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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

Faces in videos

We're going to see our second example from what we learned in the last section on faces in photos. The still image example was neat, but you might be asking about videos. Okay, let's look at that for our next example:

%pylab inline
%cd /home/test/13293

import dlib
import cv2
import os
import tkinter
from tkinter import filedialog
from IPython import display
root = tkinter.Tk()
root.withdraw()

We change to %pylab inline because having all those widgets can actually cause a problem with Jupyter when you want to display a video sequence. We'll need the same code to get started with as shown in the previous example, and only replace notebook with inline. Then, we run the same code again.

After its execution, we move on with the next part. This is actually very close to the same thing because all you have to do is iterate over each frame, and it will work just...

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