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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 plate characters

Next, we carry out our initial search to find plate characters. First, we find characters roughly, and then find candidates based on specific criteria.

Let's start with the following line in our Notebook:

%pylab notebook

We can now execute our function cell for imports, utilities, and to load our libraries:

import cv2
import numpy as np
import pickle
def getmatchingchars(char_cands):
char_list = []

for char_cand in char_cands:
ch_matches = [] \n",
for matching_candidate in char_cands:
if matching_candidate == char_cand:
continue
chardistance = np.sqrt((abs(char_cand.x_cent - matching_candidate.x_cent) ** 2) +
(abs(char_cand.y_cent - matching_candidate.y_cent)**2))
x = float(abs(char_cand.x_cent - matching_candidate.x_cent))
y = float(abs(char_cand.y_cent - matching_candidate...
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