Machine Learning and Deep learning techniques, in particular, are changing the way computers see and interact with the World. From augmented and mixed-reality applications to just gathering data, these new techniques are revolutionizing a lot of industries. OpenCV is a cross-platform library using which we can develop real-time computer vision applications. It mainly focuses on image processing, video capture, and analysis including features like face detection and object detection. This course is designed to give you a hands-on learning experience by going from the basic concepts to the most current in-depth Deep Learning methods for Computer Vision in use today.

This is the best course to quickly grasp the knowledge of Python and OpenCV and become proficient to design Computer Vision and Deep Learning solutions. With the AI-fueled organization trend getting momentum, the industry is in dire need of Computer Vision experts who are proficient in Python and OpenCV. This course has been designed to start with the basics of Python coding language comprising of Data Types, Operators, Loops, Functions, Modules, File Handling, Exception Handling along with Popular Coding Practices and then slowly take you through the advanced Python concepts such as Lambda, Map, Filter, Object Oriented Programming, Decorator, Generator, DateTime, Math, Random, Statistics, Sys, OS, Numpy, Pandas, Matplotlib and OpenPyXL in detail. Not only this, the course takes it one step further by providing comprehensive coverage of OpenCV topics including Image Thresholding, Image Noise Removal, Image Cropping & Rotation, Image Annotation, Image Detection and also OpenCV for Videos with 35 supporting notebooks available for download that contain examples for practice. The quiz at the end of each key topic helps you to assess your knowledge and identify the improvement areas.

Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.

In this tutorial, you will learn how to use multiprocessing with OpenCV and Python to perform feature extraction. You'll learn how to use multiprocessing with OpenCV to parallelize feature extraction across the system bus, including all processors and cores on your computer. Today's tutorial is inspired by PyImageSearch reader, Abigail. Hey Adrian, I just read your tutorial on image hashing with OpenCV and really enjoyed it. I'm trying to apply image hashing to my research project at the university.

If you have been keeping up with the advancements in the area of object detection, you might have got used to hearing this word'YOLO'. It has kind of become a buzzword. YOLO (You Only Look Once) is a method / way to do object detection. It is the algorithm /strategy behind how the code is going to detect objects in the image. The official implementation of this idea is available through DarkNet (neural net implementation from the ground up in'C' from the author).

These words send a shiver down my spine. But then again, they are the only comfort I get when I use Snapchat these days. "Why is Snapchat scaring this moron?" I don't know about you, BUT I SURE AS HELL don't enjoy sharing my bed with Casper or any other creepy ghosts that this otherworld-R.S.V.P-app has brought to my life. You see, every once in a while I'm doing my dog filter faces like a normal human being in 2017; but then… my cat stops moving and stares at the end of the room… the camera refocuses… and then: it finds an invisible Dalmatian filter standing by my side. I've moved twice already… but NO LONGER! A buddy of mine told me to look into the field of Computer Vision … It's a crazy field where machines learn how to extract useful information from images as we humans do.

Today's blog post is on image hashing -- and it's the hardest blog post I've ever had to write. Image hashing isn't a particularly hard technique (in fact, it's one of the easiest algorithms I've taught here on the PyImageSearch blog). But the subject matter and underlying reason of why I'm covering image hashing today of all days nearly tear my heart out to discuss. The remaining introduction to this blog post is very personal and covers events that happened in my life five years ago, nearly to this very day. If you want to skip the personal discussion and jump immediately to the image hashing content, I won't judge -- the point of PyImageSearch is to be a computer vision blog after all. To skip to the computer vision content, just scroll to the "Image Hashing with OpenCV and Python" section where I dive into the algorithm and implementation. But while PyImageSearch is a computer vision and deep learning blog, I am a very real human that writes it. And sometimes the humanity inside me needs a place to share.