百度离线人脸识别sdk的使用
1.1下载sdk运行
百度离线人脸识别sdk的使用
1.2配置环境
添加至项目，可以拖动复制或者以类库形式添加
face-resource此文件夹 放到根目录上一层
激活文件与所有dll引用放到根目录
嫌麻烦可以将sdk的根目录所有文件直接拖动至项目的根目录
1.3 使用
百度离线人脸识别sdk只支持Release方式运行，所以项目的配置也改为此方式（不然会提示baiduapi…….一系列dll找不到）
sdk使用OpenCvSharp3-AnyCPU的引用
static class Program
{
///
\[UnmanagedFunctionPointer(CallingConvention.Cdecl)\]
delegate void FaceCallback(IntPtr bytes, int size, String res);
// sdk初始化
\[DllImport("BaiduFaceApi.dll", EntryPoint = "sdk\_init", CharSet = CharSet.Ansi
, CallingConvention = CallingConvention.Cdecl)\]
private static extern int sdk\_init(bool id\_card);
// 是否授权
\[DllImport("BaiduFaceApi.dll", EntryPoint = "is\_auth", CharSet = CharSet.Ansi
, CallingConvention = CallingConvention.Cdecl)\]
private static extern bool is\_auth();
// 获取设备指纹
\[DllImport("BaiduFaceApi.dll", EntryPoint = "get\_device\_id", CharSet = CharSet.Ansi
, CallingConvention = CallingConvention.Cdecl)\]
private static extern IntPtr get\_device\_id();
// sdk销毁
\[DllImport("BaiduFaceApi.dll", EntryPoint = "sdk\_destroy", CharSet = CharSet.Ansi
, CallingConvention = CallingConvention.Cdecl)\]
private static extern void sdk\_destroy();
// 测试获取设备指纹device\_id
public static void test\_get\_device\_id()
{
IntPtr ptr = get\_device\_id();
string buf = Marshal.PtrToStringAnsi(ptr);
Console.WriteLine("device id is:" + buf);
}
\[STAThread\]
static void Main()
{
bool id = false;
int n = sdk\_init(id);
if (n != 0)
{
Console.WriteLine("auth result is {0:D}", n);
Console.ReadLine();
}
// 测试是否授权
bool authed = is\_auth();
Console.WriteLine("authed res is:" + authed);
test\_get\_device\_id();
Application.EnableVisualStyles();
Application.SetCompatibleTextRenderingDefault(false);
Application.Run(new Form1());
}
}使用时将Program类修改，不然无法使用人脸检测接口 错误报告为参数非法回传 -1
[DllImport("BaiduFaceApi.dll", EntryPoint = "track", CharSet = CharSet.Ansi
, CallingConvention = CallingConvention.Cdecl)]
private static extern IntPtr track(string file_name, int max_track_num);
public void test_track()
{
// 传入图片文件绝对路径
string file_name = "d:\\kehu2.jpg";
int max_track_num = 1; // 设置最多检测人数（多人脸检测），默认为1，最多可设为10
IntPtr ptr = track(file_name, max_track_num);
string buf = Marshal.PtrToStringAnsi(ptr);
Console.WriteLine("track res is:" + buf);
}
人脸检测接口，此方法需要在d盘根目录放一张名为“kehu2.jpg”的图片来检测是否含有人脸。
public void usb_csharp_track_face(int dev)
{
using (var window = new Window("face"))
using (VideoCapture cap = VideoCapture.FromCamera(dev))
{
if (!cap.IsOpened())
{
Console.WriteLine("open camera error");
return;
}
// Frame image buffer
Mat image = new Mat();
// When the movie playback reaches end, Mat.data becomes NULL.
while (true)
{
RotatedRect box;
cap.Read(image); // same as cvQueryFrame
if (!image.Empty())
{
int ilen = 2;//传入的人脸数
TrackFaceInfo[] track_info = new TrackFaceInfo[ilen];
for (int i = 0; i < ilen; i++)
{
track_info[i] = new TrackFaceInfo();
track_info[i].landmarks = new int[144];
track_info[i].headPose = new float[3];
track_info[i].face_id = 0;
track_info[i].score = 0;
}
int sizeTrack = Marshal.SizeOf(typeof(TrackFaceInfo));
IntPtr ptT = Marshal.AllocHGlobal(sizeTrack* ilen);
//Marshal.Copy(ptTrack, 0, ptT, ilen);
// FaceInfo[] face_info = new FaceInfo[ilen];
// face_info = new FaceInfo(0.0F,0.0F,0.0F,0.0F,0.0F);
//Cv2.ImWrite("usb\_track\_Cv2.jpg", image);
/\* trackMat
\* 传入参数: maxTrackObjNum:检测到的最大人脸数，传入外部分配人脸数，需要分配对应的内存大小。
\* 传出检测到的最大人脸数
\* 返回值: 传入的人脸数 和 检测到的人脸数 中的最小值,实际返回的人脸。
\*\*\*\*/
int faceSize = ilen;//返回人脸数 分配人脸数和检测到人脸数的最小值
int curSize = ilen;//当前人脸数 输入分配的人脸数，输出实际检测到的人脸数
faceSize = track\_mat(ptT, image.CvPtr, ref curSize);
for (int index = 0; index < faceSize; index++)
{
IntPtr ptr = new IntPtr();
if( 8 == IntPtr.Size)
{
ptr = (IntPtr)(ptT.ToInt64() + sizeTrack \* index);
}
else if(4 == IntPtr.Size)
{
ptr = (IntPtr)(ptT.ToInt32() + sizeTrack \* index);
}
track\_info\[index\] = (TrackFaceInfo)Marshal.PtrToStructure(ptr, typeof(TrackFaceInfo));
//face\_info\[index\] = (FaceInfo)Marshal.PtrToStructure(info\_ptr, typeof(FaceInfo));
Console.WriteLine("in Liveness::usb\_track face\_id is {0}:",track\_info\[index\].face\_id);
Console.WriteLine("in Liveness::usb\_track landmarks is:");
for (int k = 0; k < 1; k ++)
{
Console.WriteLine("{0},{1},{2},{3},{4},{5},{6},{7},{8},{9},",
track\_info\[index\].landmarks\[k\], track\_info\[index\].landmarks\[k+1\],
track\_info\[index\].landmarks\[k+2\], track\_info\[index\].landmarks\[k + 3\],
track\_info\[index\].landmarks\[k+4\], track\_info\[index\].landmarks\[k + 5\],
track\_info\[index\].landmarks\[k+6\], track\_info\[index\].landmarks\[k + 7\],
track\_info\[index\].landmarks\[k+8\], track\_info\[index\].landmarks\[k + 9\]
);
}
for (int k = 0; k < track\_info\[index\].headPose.Length; k++)
{
Console.WriteLine("in Liveness::usb\_track angle is:{0:f}", track\_info\[index\].headPose\[k\]);
}
Console.WriteLine("in Liveness::usb\_track score is:{0:f}", track\_info\[index\].score);
// 角度
Console.WriteLine("in Liveness::usb\_track mAngle is:{0:f}", track\_info\[index\].box.mAngle);
// 人脸宽度
Console.WriteLine("in Liveness::usb\_track mWidth is:{0:f}", track\_info\[index\].box.mWidth);
// 中心点X,Y坐标
Console.WriteLine("in Liveness::usb\_track mCenter\_x is:{0:f}", track\_info\[index\].box.mCenter\_x);
Console.WriteLine("in Liveness::usb\_track mCenter\_y is:{0:f}", track\_info\[index\].box.mCenter\_y);
画人脸框
box = bounding\_box(track\_info\[index\].landmarks, track\_info\[index\].landmarks.Length);
draw\_rotated\_box(ref image, ref box, new Scalar(0, 255, 0));
// 实时检测人脸属性和质量可能会视频卡顿，若一定必要可考虑跳帧检测
// 获取人脸属性（通过传入人脸信息）
//IntPtr ptrAttr = FaceAttr.face\_attr\_by\_face(image.CvPtr, ref track\_info\[index\]);
//string buf = Marshal.PtrToStringAnsi(ptrAttr);
//Console.WriteLine("attr res is:" + buf);
获取人脸质量（通过传入人脸信息）
//IntPtr ptrQua = FaceQuality.face\_quality\_by\_face(image.CvPtr, ref track\_info\[index\]);
//buf = Marshal.PtrToStringAnsi(ptrQua);
//Console.WriteLine("quality res is:" + buf);
实时检测人脸特征值可能会视频卡顿，若一定必要可考虑跳帧检测
//float\[\] feature = new float\[512\];
//IntPtr ptrfea = new IntPtr();
//int count = FaceCompare.get\_face\_feature\_by\_face(image.CvPtr, ref track\_info\[index\], ref ptrfea);
返回值为512表示取到了特征值
//if(ptrfea == IntPtr.Zero)
//{
// Console.WriteLine("Get feature failed!");
// continue;
//}
//if (count == 512)
//{
// for (int i = 0; i < count; i++)
// {
// IntPtr floptr = new IntPtr();
// if ( 8 == IntPtr.Size)
// {
// floptr = (IntPtr)(ptrfea.ToInt64() + i \* count \* Marshal.SizeOf(typeof(float)));
// }
// else if( 4 == IntPtr.Size)
// {
// floptr = (IntPtr)(ptrfea.ToInt32() + i \* count \* Marshal.SizeOf(typeof(float)));
// }
// feature\[i\] = (float)Marshal.PtrToStructure(floptr, typeof(float));
// Console.WriteLine("feature {0} is: {1:e8}", i, feature\[i\]);
// }
// Console.WriteLine("feature count is:{0}", count);
//}
}
Marshal.FreeHGlobal(ptT);
window.ShowImage(image);
Cv2.WaitKey(1);
Console.WriteLine("mat not empty");
}
else
{
Console.WriteLine("mat is empty");
}
}
image.Release();
}
}以上为原sdk方法；
只可检测人脸 不可活体检测；
static VideoCapture camera1 = null;
static VideoCapture camera2 = null;
public bool usb_csharp_track_face(int dev)
{
string buf = "";
int faceSize = 0;
int faceNum = 2;//传入的人脸数
int face_size = faceNum;//当前传入人脸数，传出人脸数
//VideoCapture cap = VideoCapture.FromCamera(dev);
//Mat image = new Mat();
long ir_time = 0;
// 序号0为电脑识别的usb摄像头编号，本demo中0为ir红外摄像头
// 不同摄像头和电脑识别可能有区别
// 编号一般从0-10 */
int device = select_usb_device_id();
camera1 = VideoCapture.FromCamera(device);
if (!camera1.IsOpened())
{
Console.WriteLine("camera1 open error");
return false;
}
camera1.Set(CaptureProperty.FrameWidth, 1440);
camera1.Set(CaptureProperty.FrameHeight, 1080);
camera2 = VideoCapture.FromCamera(device + 1);
if (!camera2.IsOpened())
{
Console.WriteLine("camera2 open error");
return false;
}
RotatedRect box;
Mat frame1 = new Mat();
Mat frame2 = new Mat();
Mat rgb\_mat = new Mat();
Mat ir\_mat = new Mat();
//var window\_ir = new Window("ir\_face");
//var window\_rgb = new Window("rgb\_face");
if (!camera1.IsOpened())
{
return false;
}
#region 尝试
while (true)
{
//Thread.Sleep(1000);
camera1.Read(frame1);
camera2.Read(frame2);
if (!frame1.Empty() && !frame2.Empty())
{
int ilen = 2;//传入的人脸数
TrackFaceInfo\[\] track\_info = new TrackFaceInfo\[ilen\];
for (int i = 0; i < ilen; i++)
{
track\_info\[i\] = new TrackFaceInfo();
track\_info\[i\].landmarks = new int\[144\];
track\_info\[i\].headPose = new float\[3\];
track\_info\[i\].face\_id = 0;
track\_info\[i\].score = 0;
}
int sizeTrack = Marshal.SizeOf(typeof(TrackFaceInfo));
IntPtr ptT = Marshal.AllocHGlobal(sizeTrack \* ilen);
#region 原
if (frame1.Size(0) > frame2.Size(0))
{
rgb\_mat = frame1;
ir\_mat = frame2;
}
else
{
rgb\_mat = frame2;
ir\_mat = frame1;
}
#endregion
float rgb\_score = 0;
float ir\_score = 0;
//Marshal.Copy(ptTrack, 0, ptT, ilen);
//FaceInfo\[\] face\_info = new FaceInfo\[ilen\];
//face\_info = new FaceInfo(0.0F,0.0F,0.0F,0.0F,0.0F);
//Cv2.ImWrite("usb\_track\_Cv2.jpg", image);
/\* trackMat
\* 传入参数: maxTrackObjNum:检测到的最大人脸数，传入外部分配人脸数，需要分配对应的内存大小。
\* 传出检测到的最大人脸数
\* 返回值: 传入的人脸数 和 检测到的人脸数 中的最小值,实际返回的人脸。
\*\*\*\*/
faceSize = ilen;//返回人脸数 分配人脸数和检测到人脸数的最小值
int curSize = ilen;//当前人脸数 输入分配的人脸数，输出实际检测到的人脸数
faceSize = track\_mat(ptT, frame1.CvPtr, ref curSize);
IntPtr ptr1 = rgb\_ir\_liveness\_check\_faceinfo(rgb\_mat.CvPtr, ir\_mat.CvPtr, ref rgb\_score, ref ir\_score, ref faceSize, ref ir\_time, ptT);
curSize = ilen;
faceSize = track\_mat(ptT, frame1.CvPtr, ref curSize);
//Console.WriteLine(rgb\_score.ToString());
//Console.WriteLine(ir\_score.ToString());
//textBox1.Text = faceSize.ToString();
for (int index = 0; index < faceSize; index++)
{
IntPtr ptr = new IntPtr();
if (8 == IntPtr.Size)
{
ptr = (IntPtr)(ptT.ToInt64() + sizeTrack \* index);
}
else if (4 == IntPtr.Size)
{
ptr = (IntPtr)(ptT.ToInt32() + sizeTrack \* index);
}
track\_info\[index\] = (TrackFaceInfo)Marshal.PtrToStructure(ptr, typeof(TrackFaceInfo));
IntPtr ptrQua = FaceQuality.face\_quality\_by\_face(frame1.CvPtr, ref track\_info\[index\]);
buf = Marshal.PtrToStringAnsi(ptrQua);
}
textBox2.Text = buf.ToString();
Marshal.FreeHGlobal(ptT);
//window.ShowImage(image);
pictureBox1.Image = frame1.ToBitmap();
Cv2.WaitKey(1);
#region 测试
if (rgb\_score != 0 && ir\_score != 0)
{
if (faceSize >= 1)
{
JObject jObject = (JObject)JsonConvert.DeserializeObject(buf);
var json = (JObject)JsonConvert.DeserializeObject(jObject\["data"\].ToString());
string result = json\["result"\].ToString();
// textBox1.Text = json\["result"\].ToString();
var json1 = (JObject)JsonConvert.DeserializeObject(result);
if (double.Parse(json1\["bluriness"\].ToString()) <= 0.00001 && double.Parse(json1\["occl\_r\_eye"\].ToString()) < 0.1 && double.Parse(json1\["occl\_l\_eye"\].ToString()) < 0.1 &&
double.Parse(json1\["occl\_mouth"\].ToString()) <= 0 && double.Parse(json1\["occl\_nose"\].ToString()) < 0.11 && double.Parse(json1\["occl\_r\_contour"\].ToString()) < 0.1
&& double.Parse(json1\["occl\_chin"\].ToString()) <= 0 && double.Parse(json1\["occl\_l\_contour"\].ToString()) < 0.1)
{
string fileName = DateTime.Now.ToString("yyyyMMdd") + ".jpg";
string path = AppDomain.CurrentDomain.BaseDirectory;
string picture = path + @"img" + "\\\\" + fileName;
frame1.SaveImage(picture);
falnem = picture;
if (Sdistinguish() != "0")
{
Class1 c = new Class1();
Thread.Sleep(2000);
dtTo = new TimeSpan(0, 0, 2);
camera2.Release();
camera2.Release();
thread.Abort();
return true;
}
else
{
Thread.Sleep(2000);
dtTo = new TimeSpan(0, 0, 2);
camera2.Release();
camera2.Release();
thread.Abort();
//msg2 msg2 = new msg2();
//msg2.Show();
return true;
this.Close();
}
Thread.Sleep(2000);
thread.Abort();
//msg2 msg2 = new msg2();
//msg2.Show();
this.Close();
//window.Close();
}
}
}
#endregion
//return faceSize;
}
else
{
Console.WriteLine("mat is empty");
}
}
#endregion
frame1.Release();
return false;
//usb\_csharp\_track\_face(1);
}以上为我修改后的；
private string distinguish()
{
dz = "1";
//string fileName = DateTime.Now.ToString("yyyyMMddHH") + ".jpg";
string fileName = falnem;
//string fileName = "" + ".jpg";
//SavePicture(fileName);
FaceCompare faceCompare = new FaceCompare();
JObject jObject = (JObject)JsonConvert.DeserializeObject(faceCompare.test\_identify(fileName, "group", ""));
// JObject jObject = (JObject)JsonConvert.DeserializeObject(faceCompare.test\_identify("d:\\\\4.jpg", "test\_group", "u1"));
var json = (JObject)JsonConvert.DeserializeObject(jObject\["data"\].ToString());
var json1 = (JObject)JsonConvert.DeserializeObject(json\["result"\].ToString());
var json1 = (JArray)json\["result"\].ToString();
textBox1.Text = "对比值：" + json1\["score"\].ToString() +"/r用户："+json1\["user\_id"\].ToString() ;
textBox1.Text= faceCompare.test\_identify("d:\\\\4.jpg", "test\_group", "test\_user");
string result = json\["result"\].ToString().Substring(1, json\["result"\].ToString().Length - 2);
// textBox1.Text = json\["result"\].ToString();
var json1 = (JObject)JsonConvert.DeserializeObject(result);
//textBox2.Text = "对比值：" + json1\["score"\].ToString() + "\\r\\n用户：" + json1\["user\_id"\].ToString();
string user = json1\["user\_id"\].ToString();
string lockes = "0";
#region 查询数据库
using (var db = new ces(dbPath))
{
//List<TestFaceDB> testFaces = db.testFaces.Where(x => x.User == json1\["user\_id"\].ToString()).ToList();
var testFaces = db.testFaces.Where(x => x.User == user).ToList();
//string testFaces = db.testFaces.Select(\*).Where(x => x.User == json1\["user\_id"\].ToString()).ToString();
//this.Text = $"{DateTime.Now}, 查到{books.Count}条记录";
lockes = testFaces\[0\].Locks;
}
int lo = int.Parse(lockes);
if (int.Parse(lockes) > 100)
{
int d = 0;
while (lo > 100)
{
lo -= 100;
d++;
}
int z = 1;
for (int i = 0; i < d - 1; i++)
{
z = z \* 2;
};
dz = "0" + z.ToString();
}
#endregion
double score = double.Parse(json1\["score"\].ToString());
lockes = lo.ToString();
if (score > 80)
{
if (int.Parse(lockes) < 10) lockes = "0" + lockes;
return lockes;
}
else
{
return lockes;
}
} //1：N比较
public /*void*/string test_identify(string str, string usr_grp, string usr_id)
{
string file1 = str;//"d:\\6.jpg";
string user_group = usr_grp;//"test_group";
string user_id = usr_id;//"test_user";
IntPtr ptr = identify(file1, user_group, user_id);
string buf = Marshal.PtrToStringAnsi(ptr);
//Console.WriteLine("identify res is:" + buf);
return buf;
}
distinguish（）方法为识别后在数据库中寻找匹配人脸数据；
将活体实时检测与人脸实时检测合并为一个方法，关闭了window显示窗口，修改为pictureBox控件 通过线程调用（不然死循环会崩溃）；关闭摄像头为 Release();方法
数据均为json数据 ；
具体数据参数见百度文档
根据需求修改判断条件是否符合项目中的人脸标准；
识别流程为
启动线程->打开摄像头->检测到人脸->将人脸图片保存->用图片传入调用人脸对比方法;
string user_id = User.Text;
string group_id = "group";
string file_name = falnem;
string lockse = group.Text;
string user_info = info.Text;
string zc = faceManager.test_user_add(user_id, group_id, file_name, user_info);
JObject jObjectzc = (JObject)JsonConvert.DeserializeObject(zc);
if(jObjectzc["msg"].ToString()== "success")
{
List
{
new TestFaceDB() {User = user_id,Locks = lockse,Phone = user_info}
};
textBox1.Text = dbPath;
using (var db = new ces(dbPath))
{
int count = db.InsertAll(testFaceDBs);
//this.Text = $"{DateTime.Now}, 插入{count}条记录";
}
msg msg = new msg();
msg.ShowDialog();
Thread.Sleep(5000);
dtTo = new TimeSpan(0, 0, 2);
camera1.Release();
camera2.Release();
return true;
}
Thread.Sleep(3000);同上的方法 先检测摄像头中是否使活体人脸并存图片文件，保存后调用人脸注册
public /*void*/ string test_user_add(string uid,string gid,string fil,string uin)
{
//人脸注册
//string user_id = "test_user";
//string group_id = "test_group";
//string file_name = "d:\\2.jpg";
//string user_info = "user_info";
string user_id = uid;
string group_id = gid;
string file_name = fil;
string user_info = uin;
IntPtr ptr = user_add(user_id, group_id, file_name, user_info);
string buf = Marshal.PtrToStringAnsi(ptr);
return buf;
}
这个我修改添加了参数；
因sdk带的SQLite数据库字段比较少，并且我也没有花心思想办法去修改它创建的数据库，所以创建个新的和sdk中数据库以一个字段相连，就当扩展数据库了；
在注册的时候顺带存新数据库一份就可以，要什么字段看自己需求；
当查找数据库中数据的时候查到就取那个字段，然后去新数据库中查找对应的其他字段。
1.4 打包
打包简单方法直接将根目录数据放到一个文件夹然后添加到Application Folder文件夹，然后在同目录将face-resource放进去就可以了，在安装的时候不能安装到系统盘 不然接口会出问题；不知道什么原因
安装到一个电脑的时候需要使用百度离线识别的sdk序列号激活不然不可使用；因为百度离线人脸识别sdk 是通过电脑指纹判断是否激活，如果遇到LicenseTool.exe打不开弹出缺少系统dll组件请去网络下载，或许因为电脑系统为盗版系统组件未安装齐
也可以在百度下载激活的压缩文件；
在添加输出的时候将sdk作为本地化资源输出，项目作为主输出，输出类型为Release；
「码农_半只龙虾」 也是我 ————————————————
版权声明：本文为_csdn博主「码农_半只龙虾」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原csdn链接：https://blog.csdn.net/weixin_44986330/article/details/106297549_
手机扫一扫
移动阅读更方便
你可能感兴趣的文章