraries with the help of examples. This book is a practical tutorial that covers various examples at different levels, teaching you about the different functions of OpenCV and their actual implementation. By the end of this book, you will have acquired the skills to use OpenCV and Python to develop real-world computer vision applications.
Table of Contents (17 chapters)
Title Page
Copyright and Credits
Packt Upsell

What is a dense feature detector?

In order to extract a meaningful amount of information from the images, we need to make sure our feature extractor extracts features from all parts of a given image. Consider the following image:

If you extract features using a feature extractor as we did in Chapter 5Extracting Features from an Image, it will look like this:

If you used to use the cv2.FeaturetureDetector_create("Dense") detector, unfortunately, that was removed from OpenCV 3.2 onwards, so we would need to implement our own one iterating over the grid and obtaining the keypoints:

We can control the density as well. Let's make it sparse:

By doing this, we can make sure that every single part in the image is processed. Here is the code to do it:

import sys
import cv2 
import numpy as np 

class DenseDetector(): 
    def __init__(self, step_size=20, feature_scale=20, img_bound=20): 
        # Create a dense feature detector 
        self.initXyStep = step_size
        self.initFeatureScale = feature_scale...