Computer-Vision

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Computer-Vision

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Coursework in CV

Computer-Vision 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# Computer-Vision

## Description

This repository contains all files for an introduction into computer vision.

## Contents

### 0) Numpy and Image Basics

A basic introduction of Numpy and Image handling in Python. Course chapter indexing starts with 0. Because why just python when you can python while you python.

### 1) Image basics with OpenCV

A basic introduction to Image handling using OpenCV. OpenCV can interface with Python but has core functionality in C++, making it fast enough to process real time image data.

### 2) Image Processing

An introduction to image visualization and manipulation with techniques including: color mappings, blending and pasting, thresholding, blurring and smoothing, morphological operators, gradients, and histograms.

### 3) Video Basics

Intructions on how to open and use saved video files, and pull video feed from camera hardware.

### 4) Object Detection

Introduces and explains how to track objects in Python and OpenCV. Contained are instructions for what features the software detects and how the software can detect these features. This is where this fun begins.

### 5) Object Tracking

This folder is empty. Unfortunately all material is part of the course and there is no code to share.

### 6) Deep Learning with Computer Vision

This is the introdcution to automated computerized image processing. All deep learning is done with the Keras library. Note that this folder also contains .h5 files. These are model files, more info inside tha code.

### 7) Capstone Project

The capstone projecet in this course is a finger counter. This uses the live video from a camera and in a segment of the image feed counts how many fingers are being held up. More description to be found in the code.

## Course

The course in its original form is found on udemy.com: https://www.udemy.com/course/python-for-computer-vision-with-opencv-and-deep-learning/.

## Environment

All files writen in Jupyter Notebook. I recomend running this file through Anaconda (https://www.anaconda.com/). It is important that you run the cvcourse_xxxxx.yml

Make sure that the xxxxx in cvcourse_xxxxx.yml matches your operating system. This .yml file loads a specfici environment of anaconda with all libraries and packages in the correst release.

After runnning this file, make sure that the correct envirnment is selected in anaconda navigator **before** loading Jupyter Notebook.

Thank you!

Computer-Vision

Computer-Vision 최신버젼 다운로드

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