Today, its 27th of October 2017 15:00 EEST, i am still in transit to home, after attending Spark Summit Europe 2017. The top two trends of this summit was
1. Machine Learning
2. Streaming workloads
I am fascinated by the advancement happened lately in the field of Machine Learning, Deep Learning and Artificial Intelligence.This technology is the biggest thing now, and is destined to be human scale.
As they say “The hardest part of any journey is taking that first step”
So with that, i am all set to take my first step, where i am going to do my first Machine Learning toy lesson
Project #1: Face Recognition using face_recogination library (without coding)
- Install dependencies
boost-pythonwhich are required for
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- Once installation is done verify libraries
face_recoginationby importing them in python shell. If you encounter any problems while importing these libraries, you need to fix those before moving forward.
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- To get our first image recognition project working you need a few images. Training images and images that you need to recognize.
- For simplicity create two directories
- As the name suggest under
known_imagesdirectory add a few images of faces your want your system to know (save the images as the label you want to be known).
- Similarly for
unknown_imagesdirectory add images your system wil detect based on
- In my setup i have 3
unknown_imagesas shown below
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- Now i have both known and unknown images. I will trigger
face_recoginationcommand which will use
face_reciginationlibrary under the hood and detect faces in unknown images.
- This program will detect faces in the unknown images and return the lable (name), as shown below.
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\o/ YaY .. we just created the most easiest face recogination system ever !!!
I would like to thank Adam Geitgey for creating this super handy and easy to use library.