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OpenCV 3 Blueprints

You're reading from   OpenCV 3 Blueprints Expand your knowledge of computer vision by building amazing projects with OpenCV 3

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Product type Paperback
Published in Nov 2015
Publisher
ISBN-13 9781784399757
Length 382 pages
Edition 1st Edition
Tools
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Toc

Table of Contents (9) Chapters Close

Preface 1. Getting the Most out of Your Camera System FREE CHAPTER 2. Photographing Nature and Wildlife with an Automated Camera 3. Recognizing Facial Expressions with Machine Learning 4. Panoramic Image Stitching Application Using Android Studio and NDK 5. Generic Object Detection for Industrial Applications 6. Efficient Person Identification Using Biometric Properties 7. Gyroscopic Video Stabilization Index

Rolling shutter compensation


At this point, our video is stable, however, when objects in the scene are moving quickly, the rolling shutter effects become more pronounced.

To fix this, we'll need to do a few things. First, incorporate the rolling shutter speed into our calibration code. Second, when warping images, we need to unwarp the rolling shutter as well.

Calibrating the rolling shutter

To start calibrating the rolling shutter duration, we need to tweak the error function to incorporate another term. Let's start by looking at the calcErrorAcrossVideo method. The part we're interested in is:

def calcErrorAcrossVideo(videoObj, theta, timestamp, focal_length, gyro_delay=None, gyro_drift=None, rolling_shutter=None):
    total_error = 0
    ...
        transform = getAccumulatedRotation(...)
        transformed_corners = cv2.perspectiveTransform(old_corners, transform)
    ...

Also, we'll need to add logic to transform a corner based on its location—a corner in the upper part of the image is...

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