删除m55库，完善md文档

2021-01-27 01:01:53 +08:00
parent d29c1b0446 b25643e8c8
commit 398328c0a1
20 changed files with 23826 additions and 27493 deletions
--- a/board/ALIENTEK_STM32F429/KEIL/fatfs/TencentOS_tiny.uvguix.yangq
+++ b/board/ALIENTEK_STM32F429/KEIL/fatfs/TencentOS_tiny.uvguix.yangq
--- a/board/ALIENTEK_STM32F429/KEIL/tcp_server_base_lwip/TencentOS_tiny.uvguix.yangq
+++ b/board/ALIENTEK_STM32F429/KEIL/tcp_server_base_lwip/TencentOS_tiny.uvguix.yangq
--- a/board/NUCLEO_STM32L496ZG/BSP/Hardware/Inc/ov2640.h
+++ b/board/NUCLEO_STM32L496ZG/BSP/Hardware/Inc/ov2640.h
@@ -2,6 +2,18 @@
 #define _OV2640_H
 #include "sys.h"
 #include "sccb.h"
+//////////////////////////////////////////////////////////////////////////////////	 
+//本程序只供学习使用，未经作者许可，不得用于其它任何用途
+//ALIENTEK STM32F407开发板
+//OV2640 驱动代码	   
+//正点原子@ALIENTEK
+//技术论坛:www.openedv.com
+//创建日期:2014/5/14
+//版本：V1.0
+//版权所有，盗版必究。
+//Copyright(C) 广州市星翼电子科技有限公司 2014-2024
+//All rights reserved									  
+////////////////////////////////////////////////////////////////////////////////// 

 /*
 *  picture size
@@ -9,8 +21,8 @@
 #define OV2640_PIXEL_WIDTH  ((uint16_t)96)
 #define OV2640_PIXEL_HEIGHT ((uint16_t)96)

-//#define OV2640_PWDN  	PGout(9)			//POWER DOWN<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ź<EFBFBD> 
-//#define OV2640_RST  	PGout(15)			//<EFBFBD><EFBFBD>λ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ź<EFBFBD> 
+//#define OV2640_PWDN  	PGout(9)			//POWER DOWN控制信号 
+//#define OV2640_RST  	PGout(15)			//复位控制信号 
 void OV2640_PWDN(uint8_t signal);
 void OV2640_RST(uint8_t signal);
 ////////////////////////////////////////////////////////////////////////////////// 
@@ -18,7 +30,7 @@ void OV2640_RST(uint8_t signal);
 #define OV2640_PID				0X2642
 

-//<EFBFBD><EFBFBD>ѡ<EFBFBD><EFBFBD>DSP<EFBFBD><EFBFBD>ַ(0XFF=0X00)ʱ,OV2640<EFBFBD><EFBFBD>DSP<EFBFBD>Ĵ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ַӳ<EFBFBD><EFBFBD><EFBFBD><EFBFBD>
+//当选择DSP地址(0XFF=0X00)时,OV2640的DSP寄存器地址映射表
 #define OV2640_DSP_R_BYPASS     0x05
 #define OV2640_DSP_Qs           0x44
 #define OV2640_DSP_CTRL         0x50
@@ -54,7 +66,7 @@ void OV2640_RST(uint8_t signal);
 #define OV2640_DSP_P_STATUS     0xFE
 #define OV2640_DSP_RA_DLMT      0xFF 

-//<EFBFBD><EFBFBD>ѡ<EFBFBD>񴫸<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ַ(0XFF=0X01)ʱ,OV2640<EFBFBD><EFBFBD>DSP<EFBFBD>Ĵ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ַӳ<EFBFBD><EFBFBD><EFBFBD><EFBFBD>
+//当选择传感器地址(0XFF=0X01)时,OV2640的DSP寄存器地址映射表
 #define OV2640_SENSOR_GAIN       0x00
 #define OV2640_SENSOR_COM1       0x03
 #define OV2640_SENSOR_REG04      0x04
--- a/board/NUCLEO_STM32L496ZG/BSP/Hardware/Inc/sccb.h
+++ b/board/NUCLEO_STM32L496ZG/BSP/Hardware/Inc/sccb.h
@@ -2,17 +2,26 @@
 #define __SCCB_H
 #include "sys.h"
 #include "gpio.h"
+//////////////////////////////////////////////////////////////////////////////////	 
+//本程序参考自网友guanfu_wang代码。
+//ALIENTEK STM32F103开发板 
+//SCCB 驱动代码	   
+//正点原子@ALIENTEK
+//技术论坛:www.openedv.com
+//创建日期:2015/4/16
+//版本：V1.0		    							    							  
+//////////////////////////////////////////////////////////////////////////////////

-//IO<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>	 
-#define SCCB_SDA_IN()  {GPIOB->MODER&=~(3<<(5*2));GPIOB->MODER|=0<<5*2;}	//PD7 <EFBFBD><EFBFBD><EFBFBD><EFBFBD>
-#define SCCB_SDA_OUT() {GPIOB->MODER&=~(3<<(5*2));GPIOB->MODER|=1<<5*2;} 	//PD7 <EFBFBD><EFBFBD><EFBFBD><EFBFBD>
-#define SCCB_ID   			0X60  			//OV2640<EFBFBD><EFBFBD>ID
+//IO操作函数	 
+#define SCCB_SDA_IN()  {GPIOB->MODER&=~(3<<(5*2));GPIOB->MODER|=0<<5*2;}	//PD7 输入
+#define SCCB_SDA_OUT() {GPIOB->MODER&=~(3<<(5*2));GPIOB->MODER|=1<<5*2;} 	//PD7 输出
+#define SCCB_ID   			0X60  			//OV2640的ID

-//IO<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
-#define SCCB_SDA_IN()  {GPIOB->MODER&=~(3<<(5*2));GPIOB->MODER|=0<<5*2;}	//PD7 <EFBFBD><EFBFBD><EFBFBD><EFBFBD>
-#define SCCB_SDA_OUT() {GPIOB->MODER&=~(3<<(5*2));GPIOB->MODER|=1<<5*2;} 	//PD7 <EFBFBD><EFBFBD><EFBFBD><EFBFBD>
+//IO方向设置
+#define SCCB_SDA_IN()  {GPIOB->MODER&=~(3<<(5*2));GPIOB->MODER|=0<<5*2;}	//PD7 输入
+#define SCCB_SDA_OUT() {GPIOB->MODER&=~(3<<(5*2));GPIOB->MODER|=1<<5*2;} 	//PD7 输出

-#define SCCB_ID   			0X60  			//OV2640<EFBFBD><EFBFBD>ID
+#define SCCB_ID   			0X60  			//OV2640的ID

 void SCCB_Init(void);
 void SCCB_Start(void);
--- a/board/NUCLEO_STM32L496ZG/BSP/Hardware/Src/lcd_2inch4.c
+++ b/board/NUCLEO_STM32L496ZG/BSP/Hardware/Src/lcd_2inch4.c
@@ -1,3 +1,32 @@
+/*****************************************************************************
+* | File      	:   LCD_2IN4_Driver.c
+* | Author      :   Waveshare team
+* | Function    :   LCD driver
+* | Info        :
+*----------------
+* |	This version:   V1.0
+* | Date        :   2020-07-29
+* | Info        :   
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documnetation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to  whom the Software is
+# furished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in
+# all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS OR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+# THE SOFTWARE.
+#
+******************************************************************************/
 #include "lcd_2inch4.h"
 #include <string.h>
 /*******************************************************************************
@@ -50,8 +79,8 @@ void LCD_2IN4_Init(void)
 {
 	LCD_2IN4_Reset();

-	LCD_2IN4_SetBackLight(500);//<2F>򿪱<EFBFBD><F2BFAAB1><EFBFBD>
-  HAL_Delay(100);
+	LCD_2IN4_SetBackLight(500);
+  	HAL_Delay(100);
 	
 	//************* Start Initial Sequence **********//
 	LCD_2IN4_Write_Command(0x11); //Sleep out 
--- a/board/NUCLEO_STM32L496ZG/BSP/Hardware/Src/ov2640.c
+++ b/board/NUCLEO_STM32L496ZG/BSP/Hardware/Src/ov2640.c
@@ -6,11 +6,22 @@
 #include "usart.h"			 
 #include "sccb.h"	
 #include "stdio.h"
-  
-//<2F><>ʼ<EFBFBD><CABC>OV2640 
-//<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>Ժ<EFBFBD>,Ĭ<><C4AC><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>1600*1200<30>ߴ<EFBFBD><DFB4><EFBFBD>ͼƬ!! 
-//<EFBFBD><EFBFBD><EFBFBD><EFBFBD>ֵ:0,<2C>ɹ<EFBFBD>
-//    <20><><EFBFBD><EFBFBD>,<2C><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
+//////////////////////////////////////////////////////////////////////////////////	 
+//本程序只供学习使用，未经作者许可，不得用于其它任何用途
+//ALIENTEK STM32F103开发板
+//OV2640 驱动代码	   
+//正点原子@ALIENTEK
+//技术论坛:www.openedv.com
+//创建日期:2015/4/16
+//版本：V1.0
+//版权所有，盗版必究。
+//Copyright(C) 广州市星翼电子科技有限公司 2014-2024
+//All rights reserved									  
+//////////////////////////////////////////////////////////////////////////////////   
+//初始化OV2640 
+//配置完以后,默认输出是1600*1200尺寸的图片!! 
+//返回值:0,成功
+//    其他,错误代码
 void OV2640_PWDN(uint8_t signal)
 {
 	HAL_GPIO_WritePin(GPIOB, GPIO_PIN_15, (GPIO_PinState)signal);
@@ -25,38 +36,38 @@ uint8_t OV2640_Init(void)
 { 
 	uint16_t i=0;
 	uint16_t reg;
-	//<EFBFBD><EFBFBD><EFBFBD><EFBFBD>IO     	   
+	//设置IO     	   
 	GPIO_InitTypeDef  GPIO_InitStructure;
-	__HAL_RCC_GPIOG_CLK_ENABLE();           //ʹ<EFBFBD><EFBFBD>GPIOBʱ<EFBFBD><EFBFBD>
+	__HAL_RCC_GPIOG_CLK_ENABLE();           //使能GPIOB时钟
 	HAL_GPIO_WritePin(GPIOB, GPIO_PIN_13|GPIO_PIN_15, GPIO_PIN_RESET);
-  //GPIOF9,F10<EFBFBD><EFBFBD>ʼ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
+  //GPIOF9,F10初始化设置
  GPIO_InitStructure.Pin = GPIO_PIN_13|GPIO_PIN_15;
  GPIO_InitStructure.Mode = GPIO_MODE_OUTPUT_PP;  
  GPIO_InitStructure.Speed = GPIO_SPEED_FAST;
-  GPIO_InitStructure.Pull = GPIO_PULLUP;//<EFBFBD><EFBFBD><EFBFBD><EFBFBD>
-  HAL_GPIO_Init(GPIOB, &GPIO_InitStructure);//<EFBFBD><EFBFBD>ʼ<EFBFBD>
+  GPIO_InitStructure.Pull = GPIO_PULLUP;//上拉
+  HAL_GPIO_Init(GPIOB, &GPIO_InitStructure);//初始<EFBFBD>
 	
 	OV2640_PWDN(0);	//POWER ON
 	delay_ms(10);
-	OV2640_RST(0);	//<EFBFBD><EFBFBD>λOV2640
+	OV2640_RST(0);	//复位OV2640
 	delay_ms(10);
-	OV2640_RST(1);	//<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>λ 
-  SCCB_Init();        		//<EFBFBD><EFBFBD>ʼ<EFBFBD><EFBFBD>SCCB <20><>IO<49><4F>	 
-	SCCB_WR_Reg(OV2640_DSP_RA_DLMT, 0x01);	//<EFBFBD><EFBFBD><EFBFBD><EFBFBD>sensor<EFBFBD>Ĵ<EFBFBD><EFBFBD><EFBFBD>
- 	SCCB_WR_Reg(OV2640_SENSOR_COM7, 0x80);	//<EFBFBD><EFBFBD><EFBFBD><EFBFBD>λOV2640
+	OV2640_RST(1);	//结束复位 
+  SCCB_Init();        		//初始化SCCB 的IO口	 
+	SCCB_WR_Reg(OV2640_DSP_RA_DLMT, 0x01);	//操作sensor寄存器
+ 	SCCB_WR_Reg(OV2640_SENSOR_COM7, 0x80);	//软复位OV2640
 	delay_ms(50); 
-	reg=SCCB_RD_Reg(OV2640_SENSOR_MIDH);	//<EFBFBD><EFBFBD>ȡ<EFBFBD><EFBFBD><EFBFBD><EFBFBD>ID <20>߰<EFBFBD>λ
+	reg=SCCB_RD_Reg(OV2640_SENSOR_MIDH);	//读取厂家ID 高八位
 	reg<<=8;
-	reg|=SCCB_RD_Reg(OV2640_SENSOR_MIDL);	//<EFBFBD><EFBFBD>ȡ<EFBFBD><EFBFBD><EFBFBD><EFBFBD>ID <20>Ͱ<EFBFBD>λ
+	reg|=SCCB_RD_Reg(OV2640_SENSOR_MIDL);	//读取厂家ID 低八位
 	printf("OV2640_MID = %#X\n" , reg);
 	if(reg!=OV2640_MID)
 	{
 		printf("MID:%d\r\n",reg);
 		return 1;
 	}
-	reg=SCCB_RD_Reg(OV2640_SENSOR_PIDH);	//<EFBFBD><EFBFBD>ȡ<EFBFBD><EFBFBD><EFBFBD><EFBFBD>ID <20>߰<EFBFBD>λ
+	reg=SCCB_RD_Reg(OV2640_SENSOR_PIDH);	//读取厂家ID 高八位
 	reg<<=8;
-	reg|=SCCB_RD_Reg(OV2640_SENSOR_PIDL);	//<EFBFBD><EFBFBD>ȡ<EFBFBD><EFBFBD><EFBFBD><EFBFBD>ID <20>Ͱ<EFBFBD>λ
+	reg|=SCCB_RD_Reg(OV2640_SENSOR_PIDL);	//读取厂家ID 低八位
 	if(reg!=OV2640_PID)
 	{
 		printf("HID:%d\r\n",reg);
@@ -69,34 +80,34 @@ uint8_t OV2640_Init(void)
 	printf("OV2640_init SUCCESS\n");
  return 0x00; 	//ok
 } 
-//OV2640<EFBFBD>л<EFBFBD>ΪJPEGģʽ
+//OV2640切换为JPEG模式
 void OV2640_JPEG_Mode(void) 
 {
 	uint16_t i=0;
-	//<EFBFBD><EFBFBD><EFBFBD><EFBFBD>:YUV422<EFBFBD><EFBFBD>ʽ
+	//设置:YUV422格式
 	for(i=0;i<(sizeof(ov2640_yuv422_reg_tbl)/2);i++)
 	{
 		SCCB_WR_Reg(ov2640_yuv422_reg_tbl[i][0],ov2640_yuv422_reg_tbl[i][1]); 
 	} 
 	
-	//<EFBFBD><EFBFBD><EFBFBD><EFBFBD>:<3A><><EFBFBD><EFBFBD>JPEG<45><47><EFBFBD><EFBFBD>
+	//设置:输出JPEG数据
 	for(i=0;i<(sizeof(ov2640_jpeg_reg_tbl)/2);i++)
 	{
 		SCCB_WR_Reg(ov2640_jpeg_reg_tbl[i][0],ov2640_jpeg_reg_tbl[i][1]);  
 	}  
 }
-//OV2640<EFBFBD>л<EFBFBD>ΪRGB565ģʽ
+//OV2640切换为RGB565模式
 void OV2640_RGB565_Mode(void) 
 {
 	uint16_t i=0;
-	//<EFBFBD><EFBFBD><EFBFBD><EFBFBD>:RGB565<EFBFBD><EFBFBD><EFBFBD><EFBFBD>
+	//设置:RGB565输出
 	for(i=0;i<(sizeof(ov2640_rgb565_reg_tbl)/2);i++)
 	{
 		SCCB_WR_Reg(ov2640_rgb565_reg_tbl[i][0],ov2640_rgb565_reg_tbl[i][1]); 
 	}
 	printf("OV2640_RGB565 SET!\n");
 } 
-//<EFBFBD>Զ<EFBFBD><EFBFBD>ع<EFBFBD><EFBFBD><EFBFBD><EFBFBD>ò<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>,֧<><D6A7>5<EFBFBD><35><EFBFBD>ȼ<EFBFBD>
+//自动曝光设置参数表,支持5个等级
 const static uint8_t OV2640_AUTOEXPOSURE_LEVEL[5][8]=
 {
 	{
@@ -130,7 +141,7 @@ const static uint8_t OV2640_AUTOEXPOSURE_LEVEL[5][8]=
 		0x26,0x92,	
 	},
 }; 
-//OV2640<EFBFBD>Զ<EFBFBD><EFBFBD>ع<EFBFBD><EFBFBD>ȼ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
+//OV2640自动曝光等级设置
 //level:0~4
 void OV2640_Auto_Exposure(uint8_t level)
 {  
@@ -141,12 +152,12 @@ void OV2640_Auto_Exposure(uint8_t level)
 		SCCB_WR_Reg(p[i*2],p[i*2+1]); 
 	} 
 }  
-//<EFBFBD><EFBFBD>ƽ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
-//0:<EFBFBD>Զ<EFBFBD>
-//1:̫<EFBFBD><EFBFBD>sunny
-//2,<EFBFBD><EFBFBD><EFBFBD><EFBFBD>cloudy
-//3,<EFBFBD>칫<EFBFBD><EFBFBD>office
-//4,<EFBFBD><EFBFBD><EFBFBD><EFBFBD>home
+//白平衡设置
+//0:自动
+//1:太阳sunny
+//2,阴天cloudy
+//3,办公室office
+//4,家里home
 void OV2640_Light_Mode(uint8_t mode)
 {
 	uint8_t regccval=0X5E;//Sunny 
@@ -180,7 +191,7 @@ void OV2640_Light_Mode(uint8_t mode)
 	SCCB_WR_Reg(0XCD,regcdval); 
 	SCCB_WR_Reg(0XCE,regceval);  
 }
-//ɫ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
+//色度设置
 //0:-2
 //1:-1
 //2,0
@@ -196,7 +207,7 @@ void OV2640_Color_Saturation(uint8_t sat)
 	SCCB_WR_Reg(0X7D,reg7dval);			
 	SCCB_WR_Reg(0X7D,reg7dval); 		
 }
-//<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
+//亮度设置
 //0:(0X00)-2
 //1:(0X10)-1
 //2,(0X20) 0
@@ -211,7 +222,7 @@ void OV2640_Brightness(uint8_t bright)
  SCCB_WR_Reg(0x7d, bright<<4); 
  SCCB_WR_Reg(0x7d, 0x00); 
 }
-//<EFBFBD>Աȶ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
+//对比度设置
 //0:-2
 //1:-1
 //2,0
@@ -219,7 +230,7 @@ void OV2640_Brightness(uint8_t bright)
 //4,+2
 void OV2640_Contrast(uint8_t contrast)
 {
-	uint8_t reg7d0val=0X20;//Ĭ<EFBFBD><EFBFBD>Ϊ<EFBFBD><EFBFBD>ͨģʽ
+	uint8_t reg7d0val=0X20;//默认为普通模式
 	uint8_t reg7d1val=0X20;
  	switch(contrast)
 	{
@@ -249,43 +260,43 @@ void OV2640_Contrast(uint8_t contrast)
 	SCCB_WR_Reg(0x7d,reg7d1val);
 	SCCB_WR_Reg(0x7d,0x06);
 }
-//<EFBFBD><EFBFBD>Ч<EFBFBD><EFBFBD><EFBFBD><EFBFBD>
-//0:<EFBFBD><EFBFBD>ͨģʽ    
-//1,<EFBFBD><EFBFBD>Ƭ
-//2,<EFBFBD>ڰ<EFBFBD>   
-//3,ƫ<EFBFBD><EFBFBD>ɫ
-//4,ƫ<EFBFBD><EFBFBD>ɫ
-//5,ƫ<EFBFBD><EFBFBD>ɫ
-//6,<EFBFBD><EFBFBD><EFBFBD><EFBFBD>	    
+//特效设置
+//0:普通模式    
+//1,负片
+//2,黑白   
+//3,偏红色
+//4,偏绿色
+//5,偏蓝色
+//6,复古	    
 void OV2640_Special_Effects(uint8_t eft)
 {
-	uint8_t reg7d0val=0X00;//Ĭ<EFBFBD><EFBFBD>Ϊ<EFBFBD><EFBFBD>ͨģʽ
+	uint8_t reg7d0val=0X00;//默认为普通模式
 	uint8_t reg7d1val=0X80;
 	uint8_t reg7d2val=0X80; 
 	switch(eft)
 	{
-		case 1://<EFBFBD><EFBFBD>Ƭ
+		case 1://负片
 			reg7d0val=0X40; 
 			break;	
-		case 2://<EFBFBD>ڰ<EFBFBD>
+		case 2://黑白
 			reg7d0val=0X18; 
 			break;	 
-		case 3://ƫ<EFBFBD><EFBFBD>ɫ
+		case 3://偏红色
 			reg7d0val=0X18; 
 			reg7d1val=0X40;
 			reg7d2val=0XC0; 
 			break;	
-		case 4://ƫ<EFBFBD><EFBFBD>ɫ
+		case 4://偏绿色
 			reg7d0val=0X18; 
 			reg7d1val=0X40;
 			reg7d2val=0X40; 
 			break;	
-		case 5://ƫ<EFBFBD><EFBFBD>ɫ
+		case 5://偏蓝色
 			reg7d0val=0X18; 
 			reg7d1val=0XA0;
 			reg7d2val=0X40; 
 			break;	
-		case 6://<EFBFBD><EFBFBD><EFBFBD><EFBFBD>
+		case 6://复古
 			reg7d0val=0X18; 
 			reg7d1val=0X40;
 			reg7d2val=0XA6; 
@@ -298,9 +309,9 @@ void OV2640_Special_Effects(uint8_t eft)
 	SCCB_WR_Reg(0x7d,reg7d1val);
 	SCCB_WR_Reg(0x7d,reg7d2val); 
 }
-//<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
-//sw:0,<EFBFBD>رղ<EFBFBD><EFBFBD><EFBFBD>
-//   1,<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>(ע<><D7A2>OV2640<34>Ĳ<EFBFBD><C4B2><EFBFBD><EFBFBD>ǵ<EFBFBD><C7B5><EFBFBD><EFBFBD><EFBFBD>ͼ<EFBFBD><CDBC><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>)
+//彩条测试
+//sw:0,关闭彩条
+//   1,开启彩条(注意OV2640的彩条是叠加在图像上面的)
 void OV2640_Color_Bar(uint8_t sw)
 {
 	uint8_t reg;
@@ -310,9 +321,9 @@ void OV2640_Color_Bar(uint8_t sw)
 	if(sw)reg|=1<<1; 
 	SCCB_WR_Reg(0X12,reg);
 }
-//<EFBFBD><EFBFBD><EFBFBD><EFBFBD>ͼ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> 
-//sx,sy,<EFBFBD><EFBFBD>ʼ<EFBFBD><EFBFBD>ַ
-//width,height:<EFBFBD><EFBFBD><EFBFBD><EFBFBD>(<28><>Ӧ:horizontal)<EFBFBD>͸߶<EFBFBD>(<28><>Ӧ:vertical)
+//设置图像输出窗口 
+//sx,sy,起始地址
+//width,height:宽度(对应:horizontal)和高度(对应:vertical)
 void OV2640_Window_Set(uint16_t sx,uint16_t sy,uint16_t width,uint16_t height)
 {
 	uint16_t endx;
@@ -322,25 +333,25 @@ void OV2640_Window_Set(uint16_t sx,uint16_t sy,uint16_t width,uint16_t height)
 	endy=sy+height/2;
 	
 	SCCB_WR_Reg(0XFF,0X01);			
-	temp=SCCB_RD_Reg(0X03);				//<EFBFBD><EFBFBD>ȡVref֮ǰ<EFBFBD><EFBFBD>ֵ
+	temp=SCCB_RD_Reg(0X03);				//读取Vref之前的值
 	temp&=0XF0;
 	temp|=((endy&0X03)<<2)|(sy&0X03);
-	SCCB_WR_Reg(0X03,temp);				//<EFBFBD><EFBFBD><EFBFBD><EFBFBD>Vref<EFBFBD><EFBFBD>start<EFBFBD><EFBFBD>end<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>2λ
-	SCCB_WR_Reg(0X19,sy>>2);			//<EFBFBD><EFBFBD><EFBFBD><EFBFBD>Vref<EFBFBD><EFBFBD>start<EFBFBD><EFBFBD>8λ
-	SCCB_WR_Reg(0X1A,endy>>2);			//<EFBFBD><EFBFBD><EFBFBD><EFBFBD>Vref<EFBFBD><EFBFBD>end<EFBFBD>ĸ<EFBFBD>8λ
+	SCCB_WR_Reg(0X03,temp);				//设置Vref的start和end的最低2位
+	SCCB_WR_Reg(0X19,sy>>2);			//设置Vref的start高8位
+	SCCB_WR_Reg(0X1A,endy>>2);			//设置Vref的end的高8位
 	
-	temp=SCCB_RD_Reg(0X32);				//<EFBFBD><EFBFBD>ȡHref֮ǰ<EFBFBD><EFBFBD>ֵ
+	temp=SCCB_RD_Reg(0X32);				//读取Href之前的值
 	temp&=0XC0;
 	temp|=((endx&0X07)<<3)|(sx&0X07);
-	SCCB_WR_Reg(0X32,temp);				//<EFBFBD><EFBFBD><EFBFBD><EFBFBD>Href<EFBFBD><EFBFBD>start<EFBFBD><EFBFBD>end<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>3λ
-	SCCB_WR_Reg(0X17,sx>>3);			//<EFBFBD><EFBFBD><EFBFBD><EFBFBD>Href<EFBFBD><EFBFBD>start<EFBFBD><EFBFBD>8λ
-	SCCB_WR_Reg(0X18,endx>>3);			//<EFBFBD><EFBFBD><EFBFBD><EFBFBD>Href<EFBFBD><EFBFBD>end<EFBFBD>ĸ<EFBFBD>8λ
+	SCCB_WR_Reg(0X32,temp);				//设置Href的start和end的最低3位
+	SCCB_WR_Reg(0X17,sx>>3);			//设置Href的start高8位
+	SCCB_WR_Reg(0X18,endx>>3);			//设置Href的end的高8位
 }
-//<EFBFBD><EFBFBD><EFBFBD><EFBFBD>ͼ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>С
-//OV2640<EFBFBD><EFBFBD><EFBFBD><EFBFBD>ͼ<EFBFBD><EFBFBD><EFBFBD>Ĵ<EFBFBD>С(<28>ֱ<EFBFBD><D6B1><EFBFBD>),<2C><>ȫ<EFBFBD>ɸĺ<C9B8><C4BA><EFBFBD>ȷ<EFBFBD><C8B7>
-//width,height:<EFBFBD><EFBFBD><EFBFBD><EFBFBD>(<28><>Ӧ:horizontal)<EFBFBD>͸߶<EFBFBD>(<28><>Ӧ:vertical),width<EFBFBD><EFBFBD>height<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>4<EFBFBD>ı<EFBFBD><EFBFBD><EFBFBD>
-//<EFBFBD><EFBFBD><EFBFBD><EFBFBD>ֵ:0,<2C><><EFBFBD>óɹ<C3B3>
-//    <EFBFBD><EFBFBD><EFBFBD><EFBFBD>,<2C><><EFBFBD><EFBFBD>ʧ<EFBFBD><CAA7>
+//设置图像输出大小
+//OV2640输出图像的大小(分辨率),完全由改函数确定
+//width,height:宽度(对应:horizontal)和高度(对应:vertical),width和height必须是4的倍数
+//返回值:0,设置成功
+//    其他,设置失败
 uint8_t OV2640_OutSize_Set(uint16_t width,uint16_t height)
 {
 	uint16_t outh;
@@ -352,23 +363,23 @@ uint8_t OV2640_OutSize_Set(uint16_t width,uint16_t height)
 	outh=height/4; 
 	SCCB_WR_Reg(0XFF,0X00);	
 	SCCB_WR_Reg(0XE0,0X04);			
-	SCCB_WR_Reg(0X5A,outw&0XFF);		//<EFBFBD><EFBFBD><EFBFBD><EFBFBD>OUTW<EFBFBD>ĵͰ<EFBFBD>λ
-	SCCB_WR_Reg(0X5B,outh&0XFF);		//<EFBFBD><EFBFBD><EFBFBD><EFBFBD>OUTH<EFBFBD>ĵͰ<EFBFBD>λ
+	SCCB_WR_Reg(0X5A,outw&0XFF);		//设置OUTW的低八位
+	SCCB_WR_Reg(0X5B,outh&0XFF);		//设置OUTH的低八位
 	temp=(outw>>8)&0X03;
 	temp|=(outh>>6)&0X04;
-	SCCB_WR_Reg(0X5C,temp);				//<EFBFBD><EFBFBD><EFBFBD><EFBFBD>OUTH/OUTW<EFBFBD>ĸ<EFBFBD>λ 
+	SCCB_WR_Reg(0X5C,temp);				//设置OUTH/OUTW的高位 
 	SCCB_WR_Reg(0XE0,0X00);	
 	return 0;
 }
-//<EFBFBD><EFBFBD><EFBFBD><EFBFBD>ͼ<EFBFBD>񿪴<EFBFBD><EFBFBD><EFBFBD>С
-//<EFBFBD><EFBFBD>:OV2640_ImageSize_Setȷ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ֱ<EFBFBD><EFBFBD>ʴӴ<EFBFBD>С.
-//<EFBFBD>ú<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>Χ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>п<EFBFBD><EFBFBD><EFBFBD>,<2C><><EFBFBD><EFBFBD>OV2640_OutSize_Set<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
-//ע<EFBFBD><EFBFBD>:<3A><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>Ŀ<EFBFBD><C4BF>Ⱥ͸߶<CDB8>,<2C><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ڵ<EFBFBD><DAB5><EFBFBD>OV2640_OutSize_Set<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>Ŀ<EFBFBD><EFBFBD>Ⱥ͸߶<EFBFBD>
-//     OV2640_OutSize_Set<EFBFBD><EFBFBD><EFBFBD>õĿ<EFBFBD><EFBFBD>Ⱥ͸߶<EFBFBD>,<2C><><EFBFBD>ݱ<EFBFBD><DDB1><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>õĿ<C3B5><C4BF>Ⱥ͸߶<CDB8>,<2C><>DSP
-//     <EFBFBD>Զ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ű<EFBFBD><EFBFBD><EFBFBD>,<2C><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ⲿ<EFBFBD>豸.
-//width,height:<EFBFBD><EFBFBD><EFBFBD><EFBFBD>(<28><>Ӧ:horizontal)<EFBFBD>͸߶<EFBFBD>(<28><>Ӧ:vertical),width<EFBFBD><EFBFBD>height<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>4<EFBFBD>ı<EFBFBD><EFBFBD><EFBFBD>
-//<EFBFBD><EFBFBD><EFBFBD><EFBFBD>ֵ:0,<2C><><EFBFBD>óɹ<C3B3>
-//    <EFBFBD><EFBFBD><EFBFBD><EFBFBD>,<2C><><EFBFBD><EFBFBD>ʧ<EFBFBD><CAA7>
+//设置图像开窗大小
+//由:OV2640_ImageSize_Set确定传感器输出分辨率从大小.
+//该函数则在这个范围上面进行开窗,用于OV2640_OutSize_Set的输出
+//注意:本函数的宽度和高度,必须大于等于OV2640_OutSize_Set函数的宽度和高度
+//     OV2640_OutSize_Set设置的宽度和高度,根据本函数设置的宽度和高度,由DSP
+//     自动计算缩放比例,输出给外部设备.
+//width,height:宽度(对应:horizontal)和高度(对应:vertical),width和height必须是4的倍数
+//返回值:0,设置成功
+//    其他,设置失败
 uint8_t OV2640_ImageWin_Set(uint16_t offx,uint16_t offy,uint16_t width,uint16_t height)
 {
 	uint16_t hsize;
@@ -380,31 +391,31 @@ uint8_t OV2640_ImageWin_Set(uint16_t offx,uint16_t offy,uint16_t width,uint16_t
 	vsize=height/4;
 	SCCB_WR_Reg(0XFF,0X00);	
 	SCCB_WR_Reg(0XE0,0X04);					
-	SCCB_WR_Reg(0X51,hsize&0XFF);		//<EFBFBD><EFBFBD><EFBFBD><EFBFBD>H_SIZE<EFBFBD>ĵͰ<EFBFBD>λ
-	SCCB_WR_Reg(0X52,vsize&0XFF);		//<EFBFBD><EFBFBD><EFBFBD><EFBFBD>V_SIZE<EFBFBD>ĵͰ<EFBFBD>λ
-	SCCB_WR_Reg(0X53,offx&0XFF);		//<EFBFBD><EFBFBD><EFBFBD><EFBFBD>offx<EFBFBD>ĵͰ<EFBFBD>λ
-	SCCB_WR_Reg(0X54,offy&0XFF);		//<EFBFBD><EFBFBD><EFBFBD><EFBFBD>offy<EFBFBD>ĵͰ<EFBFBD>λ
+	SCCB_WR_Reg(0X51,hsize&0XFF);		//设置H_SIZE的低八位
+	SCCB_WR_Reg(0X52,vsize&0XFF);		//设置V_SIZE的低八位
+	SCCB_WR_Reg(0X53,offx&0XFF);		//设置offx的低八位
+	SCCB_WR_Reg(0X54,offy&0XFF);		//设置offy的低八位
 	temp=(vsize>>1)&0X80;
 	temp|=(offy>>4)&0X70;
 	temp|=(hsize>>5)&0X08;
 	temp|=(offx>>8)&0X07; 
-	SCCB_WR_Reg(0X55,temp);				//<EFBFBD><EFBFBD><EFBFBD><EFBFBD>H_SIZE/V_SIZE/OFFX,OFFY<EFBFBD>ĸ<EFBFBD>λ
-	SCCB_WR_Reg(0X57,(hsize>>2)&0X80);	//<EFBFBD><EFBFBD><EFBFBD><EFBFBD>H_SIZE/V_SIZE/OFFX,OFFY<EFBFBD>ĸ<EFBFBD>λ
+	SCCB_WR_Reg(0X55,temp);				//设置H_SIZE/V_SIZE/OFFX,OFFY的高位
+	SCCB_WR_Reg(0X57,(hsize>>2)&0X80);	//设置H_SIZE/V_SIZE/OFFX,OFFY的高位
 	SCCB_WR_Reg(0XE0,0X00);	
 	return 0;
 } 
-//<EFBFBD>ú<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ͼ<EFBFBD><EFBFBD><EFBFBD>ߴ<EFBFBD><EFBFBD><EFBFBD>С,Ҳ<><D2B2><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ѡ<EFBFBD><D1A1>ʽ<EFBFBD><CABD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ֱ<EFBFBD><D6B1><EFBFBD>
+//该函数设置图像尺寸大小,也就是所选格式的输出分辨率
 //UXGA:1600*1200,SVGA:800*600,CIF:352*288
-//width,height:ͼ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>Ⱥ<EFBFBD>ͼ<EFBFBD><EFBFBD><EFBFBD>߶<EFBFBD>
-//<EFBFBD><EFBFBD><EFBFBD><EFBFBD>ֵ:0,<2C><><EFBFBD>óɹ<C3B3>
-//    <EFBFBD><EFBFBD><EFBFBD><EFBFBD>,<2C><><EFBFBD><EFBFBD>ʧ<EFBFBD><CAA7>
+//width,height:图像宽度和图像高度
+//返回值:0,设置成功
+//    其他,设置失败
 uint8_t OV2640_ImageSize_Set(uint16_t width,uint16_t height)
 { 
 	uint8_t temp; 
 	SCCB_WR_Reg(0XFF,0X00);			
 	SCCB_WR_Reg(0XE0,0X04);			
-	SCCB_WR_Reg(0XC0,(width)>>3&0XFF);		//<EFBFBD><EFBFBD><EFBFBD><EFBFBD>HSIZE<EFBFBD><EFBFBD>10:3λ
-	SCCB_WR_Reg(0XC1,(height)>>3&0XFF);		//<EFBFBD><EFBFBD><EFBFBD><EFBFBD>VSIZE<EFBFBD><EFBFBD>10:3λ
+	SCCB_WR_Reg(0XC0,(width)>>3&0XFF);		//设置HSIZE的10:3位
+	SCCB_WR_Reg(0XC1,(height)>>3&0XFF);		//设置VSIZE的10:3位
 	temp=(width&0X07)<<3;
 	temp|=height&0X07;
 	temp|=(width>>4)&0X80; 
--- 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
@@ -2,11 +2,11 @@

 **作者：**

-Github ID: [Derekduke](https://github.com/Derekduke)   E-mail:  dkeji627@gmail.com 
+Github: [Derekduke](https://github.com/Derekduke)   E-mail:  dkeji627@gmail.com 

-Github ID: [QingChuanWS](https://github.com/QingChuanWS)  E-mail: bingshan45@163.com
+Github: [QingChuanWS](https://github.com/QingChuanWS)  E-mail: bingshan45@163.com

-Github ID: [yangqings](https://github.com/yangqings)  E-mail: yangqingsheng12@outlook.com
+Github: [yangqings](https://github.com/yangqings)  E-mail: yangqingsheng12@outlook.com

 ## 概述

@@ -50,7 +50,7 @@ Github ID: [yangqings](https://github.com/yangqings)  E-mail: yangqingsheng12@ou
 有三种方式获取tflite_micro：

 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
+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仓库地址为：https://github.com/tensorflow/tensorflow ，可根据google TFLite Micro官方教程获得Tensorflow Lite Micro的全部源码。

 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。
@@ -61,16 +61,17 @@ Github ID: [yangqings](https://github.com/yangqings)  E-mail: yangqingsheng12@ou

 以下是整个例程的目录规划：

-| 一级目录  |           二级目录           |   三级目录   |                             说明                             |
-| :-------: | :--------------------------: | :----------: | :----------------------------------------------------------: |
-|   arch    |             arm              |              | TencentOS tiny适配的IP核架构（含M核中断、调度、tick相关代码） |
-|   board   |      NUCLEO_STM32L496ZG      |              |                    移植目标芯片的工程文件                    |
-|           |                              |     BSP      |            板级支持包，外设驱动代码在Hardware目录            |
-| component |              ai              | tflite_micro |                       tflite_micro源码                       |
-| examples  | tflitemicro_person_detection |              |                       行人检测demo示例                       |
-|  kernel   |             core             |              |                    TencentOS tiny内核源码                    |
-|           |              pm              |              |                 TencentOS tiny低功耗模块源码                 |
-|   osal    |           cmsis_os           |              |              TencentOS tiny提供的cmsis os 适配               |
+| 一级目录  |           二级目录           |       三级目录        |                             说明                             |
+| :-------: | :--------------------------: | :-------------------: | :----------------------------------------------------------: |
+|   arch    |             arm              |                       | TencentOS tiny适配的IP核架构（含M核中断、调度、tick相关代码） |
+|   board   |      NUCLEO_STM32L496ZG      |                       |                    移植目标芯片的工程文件                    |
+|           |                              |          BSP          |            板级支持包，外设驱动代码在Hardware目录            |
+| component |              ai              |     tflite_micro      |                 tflite_micro源码及有关库文件                 |
+| examples  | tflitemicro_person_detection |                       |                       行人检测demo示例                       |
+|           |                              | tflu_person_detection |                       行人检测实例代码                       |
+|  kernel   |             core             |                       |                    TencentOS tiny内核源码                    |
+|           |              pm              |                       |                 TencentOS tiny低功耗模块源码                 |
+|   osal    |           cmsis_os           |                       |              TencentOS tiny提供的cmsis os 适配               |

 完成TencentOS tiny基础keil工程准备工作后，在这个keil工程的基础上继续添加外设驱动代码。

@@ -191,7 +192,7 @@ void task1(void *arg)

 ### 1. tflite_micro组件加入到keil工程

-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。
+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

@@ -209,9 +210,9 @@ void task1(void *arg)

 其中，retarget.c的路径为：`TencentOS-tiny\components\ai\tflite_micro\KEIL\retarget.c`

-tensorflow_lite_micro.lib的路径为：`TencentOS-tiny\components\ai\tflite_micro\ARM_CortexM4_lib\tensorflow_lite_micro.lib`
+tensorflow_lite_micro.lib的路径为：`TencentOS-stiny\components\ai\tflite_micro\ARM_CortexM4_lib\tensorflow_lite_micro.lib`

-其余.cc文件均在当前目录下的`tflu_person_detection`文件夹中。
+其余.cc文件和.h均在`examples\tflu_person_detection\tflu_person_detection`文件夹中。

 #### 1.3 关闭Keil的MicroLib库

@@ -237,13 +238,13 @@ TencentOS-tiny\components\ai\tflite_micro\ARM_CortexM4_lib\tensorflow\lite\micro
 <img src="./image/tflu_STM32496宏.png" width=80% />
 </div>

-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。
+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
+`TencentOS-tiny\examples\tflitemicro_person_detection\tflitemicro_person_detection.c`

 #### 2.1 图像预处理

@@ -311,13 +312,13 @@ 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：
+通过串行输出实时打印信息，移动摄像头，没有对准行人时，输出如下：

 <div align=center>
 <img src="./image/reasult_no_person.png" width=70% />
 </div>

-当镜头对准行人时，输出如下：
+当摄像头对准行人时，输出如下：

 <div align=center>
 <img src="./image/reasult_person.png" width=70% />
--- a/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TencentOS_tiny.uvoptx
+++ b/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TencentOS_tiny.uvoptx
@@ -103,7 +103,7 @@
        <bEvRecOn>1</bEvRecOn>
        <bSchkAxf>0</bSchkAxf>
        <bTchkAxf>0</bTchkAxf>
-        <nTsel>0</nTsel>
+        <nTsel>6</nTsel>
        <sDll></sDll>
        <sDllPa></sDllPa>
        <sDlgDll></sDlgDll>
@@ -114,7 +114,7 @@
        <tDlgDll></tDlgDll>
        <tDlgPa></tDlgPa>
        <tIfile></tIfile>
-        <pMon>BIN\UL2CM3.DLL</pMon>
+        <pMon>STLink\ST-LINKIII-KEIL_SWO.dll</pMon>
      </DebugOpt>
      <TargetDriverDllRegistry>
        <SetRegEntry>
@@ -622,7 +622,7 @@

  <Group>
    <GroupName>Drivers/CMSIS</GroupName>
-    <tvExp>0</tvExp>
+    <tvExp>1</tvExp>
    <tvExpOptDlg>0</tvExpOptDlg>
    <cbSel>0</cbSel>
    <RteFlg>0</RteFlg>
@@ -642,7 +642,7 @@

  <Group>
    <GroupName>tos/arch</GroupName>
-    <tvExp>0</tvExp>
+    <tvExp>1</tvExp>
    <tvExpOptDlg>0</tvExpOptDlg>
    <cbSel>0</cbSel>
    <RteFlg>0</RteFlg>
@@ -982,7 +982,7 @@

  <Group>
    <GroupName>tos/cmsis_os</GroupName>
-    <tvExp>0</tvExp>
+    <tvExp>1</tvExp>
    <tvExpOptDlg>0</tvExpOptDlg>
    <cbSel>0</cbSel>
    <RteFlg>0</RteFlg>
@@ -1002,7 +1002,7 @@

  <Group>
    <GroupName>hal</GroupName>
-    <tvExp>0</tvExp>
+    <tvExp>1</tvExp>
    <tvExpOptDlg>0</tvExpOptDlg>
    <cbSel>0</cbSel>
    <RteFlg>0</RteFlg>
@@ -1070,7 +1070,7 @@

  <Group>
    <GroupName>examples</GroupName>
-    <tvExp>0</tvExp>
+    <tvExp>1</tvExp>
    <tvExpOptDlg>0</tvExpOptDlg>
    <cbSel>0</cbSel>
    <RteFlg>0</RteFlg>
@@ -1090,13 +1090,25 @@

  <Group>
    <GroupName>tensorflow</GroupName>
-    <tvExp>0</tvExp>
+    <tvExp>1</tvExp>
    <tvExpOptDlg>0</tvExpOptDlg>
    <cbSel>0</cbSel>
    <RteFlg>0</RteFlg>
    <File>
      <GroupNumber>10</GroupNumber>
      <FileNumber>71</FileNumber>
+      <FileType>1</FileType>
+      <tvExp>0</tvExp>
+      <tvExpOptDlg>0</tvExpOptDlg>
+      <bDave2>0</bDave2>
+      <PathWithFileName>..\..\..\..\components\ai\tflite_micro\KEIL\retarget.c</PathWithFileName>
+      <FilenameWithoutPath>retarget.c</FilenameWithoutPath>
+      <RteFlg>0</RteFlg>
+      <bShared>0</bShared>
+    </File>
+    <File>
+      <GroupNumber>10</GroupNumber>
+      <FileNumber>72</FileNumber>
      <FileType>4</FileType>
      <tvExp>0</tvExp>
      <tvExpOptDlg>0</tvExpOptDlg>
@@ -1106,18 +1118,6 @@
      <RteFlg>0</RteFlg>
      <bShared>0</bShared>
    </File>
-    <File>
-      <GroupNumber>10</GroupNumber>
-      <FileNumber>72</FileNumber>
-      <FileType>8</FileType>
-      <tvExp>0</tvExp>
-      <tvExpOptDlg>0</tvExpOptDlg>
-      <bDave2>0</bDave2>
-      <PathWithFileName>.\tflu_person_detection\detection_responder.cc</PathWithFileName>
-      <FilenameWithoutPath>detection_responder.cc</FilenameWithoutPath>
-      <RteFlg>0</RteFlg>
-      <bShared>0</bShared>
-    </File>
    <File>
      <GroupNumber>10</GroupNumber>
      <FileNumber>73</FileNumber>
@@ -1125,8 +1125,8 @@
      <tvExp>0</tvExp>
      <tvExpOptDlg>0</tvExpOptDlg>
      <bDave2>0</bDave2>
-      <PathWithFileName>.\tflu_person_detection\image_provider.cc</PathWithFileName>
-      <FilenameWithoutPath>image_provider.cc</FilenameWithoutPath>
+      <PathWithFileName>..\..\..\..\examples\tflitemicro_person_detection\tflu_person_detection\detection_responder.cc</PathWithFileName>
+      <FilenameWithoutPath>detection_responder.cc</FilenameWithoutPath>
      <RteFlg>0</RteFlg>
      <bShared>0</bShared>
    </File>
@@ -1137,8 +1137,8 @@
      <tvExp>0</tvExp>
      <tvExpOptDlg>0</tvExpOptDlg>
      <bDave2>0</bDave2>
-      <PathWithFileName>.\tflu_person_detection\main_functions.cc</PathWithFileName>
-      <FilenameWithoutPath>main_functions.cc</FilenameWithoutPath>
+      <PathWithFileName>..\..\..\..\examples\tflitemicro_person_detection\tflu_person_detection\image_provider.cc</PathWithFileName>
+      <FilenameWithoutPath>image_provider.cc</FilenameWithoutPath>
      <RteFlg>0</RteFlg>
      <bShared>0</bShared>
    </File>
@@ -1149,8 +1149,8 @@
      <tvExp>0</tvExp>
      <tvExpOptDlg>0</tvExpOptDlg>
      <bDave2>0</bDave2>
-      <PathWithFileName>.\tflu_person_detection\model_settings.cc</PathWithFileName>
-      <FilenameWithoutPath>model_settings.cc</FilenameWithoutPath>
+      <PathWithFileName>..\..\..\..\examples\tflitemicro_person_detection\tflu_person_detection\main_functions.cc</PathWithFileName>
+      <FilenameWithoutPath>main_functions.cc</FilenameWithoutPath>
      <RteFlg>0</RteFlg>
      <bShared>0</bShared>
    </File>
@@ -1161,7 +1161,19 @@
      <tvExp>0</tvExp>
      <tvExpOptDlg>0</tvExpOptDlg>
      <bDave2>0</bDave2>
-      <PathWithFileName>.\tflu_person_detection\person_detect_model_data.cc</PathWithFileName>
+      <PathWithFileName>..\..\..\..\examples\tflitemicro_person_detection\tflu_person_detection\model_settings.cc</PathWithFileName>
+      <FilenameWithoutPath>model_settings.cc</FilenameWithoutPath>
+      <RteFlg>0</RteFlg>
+      <bShared>0</bShared>
+    </File>
+    <File>
+      <GroupNumber>10</GroupNumber>
+      <FileNumber>77</FileNumber>
+      <FileType>8</FileType>
+      <tvExp>0</tvExp>
+      <tvExpOptDlg>0</tvExpOptDlg>
+      <bDave2>0</bDave2>
+      <PathWithFileName>..\..\..\..\examples\tflitemicro_person_detection\tflu_person_detection\person_detect_model_data.cc</PathWithFileName>
      <FilenameWithoutPath>person_detect_model_data.cc</FilenameWithoutPath>
      <RteFlg>0</RteFlg>
      <bShared>0</bShared>
--- a/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TencentOS_tiny.uvprojx
+++ b/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TencentOS_tiny.uvprojx
@@ -339,7 +339,7 @@
              <MiscControls></MiscControls>
              <Define>USE_HAL_DRIVER,STM32L496xx,NUCLEO_STM32L496ZG</Define>
              <Undefine></Undefine>
-              <IncludePath>..\..\BSP\Inc;..\..\..\..\platform\vendor_bsp\st\STM32L4xx_HAL_Driver\Inc;..\..\..\..\platform\vendor_bsp\st\STM32L4xx_HAL_Driver\Inc\Legacy;..\..\..\..\platform\vendor_bsp\st\CMSIS\Device\ST\STM32L4xx\Include;..\..\..\..\platform\vendor_bsp\st\CMSIS\Include;..\..\..\..\arch\arm\arm-v7m\common\include;..\..\..\..\arch\arm\arm-v7m\cortex-m4\armcc;..\..\..\..\kernel\core\include;..\..\..\..\kernel\pm\include;..\..\..\..\osal\cmsis_os;..\..\..\..\examples\hello_world;..\..\TOS_CONFIG;..\..\..\..\net\at\include;..\..\..\..\kernel\hal\include;..\..\BSP\Hardware\Inc;..\..\..\..\components\ai\tflite_micro\ARM_CortexM4_lib;..\..\..\..\components\ai\tflite_micro\ARM_CortexM4_lib\third_party\flatbuffers\include;..\..\..\..\components\ai\tflite_micro\ARM_CortexM4_lib\third_party\gemmlowp;..\..\..\..\components\ai\tflite_micro\ARM_CortexM4_lib\third_party\kissfft;..\..\..\..\components\ai\tflite_micro\ARM_CortexM4_lib\third_party\ruy;..\..\..\..\components\ai\tflite_micro\ARM_CortexM4_lib\tensorflow\lite\micro\tools\make\downloads</IncludePath>
+              <IncludePath>..\..\BSP\Inc;..\..\..\..\platform\vendor_bsp\st\STM32L4xx_HAL_Driver\Inc;..\..\..\..\platform\vendor_bsp\st\STM32L4xx_HAL_Driver\Inc\Legacy;..\..\..\..\platform\vendor_bsp\st\CMSIS\Device\ST\STM32L4xx\Include;..\..\..\..\platform\vendor_bsp\st\CMSIS\Include;..\..\..\..\arch\arm\arm-v7m\common\include;..\..\..\..\arch\arm\arm-v7m\cortex-m4\armcc;..\..\..\..\kernel\core\include;..\..\..\..\kernel\pm\include;..\..\..\..\osal\cmsis_os;..\..\..\..\examples\hello_world;..\..\TOS_CONFIG;..\..\..\..\net\at\include;..\..\..\..\kernel\hal\include;..\..\BSP\Hardware\Inc;..\..\..\..\components\ai\tflite_micro\ARM_CortexM4_lib;..\..\..\..\components\ai\tflite_micro\ARM_CortexM4_lib\third_party\flatbuffers\include;..\..\..\..\components\ai\tflite_micro\ARM_CortexM4_lib\third_party\gemmlowp;..\..\..\..\components\ai\tflite_micro\ARM_CortexM4_lib\third_party\kissfft;..\..\..\..\components\ai\tflite_micro\ARM_CortexM4_lib\third_party\ruy;..\..\..\..\components\ai\tflite_micro\ARM_CortexM4_lib\tensorflow\lite\micro\tools\make\downloads;..\..\..\..\examples\tflitemicro_person_detection\tflu_person_detection</IncludePath>
            </VariousControls>
          </Cads>
          <Aads>
@@ -778,6 +778,11 @@
        <Group>
          <GroupName>tensorflow</GroupName>
          <Files>
+            <File>
+              <FileName>retarget.c</FileName>
+              <FileType>1</FileType>
+              <FilePath>..\..\..\..\components\ai\tflite_micro\KEIL\retarget.c</FilePath>
+            </File>
            <File>
              <FileName>tensorflow_lite_micro_M4.lib</FileName>
              <FileType>4</FileType>
@@ -786,27 +791,27 @@
            <File>
              <FileName>detection_responder.cc</FileName>
              <FileType>8</FileType>
-              <FilePath>.\tflu_person_detection\detection_responder.cc</FilePath>
+              <FilePath>..\..\..\..\examples\tflitemicro_person_detection\tflu_person_detection\detection_responder.cc</FilePath>
            </File>
            <File>
              <FileName>image_provider.cc</FileName>
              <FileType>8</FileType>
-              <FilePath>.\tflu_person_detection\image_provider.cc</FilePath>
+              <FilePath>..\..\..\..\examples\tflitemicro_person_detection\tflu_person_detection\image_provider.cc</FilePath>
            </File>
            <File>
              <FileName>main_functions.cc</FileName>
              <FileType>8</FileType>
-              <FilePath>.\tflu_person_detection\main_functions.cc</FilePath>
+              <FilePath>..\..\..\..\examples\tflitemicro_person_detection\tflu_person_detection\main_functions.cc</FilePath>
            </File>
            <File>
              <FileName>model_settings.cc</FileName>
              <FileType>8</FileType>
-              <FilePath>.\tflu_person_detection\model_settings.cc</FilePath>
+              <FilePath>..\..\..\..\examples\tflitemicro_person_detection\tflu_person_detection\model_settings.cc</FilePath>
            </File>
            <File>
              <FileName>person_detect_model_data.cc</FileName>
              <FileType>8</FileType>
-              <FilePath>.\tflu_person_detection\person_detect_model_data.cc</FilePath>
+              <FilePath>..\..\..\..\examples\tflitemicro_person_detection\tflu_person_detection\person_detect_model_data.cc</FilePath>
            </File>
          </Files>
        </Group>
--- a/examples/tflitemicro_person_detection/tflitemicro_person_detection.c
+++ b/examples/tflitemicro_person_detection/tflitemicro_person_detection.c
@@ -1,70 +1,80 @@
-#include "cmsis_os.h"
-#include "mcu_init.h"
-
-extern uint16_t camera_buffer[];
-extern uint8_t frame_flag;
-static uint8_t model_buffer[96*96];
-
-#define TASK1_STK_SIZE          1024
-void task1(void *arg);
-osThreadDef(task1, osPriorityNormal, 1, TASK1_STK_SIZE);
-
-#define TASK2_STK_SIZE          1024
-void task2(void *arg);
-osThreadDef(task2, osPriorityNormal, 1, TASK2_STK_SIZE);
-
-uint8_t rgb565_to_gray(uint16_t bg_color)
-{
-    uint8_t bg_r = 0;
-    uint8_t bg_g = 0;
-    uint8_t bg_b = 0;
-    bg_r = ((bg_color>>11)&0xff)<<3;
-    bg_g = ((bg_color>>5)&0x3f)<<2;
-    bg_b = (bg_color&0x1f)<<2;
-    uint8_t gray = (bg_r*299 + bg_g*587 + bg_b*114 + 500) / 1000;
-    return gray;
-}
-
-void input_convert(uint16_t* camera_buffer , uint8_t* model_buffer)
-{
-	for(int i=0 ; i<OV2640_PIXEL_WIDTH*OV2640_PIXEL_HEIGHT ; i++)
-	{
-		model_buffer[i] = rgb565_to_gray(camera_buffer[i]);
-	}
-}
-
-void task1(void *arg)
-{
-    while (1) {
-      if(frame_flag == 1){
-				
-				if(HAL_DCMI_Stop(&hdcmi))Error_Handler(); //stop DCMI
-				input_convert(camera_buffer,model_buffer);
-				person_detect(model_buffer);
-				LCD_2IN4_Display(camera_buffer,OV2640_PIXEL_WIDTH,OV2640_PIXEL_HEIGHT);
-        
-				frame_flag = 0;
-				
-				if(HAL_DCMI_Start_DMA(&hdcmi,DCMI_MODE_CONTINUOUS,(uint32_t)camera_buffer ,\
-					 (OV2640_PIXEL_WIDTH*OV2640_PIXEL_HEIGHT)/2))Error_Handler(); //restart DCMI
-			}
-			osDelay(50);
-    }
-}
-
-void task2(void *arg)
-{
-    while (1) {
-        printf("***person detection task\r\n");
-        osDelay(50);
-    }
-}
-
-
-void application_entry(void *arg)
-{
-    printf("***Start person detection task! \r\n");
-    osThreadCreate(osThread(task1), NULL); // Create task1
-    osThreadCreate(osThread(task2), NULL); // Create task2
-}
-
+/**
+ * @file tflitemicro_person_detection.c
+ * @author Derekduke (dkeji627@gmail.com); QingChuanWS (bingshan45@163.com); yangqings (yangqingsheng12@outlook.com)
+ * @brief  Person detection example based on TencentOS-Tiny and TFlite_Micro.
+ * @version 0.1
+ * @date 2021-01-05
+ * @copyright Copyright (c) 2021
+ * 
+ */
+
+#include "cmsis_os.h"
+#include "mcu_init.h"
+
+extern uint16_t camera_buffer[];
+extern uint8_t frame_flag;
+static uint8_t model_buffer[96*96];
+
+#define TASK1_STK_SIZE          1024
+void task1(void *arg);
+osThreadDef(task1, osPriorityNormal, 1, TASK1_STK_SIZE);
+
+#define TASK2_STK_SIZE          1024
+void task2(void *arg);
+osThreadDef(task2, osPriorityNormal, 1, TASK2_STK_SIZE);
+
+uint8_t rgb565_to_gray(uint16_t bg_color)
+{
+    uint8_t bg_r = 0;
+    uint8_t bg_g = 0;
+    uint8_t bg_b = 0;
+    bg_r = ((bg_color>>11)&0xff)<<3;
+    bg_g = ((bg_color>>5)&0x3f)<<2;
+    bg_b = (bg_color&0x1f)<<2;
+    uint8_t gray = (bg_r*299 + bg_g*587 + bg_b*114 + 500) / 1000;
+    return gray;
+}
+
+void input_convert(uint16_t* camera_buffer , uint8_t* model_buffer)
+{
+	for(int i=0 ; i<OV2640_PIXEL_WIDTH*OV2640_PIXEL_HEIGHT ; i++)
+	{
+		model_buffer[i] = rgb565_to_gray(camera_buffer[i]);
+	}
+}
+
+void task1(void *arg)
+{
+    while (1) {
+      if(frame_flag == 1){
+				printf("*person_detect_task\r\n");
+				if(HAL_DCMI_Stop(&hdcmi))Error_Handler(); //stop DCMI
+				input_convert(camera_buffer,model_buffer);
+				person_detect(model_buffer);
+				LCD_2IN4_Display(camera_buffer,OV2640_PIXEL_WIDTH,OV2640_PIXEL_HEIGHT);
+        
+				frame_flag = 0;
+				
+				if(HAL_DCMI_Start_DMA(&hdcmi,DCMI_MODE_CONTINUOUS,(uint32_t)camera_buffer ,\
+					 (OV2640_PIXEL_WIDTH*OV2640_PIXEL_HEIGHT)/2))Error_Handler(); //restart DCMI
+			}
+			osDelay(50);
+    }
+}
+
+void task2(void *arg)
+{
+    while (1) {
+        //printf("***task2\r\n");
+        osDelay(200);
+    }
+}
+
+
+void application_entry(void *arg)
+{
+    printf("***Start person detection task! \r\n");
+    osThreadCreate(osThread(task1), NULL); // Create task1
+    osThreadCreate(osThread(task2), NULL); // Create task2
+}
+
--- a/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/tflu_person_detection/detection_responder.cc
+++ b/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/tflu_person_detection/detection_responder.cc
@@ -1,25 +1,25 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/person_detection_experimental/detection_responder.h"
-
-// This dummy implementation writes person and no person scores to the error
-// console. Real applications will want to take some custom action instead, and
-// should implement their own versions of this function.
-void RespondToDetection(tflite::ErrorReporter* error_reporter,
-                        int8_t person_score, int8_t no_person_score) {
-  TF_LITE_REPORT_ERROR(error_reporter, "person score:%d no person score %d",
-                       person_score, no_person_score);
-}
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/lite/micro/examples/person_detection_experimental/detection_responder.h"
+
+// This dummy implementation writes person and no person scores to the error
+// console. Real applications will want to take some custom action instead, and
+// should implement their own versions of this function.
+void RespondToDetection(tflite::ErrorReporter* error_reporter,
+                        int8_t person_score, int8_t no_person_score) {
+  TF_LITE_REPORT_ERROR(error_reporter, "person score:%d no person score %d",
+                       person_score, no_person_score);
+}
--- a/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/tflu_person_detection/detection_responder.h
+++ b/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/tflu_person_detection/detection_responder.h
@@ -1,34 +1,34 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// Provides an interface to take an action based on the output from the person
-// detection model.
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_DETECTION_RESPONDER_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_DETECTION_RESPONDER_H_
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/micro/micro_error_reporter.h"
-
-// Called every time the results of a person detection run are available. The
-// `person_score` has the numerical confidence that the captured image contains
-// a person, and `no_person_score` has the numerical confidence that the image
-// does not contain a person. Typically if person_score > no person score, the
-// image is considered to contain a person.  This threshold may be adjusted for
-// particular applications.
-void RespondToDetection(tflite::ErrorReporter* error_reporter,
-                        int8_t person_score, int8_t no_person_score);
-
-#endif  // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_DETECTION_RESPONDER_H_
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// Provides an interface to take an action based on the output from the person
+// detection model.
+
+#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_DETECTION_RESPONDER_H_
+#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_DETECTION_RESPONDER_H_
+
+#include "tensorflow/lite/c/common.h"
+#include "tensorflow/lite/micro/micro_error_reporter.h"
+
+// Called every time the results of a person detection run are available. The
+// `person_score` has the numerical confidence that the captured image contains
+// a person, and `no_person_score` has the numerical confidence that the image
+// does not contain a person. Typically if person_score > no person score, the
+// image is considered to contain a person.  This threshold may be adjusted for
+// particular applications.
+void RespondToDetection(tflite::ErrorReporter* error_reporter,
+                        int8_t person_score, int8_t no_person_score);
+
+#endif  // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_DETECTION_RESPONDER_H_
--- a/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/tflu_person_detection/image_provider.cc
+++ b/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/tflu_person_detection/image_provider.cc
@@ -1,26 +1,26 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/person_detection_experimental/image_provider.h"
-#include "tensorflow/lite/micro/examples/person_detection_experimental/model_settings.h"
-
-TfLiteStatus GetImage(tflite::ErrorReporter* error_reporter, int image_width,
-                      int image_height, int channels, int8_t* image_data, 
-					  uint8_t * hardware_input) {
-  for (int i = 0; i < image_width * image_height * channels; ++i) {
-	image_data[i] = hardware_input[i]; 
-  }
-  return kTfLiteOk;
-}
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/lite/micro/examples/person_detection_experimental/image_provider.h"
+#include "tensorflow/lite/micro/examples/person_detection_experimental/model_settings.h"
+
+TfLiteStatus GetImage(tflite::ErrorReporter* error_reporter, int image_width,
+                      int image_height, int channels, int8_t* image_data, 
+					  uint8_t * hardware_input) {
+  for (int i = 0; i < image_width * image_height * channels; ++i) {
+	image_data[i] = hardware_input[i]; 
+  }
+  return kTfLiteOk;
+}
--- a/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/tflu_person_detection/image_provider.h
+++ b/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/tflu_person_detection/image_provider.h
@@ -1,40 +1,40 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_IMAGE_PROVIDER_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_IMAGE_PROVIDER_H_
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/micro/micro_error_reporter.h"
-
-// This is an abstraction around an image source like a camera, and is
-// expected to return 8-bit sample data.  The assumption is that this will be
-// called in a low duty-cycle fashion in a low-power application.  In these
-// cases, the imaging sensor need not be run in a streaming mode, but rather can
-// be idled in a relatively low-power mode between calls to GetImage().  The
-// assumption is that the overhead and time of bringing the low-power sensor out
-// of this standby mode is commensurate with the expected duty cycle of the
-// application.  The underlying sensor may actually be put into a streaming
-// configuration, but the image buffer provided to GetImage should not be
-// overwritten by the driver code until the next call to GetImage();
-//
-// The reference implementation can have no platform-specific dependencies, so
-// it just returns a static image. For real applications, you should
-// ensure there's a specialized implementation that accesses hardware APIs.
-TfLiteStatus GetImage(tflite::ErrorReporter* error_reporter, int image_width,
-                      int image_height, int channels, int8_t* image_data, 
-					  uint8_t * hardware_input);
-
-#endif  // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_IMAGE_PROVIDER_H_
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_IMAGE_PROVIDER_H_
+#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_IMAGE_PROVIDER_H_
+
+#include "tensorflow/lite/c/common.h"
+#include "tensorflow/lite/micro/micro_error_reporter.h"
+
+// This is an abstraction around an image source like a camera, and is
+// expected to return 8-bit sample data.  The assumption is that this will be
+// called in a low duty-cycle fashion in a low-power application.  In these
+// cases, the imaging sensor need not be run in a streaming mode, but rather can
+// be idled in a relatively low-power mode between calls to GetImage().  The
+// assumption is that the overhead and time of bringing the low-power sensor out
+// of this standby mode is commensurate with the expected duty cycle of the
+// application.  The underlying sensor may actually be put into a streaming
+// configuration, but the image buffer provided to GetImage should not be
+// overwritten by the driver code until the next call to GetImage();
+//
+// The reference implementation can have no platform-specific dependencies, so
+// it just returns a static image. For real applications, you should
+// ensure there's a specialized implementation that accesses hardware APIs.
+TfLiteStatus GetImage(tflite::ErrorReporter* error_reporter, int image_width,
+                      int image_height, int channels, int8_t* image_data, 
+					  uint8_t * hardware_input);
+
+#endif  // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_IMAGE_PROVIDER_H_
--- a/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/tflu_person_detection/main_functions.cc
+++ b/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/tflu_person_detection/main_functions.cc
@@ -1,119 +1,119 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/person_detection_experimental/main_functions.h"
-
-#include "tensorflow/lite/micro/examples/person_detection_experimental/detection_responder.h"
-#include "tensorflow/lite/micro/examples/person_detection_experimental/image_provider.h"
-#include "tensorflow/lite/micro/examples/person_detection_experimental/model_settings.h"
-#include "tensorflow/lite/micro/examples/person_detection_experimental/person_detect_model_data.h"
-#include "tensorflow/lite/micro/micro_error_reporter.h"
-#include "tensorflow/lite/micro/micro_interpreter.h"
-#include "tensorflow/lite/micro/micro_mutable_op_resolver.h"
-#include "tensorflow/lite/schema/schema_generated.h"
-#include "tensorflow/lite/version.h"
-
-// Globals, used for compatibility with Arduino-style sketches.
-namespace {
-tflite::ErrorReporter* error_reporter = nullptr;
-const tflite::Model* model = nullptr;
-tflite::MicroInterpreter* interpreter = nullptr;
-TfLiteTensor* input = nullptr;
-
-// In order to use optimized tensorflow lite kernels, a signed int8_t quantized
-// model is preferred over the legacy unsigned model format. This means that
-// throughout this project, input images must be converted from unisgned to
-// signed format. The easiest and quickest way to convert from unsigned to
-// signed 8-bit integers is to subtract 128 from the unsigned value to get a
-// signed value.
-
-// An area of memory to use for input, output, and intermediate arrays.
-constexpr int kTensorArenaSize = 115 * 1024;
-static uint8_t tensor_arena[kTensorArenaSize];
-}  // namespace
-
-// The name of this function is important for Arduino compatibility.
-void person_detect_init() {
-  // Set up logging. Google style is to avoid globals or statics because of
-  // lifetime uncertainty, but since this has a trivial destructor it's okay.
-  // NOLINTNEXTLINE(runtime-global-variables)
-  static tflite::MicroErrorReporter micro_error_reporter;
-  error_reporter = &micro_error_reporter;
-
-  // Map the model into a usable data structure. This doesn't involve any
-  // copying or parsing, it's a very lightweight operation.
-  model = tflite::GetModel(g_person_detect_model_data);
-  if (model->version() != TFLITE_SCHEMA_VERSION) {
-    TF_LITE_REPORT_ERROR(error_reporter,
-                         "Model provided is schema version %d not equal "
-                         "to supported version %d.",
-                         model->version(), TFLITE_SCHEMA_VERSION);
-    return;
-  }
-
-  // Pull in only the operation implementations we need.
-  // This relies on a complete list of all the ops needed by this graph.
-  // An easier approach is to just use the AllOpsResolver, but this will
-  // incur some penalty in code space for op implementations that are not
-  // needed by this graph.
-  //
-  // tflite::AllOpsResolver resolver;
-  // NOLINTNEXTLINE(runtime-global-variables)
-  static tflite::MicroMutableOpResolver<5> micro_op_resolver;
-  micro_op_resolver.AddAveragePool2D();
-  micro_op_resolver.AddConv2D();
-  micro_op_resolver.AddDepthwiseConv2D();
-  micro_op_resolver.AddReshape();
-  micro_op_resolver.AddSoftmax();
-
-  // Build an interpreter to run the model with.
-  // NOLINTNEXTLINE(runtime-global-variables)
-  static tflite::MicroInterpreter static_interpreter(
-      model, micro_op_resolver, tensor_arena, kTensorArenaSize, error_reporter);
-  interpreter = &static_interpreter;
-
-  // Allocate memory from the tensor_arena for the model's tensors.
-  TfLiteStatus allocate_status = interpreter->AllocateTensors();
-  if (allocate_status != kTfLiteOk) {
-    TF_LITE_REPORT_ERROR(error_reporter, "AllocateTensors() failed");
-    return;
-  }
-
-  // Get information about the memory area to use for the model's input.
-  input = interpreter->input(0);
-}
-
-// The name of this function is important for Arduino compatibility.
-int person_detect(uint8_t * hardware_input) {
-  // Get image from provider.
-  if (kTfLiteOk != GetImage(error_reporter, kNumCols, kNumRows, kNumChannels,
-                            input->data.int8, hardware_input)) {
-    TF_LITE_REPORT_ERROR(error_reporter, "Image capture failed.");
-  }
-
-  // Run the model on this input and make sure it succeeds.
-  if (kTfLiteOk != interpreter->Invoke()) {
-    TF_LITE_REPORT_ERROR(error_reporter, "Invoke failed.");
-  }
-
-  TfLiteTensor* output = interpreter->output(0);
-
-  // Process the inference results.
-  int8_t person_score = output->data.uint8[kPersonIndex];
-  int8_t no_person_score = output->data.uint8[kNotAPersonIndex];
-  RespondToDetection(error_reporter, person_score, no_person_score);
-	if(person_score >= no_person_score + 50) return 1;
-  else return 0;
-}
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/lite/micro/examples/person_detection_experimental/main_functions.h"
+
+#include "tensorflow/lite/micro/examples/person_detection_experimental/detection_responder.h"
+#include "tensorflow/lite/micro/examples/person_detection_experimental/image_provider.h"
+#include "tensorflow/lite/micro/examples/person_detection_experimental/model_settings.h"
+#include "tensorflow/lite/micro/examples/person_detection_experimental/person_detect_model_data.h"
+#include "tensorflow/lite/micro/micro_error_reporter.h"
+#include "tensorflow/lite/micro/micro_interpreter.h"
+#include "tensorflow/lite/micro/micro_mutable_op_resolver.h"
+#include "tensorflow/lite/schema/schema_generated.h"
+#include "tensorflow/lite/version.h"
+
+// Globals, used for compatibility with Arduino-style sketches.
+namespace {
+tflite::ErrorReporter* error_reporter = nullptr;
+const tflite::Model* model = nullptr;
+tflite::MicroInterpreter* interpreter = nullptr;
+TfLiteTensor* input = nullptr;
+
+// In order to use optimized tensorflow lite kernels, a signed int8_t quantized
+// model is preferred over the legacy unsigned model format. This means that
+// throughout this project, input images must be converted from unisgned to
+// signed format. The easiest and quickest way to convert from unsigned to
+// signed 8-bit integers is to subtract 128 from the unsigned value to get a
+// signed value.
+
+// An area of memory to use for input, output, and intermediate arrays.
+constexpr int kTensorArenaSize = 115 * 1024;
+static uint8_t tensor_arena[kTensorArenaSize];
+}  // namespace
+
+// The name of this function is important for Arduino compatibility.
+void person_detect_init() {
+  // Set up logging. Google style is to avoid globals or statics because of
+  // lifetime uncertainty, but since this has a trivial destructor it's okay.
+  // NOLINTNEXTLINE(runtime-global-variables)
+  static tflite::MicroErrorReporter micro_error_reporter;
+  error_reporter = &micro_error_reporter;
+
+  // Map the model into a usable data structure. This doesn't involve any
+  // copying or parsing, it's a very lightweight operation.
+  model = tflite::GetModel(g_person_detect_model_data);
+  if (model->version() != TFLITE_SCHEMA_VERSION) {
+    TF_LITE_REPORT_ERROR(error_reporter,
+                         "Model provided is schema version %d not equal "
+                         "to supported version %d.",
+                         model->version(), TFLITE_SCHEMA_VERSION);
+    return;
+  }
+
+  // Pull in only the operation implementations we need.
+  // This relies on a complete list of all the ops needed by this graph.
+  // An easier approach is to just use the AllOpsResolver, but this will
+  // incur some penalty in code space for op implementations that are not
+  // needed by this graph.
+  //
+  // tflite::AllOpsResolver resolver;
+  // NOLINTNEXTLINE(runtime-global-variables)
+  static tflite::MicroMutableOpResolver<5> micro_op_resolver;
+  micro_op_resolver.AddAveragePool2D();
+  micro_op_resolver.AddConv2D();
+  micro_op_resolver.AddDepthwiseConv2D();
+  micro_op_resolver.AddReshape();
+  micro_op_resolver.AddSoftmax();
+
+  // Build an interpreter to run the model with.
+  // NOLINTNEXTLINE(runtime-global-variables)
+  static tflite::MicroInterpreter static_interpreter(
+      model, micro_op_resolver, tensor_arena, kTensorArenaSize, error_reporter);
+  interpreter = &static_interpreter;
+
+  // Allocate memory from the tensor_arena for the model's tensors.
+  TfLiteStatus allocate_status = interpreter->AllocateTensors();
+  if (allocate_status != kTfLiteOk) {
+    TF_LITE_REPORT_ERROR(error_reporter, "AllocateTensors() failed");
+    return;
+  }
+
+  // Get information about the memory area to use for the model's input.
+  input = interpreter->input(0);
+}
+
+// The name of this function is important for Arduino compatibility.
+int person_detect(uint8_t * hardware_input) {
+  // Get image from provider.
+  if (kTfLiteOk != GetImage(error_reporter, kNumCols, kNumRows, kNumChannels,
+                            input->data.int8, hardware_input)) {
+    TF_LITE_REPORT_ERROR(error_reporter, "Image capture failed.");
+  }
+
+  // Run the model on this input and make sure it succeeds.
+  if (kTfLiteOk != interpreter->Invoke()) {
+    TF_LITE_REPORT_ERROR(error_reporter, "Invoke failed.");
+  }
+
+  TfLiteTensor* output = interpreter->output(0);
+
+  // Process the inference results.
+  int8_t person_score = output->data.uint8[kPersonIndex];
+  int8_t no_person_score = output->data.uint8[kNotAPersonIndex];
+  RespondToDetection(error_reporter, person_score, no_person_score);
+	if(person_score >= no_person_score + 50) return 1;
+  else return 0;
+}
--- a/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/tflu_person_detection/main_functions.h
+++ b/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/tflu_person_detection/main_functions.h
@@ -1,30 +1,30 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_MAIN_FUNCTIONS_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_MAIN_FUNCTIONS_H_
-
-#include "tensorflow/lite/c/common.h"
-
-// Initializes all data needed for the example. The name is important, and needs
-// to be setup() for Arduino compatibility.
-extern "C" void person_detect_init();
-
-// Runs one iteration of data gathering and inference. This should be called
-// repeatedly from the application code. The name needs to be loop() for Arduino
-// compatibility.
-extern "C" int person_detect(uint8_t * hardware_input);
-
-#endif  // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_MAIN_FUNCTIONS_H_
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_MAIN_FUNCTIONS_H_
+#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_MAIN_FUNCTIONS_H_
+
+#include "tensorflow/lite/c/common.h"
+
+// Initializes all data needed for the example. The name is important, and needs
+// to be setup() for Arduino compatibility.
+extern "C" void person_detect_init();
+
+// Runs one iteration of data gathering and inference. This should be called
+// repeatedly from the application code. The name needs to be loop() for Arduino
+// compatibility.
+extern "C" int person_detect(uint8_t * hardware_input);
+
+#endif  // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_MAIN_FUNCTIONS_H_
--- a/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/tflu_person_detection/model_settings.cc
+++ b/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/tflu_person_detection/model_settings.cc
@@ -1,21 +1,21 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/examples/person_detection_experimental/model_settings.h"
-
-const char* kCategoryLabels[kCategoryCount] = {
-    "notperson",
-    "person",
-};
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/lite/micro/examples/person_detection_experimental/model_settings.h"
+
+const char* kCategoryLabels[kCategoryCount] = {
+    "notperson",
+    "person",
+};
--- a/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/tflu_person_detection/model_settings.h
+++ b/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/tflu_person_detection/model_settings.h
@@ -1,35 +1,35 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_MODEL_SETTINGS_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_MODEL_SETTINGS_H_
-
-// Keeping these as constant expressions allow us to allocate fixed-sized arrays
-// on the stack for our working memory.
-
-// All of these values are derived from the values used during model training,
-// if you change your model you'll need to update these constants.
-constexpr int kNumCols = 96;
-constexpr int kNumRows = 96;
-constexpr int kNumChannels = 1;
-
-constexpr int kMaxImageSize = kNumCols * kNumRows * kNumChannels;
-
-constexpr int kCategoryCount = 2;
-constexpr int kPersonIndex = 1;
-constexpr int kNotAPersonIndex = 0;
-extern const char* kCategoryLabels[kCategoryCount];
-
-#endif  // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_MODEL_SETTINGS_H_
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_MODEL_SETTINGS_H_
+#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_MODEL_SETTINGS_H_
+
+// Keeping these as constant expressions allow us to allocate fixed-sized arrays
+// on the stack for our working memory.
+
+// All of these values are derived from the values used during model training,
+// if you change your model you'll need to update these constants.
+constexpr int kNumCols = 96;
+constexpr int kNumRows = 96;
+constexpr int kNumChannels = 1;
+
+constexpr int kMaxImageSize = kNumCols * kNumRows * kNumChannels;
+
+constexpr int kCategoryCount = 2;
+constexpr int kPersonIndex = 1;
+constexpr int kNotAPersonIndex = 0;
+extern const char* kCategoryLabels[kCategoryCount];
+
+#endif  // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_MODEL_SETTINGS_H_
--- a/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/tflu_person_detection/person_detect_model_data.cc
+++ b/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/tflu_person_detection/person_detect_model_data.cc
--- a/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/tflu_person_detection/person_detect_model_data.h
+++ b/board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/tflu_person_detection/person_detect_model_data.h
@@ -1,27 +1,27 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// This is a standard TensorFlow Lite model file that has been converted into a
-// C data array, so it can be easily compiled into a binary for devices that
-// don't have a file system. It was created using the command:
-// xxd -i person_detect.tflite > person_detect_model_data.cc
-
-#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_PERSON_DETECT_MODEL_DATA_H_
-#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_PERSON_DETECT_MODEL_DATA_H_
-
-extern const unsigned char g_person_detect_model_data[];
-extern const int g_person_detect_model_data_len;
-
-#endif  // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_PERSON_DETECT_MODEL_DATA_H_
+/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// This is a standard TensorFlow Lite model file that has been converted into a
+// C data array, so it can be easily compiled into a binary for devices that
+// don't have a file system. It was created using the command:
+// xxd -i person_detect.tflite > person_detect_model_data.cc
+
+#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_PERSON_DETECT_MODEL_DATA_H_
+#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_PERSON_DETECT_MODEL_DATA_H_
+
+extern const unsigned char g_person_detect_model_data[];
+extern const int g_person_detect_model_data_len;
+
+#endif  // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_EXPERIMENTAL_PERSON_DETECT_MODEL_DATA_H_