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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

Retraining the human pose estimation model

We will now discuss how to handle videos and retrain our human pose estimation network. We have already covered face detection and how to apply a model to a video. Opening a video is pretty straightforward and OpenCV provides a mechanism for that. It's basically doing the same thing one frame at a time. The following example shows the code for this:

predictor_path = "./shape_predictor_68_face_landmarks.dat"
detector = dlib.get_fronta1_face_detector()
predictor = dlib.shape_predictor(predictor_path)

#Uncomment Line below if you want to use your webcam
#cap = cv2.VideoCapture(0) #0 is the first camera on your computer, change if you
#more than one camera

#Comment out the Line below if using webcam
cap = cv2.VideoCapture('./rollerc.mp4')
figure(100)
font = cv2.FONT_HERSHEY_SIMPLEX

First, we need to create a cv2 capture device...

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