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@@ -17,9 +17,9 @@ M5Stack recently launched the new AIoT(AI+IoT) Camera powered by Kendryte K210 -
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<br><br>
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M5StickV AI Camera features its integration with machine vision capabilities, featuring the unprocessed acceptability to AI Visioning with high energy efficiency and low cost. We co-oped with Sipeed providing the MicroPython environment makes programming onM5StickV easier.
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<br><br>
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The module comes with the OmniVision OV7740 sensor, using the OmniPixel®3-HS technology, providing a best-in-class low light sensitivity, making it ideal for machine vision. In addition to an OV7740 sensor, M5StickV features more hardware resources such as a speaker with built-in I2S Class-D DAC, MEMS Microphone, IPS screen, 6-axis IMU, 200mAh Li-po battery, and more.
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The module comes with the OmniVision OV7740 sensor, using the OmniPixel®3-HS technology, providing a best-in-class low light sensitivity, making it ideal for machine vision. In addition to an OV7740 sensor, M5StickV features more hardware resources such as a speaker with built-in I2S Class-D DAC, IPS screen, 6-axis IMU, 200mAh Li-po battery, and more.
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<br><br>
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More than the visioning, M5StickV also features the embedded APU - Audio Processor. With its hardware beam-forming support and dual 512-point FFT units, the M5StickV is also capable of a series of machine hearing works like voice wake-up to speech recognition.
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<br><br><br>
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<mark style="background-color: #007bff; color:white">Note: M5StickV does not support microphone function, the microphone function will be added in the updated WiFi version M5StickV+.</mark>
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@@ -36,7 +36,7 @@ More than the visioning, M5StickV also features the embedded APU - Audio Process
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- Dual hardware 512-point 16bit Complex FFT
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- SPI, I2C, UART, I2S, RTC, PWM, Timer Support
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- AES, SHA256 Accelerator
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- Direct Memory Access Controller (DMAC)
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- Direct Memory Access Controller (DMAC)
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- Micropython Support
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- Firmware encryption support
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- Case Material: PC + ABS
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@@ -191,9 +191,8 @@ M5StickV does not currently recognize all types of SD cards. We have tested some
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### FUNCTIONAL DESCRIPTION
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#### 1.1 KENDRYTE K210
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The Kendryte K210 is a system-on-chip (SoC) that integrates machine vision and machine hearing. Using TSMC’s ultra-low-power 28-nm advanced process with dualcore 64-bit processors for better power efficiency, stability and reliability. The SoC strives for ”zero threshold” development and to be deployable in the user’s products in the shortest possible time, giving the product artificial intelligence<br><br>
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The Kendryte K210 is a system-on-chip (SoC) that integrates machine vision. Using TSMC’s ultra-low-power 28-nm advanced process with dualcore 64-bit processors for better power efficiency, stability and reliability. The SoC strives for ”zero threshold” development and to be deployable in the user’s products in the shortest possible time, giving the product artificial intelligence<br><br>
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- Machine Vision
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- Machine Hearing
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- Better low power vision processing speed and accuracy
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- KPU high performance Convolutional Neural Network (CNN) hardware accelerator
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- Advanced TSMC 28nm process, temperature range -40°C to 125°C
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@@ -292,7 +291,7 @@ The triple-axis MEMS accelerometer in MPU-6886 includes a wide range of features
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- Obtaining size and coordinates of the target in real-time
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- Obtaining the type of detected target in real-time
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- Shape recognition
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- Video/Audio Record/Display
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- Video/Display
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- Game simulator
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@@ -82,7 +82,7 @@ Besides, a power switch is placed at the front.
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### 1. Arduino IDE
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*To get complete code, please click [here](https://github.com/m5stack/M5-ProductExampleCodes/tree/master/Hat/beetleC/stickc/Arduino/beetleC).*
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*To get complete code, please click [here](https://github.com/m5stack/M5-ProductExampleCodes/tree/master/Hat/beetleC/stickC/beetleC).*
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## Video
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@@ -16,7 +16,7 @@
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- Rated current: 0.14 A
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- Power consumption: 0.7W
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- Speed: 7500 ± 10% RPM
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- Noise: 300BA
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- Noise: 30DBA
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- Bearing: Hydraulic bearing
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- Wiring: red line positive, black line negative
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- Product Size：54.2mm x 54.2mm x 12.8mm
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@@ -12,7 +12,9 @@
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**[5. Run Recognition Program](#Run-Recognition-Program)**
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## EasyLoader
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<h4><mark>Users who have already programmed the firmware should start directly from the third step.</mark></h4>
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## EasyLoader <span class="badge badge-secondary">optional</span>
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<img src="https://m5stack.oss-cn-shenzhen.aliyuncs.com/image/EasyLoader_logo.png" width="100px" style="margin-top:20px">
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@@ -22,7 +24,7 @@
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>2, After downloaded , double click to run the app, connect the device to computer via USB cable, select the com port number, then click "Burn" to start it.
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## Download Firmware
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## Download Firmware <span class="badge badge-secondary">optional</span>
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> EasyLoader is only Window-supported. If you don't have a Windows computer or you would like to download specific file to flash , please use "Kflash, download firmware below "
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@@ -56,7 +58,7 @@
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> Material Training requires SD cards, users could downloade boot code zip files, unzip the files to SD card.(M5StickC only recognized certain type of SD card , [click to see the supported type](en/core/m5stickv?id=sd-card-test))
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<a href="https://m5stack.oss-cn-shenzhen.aliyuncs.com/resource/docs/V-Training_boot_0823.zip"><button type="button" class="btn btn-primary">Click here download boot zip</button></a>
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<a href="https://m5stack.oss-cn-shenzhen.aliyuncs.com/resource/docs/VTraining-Client-VerA02.zip"><button type="button" class="btn btn-primary">Click here download boot zip</button></a>
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<img src="assets\img\related_documents\v-training\1.jpg" width="60%">
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@@ -76,7 +78,7 @@
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<img src="assets\img\related_documents\v-training\4.jpg" width="100%">
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<mark>note：In order to reach a certain accuracy, each Class should contains at least 120 pictures, or the Could Training would give out a rejection</mark>
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<mark>note：In order to reach a certain accuracy, each Class should contains at least 35 pictures, or the Could Training would give out a rejection</mark>
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## Material Checking and Compress
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@@ -87,7 +89,7 @@
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> Inside folder "train","vaild", they share exact the same folder directory, when we switch Class, the program will generate the same folder (with a name of Class number) in both "train" and "vaild". The phtotos will placed either in "train" or "vaild", underneath the coorespondent Class folder.
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> Before we compress the package, we should check the photo and photo number, make sure for the same Class, the number of photos in the coorespondent Class Folder in
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<mark>"train" and "vaild" should add up over 120. (like n1-n100 in train, n100-n120 in vaild). If any Class photos total amount were under 120, please either delete it or copy for back up. After finish the checking, let's compress the "train" and "vaild" to ZIP.
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<mark>"train" and "vaild" should add up over 35. If any Class photos total amount were under 35, please either delete it or copy for back up. After finish the checking, let's compress the "train" and "vaild" to ZIP.
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</mark>
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## Upload Data to Cloud
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@@ -21,8 +21,6 @@ M5stickV AI 摄像头具备机器视觉能力，支持多种视觉识别能力
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<br><br>
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配备OmniVision OV7740图像传感器，采用OmniPixel®3-HS技术，提供相比同类最佳的低光灵敏度，是机器视觉项目的理想选择.
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M5StickV不仅具备视觉识别能力，其内置的嵌入式APU - 音频处理器. 能够进行一系列机器听觉工作，同时配备I2S D类DAC的扬声器，IPS屏幕，6轴IMU，200mAh锂电池等硬件，能够为你的项目提供极好的硬件条件.
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<mark style="background-color: #007bff; color:white">注意: M5StickV当前版本不支持麦克风功能， 该功能将会在WIFI版的M5StickV+中集成.</mark>
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<br><br><br>
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@@ -195,9 +193,8 @@ M5StickV目前并不能识别所有类型的SD卡，我们对一些常见的SD
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### 功能描述
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#### 1.1 KENDRYTE K210
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Kendryte K210 是集成机器视觉与机器听觉能力的系统级芯片 (SoC)。使用台积电 (TSMC) 超低功耗的 28 纳米先进制程，具有双核 64 位处理器，拥有较好的功耗性能，稳定性与可靠性。该方案力求零门槛开发，可在最短时效部署于用户的产品中，赋予产品人工智能.<br><br>
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Kendryte K210 是集成机器视觉能力的系统级芯片 (SoC)。使用台积电 (TSMC) 超低功耗的 28 纳米先进制程，具有双核 64 位处理器，拥有较好的功耗性能，稳定性与可靠性。该方案力求零门槛开发，可在最短时效部署于用户的产品中，赋予产品人工智能.<br><br>
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- 具备机器视觉能力
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- 具备机器听觉能力
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- 更好的低功耗视觉处理速度与准确率
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- 具备卷积人工神经网络硬件加速器 KPU，可高性能进行卷积人工神经网络运算
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- TSMC 28nm 先进制程，温度范围-40°C 到 125°C，稳定可靠
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@@ -293,7 +290,7 @@ MPU-6886中的三轴MEMS加速度计包括多种功能：
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- 实时获取目标的大小和坐标
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- 实时获取检测到的目标类型
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- 形状识别
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- 视频/音频记录/显示
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- 视频/显示
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- 游戏模拟器
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@@ -80,7 +80,7 @@ Beetlec底座需要结合M5StickC控制器使用.在底座上，配备了两个
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### 1. Arduino IDE
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[点击此处](https://github.com/m5stack/M5-ProductExampleCodes/tree/master/Hat/beetleC/stickc/Arduino/beetleC)，获取完整程序.
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[点击此处](https://github.com/m5stack/M5-ProductExampleCodes/tree/master/Hat/beetleC/stickC/beetleC)，获取完整程序.
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## 相关视频
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@@ -16,7 +16,7 @@
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- 额定电流：0.14 A
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- 消耗功率：0.7W
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- 转速：7500±10%RPM
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- 噪音：300BA
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- 噪音：30DBA
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- 轴承：液压轴承
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- 接线：红线正极，黑线负极
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- 产品尺寸：54.2mm x 54.2mm x 12.8mm
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@@ -12,7 +12,9 @@
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**[5. 运行识别程序](#运行识别程序)**
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## EasyLoader
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<h4><mark>已经烧录了固件程序的用户请直接从第三步开始</mark></h4>
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## EasyLoader <span class="badge badge-secondary">可选</span>
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<img src="https://m5stack.oss-cn-shenzhen.aliyuncs.com/image/EasyLoader_logo.png" width="100px" style="margin-top:20px">
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@@ -22,7 +24,7 @@
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>2.下载软件后，双击运行应用程序，将M5设备通过数据线连接至电脑,选择端口参数，点击 **"Burn"** 即可开始烧录
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## 下载固件
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## 下载固件 <span class="badge badge-secondary">可选</span>
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> 需要指定烧录文件的用户可以选用**Kflash**进行固件烧录.
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@@ -49,7 +51,6 @@
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>3.对于习惯使用命令行操作的用户来说还可以选择Kflash作为固件烧录工具.[点击此处查看详情](https://github.com/kendryte/kflash.py)
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## 训练素材拍摄
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### boot程序
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@@ -57,7 +58,7 @@
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> 拍摄训练素材需要使用到SD卡，用户需下载boot程序压缩包，并将压缩包内的所有文件解压放置到SD卡中（M5StickV对SD卡的选型有所要求，[点击此处查看支持类型](zh_CN/core/m5stickv?id=sd卡测试)）
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<a href="https://m5stack.oss-cn-shenzhen.aliyuncs.com/resource/docs/V-Training_boot_0823.zip"><button type="button" class="btn btn-primary">点击下载boot程序压缩包</button></a>
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<a href="https://m5stack.oss-cn-shenzhen.aliyuncs.com/resource/docs/VTraining-Client-VerA02.zip"><button type="button" class="btn btn-primary">点击下载boot程序压缩包</button></a>
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<img src="assets\img\related_documents\v-training\1.jpg" width="60%">
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@@ -78,7 +79,7 @@
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<img src="assets\img\related_documents\v-training\4.jpg" width="100%">
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<mark>注意：为了保证识别的准确率，每组Class拍摄素材张数需要超过120张，否则在进行云端训练时将不给予通过.</mark>
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<mark>注意：为了保证识别的准确率，每组Class拍摄素材张数需要超过35张，否则在进行云端训练时将不给予通过. 素材的数量越多，识别训练的效果越好，识别率越高</mark>
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## 素材检查与压制
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@@ -86,9 +87,9 @@
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<img src="assets\img\related_documents\v-training\5.jpg" width="60%">
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>"train"、"vaild"两个文件夹中的Class序号文件夹目录是保持一致的，当切换Class并拍摄素材时，程序将会在"train"、"vaild"中同时创建Class序号一致的文件夹，并按照存放规则将所拍摄的图片分别存储到"train"、"vaild"各自目录下的Class文件夹中.（"train"文件夹中将存放拍摄的1 ~ 100、121 ~ n号素材图片."vaild"文件夹中将存放拍摄的101 ~ 120号素材图片.）
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>"train"、"vaild"两个文件夹中的Class序号文件夹目录是保持一致的，当切换Class并拍摄素材时，程序将会在"train"、"vaild"中同时创建Class序号一致的文件夹，并按照存放规则将所拍摄的图片分别存储到"train"、"vaild"各自目录下的Class文件夹中.
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><mark>在压制打包前除了检查图片内容的正确性以外，必须确保"train"、"vaild"两个文件夹中同一Class序号目录里的素材图片总和大于120.数量总和小于120时的Class序号目录请自行删除或是备份处理.</mark>完成了检查工作，接下来要做就是素材文件的压制.将"train"、"vaild"两个文件夹通过压制工具压制为"zip"格式的压缩包.
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><mark>在压制打包前除了检查图片内容的正确性以外，必须确保"train"、"vaild"两个文件夹中同一Class序号目录里的素材图片总和大于35.数量总和小于35时的Class序号目录请自行删除或是备份处理.</mark>完成了检查工作，接下来要做就是素材文件的压制.将"train"、"vaild"两个文件夹通过压制工具压制为"zip"格式的压缩包.
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## 数据上传云端
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@@ -51,23 +51,27 @@
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</div>
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</div>
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<div class="row">
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<div class="col-md-4">
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<h2>Units</h2>
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<p class="uiflow_p">UIFlow相关的Units使用方法，如ToF,ENV,Realy等模块，包含I/O类和I2C及UART接口的硬件. </p>
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<p><a class="btn btn-secondary" href="#/zh_CN/uiflow/data_structure" role="button">View details »</a></p>
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<p><a class="btn btn-secondary" href="#/zh_CN/uiflow/Units" role="button">View details »</a></p>
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</div>
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<div class="col-md-4">
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<h2>Modules</h2>
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<p class="uiflow_p">UIFlow中配合Modules使用的操作说明. </p>
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<p><a class="btn btn-secondary" href="#/zh_CN/uiflow/logic" role="button">View details »</a></p>
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<p><a class="btn btn-secondary" href="#/zh_CN/uiflow/Modules" role="button">View details »</a></p>
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</div>
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<div class="col-md-4">
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<h2>FACES</h2>
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<p class="uiflow_p">UIFlow中使用FACES面板进行界面交互或数据操作,提高输入效率.</p>
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<p><a class="btn btn-secondary" href="#/zh_CN/uiflow/advanced" role="button">View details »</a></p>
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<p><a class="btn btn-secondary" href="#/zh_CN/uiflow/FACES" role="button">View details »</a></p>
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</div>
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</div>
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</div>
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<br><br><br><br>
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@@ -8,7 +8,7 @@
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## 描述
|
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**HEART** 是一款血氧心率传感器.集成**MAX30110**，提供完整的脉搏血氧仪和心率传感器系统解决方案.这是一款非侵入式的血氧心率传感器,集成了两个红外发光二极管和一个光检测器.其检测原理是通过红外 led 灯照射，检测携带氧气和非携带氧气的红血球数量比例，从而得到血氧含量.
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**HEART** 是一款血氧心率传感器.集成**MAX30110**，提供完整的脉搏血氧仪和心率传感器系统解决方案.这是一款非插入式的血氧心率传感器,集成了两个红外发光二极管和一个光检测器.其检测原理是通过红外 led 灯照射，检测携带氧气和非携带氧气的红血球数量比例，从而得到血氧含量.
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**测试方式: 程序运行后，将手指放置在检测区域.**
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||||
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||||
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Reference in New Issue
Block a user