完成tflite_micro库目录结构，完成496TF移植部分文档

board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TFlite_Micro_Demo移植参考指南（Keil版）.md

@@ -60,16 +60,16 @@
 
 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 |         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                       |
+| 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               |
 
 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。
 
@@ -128,23 +128,59 @@ void HAL_DCMI_FrameEventCallback(DCMI_HandleTypeDef *hdcmi)
 
 ## 三、Tensorflow Lite Micro移植
 
-### 1. tflite_micro源码加入到keil工程
+### 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。
+
+#### 1.1 在project中加入新的文件夹tensorflow：
+
+<div align=center>
+<img src="./image/tflu_tensorflow文件夹增加的内容.png" width=80% />
+</div>
+
+#### 1.2 添加本次与行人检测demo有关的源文件
+
+<div align=center>
+<img src="./image/tflu_需要添加的文件.png" width=80% />
+</div>
 
 
 
-### 2. 解决编译错误
+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`
+
+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：
+
+<div align=center>
+<img src="./image/tflu_取消Microlib.png" width=80% />
+</div>
+#### 1.4 添加tflite_micro需要的头文件：
+
+<div align=center>
+<img src="./image/tflu_添加include.png" width=80% />
+</div>
+
+注：最下方的路径为：
+
+```
+TencentOS-tiny\components\ai\tflite_micro\ARM_CortexM4_lib\tensorflow\lite\micro\tools\make\downloads
+```
+
+#### 1.5 调整优化等级和tflite_micro的交互信息输出串口：
+
+<div align=center>
+<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。
 
 
+### 2. 编写Person_Detection 任务函数
 
-### 3. 测试行人检测模型
-
-
-
-### 4. 编写Person_Detection 任务函数
-
-
-
-#### 4.1 图像预处理
+#### 2.1 图像预处理
 
 <div align=center>
 <img src="./image/RGB565.jpg" width=50% />
@@ -174,9 +210,7 @@ void input_convert(uint16_t* camera_buffer , uint8_t* model_buffer)
 }
 ```
 
-
-
-#### 4.2 行人检测线程任务函数
+#### 3.2 行人检测线程任务函数
 
 ```c
 void task1(void *arg)

board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TencentOS_tiny.uvoptx

@@ -12,7 +12,7 @@
     <lExt>*.lib</lExt>
     <tExt>*.txt; *.h; *.inc</tExt>
     <pExt>*.plm</pExt>
-    <CppX>*.cpp</CppX>
+    <CppX>*.cpp;*.cc</CppX>
     <nMigrate>0</nMigrate>
   </Extensions>
 
@@ -1051,7 +1051,7 @@
 
   <Group>
     <GroupName>hal</GroupName>
-    <tvExp>1</tvExp>
+    <tvExp>0</tvExp>
     <tvExpOptDlg>0</tvExpOptDlg>
     <cbSel>0</cbSel>
     <RteFlg>0</RteFlg>
@@ -1119,7 +1119,7 @@
 
   <Group>
     <GroupName>examples</GroupName>
-    <tvExp>1</tvExp>
+    <tvExp>0</tvExp>
     <tvExpOptDlg>0</tvExpOptDlg>
     <cbSel>0</cbSel>
     <RteFlg>0</RteFlg>
@@ -1146,24 +1146,84 @@
     <File>
       <GroupNumber>10</GroupNumber>
       <FileNumber>71</FileNumber>
-      <FileType>1</FileType>
+      <FileType>8</FileType>
       <tvExp>0</tvExp>
       <tvExpOptDlg>0</tvExpOptDlg>
       <bDave2>0</bDave2>
-      <PathWithFileName>..\..\..\..\components\tflite_micro\KEIL\regarget.c</PathWithFileName>
-      <FilenameWithoutPath>regarget.c</FilenameWithoutPath>
+      <PathWithFileName>.\tflu_person_detection\person_detect_model_data.cc</PathWithFileName>
+      <FilenameWithoutPath>person_detect_model_data.cc</FilenameWithoutPath>
       <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\model_settings.cc</PathWithFileName>
+      <FilenameWithoutPath>model_settings.cc</FilenameWithoutPath>
+      <RteFlg>0</RteFlg>
+      <bShared>0</bShared>
+    </File>
+    <File>
+      <GroupNumber>10</GroupNumber>
+      <FileNumber>73</FileNumber>
+      <FileType>8</FileType>
+      <tvExp>0</tvExp>
+      <tvExpOptDlg>0</tvExpOptDlg>
+      <bDave2>0</bDave2>
+      <PathWithFileName>.\tflu_person_detection\main_functions.cc</PathWithFileName>
+      <FilenameWithoutPath>main_functions.cc</FilenameWithoutPath>
+      <RteFlg>0</RteFlg>
+      <bShared>0</bShared>
+    </File>
+    <File>
+      <GroupNumber>10</GroupNumber>
+      <FileNumber>74</FileNumber>
+      <FileType>8</FileType>
+      <tvExp>0</tvExp>
+      <tvExpOptDlg>0</tvExpOptDlg>
+      <bDave2>0</bDave2>
+      <PathWithFileName>.\tflu_person_detection\image_provider.cc</PathWithFileName>
+      <FilenameWithoutPath>image_provider.cc</FilenameWithoutPath>
+      <RteFlg>0</RteFlg>
+      <bShared>0</bShared>
+    </File>
+    <File>
+      <GroupNumber>10</GroupNumber>
+      <FileNumber>75</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>76</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>77</FileNumber>
       <FileType>4</FileType>
       <tvExp>0</tvExp>
       <tvExpOptDlg>0</tvExpOptDlg>
       <bDave2>0</bDave2>
-      <PathWithFileName>..\..\..\..\components\tflite_micro\ARM_CortexM4_lib\tflm_person_detection.lib</PathWithFileName>
-      <FilenameWithoutPath>tflm_person_detection.lib</FilenameWithoutPath>
+      <PathWithFileName>..\..\..\..\components\ai\tflite_micro\ARM_CortexM4_lib\tensorflow_lite_micro_M4.lib</PathWithFileName>
+      <FilenameWithoutPath>tensorflow_lite_micro_M4.lib</FilenameWithoutPath>
       <RteFlg>0</RteFlg>
       <bShared>0</bShared>
     </File>

board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/TencentOS_tiny.uvprojx

@@ -16,8 +16,8 @@
         <TargetCommonOption>
           <Device>STM32L496ZGTx</Device>
           <Vendor>STMicroelectronics</Vendor>
-          <PackID>Keil.STM32L4xx_DFP.2.4.0</PackID>
-          <PackURL>http://www.keil.com/pack/</PackURL>
+          <PackID>Keil.STM32L4xx_DFP.2.5.0</PackID>
+          <PackURL>https://www.keil.com/pack/</PackURL>
           <Cpu>IRAM(0x20000000-0x2004FFFF) IROM(0x8000000-0x80FFFFF) CLOCK(8000000) FPU2 CPUTYPE("Cortex-M4")</Cpu>
           <FlashUtilSpec></FlashUtilSpec>
           <StartupFile></StartupFile>
@@ -313,7 +313,7 @@
           </ArmAdsMisc>
           <Cads>
             <interw>1</interw>
-            <Optim>7</Optim>
+            <Optim>4</Optim>
             <oTime>0</oTime>
             <SplitLS>0</SplitLS>
             <OneElfS>1</OneElfS>
@@ -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\tflite_micro\ARM_CortexM4_lib;..\..\..\..\components\tflite_micro\ARM_CortexM4_lib\third_party\flatbuffers\include;..\..\..\..\components\tflite_micro\ARM_CortexM4_lib\third_party\gemmlowp;..\..\..\..\components\tflite_micro\ARM_CortexM4_lib\third_party\kissfft;..\..\..\..\components\tflite_micro\ARM_CortexM4_lib\third_party\ruy;..\..\..\..\components\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</IncludePath>
             </VariousControls>
           </Cads>
           <Aads>
@@ -779,14 +779,39 @@
           <GroupName>tensorflow</GroupName>
           <Files>
             <File>
-              <FileName>regarget.c</FileName>
-              <FileType>1</FileType>
-              <FilePath>..\..\..\..\components\tflite_micro\KEIL\regarget.c</FilePath>
+              <FileName>person_detect_model_data.cc</FileName>
+              <FileType>8</FileType>
+              <FilePath>.\tflu_person_detection\person_detect_model_data.cc</FilePath>
             </File>
             <File>
-              <FileName>tflm_person_detection.lib</FileName>
+              <FileName>model_settings.cc</FileName>
+              <FileType>8</FileType>
+              <FilePath>.\tflu_person_detection\model_settings.cc</FilePath>
+            </File>
+            <File>
+              <FileName>main_functions.cc</FileName>
+              <FileType>8</FileType>
+              <FilePath>.\tflu_person_detection\main_functions.cc</FilePath>
+            </File>
+            <File>
+              <FileName>image_provider.cc</FileName>
+              <FileType>8</FileType>
+              <FilePath>.\tflu_person_detection\image_provider.cc</FilePath>
+            </File>
+            <File>
+              <FileName>detection_responder.cc</FileName>
+              <FileType>8</FileType>
+              <FilePath>.\tflu_person_detection\detection_responder.cc</FilePath>
+            </File>
+            <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>
-              <FilePath>..\..\..\..\components\tflite_micro\ARM_CortexM4_lib\tflm_person_detection.lib</FilePath>
+              <FilePath>..\..\..\..\components\ai\tflite_micro\ARM_CortexM4_lib\tensorflow_lite_micro_M4.lib</FilePath>
             </File>
           </Files>
         </Group>

board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/tflu_person_detection/detection_responder.cc

@@ -0,0 +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);
+}

board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/tflu_person_detection/image_provider.cc

@@ -0,0 +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;
+}

board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/tflu_person_detection/main_functions.cc

@@ -0,0 +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;
+}

board/NUCLEO_STM32L496ZG/KEIL/tflitemicro_person_detection/tflu_person_detection/model_settings.cc

@@ -0,0 +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",
+};