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README
Apache-2.0

凌智视觉模块(Lockzhiner Vision Module)

凌智视觉模块(Lockzhiner Vision Module) 是福州市凌睿智捷电子有限公司联合百度飞桨倾力打造的一款高集成度人工智能视觉模块,专为边缘端人工智能和机器视觉应用而设计,其特色包括:

  • 计算能力:凌智视觉模块搭载单核 ARM Cortex-A7 处理器,并集成了 NEON 和 FPU, 以更出色地处理 SIMD 和浮点计算。同时,该模块支持 Int8 量化,内置 1 TOPs 的 NPU, 足以应对绝大多数的视觉应用场景。
  • 开源适配:凌智视觉模块与飞桨低代码开发工具 PaddleX 完美适配,目前支持PaddleX 中的通用图像分类模型产线, 其余任务场景正在适配中, 未来会陆续上线。
  • 简单易用:配合飞桨星河社区 Al Studio, 用户可以实现一键训练;配合凌智视觉算法部署库,用户可以实现一键部署,极大地减轻了用户的使用成本。

注:

🔄 最新日志

🎁 购买链接

💡 入门学习教程

这一部分教程旨在为你提供一个系统化的学习路径,帮助你快速上手 Lockzhiner Vision Module。通过一系列详细的教程, 你将学会如何烧录镜像、连接设备、搭建开发环境和编写简单的程序。

🔌 外设控制教程

Lockzhiner Vision Module 拥有丰富的 IO 接口,其接口图片如下图所示:

为了帮助你快速上手教程,我们提供了一系列的外设控制例程,你可以点击链接快速学习

🧠 深度学习视觉部署教程

这一部分教程将引导你从零开始训练并部署各种深度学习模型,包括分类模型、检测模型、分割模型和 OCR 模型。通过这些实战教程,你将学习到如何利用 Lockzhiner Vision Module 的高性能计算能力以及借助飞桨生态系统的丰富资源,轻松实现视觉任务的训练和部署。

👍 OpenCV 案例

OpenCV 是一个开源的计算机视觉库,它提供了一组功能强大的函数,用于处理图像和视频,并进行各种图像处理和计算机视觉任务。它由一系列 C++ 类构成,并提供了 Python 接口。

👍 目标分类案例

目标分类(Object Classification)是深度学习中的一项核心任务,旨在为输入图像分配一个或多个类别标签。这是计算机视觉的基础问题之一,主要用于识别和区分图像中的主要物体类别,而不关心物体的位置或数量。一般来说,目标分类任务的标注过程比较轻松,适合不需要知道目标位置的场景。

👍 目标检测案例

目标检测(Object Detection)是深度学习中计算机视觉领域的重要任务之一,旨在识别图像或视频中所有感兴趣的物体,并准确地定位这些物体的边界框(Bounding Box)。与目标分类不同,目标检测不仅需要预测物体的类别,还需要标注它们在图像中的位置。一般来说,目标检测任务的标注过程比较复杂,适合既需要对目标进行分类,有需要对目标进行定位的场景。

👍 条码检测识别案例

条码(二维码和条形码)检测与识别任务是计算机视觉领域的一个具体应用,旨在从图像中准确定位和解码条码的信息。条码分为一维条码(如条形码)和二维条码(如二维码),它们广泛用于商品标识、物流跟踪、电子支付等场景。

👍 人脸检测识别案例

在人脸检测与识别任务中,目标是从图像或视频中检测出人脸区域,并进一步识别身份信息。这一任务已广泛应用于安全监控、人机交互、身份验证等场景,是计算机视觉领域的重要研究方向。

👍 OCR 案例

OCR(Optical Character Recognition,光学字符识别)是一种将图像中的文字(印刷体或手写体)转换为可编辑文本的技术。

👍 C++ 开发案例

C++ 开发案例以A、B、C、D进行不同类别进行分类,方便初学者进行使用和二次开发。

  • A01 - A99: 基础外设类
  • B01 - B99: OpenCV基础函数类
  • C01 - C99: 传统OpenCV识别类
  • D01 - D99: 神经网络模型类

例程列表如下所示:

编号 类别 例程名 说明
A01 基础外设类 Capture 图像采集例程
A02 基础外设类 GPIO GPIO 控制例程
A03 基础外设类 PWM PWM 信号控制例程
A04 基础外设类 ADC ADC 传感器数据采集例程
A05 基础外设类 USART 串口通信例程
B01 OpenCV基础类 Basic Method OpenCV 基础操作
B02 OpenCV基础类 Image Information Statistics 图像信息统计
B03 OpenCV基础类 Draw 绘制图形示例
C01 传统识别类 Find Blobs Blob 检测
C02 传统识别类 Template Matching 单模板匹配
C03 传统识别类 Template Matching More 多模板匹配
C04 传统识别类 Find Contours 轮廓查找
C05 传统识别类 Fine Color and Shape 颜色与形状定位
C06 传统识别类 QR Code Detector 二维码检测
C07 传统识别类 Bar Code Detector 条形码检测
D01 神经网络类 Test Detection 目标检测模型
D02 神经网络类 Digit Hand Recognition 手写数字识别
D03 神经网络类 Face Recognition System 人脸识别系统
D04 神经网络类 Distance 距离检测
D05 神经网络类 OCR Text Recognition OCR文字识别
D06 神经网络类 OCR Text Detection OCR文字检测
D07 神经网络类 OCR Synthesis OCR综合示例

🐛 Bug反馈

如果您遇到问题,您可以前往 Lockzhiner Vision Module Issues 并点击已完成按钮查看其他用户反馈且我们已经解决的 Bug。

如果查找不到对应的解决方案,可点击新建 Issues 来反馈您的 Bug, 我们将尽快与您沟通并及时解决 Bug。

🔧 使用到的开源仓库

开源是推动技术进步和创新的重要力量,我们采用了多个开源仓库来增强功能、提升效率。

😊 致谢

凌智视觉模块(Lockzhiner Vision Module) 离不开社区的支持,特别感谢以下个人(或团体):

  • 感谢 Rockchip NPU 团队帮忙调试 PaddlePaddle 模型到 RKNN 模型的推理和优化
  • 感谢飞桨团队在适配 PaddleX 等套件上提供的帮助
  • 感谢 nihui 大佬帮忙审核并将 Lockzhiner Vision Module 的 ISP 代码合入到 opencv-mobile

📜 开源协议

Lockzhiner Vision Module 全系列仓库遵循 Apache License Version 2.0

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20小时前推送了新的提交到 develop 分支,b272bf4...ccebb74
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