Computer Vision Smart Systems : Python, Yolo And Opencv -1

Develop Your Own Computer Vision Smart System with Python and YOLO | Step-by-Step | OpenCV | Real-Time Object Detection

What you'll learn

Introduction to Image Processing and Computer Vision,

Object Detection with YOLO Algorithm

Building Custom Deep Learning Models

Practical Vehicle Counting Project

Project Development and Implementation

Requirements

No Prior Experience Required

Basic Computer Skills

Eagerness to Learn

Description

This course is designed for anyone interested in pursuing a career in artificial intelligence and computer vision or looking to implement computer vision applications in their projects. In "Computer Vision Smart Systems: Python, YOLO, and OpenCV -1," we start with the fundamentals of computer vision and cover image processing techniques using the Python programming language and OpenCV library. Then, we advance to object detection and deep learning modeling using the YOLO (You Only Look Once) algorithm. Students will learn to build custom deep learning models from scratch, work with datasets, perform object detection, and apply these models in various projects.Throughout the course, practical exercises are provided step-by-step along with theoretical knowledge, giving students the chance to apply what they've learned. Additionally, we address common challenges you may face and provide detailed solutions. Aimed at building skills from basic to intermediate levels, this course serves as a comprehensive guide for anyone interested in the field of computer vision. It empowers you to develop smart systems for your projects and enhances your expertise in this exciting domain."You are never too old to set another goal or to dream a new dream." - C.S.Lewis"Do the difficult things while they are easy and do the great things while they are small. A journey of a thousand miles begins with a single step" - Lao TzuYou get the best information that I have compiled over years of trial and error and experience!Best wishes,Yılmaz ALACA

Overview

Section 1: Introduction

Lecture 1 Hello!

Section 2: Docs - Optional

Lecture 2 File Operations

Lecture 3 applyColorMap

Section 3: Python 3

Lecture 4 Installation of Python

Lecture 5 Installation of Jupyter Notebook

Lecture 6 What Is Variable?

Lecture 7 Numerical Data Types (Variables) -1

Lecture 8 Numerical Data Types (Rules of Variables) -2

Lecture 9 Numerical Data Types (Rules of Variables) -3

Lecture 10 String Data Type -1

Lecture 11 Changing Variable (Rules of Variables) -4

Lecture 12 Standart OUTPUT Function

Lecture 13 Lists -1

Lecture 14 Lists -2

Lecture 15 Standart Input Function

Lecture 16 Type Conversions -1

Lecture 17 Type Conversions 2

Lecture 18 Tuples

Lecture 19 Logical Data Types

Lecture 20 Logical Conjuctions -1

Lecture 21 Logical Conjuctions -2

Lecture 22 What is Conditional Structure?

Lecture 23 If Condition

Lecture 24 If-Else Condition

Lecture 25 If-Elif-Else Condition

Lecture 26 What is loop?

Lecture 27 Structure "in"

Lecture 28 For Loop -1

Lecture 29 For Loop -2

Lecture 30 While Loop

Lecture 31 While - Else

Lecture 32 break and continue

Lecture 33 Functions -1

Lecture 34 Default Parameter and Parameter Order

Lecture 35 Functions -2

Lecture 36 Multiple Data in a Parameter

Lecture 37 Functions -3

Lecture 38 Functions -4

Lecture 39 Functions -5

Lecture 40 Modules -1

Lecture 41 Modules -2

Lecture 42 Modules -3

Lecture 43 Installation of PyCharm

Section 4: OpenCV

Lecture 44 Installation of OpenCV

Lecture 45 Displaying an Image

Lecture 46 Getting Size of Images

Lecture 47 Resizing

Lecture 48 Cropping a Certain Area

Lecture 49 Drawing Functions -1

Lecture 50 Drawing Operations -2

Lecture 51 Drawing Functions -3

Section 5: Coding Exercises

Section 6: Libraries | Modules

Lecture 52 Theory: Creating a Special PutText Function

Lecture 53 Creating a Special PutText Function (Coding) -1

Lecture 54 Creating a Special PutText Function (Coding) -2

Lecture 55 Creating a Special Rectangle Function (Coding) -1

Lecture 56 Creating a Special Rectangle Function (Coding) -2

Lecture 57 Creating a Object List Function for YOLO -1

Lecture 58 Creating a Object List Function for YOLO -2

Lecture 59 Creating a Object List Function for YOLO -3

Lecture 60 Creating a Object List Function for YOLO -4

Lecture 61 Creating a Stacking Images Function -1

Lecture 62 Creating a Stacking Images Function -2

Lecture 63 Creating a Dir Function

Lecture 64 Creating a pointsOfMouse Function

Section 7: Creating Custom Models with YOLO (Deep Learning)

Lecture 65 Installation of Packages

Lecture 66 Yolo (GPU)

Lecture 67 Data Collection -1 (Modules and Variables)

Lecture 68 Data Collection -2 (Modules and Variables)

Lecture 69 Data Collection -3 (Loops)

Lecture 70 Data Collection -4 (Loops)

Lecture 71 Data Collection -5 (Loops)

Lecture 72 Data Collection -6 (Loops)

Lecture 73 Solving Problems about Data Collection

Lecture 74 Collecting Data

Lecture 75 Data Cleaning -1

Lecture 76 Data Cleaning -2

Lecture 77 Data Cleaning -3

Lecture 78 Data Cleaning -4

Lecture 79 Data Cleaning -5

Lecture 80 Data Cleaning -6

Lecture 81 Data Cleaning

Lecture 82 Data Splitting

Lecture 83 Data Splitting -2

Lecture 84 Training (Custom Model)

Lecture 85 Testing -1

Lecture 86 Testing -2

Section 8: Project 1: Car Counter

Lecture 87 Creation of Variables

Lecture 88 Creation of Loop

Beginners in Programming: Individuals with no prior programming experience who want to learn Python and apply it to real-world applications in computer vision.,Aspiring Data Scientists: Those looking to enter the field of data science and machine learning, particularly in the domain of computer vision.,Students and Professionals: College students, recent graduates, or professionals seeking to expand their skill set in artificial intelligence and image processing.,Hobbyists and Enthusiasts: Anyone with a keen interest in technology, artificial intelligence, or robotics who wants to explore how computer vision systems are built.,Developers Transitioning to AI: Programmers or software developers wanting to branch into the field of artificial intelligence and machine learning, specifically in computer vision.,Project-Based Learners: Individuals who learn best through hands-on projects and practical applications, as this course includes a final project on vehicle counting.