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OpenCV with Python Blueprints
OpenCV with Python Blueprints

OpenCV with Python Blueprints: Design and develop advanced computer vision projects using OpenCV with Python

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Profile Icon Michael Beyeler (USD) Profile Icon Michael Beyeler
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.4 (10 Ratings)
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OpenCV with Python Blueprints

Chapter 2. Hand Gesture Recognition Using a Kinect Depth Sensor

The goal of this chapter is to develop an app that detects and tracks simple hand gestures in real time using the output of a depth sensor, such as that of a Microsoft Kinect 3D sensor or an Asus Xtion. The app will analyze each captured frame to perform the following tasks:

  • Hand region segmentation: The user's hand region will be extracted in each frame by analyzing the depth map output of the Kinect sensor, which is done by thresholding, applying some morphological operations, and finding connected components
  • Hand shape analysis: The shape of the segmented hand region will be analyzed by determining contours, convex hull, and convexity defects
  • Hand gesture recognition: The number of extended fingers will be determined based on the hand contour's convexity defects, and the gesture will be classified accordingly (with no extended fingers corresponding to a fist, and five extended fingers corresponding to an...

Planning the app

The final app will consist of the following modules and scripts:

  • gestures: A module that consists of an algorithm for recognizing hand gestures. We separate this algorithm from the rest of the application so that it can be used as a standalone module without the need for a GUI.
  • gestures.HandGestureRecognition: A class that implements the entire process flow of hand-gesture recognition. It accepts a single-channel depth image (acquired from the Kinect depth sensor) and returns an annotated RGB color image with an estimated number of extended fingers.
  • gui: A module that provides a wxPython GUI application to access the capture device and display the video feed. This is the same module that we used in the last chapter. In order to have it access the Kinect depth sensor instead of a generic camera, we will have to extend some of the base class functionality.
  • gui.BaseLayout: A generic layout from which more complicated layouts can be built.
  • chapter2: The main script for the chapter...

Setting up the app

Before we can get down to the nitty-gritty of our gesture recognition algorithm, we need to make sure that we can access the Kinect sensor and display a stream of depth frames in a simple GUI.

Accessing the Kinect 3D sensor

Accessing Microsoft Kinect from within OpenCV is not much different from accessing a computer's webcam or camera device. The easiest way to integrate a Kinect sensor with OpenCV is by using an OpenKinect module called freenect. For installation instructions, take a look at the preceding information box. The following code snippet grants access to the sensor using cv2.VideoCapture:

import cv2
import freenect


device = cv2.cv.CV_CAP_OPENNI
capture = cv2.VideoCapture(device)

On some platforms, the first call to cv2.VideoCapture fails to open a capture channel. In this case, we provide a workaround by opening the channel ourselves:

if not(capture.isOpened(device)):
    capture.open(device)

If you want to connect to your Asus Xtion, the device variable...

Tracking hand gestures in real time

Hand gestures are analyzed by the HandGestureRecognition class, especially by its recognize method. This class starts off with a few parameter initializations, which will be explained and used later:

class HandGestureRecognition:
    def __init__(self):
        # maximum depth deviation for a pixel to be considered # within range
        self.abs_depth_dev = 14

        # cut-off angle (deg): everything below this is a convexity 
        # point that belongs to two extended fingers
        self.thresh_deg = 80.0

The recognize method is where the real magic takes place. This method handles the entire process flow, from the raw grayscale image all the way to a recognized hand gesture. It implements the following procedure:

  1. It extracts the user's hand region by analyzing the depth map (img_gray) and returning a hand region mask (segment):
    def recognize(self, img_gray):
        segment = self._segment_arm(img_gray)
  2. It performs contour analysis on the hand region...

Hand region segmentation

The automatic detection of an arm, and later the hand region, could be designed to be arbitrarily complicated, maybe by combining information about the shape and color of an arm or hand. However, using a skin color as a determining feature to find hands in visual scenes might fail terribly in poor lighting conditions or when the user is wearing gloves. Instead, we choose to recognize the user's hand by its shape in the depth map. Allowing hands of all sorts to be present in any region of the image unnecessarily complicates the mission of the present chapter, so we make two simplifying assumptions:

  • We will instruct the user of our app to place their hand in front of the center of the screen, orienting their palm roughly parallel to the orientation of the Kinect sensor so that it is easier to identify the corresponding depth layer of the hand.
  • We will also instruct the user to sit roughly one to two meters away from the Kinect, and to slightly extend their arm...

Hand shape analysis

Now that we know (roughly) where the hand is located, we aim to learn something about its shape.

Determining the contour of the segmented hand region

The first step involves determining the contour of the segmented hand region. Luckily, OpenCV comes with a pre-canned version of such an algorithm—cv2.findContours. This function acts on a binary image and returns a set of points that are believed to be part of the contour. As there might be multiple contours present in the image, it is possible to retrieve an entire hierarchy of contours:

def _find_hull_defects(self, segment):
    contours, hierarchy = cv2.findContours(segment, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

Furthermore, because we do not know which contour we are looking for, we have to make an assumption to clean up the contour result. Since it is possible that some small cavities are left over even after the morphological closing—but we are fairly certain that our mask contains only the segmented...

Hand gesture recognition

What remains to be done is to classify the hand gesture based on the number of extended fingers. For example, if we find five extended fingers, we assume the hand to be open, whereas no extended fingers implies a fist. All that we are trying to do is count from zero to five and make the app recognize the corresponding number of fingers.

This is actually trickier than it might seem at first. For example, people in Europe might count to three by extending their thumb, index finger, and middle finger. If you do that in the US, people there might get horrendously confused, because they do not tend to use their thumbs when signaling the number two. This might lead to frustration, especially in restaurants (trust me). If we could find a way to generalize these two scenarios—maybe by appropriately counting the number of extended fingers—we would have an algorithm that could teach simple hand gesture recognition to not only a machine but also (maybe) to an...

Planning the app


The final app will consist of the following modules and scripts:

  • gestures: A module that consists of an algorithm for recognizing hand gestures. We separate this algorithm from the rest of the application so that it can be used as a standalone module without the need for a GUI.

  • gestures.HandGestureRecognition: A class that implements the entire process flow of hand-gesture recognition. It accepts a single-channel depth image (acquired from the Kinect depth sensor) and returns an annotated RGB color image with an estimated number of extended fingers.

  • gui: A module that provides a wxPython GUI application to access the capture device and display the video feed. This is the same module that we used in the last chapter. In order to have it access the Kinect depth sensor instead of a generic camera, we will have to extend some of the base class functionality.

  • gui.BaseLayout: A generic layout from which more complicated layouts can be built.

  • chapter2: The main script for the chapter...

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Description

Design and develop advanced computer vision projects using OpenCV with Python About This Book Program advanced computer vision applications in Python using different features of the OpenCV library Practical end-to-end project covering an important computer vision problem All projects in the book include a step-by-step guide to create computer vision applications Who This Book Is For This book is for intermediate users of OpenCV who aim to master their skills by developing advanced practical applications. Readers are expected to be familiar with OpenCV’s concepts and Python libraries. Basic knowledge of Python programming is expected and assumed. What You Will Learn Generate real-time visual effects using different filters and image manipulation techniques such as dodging and burning Recognize hand gestures in real time and perform hand-shape analysis based on the output of a Microsoft Kinect sensor Learn feature extraction and feature matching for tracking arbitrary objects of interest Reconstruct a 3D real-world scene from 2D camera motion and common camera reprojection techniques Track visually salient objects by searching for and focusing on important regions of an image Detect faces using a cascade classifier and recognize emotional expressions in human faces using multi-layer peceptrons (MLPs) Recognize street signs using a multi-class adaptation of support vector machines (SVMs) Strengthen your OpenCV2 skills and learn how to use new OpenCV3 features In Detail OpenCV is a native cross platform C++ Library for computer vision, machine learning, and image processing. It is increasingly being adopted in Python for development. OpenCV has C++/C, Python, and Java interfaces with support for Windows, Linux, Mac, iOS, and Android. Developers using OpenCV build applications to process visual data; this can include live streaming data from a device like a camera, such as photographs or videos. OpenCV offers extensive libraries with over 500 functions This book demonstrates how to develop a series of intermediate to advanced projects using OpenCV and Python, rather than teaching the core concepts of OpenCV in theoretical lessons. Instead, the working projects developed in this book teach the reader how to apply their theoretical knowledge to topics such as image manipulation, augmented reality, object tracking, 3D scene reconstruction, statistical learning, and object categorization. By the end of this book, readers will be OpenCV experts whose newly gained experience allows them to develop their own advanced computer vision applications. Style and approach This book covers independent hands-on projects that teach important computer vision concepts like image processing and machine learning for OpenCV with multiple examples.

Who is this book for?

This book is for intermediate users of OpenCV who aim to master their skills by developing advanced practical applications. Readers are expected to be familiar with OpenCV&apos

What you will learn

  • Generate real-time visual effects using different filters and image manipulation techniques such as dodging and burning
  • Recognize hand gestures in real time and perform hand-shape analysis based on the output of a Microsoft Kinect sensor
  • Learn feature extraction and feature matching for tracking arbitrary objects of interest
  • Reconstruct a 3D real-world scene from 2D camera motion and common camera reprojection techniques
  • Track visually salient objects by searching for and focusing on important regions of an image
  • Detect faces using a cascade classifier and recognize emotional expressions in human faces using multi-layer peceptrons (MLPs)
  • Recognize street signs using a multi-class adaptation of support vector machines (SVMs)
  • Strengthen your OpenCV2 skills and learn how to use new OpenCV3 features
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Table of Contents

8 Chapters
1. Fun with Filters Chevron down icon Chevron up icon
2. Hand Gesture Recognition Using a Kinect Depth Sensor Chevron down icon Chevron up icon
3. Finding Objects via Feature Matching and Perspective Transforms Chevron down icon Chevron up icon
4. 3D Scene Reconstruction Using Structure from Motion Chevron down icon Chevron up icon
5. Tracking Visually Salient Objects Chevron down icon Chevron up icon
6. Learning to Recognize Traffic Signs Chevron down icon Chevron up icon
7. Learning to Recognize Emotions on Faces Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

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Full star icon Full star icon Full star icon Full star icon Half star icon 4.4
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Amazon Customer May 17, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
"OpenCV with Python Blueprints" is a great introduction to intermediate and advanced OpenCV concepts. Technical, but not too much. It's really helpful if you want to build your own functional apps.The book focuses on the practical side of things: Every chapter describes a dedicated, stand-alone project from A-Z. I'm big on 3D scene reconstruction, so I really enjoyed the chapter about optic flow and structure-from-motion. For a book that has to cover a lot of breadth, I was impressed by how much detail was contained in just one chapter. You get a really good value here.I also liked the structure of the book a lot: At the beginning of a chapter, the author tells you the goal of the project and outlines how exactly to get there step-by-step. If there is some theory or background information you need, he will introduce the concepts and explain them using a simple example. Very easy to follow. Every chapter finishes with the finished app, so you know exactly what you'll get in the end.I highly recommend this book to anyone with the requisite background trying to get a better understanding of the more advanced OpenCV tools.
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Nick Nov 17, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book covers multiple computer vision algorithms used in the industry today. Beyeler focuses on a practical approach by providing detailed Python code to perform image manipulation, augmented reality, object tracking, 3D scene reconstruction, statistical learning, and object categorization.I particularly liked the last chapter on facial expression recognition using Haar Cascade Classifiers and multi-layer perceptrons, where the authors described very clearly their implementation and usage with openCV. Finally, having access to all the source code on Github is great and really useful in order to quickly test the models, and modify them yourself.I recommend this book for people who already have some experience with computer vision and who want to improve their skills by developing advanced practical applications with OpenCV.
Amazon Verified review Amazon
charliesixpack Jun 13, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I cannot get the code to work with either of the platforms I have tried. These are common platforms I would expect the code to be compatible with.Macbook Pro running OS X 10.11.5: The code in the book requires the wxPython package. The download "wxPython3.0-osx-3.0.2.0-cocoa-py2.7.dmg" simply does not install. This problem is documented and unresolved. An error message appears during install about the downloaded software not being found by the installer. Without this package the code is useless. This is not a problem with the book but without the package the book is useless.Raspberry Pi 3 with raspberry pi camera: The opencv function VideoCapture(0) does not work with the Raspberry Pi camera. So I spent a couple of days trying to get the equivalent picamera.capture in conjunction with picamera.array.PiRGBArray to work with the code by producing an equivalent numpy three dimensional array for the frame the code would work with. My syntax and data types check out but I get a runtime error "Failed to gain raw access to bitmap data." What does that mean?I am frustrated and disappointed that neither of my platforms can use the code in the book. If the book code worked with tweaking I would give it 5 stars.Update: I have finally gotten a wxPython frame to display from the pi camera on the Raspberry Pi by using the hint from G10DRAS on [...]So I am off and running and have revised my rating.
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Amazon Customer Mar 25, 2016
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Excellent book to build practical OpenCV projects! I'm still relatively new to OpenCV, but all examples are well laid out and easy to follow. The author does a good job explaining the concepts in detail and shows how they apply in real life. As a professional programmer, I especially love that you can just fork the code from GitHub and follow along. Strongly recommend to readers with basic knowledge of computer vision, machines learning, and Python!
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JrB May 11, 2018
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There a lot of information in this book. It is to the point. I think it is great for a beginner to semi-advanced users. Plenty of examples to help you really understand what is going on.
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