1.加载深度学习模型
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void visionagin:: Myreadnet()
{
String model = "C:\\Users\\86176\\Downloads\\visionimage\\bvlc_googlenet.caffemodel";
String config = "C:\\Users\\86176\\Downloads\\visionimage\\bvlc_googlenet.prototxt";
dnn::Net net = dnn::readNet(model, config);
if (net.empty())
{
cout << "read failed !" << endl;
}
vector<String> layernames;
layernames=net.getLayerNames();
for (int i = 0; i < layernames.size(); ++i)
{
int id = net.getLayerId(layernames[i]);
Ptr<dnn::Layer> layer = net.getLayer(id);
cout << "第" << to_string(id) << "层网络名：" << layernames[i] << " ,网络类型：" << layer->type.c_str() << endl;
}
}
2.图像识别
void visionagin:: Myimagedetect()
{
Mat img = imread("C:\\Users\\86176\\Downloads\\visionimage\\plane.jpeg");
if (!img.data)
{
cout << "picture read failed!" << endl;
}
String model = "C:\\Users\\86176\\Downloads\\visionimage\\image_pattern\\tensorflow_inception_graph.pb";
//字符串数组存放读出来的分类表
vector<String>typelist;
String txt= "C:\\Users\\86176\\Downloads\\visionimage\\image_pattern\\imagenet_comp_graph_label_strings.txt";
//创建文件流
ifstream file(txt);
if (!file.is_open())
{
cout << "文件流打开失败！" << endl;
}
String type;
while (!file.eof())
{
getline(file, type);
typelist.push_back(type);
}
file.close();
dnn::Net net = dnn::readNet(model);
if (net.empty())
{
cout << "模型读取失败！" << endl;
}
//变换尺寸
Mat blob=dnn::blobFromImage(img, 1.0f, Size(224, 224));
//进行图像预测
net.setInput(blob,"input");
Mat prob;
prob = net.forward("softmax2");
Mat result = prob.reshape(1, 1);
double maxval;//匹配的最大可能性
Point maxp;//最大可能性坐标
minMaxLoc(result, NULL, &maxval, NULL, &maxp);
String outtype = typelist[maxp.x].c_str();
cout << "识别结果是：" << outtype << endl;
cout << "最大可能性：" << maxval << endl;
}3.风格迁移
void visionagin:: MyneuralStyle()
{
Mat img = imread("C:\\Users\\86176\\Downloads\\visionimage\\women.jfif");
imshow("原图", img);
//计算图像各通道均值
Scalar meanimg = mean(img);
Mat blob=dnn::blobFromImage(img, 1.0f, Size(256, 256), meanimg);
String temp = "C:\\Users\\86176\\Downloads\\visionimage\\fast_style\\";
String neuralstyle[] = { "the_wave.t7","mosaic.t7","feathers.t7","candy.t7","udnie.t7" };
for (int i = 0; i < size(neuralstyle); ++i)
{
dnn::Net net = dnn::readNet(temp + neuralstyle[i]);
net.setInput(blob);
Mat out;//预测结果
net.forward(out);
int outchannels = out.size[1];
int outrows = out.size[2];
int outcols=out.size[3];
Mat result = Mat::zeros(outrows, outcols, CV_32FC3);//绘制结果的画布
float* data = out.ptr<float>();
for (int k = 0; k < outchannels; ++k)
{
for (int m = 0; m < outrows; ++m)
{
for (int n = 0; n < outcols; ++n)
{
result.at<Vec3f>(m, n)[k] = *data++;
}
}
}
//加上减去的均值
result += meanimg;
//result /= 255;//result为cv_32f类型，需归一化到0-1；
result.convertTo(result, CV_8UC3);
resize(result, result, Size(img.cols, img.rows));
imshow("迁移后图" + to_string(i), result);
}
}
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4.性别检测
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void visionagin:: Myfaceandgenderdetect()
{
Mat image = imread("C:\\Users\\86176\\Downloads\\visionimage\\faces.jpg");
resize(image, image, Size(800, 600));
GaussianBlur(image, image, Size(3, 3),20);
dnn::Net net = dnn::readNet("C:\\Users\\86176\\Downloads\\visionimage\\ch12_face_age\\opencv_face_detector_uint8.pb", "C:\\Users\\86176\\Downloads\\visionimage\\ch12_face_age\\opencv_face_detector.pbtxt");
Mat inputimg = dnn::blobFromImage(image, 1.0f, Size(300, 300));
Mat output1;
net.setInput(inputimg,"data");//输入数据
net.forward(output1,"detection_out");//进行预测
//检测出人脸的区域的位置
Mat facearea = Mat(output1.size[2], output1.size[3], CV_32F, output1.ptr<float>());
//读取性别检测模型
dnn::Net gendermodel = dnn::readNet("C:\\Users\\86176\\Downloads\\visionimage\\ch12_face_age\\gender_net.caffemodel", "C:\\Users\\86176\\Downloads\\visionimage\\ch12_face_age\\gender_deploy.prototxt");
String gender[2] = { "boy","girl" };
//对每个检测出的人脸
for (int i = 0; i < facearea.rows; ++i)
{
double rate = facearea.at<float>(i, 2);//人脸的概率
if (rate > 0.5)
{
int top_x = facearea.at<float>(i, 3) * image.cols;//facearea每行的第2列是概率，3,4,5,6是坐标位置，以比例表示
int top_y = facearea.at<float>(i, 4) * image.rows;
int down_x = facearea.at<float>(i, 5) * image.cols;
int down_y = facearea.at<float>(i, 6) * image.rows;
Rect rect = Rect(top_x, top_y, down_x - top_x, down_y - top_y);
//对检测出的图像进行扩充，防止尺寸在真实图像之外
Rect rectagin;
rectagin.x = max(0, rect.x - 25);
rectagin.y = max(0, rect.y - 25);
rectagin.width = min(rect.width + 25, image.cols - 1);
rectagin.height = min(rect.height+ 25, image.rows - 1);
//人脸区域
Mat face = image(rectagin);
//对每个人脸变换尺寸
Mat inputface = dnn::blobFromImage(face, 1.0, Size(227, 227));
gendermodel.setInput(inputface);
//预测性别男女概率,是2*1的矩阵
Mat outputgenderrate=gendermodel.forward();
float boy,girl;
boy = outputgenderrate.at<float>(0);
girl = outputgenderrate.at<float>(1);
cout << boy << "-" << girl << endl;
int num=0;
if (boy > girl)
{
num=0;
}
else
{
num=1;
}
String gen = gender[num];
//画框
rectangle(image, rect, Scalar(0, 200, 0), 2, 8);
putText(image, gen.c_str(), rect.tl(), FONT_HERSHEY_SIMPLEX, 0.8, Scalar(0, 0, 200));
}
}
imshow("result", image);
}
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