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WEBVTT
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So while in the open Zeevi in Python.
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Well Python is becoming more and more popular and that's because it's one of the easiest languages for
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beginners.
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It allows us to focus on building complex computer vision apps without being bogged down by the intricacies
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of the language itself.
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And I'm looking at you C++.
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However Python is still extremely powerful especially for science and machine learning applications
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which are an essential part of the can be division world.
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Finally it allows us to store images and non-payers which allows us to do some very powerful operations
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quite easily.
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So let's take a quick look at exactly what you'll be learning on this course so fiercely given the current
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state of can be division.
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I try to give you an excellent foundation that exposes you to all key areas of computer vision.
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We start off by doing the basics where we get into some simple image manipulations and segmentation.
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We then implement some basic object detection followed by feature detection and call and people detection
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as well.
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We don't take a look at this analysis and filters.
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After that we go into some basic machine learning in computer vision and then we get into some more
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motion analysis and object tracking.
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I've also included a short mini project based on competition of photography and then we wrap up the
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course where I give you some advice and resources on how to become an expert and can be division.
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I also show you some of the latest research areas and also give you some very cool startup ideas that
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involve computer.
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Computer vision and best of all in discourse.
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You get to implement almost 50 different computer vision exercises and implement 12 very fun many projects.
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So it should come as no surprise that this is a very practical course where we're going to spend more
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than half of our time coding.
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However before we dive into it could always teach the theory first before hand unless I'm using the
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code to actually teach at that topic as well.
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And the is always explained line by line except in cases where it becomes a bit redundant.
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Or the theory at hand is a bit too complex for this group of discourse.
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So I may have mentioned 12 mini projects before.
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So what exactly are these mini projects.
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Let's take a look.
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So here they are in all their glory all 12 many projects on one slide so fiercely.
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You're getting to make an awesome Live sketch of yourself using a webcam.
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We don't get to implement is simple ship matching project followed by an app that actually comes a number
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of circles and ellipses in an image.
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We don't move on to.
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We're finding Waldo projec followed by a simple object detection project.
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You don't get to implement fi's pedestrian encored detection.
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After that you get to implement a very cool life swapping up here where you can play a Donald Trump
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or Kim Kardashian's or anyone else's face in real time.
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And then we implement a simple human detection up after which you get to make a basic machine learning
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app that actually understands handwritten digits.
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This is then followed by a face recognition app and you don't get to implement a simple ball trucking
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up and I know this isn't a ball it's actually a clock.
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I couldn't find my ball but that's OK.
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And lastly we get to do a simple photo restoration app we can remove this line from his photo right
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here.
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So you're definitely getting a lot of practical experience making computer vision applications so I
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really hope you enjoy doing these projects.
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The requirements for the scores are actually pretty low.
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Basic programming would actually be very helpful as well as exposure to non-pay.
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However it's not needed as I actually go through the code line by line.
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Secondly a high school level math would actually be very good to have to appreciate some of the high
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level concepts that we're implementing.
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And also you need to have a webcam to implement a lot of for many projects as well as some of the example
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code.
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Now we're going to install Pitre an open C.V right after in the next section.
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However I'll just point out that I used the Anaconda package solution and that allows me to use Pitre
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notebooks which are excellent for teaching since it since it allows us to use and or uncovered in court
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blocks.
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Now there is some unfortunate news regarding the latest version of Open C-v which is true point 1.
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It unfortunately no longer support some important functions such as swift and Souf which I use for object
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detection.
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So I would recommend you install 2.4 one tree instead.
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However that said there are some object tracking techniques that aren't supported in 2.4 and tree.
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So depending on what's your priority you can choose which version you would like to install accordingly.
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So who exactly is this course for.
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Well I've designed this course to suit a wide number of people starting from beginners who just have
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an interest in computer vision or even software developers and engineers looking to strengthen their
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job skills as well as college and university students looking to get a head start in the computer vision
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projects and research also startup founders who wish to use law as some sort of computer vision component
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to their companies.
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And finally hobbyists who just want to build some fun computer vision project using a Raspberry Pi perhaps.
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So let's begin our exciting journey into the world of computer vision using open C-v in Python.
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