零、性能测评
之前测试了BW21的人脸检测例程，感觉对人脸的识别还是十分的准确的。这次在例程里找到了另一个人脸检测的例程进行测试，相比于之前的例程多实现了不同人脸的识别功能，并且可以标记存储人脸记录。
实际运行效果：（真实把自己命名成USER1）
人脸识别功能整体使用体验还是很棒的，识别的帧率按串口输出看大概是十帧左右，但RTSP的视频流看起来挺流畅的，没有卡顿的感觉，只是稍微有点小延迟，但是并不影响实际的使用。
而且识别结果准确率和容错都很棒啊，我是在正面录得人脸，实际测试摄像头倾斜到三四十度斜视的时候还能准确识别。
一、功能需求
因为之前也用过安信可的雷达产品，寻思可以试着做个久坐检测的功能（打工牛马持续久坐对下肢的影响还是挺大的，建议大伙坐半小时到一小时尽量起来活动几分钟），但是使用雷达的话有可能对附近的环境会误识别，所以考虑可以用BW21人脸识别实现精准的人脸识别久坐检测并提醒。
计划实现功能：
- 标记存储要检测的人脸
- 实现对检测人脸的久坐检测
- 避免因为转头/短暂站立（丢失检测人脸）导致的久坐时长误识别
- 久坐后提醒（LED）
- 久坐信息上传实现设备联动（还没实现）
二、功能实现
以下就是本次小diy的源码。
-
通过 NTPClient获取实时的时间计算久坐时长
NTPClient timeClient(ntpUDP, "europe.pool.ntp.org", 28800, 60000);
-
使用LED提醒久坐
-
通过几个全局变量记录久坐或检测丢失的开始时间
-
宏定义配置久坐时间和活动时间（用于刷新久坐开始时间）
当前实现的功能就是当被检测到持续半小时时就会亮起蓝色LED提醒站立活动一会，活动时间超过一分钟后就会刷新久坐检测开始时间，当人脸重新被检测到时开始新一轮的久坐检测
/*
Example guide:
https://www.amebaiot.com/en/amebapro2-arduino-neuralnework-face-recognition/
Face registration commands
--------------------------
Point the camera at a target face and enter the following commands into the serial monitor,
Register face: "REG={Name}" Ensure that there is only one face detected in frame
Remove face: "DEL={Name}" Remove a registered face
Reset registered faces: "RESET" Forget all previously registered faces
Backup registered faces to flash: "BACKUP" Save registered faces to flash
Restore registered faces from flash: "RESTORE" Load registered faces from flash
NN Model Selection
-------------------
Select Neural Network(NN) task and models using modelSelect(nntask, objdetmodel, facedetmodel, facerecogmodel).
Replace with NA_MODEL if they are not necessary for your selected NN Task.
NN task
=======
OBJECT_DETECTION/ FACE_DETECTION/ FACE_RECOGNITION
Models
=======
YOLOv3 model DEFAULT_YOLOV3TINY / CUSTOMIZED_YOLOV3TINY
YOLOv4 model DEFAULT_YOLOV4TINY / CUSTOMIZED_YOLOV4TINY
YOLOv7 model DEFAULT_YOLOV7TINY / CUSTOMIZED_YOLOV7TINY
SCRFD model DEFAULT_SCRFD / CUSTOMIZED_SCRFD
MobileFaceNet model DEFAULT_MOBILEFACENET/ CUSTOMIZED_MOBILEFACENET
No model NA_MODEL
*/
#include <NTPClient.h>
#include "WiFi.h"
#include <WiFiUdp.h>
#include "StreamIO.h"
#include "VideoStream.h"
#include "RTSP.h"
#include "NNFaceDetectionRecognition.h"
#include "VideoStreamOverlay.h"
#define CHANNEL 0
#define CHANNELNN 3
// Customised resolution for NN
#define NNWIDTH 576
#define NNHEIGHT 320
#define RECOGNITION_MAX 1800
#define LOSS_MAX 60
VideoSetting config(VIDEO_FHD, 30, VIDEO_H264, 0);
VideoSetting configNN(NNWIDTH, NNHEIGHT, 10, VIDEO_RGB, 0);
NNFaceDetectionRecognition facerecog;
RTSP rtsp;
StreamIO videoStreamer(1, 1);
StreamIO videoStreamerFDFR(1, 1);
StreamIO videoStreamerRGBFD(1, 1);
char ssid[] = "XM-15959385520"; // your network SSID (name)
char pass[] = "15959385520a"; // your network password
int status = WL_IDLE_STATUS;
char* lastRecoName[100] = {0};
unsigned long recognitionTime = 0;
unsigned long lossTime = 0;
bool isSedentary = false;
IPAddress ip;
int rtsp_portnum;
WiFiUDP ntpUDP;
NTPClient timeClient(ntpUDP, "europe.pool.ntp.org", 28800, 60000);
void setup()
{
Serial.begin(115200);
pinMode(LED_B, OUTPUT);
pinMode(LED_G, OUTPUT);
digitalWrite(LED_B, LOW);
digitalWrite(LED_G, LOW);
// Attempt to connect to Wifi network:
while (status != WL_CONNECTED) {
Serial.print("Attempting to connect to WPA SSID: ");
Serial.println(ssid);
status = WiFi.begin(ssid, pass);
// wait 2 seconds for connection:
delay(2000);
}
ip = WiFi.localIP();
timeClient.begin();
// Configure camera video channels with video format information
// Adjust the bitrate based on your WiFi network quality
config.setBitrate(2 * 1024 * 1024); // Recommend to use 2Mbps for RTSP streaming to prevent network congestion
Camera.configVideoChannel(CHANNEL, config);
Camera.configVideoChannel(CHANNELNN, configNN);
Camera.videoInit();
// Configure RTSP with corresponding video format information
rtsp.configVideo(config);
rtsp.begin();
rtsp_portnum = rtsp.getPort();
// Configure Face Recognition model
// Select Neural Network(NN) task and models
facerecog.configVideo(configNN);
facerecog.modelSelect(FACE_RECOGNITION, NA_MODEL, DEFAULT_SCRFD, DEFAULT_MOBILEFACENET);
facerecog.begin();
facerecog.setResultCallback(FRPostProcess);
// Configure StreamIO object to stream data from video channel to RTSP
videoStreamer.registerInput(Camera.getStream(CHANNEL));
videoStreamer.registerOutput(rtsp);
if (videoStreamer.begin() != 0) {
Serial.println("StreamIO link start failed");
}
// Start data stream from video channel
Camera.channelBegin(CHANNEL);
// Configure StreamIO object to stream data from RGB video channel to face detection
videoStreamerRGBFD.registerInput(Camera.getStream(CHANNELNN));
videoStreamerRGBFD.setStackSize();
videoStreamerRGBFD.setTaskPriority();
videoStreamerRGBFD.registerOutput(facerecog);
if (videoStreamerRGBFD.begin() != 0) {
Serial.println("StreamIO link start failed");
}
// Start video channel for NN
Camera.channelBegin(CHANNELNN);
// Start OSD drawing on RTSP video channel
OSD.configVideo(CHANNEL, config);
OSD.begin();
facerecog.restoreRegisteredFace();
}
void loop()
{
if (Serial.available() > 0) {
String input = Serial.readString();
input.trim();
if (input.startsWith(String("REG="))) {
String name = input.substring(4);
facerecog.registerFace(name);
} else if (input.startsWith(String("DEL="))) {
String name = input.substring(4);
facerecog.removeFace(name);
} else if (input.startsWith(String("RESET"))) {
facerecog.resetRegisteredFace();
} else if (input.startsWith(String("BACKUP"))) {
facerecog.backupRegisteredFace();
} else if (input.startsWith(String("RESTORE"))) {
facerecog.restoreRegisteredFace();
}
}
delay(2000);
OSD.createBitmap(CHANNEL);
OSD.update(CHANNEL);
}
// User callback function for post processing of face recognition results
void FRPostProcess(std::vector<FaceRecognitionResult> results)
{
uint16_t im_h = config.height();
uint16_t im_w = config.width();
// Serial.print("Network URL for RTSP Streaming: ");
// Serial.print("rtsp://");
// Serial.print(ip);
// Serial.print(":");
// Serial.println(rtsp_portnum);
// Serial.println(" ");
// printf("Total number of faces detected = %d\r\n", facerecog.getResultCount());
OSD.createBitmap(CHANNEL);
if (facerecog.getResultCount() > 0) {
for (int i = 0; i < facerecog.getResultCount(); i++) {
FaceRecognitionResult item = results[i];
// Result coordinates are floats ranging from 0.00 to 1.00
// Multiply with RTSP resolution to get coordinates in pixels
int xmin = (int)(item.xMin() * im_w);
int xmax = (int)(item.xMax() * im_w);
int ymin = (int)(item.yMin() * im_h);
int ymax = (int)(item.yMax() * im_h);
uint32_t osd_color;
if (String(item.name()) == String("unknown")) {
osd_color = OSD_COLOR_RED;
// 丢失检测
timeClient.update();
if(lossTime == 0){
lossTime = timeClient.getEpochTime();
}else if((timeClient.getEpochTime() - lossTime) > LOSS_MAX){
isSedentary = false;
digitalWrite(LED_B, LOW);
recognitionTime = 0;
}
} else {
osd_color = OSD_COLOR_GREEN;
// 对比名称
if(strcmp(item.name(),lastRecoName) == 0){
// 持续存在
timeClient.update();
if(recognitionTime == 0){
recognitionTime = timeClient.getEpochTime();
}else if((timeClient.getEpochTime() - recognitionTime) > RECOGNITION_MAX){
isSedentary = true;
digitalWrite(LED_B, HIGH);
}
}else{
// 目标丢失
strcpy(lastRecoName,item.name());
timeClient.update();
if(lossTime == 0){
lossTime = timeClient.getEpochTime();
}else if((timeClient.getEpochTime() - lossTime) > LOSS_MAX){
isSedentary = false;
digitalWrite(LED_B, LOW);
recognitionTime = 0;
}
}
}
// Draw boundary box
// printf("Face %d name %s:\t%d %d %d %d\n\r", i, item.name(), xmin, xmax, ymin, ymax);
OSD.drawRect(CHANNEL, xmin, ymin, xmax, ymax, 3, osd_color);
// Print identification text above boundary box
char text_str[40];
snprintf(text_str, sizeof(text_str), "Face:%s", item.name());
OSD.drawText(CHANNEL, xmin, ymin - OSD.getTextHeight(CHANNEL), text_str, osd_color);
}
}
else{ // 没有检测到人脸 = 丢失
lastRecoName = "";
timeClient.update();
if(lossTime == 0){
lossTime = timeClient.getEpochTime();
}else if((timeClient.getEpochTime() - lossTime) > LOSS_MAX){
isSedentary = false;
digitalWrite(LED_B, LOW);
recognitionTime = 0;
}
}
OSD.update(CHANNEL);
}
提醒亮起时效果：
