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for me as a kid and still is now.

And my girlfriend who has always been positive, helpful, and supportive (even when I probably didnt deserve it).

Ive also met some demoralizing, discouraging ones.

Family members who have gone out of their way to deter me from being an entrepreneur and working for myself.

Colleagues who either disliked me or my work and chose to express their disdain in a public fashion.

And then there are those who have said some pretty disheartening things over email, Twitter, and other internet outlets.

Were all familiar with these types of people.

Yet regardless of their demeanor (whether positive or negative were all built from the same genetic material of four nucleobases: cytosine, guanine, adenine, and thymine.

These base pairs are combined in such a way that our bodies all have the same basic structure regardless of gender, race, or ethnicity.

At the most structural level we all have a head, two arms, a torso, and two legs.

We can use computer vision to exploit this semi-rigid structure and extract features to quantify the human body.

These features can be passed on to machine learning models that when trained can be used to detect and track humans in images and video streams. .

This is especially useful for the task of pedestrian detection, which is the topic well be talking about in todays blog post.

Read on to find out how you can use OpenCV and Python to perform pedestrian detection.

Looking for the source code to this post?

Jump Right To The Downloads Section.

Pedestrian Detection OpenCV, did you know that OpenCV has built-in methods to perform pedestrian detection?

OpenCV ships with a pre-trained HOG Linear SVM model that can be used to perform pedestrian detection in both images and video streams.

If youre not familiar with the Histogram of Oriented Gradients and Linear SVM method, I suggest you read this blog post where I discuss the 6 step framework.

If youre already familiar with the process (or if you just want to see some code on how pedestrian detection with OpenCV is done just open up a new file, name it , and well get coding: # import the necessary.

Object_detection import non_max_suppression from imutils import paths import numpy as np import argparse import imutils import cv2 # construct the argument parse and parse the arguments ap gumentParser d_argument -i "-images requiredTrue, help"path to images directory args vars(rse_args # initialize the HOG descriptor/person.

Well import print_function  to ensure our code is compatible with both Python.7 and Python 3 (this code will also work for OpenCV.4.X and OpenCV 3).
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