Edgeimpulse

Edge Impulse

Edge Impulse 是边缘设备机器学习的领先开发平台，对开发者免费，并受到全球企业的信赖。

• 通过 Edge Impulse，软件开发者、工程师和领域专家可以在边缘设备上使用机器学习解决实际问题，无需博士学位或高级嵌入式工程技能。从入门到生产中的 MLOps，Edge Impulse 在从 MCU 到 CPU 的各种硬件上提供最高的效率和速度。

通过 EdgeImpulse 我们可以

• 直接从设备收集数据集
• 从其他来源收集数据集，如 .zip 文件、通过 API 或其他第三方云端摄取
• 创建测试和训练数据并将它们放入不同的标签中
• 训练我们的模型
• 能够选择合适的机器学习算法 - EI 基于我们的数据集提供推荐的机器学习算法
• 将其部署到我们的硬件上
• 具有版本控制的 TinyML 项目协作
• 以及更多帮助您构建 tinyML 应用程序的功能。

快速实践体验

在学习 Edge Impulse 的完整过程之前，我们提供了完整的预生成 Arduino 库，您可以通过直接将这个 Arduino 程序从草图刷写到 XIAO ESP32S3 Sense 来查看结果。

在 Arduino 上设置 XIAO ESPS3 Sense

在开始使用 Edge impulse 库之前，我们需要在 Arduino IDE 上设置 XIAO ESPS3，请查看这里的指南

编译并上传 Blink 示例

// the setup function runs once when you press reset or power the board
void setup() {
  // initialize digital pin LED_BUILTIN as an output.
  pinMode(LED_BUILTIN, OUTPUT);
}

// the loop function runs over and over again forever
void loop() {
  digitalWrite(LED_BUILTIN, HIGH);  // turn the LED on (HIGH is the voltage level)
  delay(1000);                      // wait for a second
  digitalWrite(LED_BUILTIN, LOW);   // turn the LED off by making the voltage LOW
  delay(1000);                      // wait for a second
}

确保在上传前选择正确的开发板和正确的端口。

待办事项

• 在 Arduino IDE 上安装 XIAO ESPS3 开发板
• 在 XIAO ESPS3 上编译并上传 Blink 示例

可用的 Edge Impulse 库

我们也在并行推进，制作一些已经验证并编辑过的 Edge Impulse 导出的 Arduino 库，这些库可以直接与 XIAO ESP32S3 Sense 配合使用。当我们使用这些库时，我们在程序中有更多的控制权，可以做出决策。

• 语音关键词识别（Yes & No）Arduino 库
• 水果识别（苹果、香蕉、葡萄）Arduino 库

如何使用 Edge Impulse Arduino 库

这就像一个通用的 arduino 库，我们需要先在 Arduino IDE 上安装它，然后使用示例代码来运行演示。

语音关键词识别（Yes & No）Arduino 库

演示

工作原理

步骤 1. 下载 KWS 演示库

下载语音关键词识别（Yes & No）Arduino 库库作为 .Zip 文件。

步骤 2. 将 ZIP 库添加到 Arduino IDE

由于您已经下载了 zip 库，打开您的 Arduino IDE，点击 Sketch > Include Library > Add .ZIP Library。

选择您刚刚下载的 zip 文件，如果库安装正确，您将在通知窗口中看到 Library added to your libraries。这意味着库已成功安装。

步骤 3. 运行推理代码

/* Edge Impulse Arduino examples
 * Copyright (c) 2022 EdgeImpulse Inc.
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation 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
 * furnished 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 FOR 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.
 * 
 * This code was adapted by Marcelo Rovai to run on a XIAO ESP32S3
 * 29May23
 * Modified by Salman Faris
 * 14Aug23
 * 
 */

// If your target is limited in memory remove this macro to save 10K RAM
#define EIDSP_QUANTIZE_FILTERBANK 0

/*
 ** NOTE: If you run into TFLite arena allocation issue.
 **
 ** This may be due to may dynamic memory fragmentation.
 ** Try defining "-DEI_CLASSIFIER_ALLOCATION_STATIC" in boards.local.txt (create
 ** if it doesn't exist) and copy this file to
 ** `<ARDUINO_CORE_INSTALL_PATH>/arduino/hardware/<mbed_core>/<core_version>/`.
 **
 ** See
 ** (https://support.arduino.cc/hc/en-us/articles/360012076960-Where-are-the-installed-cores-located-)
 ** to find where Arduino installs cores on your machine.
 **
 ** If the problem persists then there's not enough memory for this model and application.
 */

/* Includes ---------------------------------------------------------------- */
#include <XIAO_esp32S3_YesNo_inferencing.h>

#include <I2S.h>
#define SAMPLE_RATE 16000U
#define SAMPLE_BITS 16

/** Audio buffers, pointers and selectors */
typedef struct {
  int16_t *buffer;
  uint8_t buf_ready;
  uint32_t buf_count;
  uint32_t n_samples;
} inference_t;

static inference_t inference;
static const uint32_t sample_buffer_size = 2048;
static signed short sampleBuffer[sample_buffer_size];
static bool debug_nn = false;  // Set this to true to see e.g. features generated from the raw signal
static bool record_status = true;

/**
 * @brief      Arduino setup function
 */
void setup() {
  // put your setup code here, to run once:
  Serial.begin(115200);

  pinMode(LED_BUILTIN, OUTPUT);
  // comment out the below line to cancel the wait for USB connection (needed for native USB)
  while (!Serial)
    ;
  Serial.println("Edge Impulse Inferencing Demo");

  I2S.setAllPins(-1, 42, 41, -1, -1);
  if (!I2S.begin(PDM_MONO_MODE, SAMPLE_RATE, SAMPLE_BITS)) {
    Serial.println("Failed to initialize I2S!");
    while (1)
      ;
  }

  // summary of inferencing settings (from model_metadata.h)
  ei_printf("Inferencing settings:\n");
  ei_printf("\tInterval: ");
  ei_printf_float((float)EI_CLASSIFIER_INTERVAL_MS);
  ei_printf(" ms.\n");
  ei_printf("\tFrame size: %d\n", EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE);
  ei_printf("\tSample length: %d ms.\n", EI_CLASSIFIER_RAW_SAMPLE_COUNT / 16);
  ei_printf("\tNo. of classes: %d\n", sizeof(ei_classifier_inferencing_categories) / sizeof(ei_classifier_inferencing_categories[0]));

  ei_printf("\nStarting continious inference in 2 seconds...\n");
  ei_sleep(2000);

  if (microphone_inference_start(EI_CLASSIFIER_RAW_SAMPLE_COUNT) == false) {
    ei_printf("ERR: Could not allocate audio buffer (size %d), this could be due to the window length of your model\r\n", EI_CLASSIFIER_RAW_SAMPLE_COUNT);
    return;
  }

  ei_printf("Recording...\n");
}

/**
 * @brief      Arduino main function. Runs the inferencing loop.
 */
void loop() {
  bool m = microphone_inference_record();
  if (!m) {
    ei_printf("ERR: Failed to record audio...\n");
    return;
  }

  signal_t signal;
  signal.total_length = EI_CLASSIFIER_RAW_SAMPLE_COUNT;
  signal.get_data = &microphone_audio_signal_get_data;
  ei_impulse_result_t result = { 0 };

  EI_IMPULSE_ERROR r = run_classifier(&signal, &result, debug_nn);
  if (r != EI_IMPULSE_OK) {
    ei_printf("ERR: Failed to run classifier (%d)\n", r);
    return;
  }

  int pred_index = 0;    // Initialize pred_index
  float pred_value = 0;  // Initialize pred_value

  // print the predictions
  ei_printf("Predictions ");
  ei_printf("(DSP: %d ms., Classification: %d ms., Anomaly: %d ms.)",
            result.timing.dsp, result.timing.classification, result.timing.anomaly);
  ei_printf(": \n");
  for (size_t ix = 0; ix < EI_CLASSIFIER_LABEL_COUNT; ix++) {
    ei_printf("    %s: ", result.classification[ix].label);
    ei_printf_float(result.classification[ix].value);
    ei_printf("\n");

    if (result.classification[ix].value > pred_value) {
      pred_index = ix;
      pred_value = result.classification[ix].value;
    }
  }

    // Display inference result
    if ((pred_index == 0 && (pred_value > 0.8))) {
      digitalWrite(LED_BUILTIN, HIGH);  //Turn on
    } else if ((pred_index == 2) && (pred_value > 0.8)) {
      digitalWrite(LED_BUILTIN, LOW);  //Turn off
    } else {
      //nothing to do. 
    }

#if EI_CLASSIFIER_HAS_ANOMALY == 1
    ei_printf("    anomaly score: ");
    ei_printf_float(result.anomaly);
    ei_printf("\n");
#endif
  }

  static void audio_inference_callback(uint32_t n_bytes) {
    for (int i = 0; i < n_bytes >> 1; i++) {
      inference.buffer[inference.buf_count++] = sampleBuffer[i];

      if (inference.buf_count >= inference.n_samples) {
        inference.buf_count = 0;
        inference.buf_ready = 1;
      }
    }
  }

  static void capture_samples(void *arg) {

    const int32_t i2s_bytes_to_read = (uint32_t)arg;
    size_t bytes_read = i2s_bytes_to_read;

    while (record_status) {

      /* read data at once from i2s */
      esp_i2s::i2s_read(esp_i2s::I2S_NUM_0, (void *)sampleBuffer, i2s_bytes_to_read, &bytes_read, 100);

      if (bytes_read <= 0) {
        ei_printf("Error in I2S read : %d", bytes_read);
      } else {
        if (bytes_read < i2s_bytes_to_read) {
          ei_printf("Partial I2S read");
        }

        // scale the data (otherwise the sound is too quiet)
        for (int x = 0; x < i2s_bytes_to_read / 2; x++) {
          sampleBuffer[x] = (int16_t)(sampleBuffer[x]) * 8;
        }

        if (record_status) {
          audio_inference_callback(i2s_bytes_to_read);
        } else {
          break;
        }
      }
    }
    vTaskDelete(NULL);
  }

  /**
 * @brief      Init inferencing struct and setup/start PDM
 *
 * @param[in]  n_samples  The n samples
 *
 * @return     { description_of_the_return_value }
 */
  static bool microphone_inference_start(uint32_t n_samples) {
    inference.buffer = (int16_t *)malloc(n_samples * sizeof(int16_t));

    if (inference.buffer == NULL) {
      return false;
    }

    inference.buf_count = 0;
    inference.n_samples = n_samples;
    inference.buf_ready = 0;

    //    if (i2s_init(EI_CLASSIFIER_FREQUENCY)) {
    //        ei_printf("Failed to start I2S!");
    //    }

    ei_sleep(100);

    record_status = true;

    xTaskCreate(capture_samples, "CaptureSamples", 1024 * 32, (void *)sample_buffer_size, 10, NULL);

    return true;
  }

  /**
 * @brief      Wait on new data
 *
 * @return     True when finished
 */
  static bool microphone_inference_record(void) {
    bool ret = true;

    while (inference.buf_ready == 0) {
      delay(10);
    }

    inference.buf_ready = 0;
    return ret;
  }

  /**
 * Get raw audio signal data
 */
  static int microphone_audio_signal_get_data(size_t offset, size_t length, float *out_ptr) {
    numpy::int16_to_float(&inference.buffer[offset], out_ptr, length);

    return 0;
  }

  /**
 * @brief      Stop PDM and release buffers
 */
  static void microphone_inference_end(void) {
    free(sampleBuffer);
    ei_free(inference.buffer);
  }


#if !defined(EI_CLASSIFIER_SENSOR) || EI_CLASSIFIER_SENSOR != EI_CLASSIFIER_SENSOR_MICROPHONE
#error "Invalid model for current sensor."
#endif

复制上述代码或从这里下载，然后将代码上传到XIAO。

 // print the predictions
  ei_printf("Predictions ");
  ei_printf("(DSP: %d ms., Classification: %d ms., Anomaly: %d ms.)",
            result.timing.dsp, result.timing.classification, result.timing.anomaly);
  ei_printf(": \n");
  for (size_t ix = 0; ix < EI_CLASSIFIER_LABEL_COUNT; ix++) {
    ei_printf("    %s: ", result.classification[ix].label);
    ei_printf_float(result.classification[ix].value);
    ei_printf("\n");

    if (result.classification[ix].value > pred_value) {
      pred_index = ix;
      pred_value = result.classification[ix].value;
    }
  }

    // Display inference result
    if ((pred_index == 0 && (pred_value > 0.8))) {
      digitalWrite(LED_BUILTIN, HIGH);  //Turn on
    } else if ((pred_index == 2) && (pred_value > 0.8)) {
      digitalWrite(LED_BUILTIN, LOW);  //Turn off
    } else {
      //nothing to do. 
    }

在上面的代码中，我们可以看到在哪里做出了打开 LED 和关闭 LED 的决定。我们也可以在这里放置其他逻辑，让 KWS 来控制我们的项目。

---

水果识别（苹果、香蕉、葡萄）Arduino 库

步骤 1. 下载水果识别库

下载水果识别（苹果、香蕉、葡萄）Arduino 库库文件（.Zip 格式）。

步骤 2. 将 ZIP 库添加到 Arduino IDE

由于您已经下载了 zip 库文件，请打开您的 Arduino IDE，点击 Sketch > Include Library > Add .ZIP Library。

选择您刚刚下载的 zip 文件，如果库安装正确，您将在通知窗口中看到"Library added to your libraries"。这意味着库已成功安装。

步骤 3. 运行示例代码

您可以在 Files -> Examples -> xiao-esp323-fruits-classify_inferencing -> XIAO-ESP32S3-Sense 下找到示例代码

试用演示

从这里打开数据集示例页面并打开水果图像，然后将 XIAO ESP32S3 摄像头对准水果图像，在屏幕上查看结果。

待办事项

• 在 Arduino 上安装 XIAO ESPS3
• 编译和上传
• 安装 KWS Arduino 库并运行示例。
• 安装图像识别库并运行示例。

资源

• [GITHUB] SenseCraft模型助手模型
• [GITHUB] 项目Github
• [EDGE-IMPULSE] edge impulse KWS演示
• [EDGE-IMPULSE] edge impulse水果分类演示