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Learning OpenCV 3 Computer Vision with Python (Update)

Learning OpenCV 3 Computer Vision with Python (Update)

By : Joe Minichino, Joseph Howse
2.1 (7)
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Learning OpenCV 3 Computer Vision with Python (Update)

Learning OpenCV 3 Computer Vision with Python (Update)

2.1 (7)
By: Joe Minichino, Joseph Howse

Overview of this book

OpenCV 3 is a state-of-the-art computer vision library that allows a great variety of image and video processing operations. Some of the more spectacular and futuristic features such as face recognition or object tracking are easily achievable with OpenCV 3. Learning the basic concepts behind computer vision algorithms, models, and OpenCV's API will enable the development of all sorts of real-world applications, including security and surveillance. Starting with basic image processing operations, the book will take you through to advanced computer vision concepts. Computer vision is a rapidly evolving science whose applications in the real world are exploding, so this book will appeal to computer vision novices as well as experts of the subject wanting to learn the brand new OpenCV 3.0.0. You will build a theoretical foundation of image processing and video analysis, and progress to the concepts of classification through machine learning, acquiring the technical know-how that will allow you to create and use object detectors and classifiers, and even track objects in movies or video camera feeds. Finally, the journey will end in the world of artificial neural networks, along with the development of a hand-written digits recognition application.
Table of Contents (11 chapters)
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6
6. Retrieving Images and Searching Using Image Descriptors
10
Index

Creating a mask from a disparity map


For the purposes of Cameo, we are interested in disparity maps and valid depth masks. They can help us refine our estimates of facial regions.

Using the FaceTracker function and a normal color image, we can obtain rectangular estimates of facial regions. By analyzing such a rectangular region in the corresponding disparity map, we can tell that some pixels within the rectangle are outliers—too near or too far to really be a part of the face. We can refine the facial region to exclude these outliers. However, we should only apply this test where the data is valid, as indicated by the valid depth mask.

Let's write a function to generate a mask whose values are 0 for the rejected regions of the facial rectangle and 1 for the accepted regions. This function should take a disparity map, valid depth mask, and a rectangle as arguments. We can implement it in depth.py as follows:

def createMedianMask(disparityMap, validDepthMask, rect = None):
    """Return a mask...

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