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Computer Vision Projects with OpenCV and Python 3

You're reading from   Computer Vision Projects with OpenCV and Python 3 Six end-to-end projects built using machine learning with OpenCV, Python, and TensorFlow

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781789954555
Length 182 pages
Edition 1st Edition
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Author (1):
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Matthew Rever Matthew Rever
Author Profile Icon Matthew Rever
Matthew Rever
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Table of Contents (9) Chapters Close

Preface 1. Setting Up an Anaconda Environment FREE CHAPTER 2. Image Captioning with TensorFlow 3. Reading License Plates with OpenCV 4. Human Pose Estimation with TensorFlow 5. Handwritten Digit Recognition with scikit-learn and TensorFlow 6. Facial Feature Tracking and Classification with dlib 7. Deep Learning Image Classification with TensorFlow 8. Other Books You May Enjoy

Finding and reading license plates with OpenCV

We have already found our characters, which are license plate candidates. Now we need to determine which characters match, so that we can extract the text data and map the characters within the license plates.

First, we run each plate candidate through our gray_thresh_img function, which does our de-noising and binarization. In this case, we get a cleaner output because we are using a sub-image and not the complete image.

This is the extraction code we will use:

for plate_candidate in plate_candidates: 

plate_candidate.grayimg, plate_candidate.thesholded = \
gray_thresh_img(plate_candidate.plate_im)
plate_candidate.thesholded = cv2.resize(plate_candidate.thesholded,
(0, 0), fx = 1.6, fy = 1.6)
thresholdValue, plate_candidate.thesholded = \
...
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