Learn Computer Vision with Jetson Nano using Python | 3x Projects 2021



In this course we are going to learn the basics of a computer vision along with their implementation on the Jetson Nano by Nvidia. We will start with the installations and setup, and then move on to the fundamentals of computer vision. From their we will learn the concept of modules and how we can create efficient modular code that can be used in different projects. Then we will create 3 different projects to apply what we have learnt in real life applications. The first projects is Eye tracking where we will teach the Nano to track a human face then we have a Robot car we we will build and program the car to track Objects and follow lanes like a self driving car. The last project will be Face attendance where we will learn to recognize faces and store them in a Realtime database.

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Computer Vision – Jetson Nano


By Enrolling to the course you will get access to the complete course including code and 3d printing files.

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Time Stamps:
00:00 Intro
1:28​ Downloads
4:53​ Setup
10:21​ IDE
14:02​ Chapter 1
24:37​ Chapter 2
38:21​ Chapter 3
48:43​ Chapter 4
59:51​ Concept of Modules
1:08:01​ Face Detection Module
1:30:05 Color Detection Module
1:30:52​ Shape Detection Module
1:31:07​ GPIO Basics
1:40:15 LED Module
1:49:39​ Arduino Serial Introduction
1:55:48 Arduino Install
1:57:41​ Serial Receive Arduino
2:21:07​ Serial Receive Python
2:35:21​ Serial Send Python
2:36:20​ Eye Tracking – Introduction
2:40:47​ Eye Tracking – Assembly
2:41:07​ Eye Tracking – Wiring
2:41:26​ Eye Tracking – Motor Center
2:51:15​ Eye Tracking – Serial Control
2:56:18​ Eye Tracking – Python Code
3:39:37​ Eye Tracking – Result
3:41:40​ Robot Car – Parts
3:41:59​ Robot Car – Assembly
3:42:15​ Robot Car – Wiring
3:42:40 Robot Car – Motor Test
3:52:35​ Robot Car – Motor Function
4:06:31​ Robot Car – Motor Serial
4:11:51​ Robot Car – Keyboard Control
4:28:07​ Robot Car – Keyboard Control Result
4:28:34​ Robot Car – Object Tracking Result
4:29:35​ Robot Car – Lane Following Result
4:30:57​ Face Attendance – Result

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