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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

Multi-person pose detection

Now, let's move from single-person pose detection to multi-person pose detection. With single-person pose detection, we saw that the code will take an image of a single person and generate pose estimation with all the joints labeled. We will now learn a more advanced model called ArtTrack, which will allow us to count the number of people, find people, and estimate their poses.

Let's look at the code for multi-person pose detection, which is shown in the following example:

import os
import sys
import numpy as np
import cv2 I
from imageio import imread, imsave
from config import load_config
from dataset.factory import create as create_dataset
from nnet import predict
from dataset.pose_dataset import data_to_input
from multiperson.detections import extract_detections
from multiperson.predict import SpatialModel, eval_graph, get_person_conf_mu1ticut
#...
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