V4L2采集+OpenCV+DNN人脸识别

2025-11-01

OpenCV+DNN人脸识别

v4l2进行视频采集

打开设备，设置视频参数，分辨率，样式

v4l2_fd = ::open(dev,O_RDWR);				//打开设备
if(v4l2_fd < 0){
    qDebug() << "open camera failed!";
    return false;
}
struct v4l2_format fmt;
memset(&fmt,0,sizeof(fmt));					//设置视频参数，类型、分辨率、格式
fmt.type = V4L2_BUF_TYPE_VIDEO_CAPTURE;
fmt.fmt.pix.width = v4l2_width;
fmt.fmt.pix.height =v4l2_height;
fmt.fmt.pix.pixelformat = V4L2_PIX_FMT_MJPEG;
fmt.fmt.pix.field = V4L2_FIELD_NONE;

if(ioctl(v4l2_fd,VIDIOC_S_FMT,&fmt) < 0){
    qDebug() << " set fmt failed !";
    ::close(v4l2_fd);
    return false;
}

申请缓冲区，查询缓冲区是否符合要求

struct v4l2_requestbuffers req;					//申请缓冲区
memset(&req,0,sizeof(req));
req.count = 2;
req.type = V4L2_BUF_TYPE_VIDEO_CAPTURE;
req.memory = V4L2_MEMORY_MMAP;

if(ioctl(v4l2_fd,VIDIOC_REQBUFS,&req) < 0){
    qDebug() << "request buffer failed!";
    ::close(v4l2_fd);
    return false;
}

struct v4l2_buffer buf;							//查询缓冲区
memset(&buf,0,sizeof(buf));
buf.type = V4L2_BUF_TYPE_VIDEO_CAPTURE;
buf.memory = V4L2_MEMORY_MMAP;
buf.index = 0;

if(ioctl(v4l2_fd,VIDIOC_QUERYBUF,&buf)){
    qDebug() << "search buffer failed !";
    ::close(v4l2_fd);
    return false;
}

内存映射，将缓冲区放入工作队列，等待用户采集

v4l2_buffer_length = buf.length;					//内存映射
v4l2_buffer = mmap(NULL,buf.length,PROT_READ | PROT_WRITE,MAP_SHARED,v4l2_fd,buf.m.offset);
if(v4l2_buffer == MAP_FAILED){
    qDebug() << "mmap failed !";
    ::close(v4l2_fd);
    return false;
}
if(ioctl(v4l2_fd,VIDIOC_QBUF,&buf) < 0){			//将缓冲区放入工作队列
    qDebug() << "enter queue failed!";
    ::close(v4l2_fd);
    return false;
}

获取一帧视频

从缓冲区取出视频数据

struct v4l2_buffer buf;							//从缓冲区取出视频数据
 memset(&buf, 0, sizeof(buf));
 buf.type = V4L2_BUF_TYPE_VIDEO_CAPTURE;
 buf.memory = V4L2_MEMORY_MMAP;
 buf.index = 0;
 if (ioctl(v4l2_fd, VIDIOC_DQBUF, &buf) < 0) {
     qDebug() << "DQBUF失败";
     return QImage();
 }

MJPEG解码，将MJPEG格式的图片转为QIMAG格式

// MJPEG解码
QBuffer qbuf;
qbuf.setData((const char*)v4l2_buffer, buf.bytesused);
qbuf.open(QIODevice::ReadOnly);
QImageReader reader(&qbuf, "JPG");
QImage img = reader.read();
if (img.isNull()) {
    qDebug() << "MJPEG解码失败";
}

缓冲区重新入队

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if (ioctl(v4l2_fd, VIDIOC_QBUF, &buf) < 0) {		//缓冲区重新入队
    qDebug() << "QBUF失败";
}

持续获取多帧视频，并且对视频进行一些预处理，

1、缩小视频的分辨率进行加速

2、彩色转变灰度图

3、检测60*60以上像素的人脸

4、再映射回原图

5、将人脸用矩形框框住

6、缩放显示到一个Qlabel标签上

static int detect_counter = 0;
const int detect_interval = 10; // 每10帧检测一次人脸
QElapsedTimer timerDebug;
timerDebug.start();
QImage img = getV4L2Frame(); // 获取摄像头帧
if (img.isNull()) {
    qDebug() << "未获取到V4L2帧";
    return;
}
qint64 t1 = timerDebug.elapsed();
bool doDetect = (++detect_counter % detect_interval == 0); // 控制检测频率
if (doDetect && !face_cascade.empty()) {
    QImage smallImg = img.scaled(320, 240, Qt::KeepAspectRatio); // 缩小加速
    cv::Mat mat_small(smallImg.height(), smallImg.width(), CV_8UC4, (void*)smallImg.bits(), smallImg.bytesPerLine());
    cv::Mat mat_gray;
    cv::cvtColor(mat_small, mat_gray, cv::COLOR_BGRA2GRAY); // 转灰度
    faces.clear();
    face_cascade.detectMultiScale(mat_gray, faces, 1.2, 5, 0, cv::Size(60, 60)); // 检测人脸
    double scaleX = double(img.width()) / smallImg.width();
    double scaleY = double(img.height()) / smallImg.height();
    for (auto &face : faces) {
        face.x = int(face.x * scaleX); // 坐标映射回原图
        face.y = int(face.y * scaleY);
        face.width = int(face.width * scaleX);
        face.height = int(face.height * scaleY);
    }
    qDebug() << "检测到人脸数量:" << faces.size();
} else if (face_cascade.empty()) {
    qDebug() << "人脸分类器未加载成功";
}
qint64 t3 = timerDebug.elapsed();
// 在QImage上画人脸框
QPainter painter(&img);
painter.setPen(QPen(Qt::red, 3));
for (const auto &face : faces) {
    painter.drawRect(face.x, face.y, face.width, face.height); // 画人脸框
}
painter.end();
qint64 t4 = timerDebug.elapsed();
// 缩放显示到QLabel
QSize targetSize(780, 550);
ui->camlab->setPixmap(QPixmap::fromImage(img.scaled(targetSize, Qt::KeepAspectRatio))); // 显示到QLabel
qint64 t5 = timerDebug.elapsed();

采集人脸

1、采集最大的一张人脸

2、裁剪缩放？

3、RGB转BGR

4、提取并保存特征

if (faces.empty()) {
    qDebug() << "未检测到人脸，无法采集";
    return;
}
if (collectCount >= collectTarget) {
    qDebug() << "已采集" << collectTarget << "张人脸，请勿重复采集";
    return;
}
auto maxFace = std::max_element(faces.begin(), faces.end(),
    [](const cv::Rect& a, const cv::Rect& b) { return a.area() < b.area(); }); // 取最大人脸
if (maxFace == faces.end()) return;
QImage img = getV4L2Frame();
if (img.isNull()) return;
QRect qface(maxFace->x, maxFace->y, maxFace->width, maxFace->height);
QImage faceImg = img.copy(qface).scaled(112, 112, Qt::KeepAspectRatio).convertToFormat(QImage::Format_RGB888); // 裁剪缩放
cv::Mat faceMat(faceImg.height(), faceImg.width(), CV_8UC3, (void*)faceImg.bits(), faceImg.bytesPerLine());
cv::cvtColor(faceMat, faceMat, cv::COLOR_RGB2BGR); // 转BGR
cv::Mat feat = getFaceFeature(faceMat); // 提取特征
registeredFeatures.append(feat.clone()); // 保存特征
collectCount++;
qDebug() << "第" << collectCount << "次采集成功";

获取人脸特征

1、缩放到像素112*112

2、对人脸进行归一化

3、生成blob

4、输入后前向推理

5、再L2归一化

cv::Mat Widget::getFaceFeature(const cv::Mat& faceImg) {
    cv::Mat input;
    cv::resize(faceImg, input, cv::Size(112, 112)); // 缩放到112x112
    input.convertTo(input, CV_32F, 1.0/255); // 归一化
    cv::Mat blob = cv::dnn::blobFromImage(input, 1.0, cv::Size(112,112), cv::Scalar(0,0,0), true, false); // 生成blob
    faceNet.setInput(blob); // 设置输入
    cv::Mat feat = faceNet.forward(); // 前向推理，得到特征
    cv::normalize(feat, feat); // L2归一化
    return feat.clone();
}

对比采集的人脸和检测到的人脸

1、检查是否已经采集三次人脸

2、取最大的人脸裁剪缩放

3、RGB转BGR

4、提取特征，计算余弦相似度

5、设定阈值0.5，大于阈值认定同一人

if (collectCount < collectTarget) {
    qDebug() << "请先采集" << collectTarget << "张人脸";
    return;
}
if (faces.empty()) {
    qDebug() << "未检测到人脸，无法比对";
    return;
}
auto maxFace = std::max_element(faces.begin(), faces.end(),
    [](const cv::Rect& a, const cv::Rect& b) { return a.area() < b.area(); }); // 取最大人脸
if (maxFace == faces.end()) return;
QImage img = getV4L2Frame();
if (img.isNull()) return;
QRect qface(maxFace->x, maxFace->y, maxFace->width, maxFace->height);
QImage faceImg = img.copy(qface).scaled(112, 112, Qt::KeepAspectRatio).convertToFormat(QImage::Format_RGB888); // 裁剪缩放
cv::Mat faceMat(faceImg.height(), faceImg.width(), CV_8UC3, (void*)faceImg.bits(), faceImg.bytesPerLine());
cv::cvtColor(faceMat, faceMat, cv::COLOR_RGB2BGR); // 转BGR
cv::Mat feat = getFaceFeature(faceMat); // 提取特征
double maxSim = -1;
for (const auto& regFeat : registeredFeatures) {
    double sim = feat.dot(regFeat); // 计算余弦相似度
    if (sim > maxSim) maxSim = sim;
}
qDebug() << "最大相似度:" << maxSim;
if (maxSim > 0.5) qDebug() << "比对结果：同一人";
else qDebug() << "比对结果：不同人";