用OpenCV和Python学习计算机视觉 Learn Computer Vision with OpenCV and Python

图像处理基础知识、对象检测和跟踪、深度学习、面部标志和许多特殊应用

你会学到什么
理解计算机视觉和图像处理的基础知识
使用OpenCV构建计算机视觉应用
提高Python编程技能
对象检测和跟踪示例
计算机视觉的深度学习
除了学习一些OpenCV函数，你还会有很多自己算法的特殊例子

MP4 |视频:h264，1280×720 |音频:AAC，44.1 KHz
语言：英语+中英文字幕（云桥CG资源站 机译）|大小:4.38 GB |时长:8小时36分钟


要求
有基本的Python基础更佳，但不需要编程知识。
本课程所需的所有软件都是免费和开源的。
只安装Python和OpenCV
描述
注意:你会找到真实世界的例子(不仅仅是使用OpenCV中实现的函数),到时候我会添加更多。这意味着课程内容将增加新的特殊示例！。

***新章节* * *:课程中增加了“如何准备数据集和训练你的深度学习模型”。你将学习如何准备一个简单的数据集，标记对象，并训练自己的深度学习模型。

***新的特殊应用* * *:课程中添加了“搜索团队徽标”。您将了解如何比较图像并在数据集中找到相似的图像/对象。

***新章节* * *:课程中增加了“特殊应用程序-丢失和遗弃物品检测”。你将学习如何做一个丢失物体检测和遗弃物体检测的应用程序

***新章节* * *:课程中增加了面部标志和特殊应用(实时睡眠和微笑检测)视频！

***不同的特殊应用章节* * *:不同主题的新视频将在该章节下分享。你可以看看“足球运动员检测”和“基于深度学习的物体检测API”的例子。


在这门课程中，你将从头开始学习计算机视觉和图像处理。你会接触到所有的资源，有许多例子和这些例子的解释。

解释很容易理解，你也可以问你需要的要点。

我已经与您分享了没有大量数学理论的关键概念，因此我们可以专注于实现。

也许你可以找到一些其他的资源，视频或博客来了解我的课程中解释的一些主题，但这门课程的好处是，你将通过遵循一个顺序从零开始学习计算机视觉，这样你就不会在许多不同的来源之间迷失自己。

除了基本主题之外，你还会发现许多特殊的例子。

我更喜欢使用OpenCV，它是一个开源的计算机视觉库，被很多人使用和支持！。我曾将OpenCV与Python一起使用，因为Python允许我们轻松地关注问题，而无需花费时间编写语法/复杂代码。

我希望本课程对您学习计算机视觉有所帮助，并且我们可以积极地使用“问答”区来分享信息…

你将学习这些主题

计算机视觉的关键概念& OpenCV

基本操作:直方图均衡、阈值、卷积、边缘检测、锐化、形态学操作、图像金字塔。

关键点和关键点匹配

特殊应用程序:使用关键点的迷你游戏

图像分割:分割和轮廓，轮廓属性，线检测，圆检测，斑点检测，分水岭分割。

特殊应用程序:人员计数器

对象跟踪:跟踪API，按颜色过滤。

特殊应用:移动物体的跟踪

目标检测:人脸和眼睛检测，猪行人检测

具有深度学习的对象检测

额外章节:如何准备数据集和训练你的深度学习模型

额外章节:特殊应用-丢失和遗弃物品检测

额外章节:面部标志和特殊应用(实时睡眠和微笑检测)

额外章节:不同的特殊应用(将在不同主题中更新特殊示例)

这门课是给谁的
从零开始学习计算机视觉的热情
面向寻求计算机视觉应用的学生

MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 4.38 GB | Duration: 8h 36m

Image processing basics, Object detection and tracking, Deep Learning, Facial landmarks and many special applications

What you’ll learn
Understanding the fundamentals of computer vision & image processing
Build computer vision applications using OpenCV
Improve programming skills in Python
Object detection and tracking examples
Deep Learning for Computer Vision
Beside learning some OpenCV functions, Also you will have many special examples with own algorithm

Requirements
Basic Python is a plus, but no programming knowledge is needed.
All the software needed in this course is free and open source.
Only install Python and OpenCV
Description
Note: You will find real world examples (not only using implemented functions in OpenCV) and i’ll add more by the time. It means that course content will expand with new special examples!.

***New Chapter***: “How to Prepare dataset and Train Your Deep Learning Model” was added to the course. You will learn how to prepare a simple dataset, label the objects and train your own deep learning model.

***New Special App***: “Search team logos” was added to the course. You will learn how you can compare images and find similar image/object in your dataset.

***New Chapter***: “Special Apps – Missing and Abandoned Object Detection” was added to the course. You will learn how to do an application for missing object detection and abandoned object detection

***New Chapter***: Facial Landmarks and Special Applications (real time sleep and smile detection) videos was added to the course!

***Different Special Applications Chapter***: new videos in different topics will be shared under this chapter. You can look at “Soccer players detection” and “deep learning based API for object detection” examples.

In this course, you are going to learn computer vision & image processing from scratch. You will reach all resources, have many examples and explanations of these examples.

The explanations are easy to understand and also you can ask the points you need.

I have shared key concepts with you without the heavily mathematical theory, so we can focus the implementation.

Maybe you can find some other resources, videos or blogs to learn about some of these topics explained in my course, but the advantage of this course is that, you will learn computer vision from scratch by following an order, so that you will not loss yourself between many different sources.

You will also find many special examples beside the fundamental topics.

I preferred to use OpenCV which is an open source computer vision library used and supported by many people!. I have used OpenCV with Python, because Python allows us to focus on the problem easily without spending time for programming syntax/complex codes.

I wish this course to be useful for you to learn computer vision, and Actively we can use ‘questions and answers’ area to share information…

You will learn the topics

The key concepts of computer Vision & OpenCV

Basic operations: histogram equalization,thresholding, convolution, edge detection, sharpening ,morphological operations, image pyramids.

Keypoints and keypoint matching

Special App : mini game by using key points

Image segmentation: segmentation and contours, contour properties, line detection, circle detection, blob detection, watershed segmentation.

Special App: People counter

Object tracking:Tracking APIs, Filtering by Color.

Special App: Tracking of moving object

Object detection: haarcascade face and eye detection, HOG pedestrian detection

Object detection with Deep Learning

Extra Chapter: How to Prepare dataset and Train Your Deep Learning Model

Extra Chapter: Special Apps – Missing and Abandoned Object Detection

Extra Chapter: Facial Landmarks and Special Applications (real time sleep and smile detection)

Extra Chapter: Different Special Applications ( will be updated with special examples in different topics )

Who this course is for
Passion to learn computer vision from scratch
For students looking for computer vision applications
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