Hackster平台：实现物体识别 - Hackster.io

https://www.hackster.io/user2554575/untitled-4ce9bb

1  任务前景介绍：
ESP32S3 Sense是一款集成了传感器和处理器的微控制器模块，具有强大的计算能力和丰富的接口，适合进行图像处理和模式识别任务。通过实现猫识别任务，可以学习图像处理、模式识别等相关知识。
2  项目需求

实现基于XIAO ESP32S3 Sense的cat识别功能。提供实时的识别结果，并通过合适的方式（如串口输出、Wi-Fi连接等）展示或传输给用户。可以通过调整参数选用合适的物体识别算法和模型，例如使用深度学习模型（如Edge Impulse 等）进行训练。使用合适的开发工具和编程语言，如Arduino IDE、MicroPython等，进行代码开发和调试。
3  使用技术栈：

3.1微控制器：XIAO以及其它开源硬件如Arduino、ESP32等；

3.2智能家居系统：Home Assistant；

3.3嵌入式TinyML

3.4 AI工具：AI　Studio；

3.5云平台：Edge Impulse；
4  实现思路

本次识别的是cat和lantern。使用arduino控制XIAO ESP32S3 Sense，在串口监视器中输入capture 命令并回车后，开始进入拍照模式了，提前将摄像头对准准备好的物品；将采样得到的照片进行存储；使用Edge Impulse平台对之前采取的照片进行构建、部署并运行机器学习模型。Edge Impulse 提供了从数据收集、数据预处理、模型训练到模型部署的完整工作流程，导出相应训练结果代码；将代码使用于arduion软件中验证运行打开串口监视器，进行物体识别验证。

5 完成的功能及达到的性能
5.1 视频流显示
arduino编程语言XIAO ESP32S3 Sense编译器通过网络传输到 ESP32-S3 并进行显示，并对视频处理和显示相关的算法进行优化，可以提高整体性能和稳定性。稳定的网络连接和足够的带宽可以确保视频流的流畅传输和高质量显示。

5.2 模型训练数据采集：使用 Edge Impulse 提供的工具或者自己的设备，采集并上传数据集。确保数据集的质量和多样性，这对模型的训练效果至关重要。

5.3数据预处理：在 Edge Impulse 中进行数据预处理，包括数据增强、标签定义等操作，以提高模型的泛化能力和准确率。


5.4测试和验证：在 ESP32-S3 上部署模型后，进行测试和验证。评估模型在实际场景中的性能和准确率，进行必要的调整和优化。

6.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.
 */

/* Includes ---------------------------------------------------------------- */
#include <qazwsx_985_inferencing.h>
#include "edge-impulse-sdk/dsp/image/image.hpp"

#include "esp_camera.h"

// Select camera model - find more camera models in camera_pins.h file here
// https://github.com/espressif/arduino-esp32/blob/master/libraries/ESP32/examples/Camera/CameraWebServer/camera_pins.h

#define CAMERA_MODEL_ESP_EYE // Has PSRAM
//#define CAMERA_MODEL_AI_THINKER // Has PSRAM

#define PWDN_GPIO_NUM     -1
#define RESET_GPIO_NUM    -1
#define XCLK_GPIO_NUM     10
#define SIOD_GPIO_NUM     40
#define SIOC_GPIO_NUM     39

#define Y9_GPIO_NUM       48
#define Y8_GPIO_NUM       11
#define Y7_GPIO_NUM       12
#define Y6_GPIO_NUM       14
#define Y5_GPIO_NUM       16
#define Y4_GPIO_NUM       18
#define Y3_GPIO_NUM       17
#define Y2_GPIO_NUM       15
#define VSYNC_GPIO_NUM    38
#define HREF_GPIO_NUM     47
#define PCLK_GPIO_NUM     13

/* Constant defines -------------------------------------------------------- */
#define EI_CAMERA_RAW_FRAME_BUFFER_COLS           320
#define EI_CAMERA_RAW_FRAME_BUFFER_ROWS           240
#define EI_CAMERA_FRAME_BYTE_SIZE                 3

/* Private variables ------------------------------------------------------- */
static bool debug_nn = false; // Set this to true to see e.g. features generated from the raw signal
static bool is_initialised = false;
uint8_t *snapshot_buf; //points to the output of the capture

static camera_config_t camera_config = {
    .pin_pwdn = PWDN_GPIO_NUM,
    .pin_reset = RESET_GPIO_NUM,
    .pin_xclk = XCLK_GPIO_NUM,
    .pin_sscb_sda = SIOD_GPIO_NUM,
    .pin_sscb_scl = SIOC_GPIO_NUM,

    .pin_d7 = Y9_GPIO_NUM,
    .pin_d6 = Y8_GPIO_NUM,
    .pin_d5 = Y7_GPIO_NUM,
    .pin_d4 = Y6_GPIO_NUM,
    .pin_d3 = Y5_GPIO_NUM,
    .pin_d2 = Y4_GPIO_NUM,
    .pin_d1 = Y3_GPIO_NUM,
    .pin_d0 = Y2_GPIO_NUM,
    .pin_vsync = VSYNC_GPIO_NUM,
    .pin_href = HREF_GPIO_NUM,
    .pin_pclk = PCLK_GPIO_NUM,

    //XCLK 20MHz or 10MHz for OV2640 double FPS (Experimental)
    .xclk_freq_hz = 20000000,
    .ledc_timer = LEDC_TIMER_0,
    .ledc_channel = LEDC_CHANNEL_0,

    .pixel_format = PIXFORMAT_JPEG, //YUV422,GRAYSCALE,RGB565,JPEG
    .frame_size = FRAMESIZE_QVGA,    //QQVGA-UXGA Do not use sizes above QVGA when not JPEG

    .jpeg_quality = 12, //0-63 lower number means higher quality
    .fb_count = 1,       //if more than one, i2s runs in continuous mode. Use only with JPEG
    .fb_location = CAMERA_FB_IN_PSRAM,
    .grab_mode = CAMERA_GRAB_WHEN_EMPTY,
};

/* Function definitions ------------------------------------------------------- */
bool ei_camera_init(void);
void ei_camera_deinit(void);
bool ei_camera_capture(uint32_t img_width, uint32_t img_height, uint8_t *out_buf) ;

/**
* @brief      Arduino setup function
*/
void setup()
{
    // put your setup code here, to run once:
    Serial.begin(115200);
    //comment out the below line to start inference immediately after upload
    while (!Serial);
    Serial.println("Edge Impulse Inferencing Demo");
    if (ei_camera_init() == false) {
        ei_printf("Failed to initialize Camera!\r\n");
    }
    else {
        ei_printf("Camera initialized\r\n");
    }

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

/**
* @brief      Get data and run inferencing
*
* @param[in]  debug  Get debug info if true
*/
void loop()
{

    // instead of wait_ms, we'll wait on the signal, this allows threads to cancel us...
    if (ei_sleep(5) != EI_IMPULSE_OK) {
        return;
    }

    snapshot_buf = (uint8_t*)malloc(EI_CAMERA_RAW_FRAME_BUFFER_COLS * EI_CAMERA_RAW_FRAME_BUFFER_ROWS * EI_CAMERA_FRAME_BYTE_SIZE);

    // check if allocation was successful
    if(snapshot_buf == nullptr) {
        ei_printf("ERR: Failed to allocate snapshot buffer!\n");
        return;
    }

    ei::signal_t signal;
    signal.total_length = EI_CLASSIFIER_INPUT_WIDTH * EI_CLASSIFIER_INPUT_HEIGHT;
    signal.get_data = &ei_camera_get_data;

    if (ei_camera_capture((size_t)EI_CLASSIFIER_INPUT_WIDTH, (size_t)EI_CLASSIFIER_INPUT_HEIGHT, snapshot_buf) == false) {
        ei_printf("Failed to capture image\r\n");
        free(snapshot_buf);
        return;
    }

    // Run the classifier
    ei_impulse_result_t result = { 0 };

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

    // print the predictions
    ei_printf("Predictions (DSP: %d ms., Classification: %d ms., Anomaly: %d ms.): \n",
                result.timing.dsp, result.timing.classification, result.timing.anomaly);

#if EI_CLASSIFIER_OBJECT_DETECTION == 1
    bool bb_found = result.bounding_boxes[0].value > 0;
    for (size_t ix = 0; ix < result.bounding_boxes_count; ix++) {
        auto bb = result.bounding_boxes[ix];
        if (bb.value == 0) {
            continue;
        }
        ei_printf("    %s (%f) [ x: %u, y: %u, width: %u, height: %u ]\n", bb.label, bb.value, bb.x, bb.y, bb.width, bb.height);
    }
    if (!bb_found) {
        ei_printf("    No objects found\n");
    }
#else
    for (size_t ix = 0; ix < EI_CLASSIFIER_LABEL_COUNT; ix++) {
        ei_printf("    %s: %.5f\n", result.classification[ix].label,
                                    result.classification[ix].value);
    }
#endif

#if EI_CLASSIFIER_HAS_ANOMALY == 1
        ei_printf("    anomaly score: %.3f\n", result.anomaly);
#endif


    free(snapshot_buf);

}

/**
 * @brief   Setup image sensor & start streaming
 *
 * @retval  false if initialisation failed
 */
bool ei_camera_init(void) {

    if (is_initialised) return true;

#if defined(CAMERA_MODEL_ESP_EYE)
  pinMode(13, INPUT_PULLUP);
  pinMode(14, INPUT_PULLUP);
#endif

    //initialize the camera
    esp_err_t err = esp_camera_init(&camera_config);
    if (err != ESP_OK) {
      Serial.printf("Camera init failed with error 0x%x\n", err);
      return false;
    }

    sensor_t * s = esp_camera_sensor_get();
    // initial sensors are flipped vertically and colors are a bit saturated
    if (s->id.PID == OV3660_PID) {
      s->set_vflip(s, 1); // flip it back
      s->set_brightness(s, 1); // up the brightness just a bit
      s->set_saturation(s, 0); // lower the saturation
    }

#if defined(CAMERA_MODEL_M5STACK_WIDE)
    s->set_vflip(s, 1);
    s->set_hmirror(s, 1);
#elif defined(CAMERA_MODEL_ESP_EYE)
    s->set_vflip(s, 1);
    s->set_hmirror(s, 1);
    s->set_awb_gain(s, 1);
#endif

    is_initialised = true;
    return true;
}

/**
 * @brief      Stop streaming of sensor data
 */
void ei_camera_deinit(void) {

    //deinitialize the camera
    esp_err_t err = esp_camera_deinit();

    if (err != ESP_OK)
    {
        ei_printf("Camera deinit failed\n");
        return;
    }

    is_initialised = false;
    return;
}


/**
 * @brief      Capture, rescale and crop image
 *
 * @param[in]  img_width     width of output image
 * @param[in]  img_height    height of output image
 * @param[in]  out_buf       pointer to store output image, NULL may be used
 *                           if ei_camera_frame_buffer is to be used for capture and resize/cropping.
 *
 * @retval     false if not initialised, image captured, rescaled or cropped failed
 *
 */
bool ei_camera_capture(uint32_t img_width, uint32_t img_height, uint8_t *out_buf) {
    bool do_resize = false;

    if (!is_initialised) {
        ei_printf("ERR: Camera is not initialized\r\n");
        return false;
    }

    camera_fb_t *fb = esp_camera_fb_get();

    if (!fb) {
        ei_printf("Camera capture failed\n");
        return false;
    }

   bool converted = fmt2rgb888(fb->buf, fb->len, PIXFORMAT_JPEG, snapshot_buf);

   esp_camera_fb_return(fb);

   if(!converted){
       ei_printf("Conversion failed\n");
       return false;
   }

    if ((img_width != EI_CAMERA_RAW_FRAME_BUFFER_COLS)
        || (img_height != EI_CAMERA_RAW_FRAME_BUFFER_ROWS)) {
        do_resize = true;
    }

    if (do_resize) {
        ei::image::processing::crop_and_interpolate_rgb888(
        out_buf,
        EI_CAMERA_RAW_FRAME_BUFFER_COLS,
        EI_CAMERA_RAW_FRAME_BUFFER_ROWS,
        out_buf,
        img_width,
        img_height);
    }


    return true;
}

static int ei_camera_get_data(size_t offset, size_t length, float *out_ptr)
{
    // we already have a RGB888 buffer, so recalculate offset into pixel index
    size_t pixel_ix = offset * 3;
    size_t pixels_left = length;
    size_t out_ptr_ix = 0;

    while (pixels_left != 0) {
        out_ptr[out_ptr_ix] = (snapshot_buf[pixel_ix] << 16) + (snapshot_buf[pixel_ix + 1] << 8) + snapshot_buf[pixel_ix + 2];

        // go to the next pixel
        out_ptr_ix++;
        pixel_ix+=3;
        pixels_left--;
    }
    // and done!
    return 0;
}

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

7  硬件介绍：

强大的MCU板: 采用ESP32S3 32位双核Xtensa处理器芯片，运行频率高达240 MHz，安装多个开发端口，支持Arduino/MicroPython高级功能 (用于Sense): 可拆卸的OV2640相机传感器，分辨率为1600*1200，与OV5640相机传感器兼容，内置额外的数字麦克风精心设计的电源:锂电池充电管理功能，提供4种功耗模式，可实现低至14μA的深度睡眠模式拥有更多可能性的美好记忆: 提供8MB PSRAM和8MB FLASH，支持用于外部32GB FAT内存的SD卡插槽出色的射频性能: 支持2.4GHz Wi-Fi和BLE双无线通信，连接U.FL天线时支持100m+远程通信拇指大小的紧凑型设计:21x17.5mm，用XIAO的经典外形，适用于可穿戴设备等空间有限的项目

8  流程图

9 难题

9.1计算资源限制：ESP32作为微控制器，其计算资源相对有限，特别是对于复杂的图像处理和深度学习任务而言。这可能导致在模型训练过程中出现性能瓶颈，需要针对性地优化算法和模型结构。

9.2模型选择和优化：在选择适合ESP32的图像识别模型时，需要考虑到模型的复杂度、计算开销和精度之间的平衡。需要针对ESP32的特点进行模型优化和剪枝，以提高模型在资源有限环境下的性能。

9.3数据预处理和增强：由于ESP32的计算能力有限，可能需要在训练之前对图像数据进行预处理和增强，以减少计算复杂度和提高训练效率。这包括图像尺寸调整、数据降维等方面的工作。

9.4训练时间和效率：由于ESP32的性能限制，模型训练可能需要更长的时间才能达到理想的效果。需要考虑如何在保证训练效果的同时，尽量减少训练时间，以提高整体的训练效率。

9.5实时性和响应性：如果图像识别任务需要在实时环境下运行，如物体检测、人脸识别等，需要确保模型训练后的推理过程能够在ESP32上实现实时性和高响应性，这可能需要对推理算法进行特定的优化和调整。

9.6能耗和热管理：在进行模型训练和推理时，需要考虑到ESP32的能耗和热量管理，避免过度消耗电量或导致设备过热的情况发生。这需要在算法设计和实现过程中充分考虑能效优化的策略。

1 未来的计划建议
该项目已经成功实现了物体识别的功能，并达到了预期指标。可以通过添加外设，实现更多功能，比如温度传感器、湿度传感器，也可以连接各种类型的显示屏（OLED、LCD、ePaper等），以扩展项目的功能和应用场景。这只是一些常见的外设示例，实际上，ESP32具有丰富的外设接口和功能，可以根据具体的项目需求选择适合的外设进行扩展。