pysource

Welcome! (how to use the course)

Install Python and Opencv (on Windows)

PyCharm IDE (Install, create new projects, useful shortcuts)

Install Opencv with CUDA GPU (on Windows)

Install Darknet with CUDA GPU (on Windows)

What Is OpenCV and What you'll learn in this module

1. Load and Save images

2. Load and Save videos

3. Drawing functions

4. Basic Operations with images

5. Arithmetical Operations

6. Find and draw contours

7. Contours 4 main features

4 different Object Detection methods

1.1 OBJECT DETECTION BY COLOR: The HSV Colorspace

1.2 OBJECT DETECTION BY COLOR: Detect objects on an Image and in Real Time

2.1 OBJECT DETECTION ON HOMOGENEOUS BACKGROUND: The Threshold

2.2 OBJECT DETECTION ON HOMOGENEOUS BACKGROUND: Detect objects on an Image and in Real time

3.1 OBJECT DETECTION WITH BACKGROUND SUBTRACTION: Simple background subtraction and MOG

3.2 OBJECT DETECTION WITH BACKGROUND SUBTRACTION: Detect objects on an Image and In Real Time

4.1 OBJECT DETECTION USING FEATURES: What are Features and Feature Matching

4.2 OBJECT DETECTION USING FEATURES: Detect objects on an Image and in Real Time

4.3 OBJECT DETECTION USING FEATURES: Improve the detection with Lowe's ratio test

Object detection with Deep Learning

1. Detect Objects with YOLO

2.1) Annotate your own images (to detect custom objects)

2.2 Download Images from OID (on Google Colab)

2.3 Download Images from OID (with OIDV4 Toolkit) - for big datasets

2.4 Convert OID (open Images Dataset) to YOLO

2.5 Video Annotation (Create images dataset from a video)

3.1 Train Custom object Detector (on your computer with GPU)

3.2 Train Custom object Detector (on google colab, FREE online GPU)

4. Detect Custom Objects

Run YOLO on GPU

1. Object Detection vs Object Tracking

2. Object Tracking (with Euclidean Distance)

3. Object Tracking (with SORT)

4. Object Tracking (YOLO + SORT)

5. Object Counting

6. Object Trajectory

7. Object Tracking (with Deep Sort)