diff --git a/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TFlite_Micro_Demo移植参考指南（Keil版）.md b/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TFlite_Micro_Demo移植参考指南（Keil版）.md index c1c3c693..cdf46f70 100644 --- a/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TFlite_Micro_Demo移植参考指南（Keil版）.md +++ b/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TFlite_Micro_Demo移植参考指南（Keil版）.md @@ -1,27 +1,29 @@ # TensorFlow Lite Micro移植参考指南（Keil版） -作者： +**作者：** -日期： +Github ID: [Derekduke](https://github.com/Derekduke) E-mail: dkeji627@gmail.com -联系方式： +Github ID: [QingChuanWS](https://github.com/QingChuanWS) E-mail: bingshan45@163.com + +Github ID: [yangqings](https://github.com/yangqings) E-mail: yangqingsheng12@outlook.com ## 概述 本教程是基于STM32 NUCLEO-L496ZG（Cortex-M4, 80Mhz）开发板，在运行TencentOS tiny的基础上，使用Tensorflow Lite Micro框架和CMSIS-NN库（算子加速），在STM32L496ZG上实现了**行人检测模型**的推理。 -关于Tensorflow Lite Micro组件的详细介绍可以参考`TencentOS-tiny\components\tflite_micro`目录下的TFlite_Micro_Component_User_Guide.md文档。 +关于Tensorflow Lite Micro组件的详细介绍可以参考`TencentOS-tiny\components\ai\tflite_micro`目录下的TFlite_Micro_Component_User_Guide.md文档。 本例程中，传入神经网络的RGB图像大小为 18kb（96*96 * 2byte），在STM32L496平台消耗的内存资源（经过优化后）如下： - SRAM：168 Kbyte - Flash：314 Kbyte -理论上满足以b上内存要求的STM32 Cortex-M系列MCU可以参考本指南进行移植。 +理论上满足以上内存要求的STM32 Cortex-M系列MCU可以参考本指南进行移植。 ## 一、移植前的准备 -### 1. 准备目标硬件（开发板/传感器/模组） +#### 1. 准备目标硬件（开发板/传感器/模组） 需要准备如下硬件： @@ -35,24 +37,25 @@ -### 2.准备TencentOS tiny基础keil工程代码 +#### 2.准备TencentOS tiny基础keil工程代码 - 首先，参考TencentOS tiny基于keil的移植教程进行移植： https://github.com/Tencent/TencentOS-tiny/blob/master/doc/10.Porting_Manual_for_KEIL.md -- 值得注意的是，为了方便初始化MCU的外设，后续要继续使用STM32CubeMX软件，请确保安装了该软件； +- 为了方便初始化MCU的外设，后续要继续使用STM32CubeMX软件，请确保安装了该软件； - 移植成功后，工程可以进行线程任务切换，通过串口打印"hello world"，基础keil工程代码准备完毕。 -### 3. 获取Tensorflow Lite Micro +#### 3. 获取Tensorflow Lite Micro -有两种方式获取tflite_micro源码： +有三种方式获取tflite_micro： -1. 从TencentOS tiny 代码仓库 `components/tflite_micro`目录获取； -2. 从Tensorflow代码仓库获取，TFlite_Micro的源码已经开源，github下载地址为：https://github.com/tensorflow/tensorflow ，TFlite_Micro的代码位于`tensorflow/tensorflow/lite/micro/`目录下。 +1. 从TencentOS tiny 代码仓库 `components\ai\tflite_micro`目录获取； +2. 以lib文件的形式使用tflite_micro组件，lib文件`TencentOS-tiny\components\ai\tflite_micro`的ARM_CortexM4_lib、ARM_CortexM7_lib和ARM_CortexM55_lib文件夹 +3. 从Tensorflow代码仓库获取，TFlite_Micro的源码已经开源，github下载地址为：https://github.com/tensorflow/tensorflow ，TFlite_Micro的代码位于`tensorflow\tensorflow\lite\micro\`目录下。 -如果没有tflite_micro开发经验，建议您以**第一种方式**获取tflite_micro源码，如果您希望自行获取最新tflite_micro源码，请参考`TencentOS-tiny\components\tflite_micro`目录的TFlite_Micro_Component_User_Guide.md文档。 +如果没有tflite_micro开发经验，建议以**第一种**或者**第二种**方式获取tflite_micro，希望自行获取最新源码，或者编译lib文件，请参考`TencentOS-tiny\components\tflite_micro`目录的TFlite_Micro_Component_User_Guide.md文档，本指南将直接使用TencentOS tiny 代码仓库内的tflite_micro组件。 ## 二、BSP准备 @@ -86,28 +89,67 @@
+#### 2.2 打开keil的Manage Project Items + +
+ +
+ +#### 2.3 在project中加入新的文件夹hal + +
+ +
+ + +#### 2.3 添加驱动代码 + +添加`lcd_2inch4.c`和`lcd_config.c`, + +
+ +
-#### 2.2 添加驱动代码 +添加头文件`lcd_2inch4.h`和`lcd_config.h`路径 -添加`lcd_2inch4.c`和`lcd_config.c`，添加头文件`lcd_2inch4.h`和`lcd_config.h`路径。 +
+ +
+
+ +
+外设驱动的头文件.h文件都在`TencentOS-tiny\board\NUCLEO_STM32L496ZG\BSP\Hardware\Inc`路径下。 + ### 3. 摄像头驱动 - - #### 3.1 外设初始化 +进入`TencentOS-tiny\board\NUCLEO_STM32L496ZG\BSP`目录，打开TencentOS_tiny.ioc工程，初始化DCMI外设，打开DCMI全局中断，并打开DMA通道，DMA的Direction设置为Peripheral To Memory。 + +
+ +
+ +
+ +
+ #### 3.2 添加驱动代码 +
+ +
+**在mcu_init函数重写DCMI帧中断回调函数：** -**在main函数重写DCMI帧中断回调函数：** +值得注意的是，代码需要写在CubeMx生成的注释语句内，当使用CubeMX重新配置外设并生成代码时，所添加的代码才不会被覆盖掉，如下所示，代码添加在/* USER CODE BEGIN 4 */ 和 /* USER CODE END 4 */注释语句之间： ```C /* USER CODE BEGIN 4 */ @@ -120,11 +162,32 @@ void HAL_DCMI_FrameEventCallback(DCMI_HandleTypeDef *hdcmi) /* USER CODE END 4 */ ``` - - ### 4. LCD显示摄像头图像 -经过以上步骤，BSP就准备完毕了。 +本例程的任务函数在 + +`TencentOS-tiny\examples\tflitemicro_person_detection\tflitemicro_person_detection.c` + +```c +void task1(void *arg) +{ + while (1) { + if(frame_flag == 1){ + + if(HAL_DCMI_Stop(&hdcmi))Error_Handler(); //stop DCMI + LCD_2IN4_Display(camera_buffer,OV2640_PIXEL_WIDTH,OV2640_PIXEL_HEIGHT); + //display + frame_flag = 0; + if(HAL_DCMI_Start_DMA(&hdcmi,DCMI_MODE_CONTINUOUS,\ //restart DCMI + (uint32_t)camera_buffer ,\ + (OV2640_PIXEL_WIDTH*OV2640_PIXEL_HEIGHT)/2)) + Error_Handler(); + osDelay(50); + } +} +``` + +经过以上步骤，如果能顺利地驱动摄像头，并在LCD实时显示图像，BSP就准备完毕了，如果使用的是不同的LCD或者Camera，请根据实际情况进行外设初始化和驱动的移植。 ## 三、Tensorflow Lite Micro移植 @@ -132,7 +195,7 @@ void HAL_DCMI_FrameEventCallback(DCMI_HandleTypeDef *hdcmi) 由于NUCLEO-L496ZG芯片中的内核为ARM Cortex M4，所以本次我们可以直接使用ARM Cortex M4版本的tensorflow_lite_micro.lib库来简化tflite_micro搭建流程。 -#### 1.1 在project中加入新的文件夹tensorflow： +#### 1.1 在project中加入新的文件夹tensorflow
@@ -152,13 +215,13 @@ tensorflow_lite_micro.lib的路径为：`TencentOS-tiny\components\ai\tflite_mic 其余.cc文件均在当前目录下的`tflu_person_detection`文件夹中。 -#### 1.3 关闭Keil的MicroLib库： +#### 1.3 关闭Keil的MicroLib库
-#### 1.4 添加tflite_micro需要的头文件： +#### 1.4 添加tflite_micro需要的头文件
@@ -178,9 +241,12 @@ TencentOS-tiny\components\ai\tflite_micro\ARM_CortexM4_lib\tensorflow\lite\micro 其中宏`NUCLEO_STM32L496ZG`是指定Nucleo STM32L496的hlpuart1为系统printf函数的输出串口，具体定义在Nucleo STM32L496的BSP文件夹中的`mcu_init.c`中。 - ### 2. 编写Person_Detection 任务函数 +本例程的任务函数在 + +`TencentOS-tiny\examples\tflitemicro_person_detection\tflitemicro_person_detection.c`目录下 + #### 2.1 图像预处理
@@ -211,7 +277,7 @@ void input_convert(uint16_t* camera_buffer , uint8_t* model_buffer) } ``` -#### 3.2 行人检测线程任务函数 +#### 2.2 行人检测线程任务函数 ```c void task1(void *arg) @@ -245,8 +311,21 @@ void task2(void *arg) } ``` +#### 2.3 运行效果 +通过串行输出实时打印信息，移动摄像头，镜头没有对准行人时，输出如下： -## 四、Benchmark +
+ +
+当镜头对准行人时，输出如下： + +
+ +
+ +执行一帧图像推理，耗时约633 ms。 + +更多关于tflite_micro的介绍，请参考[tensorflow](https://tensorflow.google.cn/lite/microcontrollers?hl=zh_cn)官网以及`TencentOS-tiny\components\tflite_micro`目录的TFlite_Micro_Component_User_Guide.md diff --git a/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TencentOS_tiny.uvoptx b/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TencentOS_tiny.uvoptx deleted file mode 100644 index e3edfba5..00000000 --- a/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TencentOS_tiny.uvoptx +++ /dev/null @@ -1,1240 +0,0 @@ - - - - 1.0 - -
### uVision Project, (C) Keil Software
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134235804
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..\..\..\..\components\ai\tflite_micro\ARM_CortexM4_lib\tensorflow_lite_micro_M4.lib - tensorflow_lite_micro_M4.lib - 0 - 0 - - - - - ::CMSIS - 0 - 0 - 0 - 1 - - - diff --git a/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TencentOS_tiny.uvprojx b/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TencentOS_tiny.uvprojx index d221bdcc..21576550 100644 --- a/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TencentOS_tiny.uvprojx +++ b/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TencentOS_tiny.uvprojx @@ -16,8 +16,8 @@ STM32L496ZGTx STMicroelectronics - Keil.STM32L4xx_DFP.2.5.0 - https://www.keil.com/pack/ + Keil.STM32L4xx_DFP.2.4.0 + http://www.keil.com/pack/ IRAM(0x20000000-0x2004FFFF) IROM(0x8000000-0x80FFFFF) CLOCK(8000000) FPU2 CPUTYPE("Cortex-M4") @@ -739,15 +739,20 @@ hal - lcd_config.c + delay.c 1 - ..\..\BSP\Hardware\Src\lcd_config.c + ..\..\BSP\Hardware\Src\delay.c lcd_2inch4.c 1 ..\..\BSP\Hardware\Src\lcd_2inch4.c + + lcd_config.c + 1 + ..\..\BSP\Hardware\Src\lcd_config.c + ov2640.c 1 @@ -758,11 +763,6 @@ 1 ..\..\BSP\Hardware\Src\sccb.c - - delay.c - 1 - ..\..\BSP\Hardware\Src\delay.c - diff --git a/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/image/bsp_add camera driver file.png b/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/image/bsp_add camera driver file.png new file mode 100644 index 00000000..f37e5f68 Binary files /dev/null and b/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/image/bsp_add camera driver file.png differ diff --git a/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/image/bsp_add lcd driver file.png b/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/image/bsp_add lcd driver file.png new file mode 100644 index 00000000..4e290457 Binary files /dev/null and b/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/image/bsp_add lcd driver file.png differ diff --git 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