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Learning OpenCV 4 Computer Vision with Python 3

You're reading from   Learning OpenCV 4 Computer Vision with Python 3 Get to grips with tools, techniques, and algorithms for computer vision and machine learning

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781789531619
Length 372 pages
Edition 3rd Edition
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Authors (2):
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Joe Minichino Joe Minichino
Author Profile Icon Joe Minichino
Joe Minichino
Joseph Howse Joseph Howse
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Joseph Howse
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Table of Contents (13) Chapters Close

Preface 1. Setting Up OpenCV 2. Handling Files, Cameras, and GUIs FREE CHAPTER 3. Processing Images with OpenCV 4. Depth Estimation and Segmentation 5. Detecting and Recognizing Faces 6. Retrieving Images and Searching Using Image Descriptors 7. Building Custom Object Detectors 8. Tracking Objects 9. Camera Models and Augmented Reality 10. Introduction to Neural Networks with OpenCV 11. Other Book You May Enjoy Appendix A: Bending Color Space with the Curves Filter

Capturing frames from a depth camera

Back in Chapter 2, Handling Files, Cameras, and GUIs, we discussed the concept that a computer can have multiple video capture devices and each device can have multiple channels. Suppose a given device is a depth camera. Each channel might correspond to a different lens and sensor. Also, each channel might correspond to different kinds of data, such as a normal color image versus a depth map. OpenCV, via its optional support for OpenNI 2, allows us to request any of the following channels from a depth camera (though a given camera might support only some of these channels):

  • cv2.CAP_OPENNI_DEPTH_MAP: This is a depth map—a grayscale image in which each pixel value is the estimated distance from the camera to a surface. Specifically, each pixel value is a 16-bit unsigned integer representing a depth measurement in millimeters.
  • cv2.CAP_OPENNI_POINT_CLOUD_MAP...
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