search
HomeBackend DevelopmentPHP TutorialMachine Vision and Deep Learning Technology in PHP
Machine Vision and Deep Learning Technology in PHPMay 11, 2023 am 11:31 AM
phpdeep learningmachine vision

With the continuous development of artificial intelligence technology, the application of computer vision and machine learning technology is becoming more and more widespread. Among these technologies, machine vision and deep learning are considered to be one of the most advanced and promising technologies today. PHP is widely used in web development and data processing, and it also has a lot of room for development. Therefore, this article will explore how to apply machine vision and deep learning technology in PHP.

1. What are machine vision and deep learning?

Machine vision is a technology that uses computers to process, analyze and understand images and videos. Using machine vision can enable computers to see and understand the world like humans, thereby enabling autonomous decision-making, automatic control and other functions.

Deep learning is a method of machine learning that combines the principles of computer science and neurobiology. The deep learning model is composed of multiple neural network layers. After multiple backpropagation and optimization training, it can achieve the goal of pattern recognition and analysis through large amounts of data.

2. Machine vision technology in PHP

The PHP language has good web development characteristics and ease of use. It can be easily used to process pictures and videos, and perform image analysis and Identify. The following are commonly used machine vision technologies and related tools in PHP:

  1. GD library and ImageMagick

The GD library is an image processing library built into PHP that provides image processing Functions for processing and generation. Similar ImageMagick is a free open source image processing software that can also be used for image processing in PHP.

These two libraries can implement basic image processing functions, such as image scaling, rotation, cropping, color space conversion, etc. But they do not support advanced machine vision operations, such as face recognition, target detection, etc.

  1. OpenCV

OpenCV is a cross-platform computer vision library that provides interfaces for multiple programming languages ​​such as C, Python, and Java. It is very powerful and can perform object detection, tracking, image recognition and other operations. In PHP, its functions can be called using OpenCV's PHP extension.

  1. TensorFlow

TensorFlow is a deep learning framework developed by Google that can be used to build and train deep neural network models. TensorFlow supports multiple programming languages, including Python, C, Java, etc., and also has a third-party extension support for PHP.

Using TensorFlow, you can implement some advanced machine vision operations, such as image classification, object detection, etc. However, because the way PHP calls TensorFlow is relatively complicated, it requires the use of extensions to the PHP calling system library and the TensorFlow environment needs to be configured, which can easily cause deployment troubles and other issues.

3. Deep learning technology in PHP

Compared with machine vision, the application of deep learning in PHP is relatively limited. Currently, the PHP language has no built-in deep learning library and no extensions that directly support deep learning. Therefore, using deep learning in PHP requires the help of some third-party tools.

The following are some commonly used deep learning tools in PHP and a brief description of their uses:

  1. Caffe

Caffe is a tool developed by UC Berkeley The deep learning framework supports a variety of network structures and algorithms, including CNN, RNN, LSTM, etc.

Caffe's PHP extension can be used to run trained models for image classification, target detection and other operations, but using Caffe requires certain deep learning knowledge and experience.

  1. PHP-ML

PHP-ML is a PHP machine learning library that can be used for data analysis, clustering, classification, etc. Although it is not a formal deep learning framework, it can be used to build and train some shallow neural network models.

  1. Theano

Theano is a deep learning library developed by the University of Montreal that can be used to build various types of deep neural network models. Theano can compile the model to C code on the CPU or GPU, and it can be used by calling the C language interface in PHP.

Theano is special compared to other deep learning frameworks. Its code structure and syntax are unique and require a certain learning cost.

IV. Conclusion and Outlook

In summary, although the performance and expansion mechanism of the PHP language are not suitable for the needs of machine vision and deep learning, through some open source tools and third parties Extensions to use some basic machine vision and deep learning techniques in PHP.

In the future, with the development of the PHP language and the improvement of hardware performance, I believe there will be more tools and extensions to support machine vision and deep learning in PHP. In addition, with the development of the Internet of Things, smart homes and other fields, the application prospects of machine vision and deep learning are very broad, which will bring more opportunities and challenges to the PHP language.

The above is the detailed content of Machine Vision and Deep Learning Technology in PHP. For more information, please follow other related articles on the PHP Chinese website!

Statement
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn
人工智能(AI)、机器学习(ML)和深度学习(DL):有什么区别?人工智能(AI)、机器学习(ML)和深度学习(DL):有什么区别?Apr 12, 2023 pm 01:25 PM

人工智能Artificial Intelligence(AI)、机器学习Machine Learning(ML)和深度学习Deep Learning(DL)通常可以互换使用。但是,它们并不完全相同。人工智能是最广泛的概念,它赋予机器模仿人类行为的能力。机器学习是将人工智能应用到系统或机器中,帮助其自我学习和不断改进。最后,深度学习使用复杂的算法和深度神经网络来重复训练特定的模型或模式。让我们看看每个术语的演变和历程,以更好地理解人工智能、机器学习和深度学习实际指的是什么。人工智能自过去 70 多

深度学习GPU选购指南:哪款显卡配得上我的炼丹炉?深度学习GPU选购指南:哪款显卡配得上我的炼丹炉?Apr 12, 2023 pm 04:31 PM

众所周知,在处理深度学习和神经网络任务时,最好使用GPU而不是CPU来处理,因为在神经网络方面,即使是一个比较低端的GPU,性能也会胜过CPU。深度学习是一个对计算有着大量需求的领域,从一定程度上来说,GPU的选择将从根本上决定深度学习的体验。但问题来了,如何选购合适的GPU也是件头疼烧脑的事。怎么避免踩雷,如何做出性价比高的选择?曾经拿到过斯坦福、UCL、CMU、NYU、UW 博士 offer、目前在华盛顿大学读博的知名评测博主Tim Dettmers就针对深度学习领域需要怎样的GPU,结合自

字节跳动模型大规模部署实战字节跳动模型大规模部署实战Apr 12, 2023 pm 08:31 PM

一. 背景介绍在字节跳动,基于深度学习的应用遍地开花,工程师关注模型效果的同时也需要关注线上服务一致性和性能,早期这通常需要算法专家和工程专家分工合作并紧密配合来完成,这种模式存在比较高的 diff 排查验证等成本。随着 PyTorch/TensorFlow 框架的流行,深度学习模型训练和在线推理完成了统一,开发者仅需要关注具体算法逻辑,调用框架的 Python API 完成训练验证过程即可,之后模型可以很方便的序列化导出,并由统一的高性能 C++ 引擎完成推理工作。提升了开发者训练到部署的体验

基于深度学习的Deepfake检测综述基于深度学习的Deepfake检测综述Apr 12, 2023 pm 06:04 PM

深度学习 (DL) 已成为计算机科学中最具影响力的领域之一,直接影响着当今人类生活和社会。与历史上所有其他技术创新一样,深度学习也被用于一些违法的行为。Deepfakes 就是这样一种深度学习应用,在过去的几年里已经进行了数百项研究,发明和优化各种使用 AI 的 Deepfake 检测,本文主要就是讨论如何对 Deepfake 进行检测。为了应对Deepfake,已经开发出了深度学习方法以及机器学习(非深度学习)方法来检测 。深度学习模型需要考虑大量参数,因此需要大量数据来训练此类模型。这正是

聊聊实时通信中的AI降噪技术聊聊实时通信中的AI降噪技术Apr 12, 2023 pm 01:07 PM

Part 01 概述 在实时音视频通信场景,麦克风采集用户语音的同时会采集大量环境噪声,传统降噪算法仅对平稳噪声(如电扇风声、白噪声、电路底噪等)有一定效果,对非平稳的瞬态噪声(如餐厅嘈杂噪声、地铁环境噪声、家庭厨房噪声等)降噪效果较差,严重影响用户的通话体验。针对泛家庭、办公等复杂场景中的上百种非平稳噪声问题,融合通信系统部生态赋能团队自主研发基于GRU模型的AI音频降噪技术,并通过算法和工程优化,将降噪模型尺寸从2.4MB压缩至82KB,运行内存降低约65%;计算复杂度从约186Mflop

地址标准化服务AI深度学习模型推理优化实践地址标准化服务AI深度学习模型推理优化实践Apr 11, 2023 pm 07:28 PM

导读深度学习已在面向自然语言处理等领域的实际业务场景中广泛落地,对它的推理性能优化成为了部署环节中重要的一环。推理性能的提升:一方面,可以充分发挥部署硬件的能力,降低用户响应时间,同时节省成本;另一方面,可以在保持响应时间不变的前提下,使用结构更为复杂的深度学习模型,进而提升业务精度指标。本文针对地址标准化服务中的深度学习模型开展了推理性能优化工作。通过高性能算子、量化、编译优化等优化手段,在精度指标不降低的前提下,AI模型的模型端到端推理速度最高可获得了4.11倍的提升。1. 模型推理性能优化

深度学习撞墙?LeCun与Marcus到底谁捅了马蜂窝深度学习撞墙?LeCun与Marcus到底谁捅了马蜂窝Apr 09, 2023 am 09:41 AM

今天的主角,是一对AI界相爱相杀的老冤家:Yann LeCun和Gary Marcus在正式讲述这一次的「新仇」之前,我们先来回顾一下,两位大神的「旧恨」。LeCun与Marcus之争Facebook首席人工智能科学家和纽约大学教授,2018年图灵奖(Turing Award)得主杨立昆(Yann LeCun)在NOEMA杂志发表文章,回应此前Gary Marcus对AI与深度学习的评论。此前,Marcus在杂志Nautilus中发文,称深度学习已经「无法前进」Marcus此人,属于是看热闹的不

英伟达首席科学家:深度学习硬件的过去、现在和未来英伟达首席科学家:深度学习硬件的过去、现在和未来Apr 12, 2023 pm 03:07 PM

过去十年是深度学习的“黄金十年”,它彻底改变了人类的工作和娱乐方式,并且广泛应用到医疗、教育、产品设计等各行各业,而这一切离不开计算硬件的进步,特别是GPU的革新。 深度学习技术的成功实现取决于三大要素:第一是算法。20世纪80年代甚至更早就提出了大多数深度学习算法如深度神经网络、卷积神经网络、反向传播算法和随机梯度下降等。 第二是数据集。训练神经网络的数据集必须足够大,才能使神经网络的性能优于其他技术。直至21世纪初,诸如Pascal和ImageNet等大数据集才得以现世。 第三是硬件。只有

See all articles

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

AI Hentai Generator

AI Hentai Generator

Generate AI Hentai for free.

Hot Tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

MantisBT

MantisBT

Mantis is an easy-to-deploy web-based defect tracking tool designed to aid in product defect tracking. It requires PHP, MySQL and a web server. Check out our demo and hosting services.

MinGW - Minimalist GNU for Windows

MinGW - Minimalist GNU for Windows

This project is in the process of being migrated to osdn.net/projects/mingw, you can continue to follow us there. MinGW: A native Windows port of the GNU Compiler Collection (GCC), freely distributable import libraries and header files for building native Windows applications; includes extensions to the MSVC runtime to support C99 functionality. All MinGW software can run on 64-bit Windows platforms.

WebStorm Mac version

WebStorm Mac version

Useful JavaScript development tools

Safe Exam Browser

Safe Exam Browser

Safe Exam Browser is a secure browser environment for taking online exams securely. This software turns any computer into a secure workstation. It controls access to any utility and prevents students from using unauthorized resources.