OpenCV Python Tutorial For Beginners 13 - Object Detection and Object Tracking Using HSV Color Space

In this video on OpenCV Python Tutorial For Beginners, I am going to show How to do Object Detection and Object Tracking Using HSV Color Space. So we will be Implementing color and shape-based object detection and tracking using hue-saturation-value (HSV) color model. For Choosing the correct upper and lower HSV boundaries for color detection with`cv::inRange` (OpenCV) we will use trackbar.

OpenCV is an image processing library created by Intel and later supported by Willow Garage and now maintained by Itseez. opencv is available on Mac, Windows, Linux. Works in C, C++, and Python.
it is Open Source and free. opencv is easy to use and install.

Starting with an overview of what the course will be covering, we move on to discussing morphological operations and practically learn how they work on images. We will then learn contrast enhancement using equalization and contrast limiting. Finally we will learn 3 methods to subtract the background from the video and implement them using OpenCV.

At the end of this course, you will have a firm grasp of Computer Vision techniques using OpenCV libraries. This course will be your gateway to the world of data science.

Feel the real power of Python and programming! The course offers you a unique approach of learning how to code by solving real world problems.

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[christopherwestcott]

These videos have gotten me up and running quickly with OpenCV. Thank you!!

[emoon]

Your videos are incredible, I have been watching them all day, as I've been working on a CV project and this has been so helpful! You have done such a brilliant job

[rebekahhumphreys]

Hi, this video and series really helped me with a project that made my robot track and move towards a pink blob or shape. You are an amazing teacher because before this tutorial I did not know anything about OpenCV and with your help I made something super cool happen so thank you :)

[patirosa]

l_b = np.array([102, 165, 183])
u_b = np.array([139, 255, 255])
Values that i found to the blue balls.
Thanks for the videos!

[Arnius_Music]

i felt like the first days that i enjoyed learning how to code. thank you💛

[atharvapatil]

A more visible lower and upper bound for blue color detection is :
l_b = np.array([95, 116, 50])
u_b = np.array([130, 255, 255])

[joliver]

This is great! I can really use this method for a project I’m working on right now. Thank you!

[KyranFindlater]

wow, this is actually the first time i've seen these really handy integrated highgui trackbar features. that's super handy for my image processing prototyping!

[gioacchinopellerito]

Simply amazing, I am newby about opencv, nevertheless, the exemples managed on these video series are clear as crystal water. In a whole day, i feel as confident I can begin to handle a simple test by my own. Thanks so much

[husseingharakhani]

This was my first time to work with OpenCV. Thank you so much.

[garvitgoyal]

your videos are amazing. Thanku for such content and effort.

[saidanianis]

i learn a lot from you, thanks for all the work that you done

[uchihaitachi]

Very helpful play list and especially i loved this video :)

[jeepjr]

Teacher, first congratulations for the channel. I need your help, I have a simple image with a range of 5 placements where the quantity is identified, how do I identify the biggest color type red and say where the X, Y is, and if you can help me, and only for create an alert?

[nevilparekh]

Bravo... many thanks for amazing tutorials ..

[fun-ihsc]

Sir/Anyone
How cv2.inrange function is working could you please explain a bit.
And 9:23 line no: 19, why taking same image frame two times.
Thank you in advance

[liliankotvan]

I loved this video. Thank you very much!

[lexxsonquayeson]

great one. please we need a practical video on fruits detection. thank you

[siddharthmishra]

Why do we need two same source as frame

[NguyenNhan-ygcb]

hey bro, how do you know the blue color is 82 51 51 133, please explain to me. Thank you very much