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+# add_library(speakrecognize SHARED speak_detector.cpp retinaface.cpp faceLandmarks.cpp speakcls.cpp )
+# add_executable(speakrecognize eval.cpp retinaface.cpp faceLandmarks.cpp speakcls.cpp speak_detector.cpp)
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+#include "faceLandmarks.h"
+#include <stdio.h>
+
+vector<vector<float>> FaceLandmarks::detect_landmarks(string image_path){
+    cv::Mat image=cv::imread(image_path);
+    float w_r=image.cols/input_size[0];
+    float h_r=image.rows/input_size[1];
+
+    cv::Mat blob = cv::dnn::blobFromImage(image,1.0,Size(input_size[0],input_size[1]));  // 由图片加载数据 这里还可以进行缩放、归一化等预处理
+    net.setInput(blob);  // 设置模型输入
+    std::vector<cv::Mat> netOutputImg;
+    net.forward(netOutputImg,net.getUnconnectedOutLayersNames()); // 推理出结果
+    cv::Mat predict_landmarks = netOutputImg[0].reshape(1,106);
+    vector<vector<float>> result_landmarks;
+    for(int i=0;i<predict_landmarks.rows;++i){
+        // cout<<predict_landmarks.at<float>(i,0)<<" "<<predict_landmarks.at<float>(i,1)<<endl;
+        vector<float> tmp_landm={predict_landmarks.at<float>(i,0)*w_r,predict_landmarks.at<float>(i,1)*h_r};
+        result_landmarks.push_back(tmp_landm);
+    }
+    return result_landmarks;
+}
+vector<vector<float>> FaceLandmarks::detect_image_landmarks(cv::Mat image){
+    float w_r=image.cols/input_size[0];
+    float h_r=image.rows/input_size[1];
+
+    cv::Mat blob = cv::dnn::blobFromImage(image,1.0,Size(input_size[0],input_size[1]));  // 由图片加载数据 这里还可以进行缩放、归一化等预处理
+    net.setInput(blob);  // 设置模型输入
+    std::vector<cv::Mat> netOutputImg;
+    net.forward(netOutputImg,net.getUnconnectedOutLayersNames()); // 推理出结果
+    cv::Mat predict_landmarks = netOutputImg[0].reshape(1,106);
+    vector<vector<float>> result_landmarks;
+    for(int i=0;i<predict_landmarks.rows;++i){
+        // cout<<predict_landmarks.at<float>(i,0)<<" "<<predict_landmarks.at<float>(i,1)<<endl;
+        vector<float> tmp_landm={predict_landmarks.at<float>(i,0)*w_r,predict_landmarks.at<float>(i,1)*h_r};
+        result_landmarks.push_back(tmp_landm);
+    }
+    return result_landmarks;
+}
+
+

+#ifndef FACELANDMARKS_H
+#define FACELANDMARKS_H
+
+#include <opencv2/opencv.hpp>
+
+using namespace std;
+using namespace cv;
+
+class FaceLandmarks{
+    private:
+        vector<float> input_size={112,112};
+        cv::dnn::Net net;
+        
+        
+    public:
+        FaceLandmarks(){};
+        FaceLandmarks(string model_path){
+            net=cv::dnn::readNetFromONNX(model_path);
+        }
+        vector<vector<float>> detect_landmarks(string image_path);
+        vector<vector<float>> detect_image_landmarks(Mat image);
+
+};
+
+
+#endif
\ No newline at end of file

+#ifndef RETINAFACE_H
+#define RETINAFACE_H
+#include<opencv2/opencv.hpp>
+
+using namespace std;
+using namespace cv;
+struct Bbox{
+        float xmin;
+        float ymin;
+        float xmax;
+        float ymax;
+        float score;
+        float x1;
+        float y1;
+        float x2;
+        float y2;
+        float x3;
+        float y3;
+        float x4;
+        float y4;
+        float x5;
+        float y5;
+        };
+class RetinaFace{
+
+    private:
+        bool use_gpu=true;
+        vector<float> input_size={640,640};
+        vector<float> variances={0.1,0.2};
+        vector<float> mean_data={104,117,128};
+        float confidence_threshold = 0.8;
+        float keep_top_k = 100;
+        float vis_threshold = 0.2;
+        float nms_threshold = 0.4;
+        float resize_scale = 1.0;
+        bool is_bbox_process=true;
+
+        cv::dnn::Net net;
+        vector<vector<float>> anchors;
+
+    private:
+        // 生成anchors
+        vector<vector<float>> priorBox(vector<float> image_size);
+        // 解析bounding box  landmarks 包含置信度
+        vector<Bbox> decode(Mat loc,Mat score,Mat pre,vector<vector<float>> priors,vector<float> variances);
+        // 解析landmarks
+        // vector<vector<float>> decode_landm(vector<vector<float>> pre,vector<vector<float>> priors,vector<float> variances);
+        //NMS
+        void nms_cpu(std::vector<Bbox> &bboxes, float threshold);
+        // Mat转vector
+        vector<vector<float>> mat2vector(Mat mat);
+        // 根据阈值筛选
+        vector<Bbox> select_score(vector<Bbox> bboxes,float threshold,float w_r,float h_r);
+        // vector转Bbox
+        // vector<Bbox> vec2Bbox(vector<vector<float>> bbox,float w_r,float h_r);
+        // 数据后处理
+        vector<Bbox> bbox_process(bool is_bbox_process,vector<Bbox> bboxes,float frame_w,float frame_h);
+
+    public:
+        RetinaFace(){};
+        RetinaFace(string model_path){
+            net = cv::dnn::readNetFromONNX(model_path);
+            if(use_gpu){
+                net.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA);
+                net.setPreferableTarget(cv::dnn::DNN_TARGET_CUDA);
+            }
+            anchors=priorBox(input_size);
+        }
+
+        // 推理
+        vector<Bbox> detect(string image_path);
+        vector<Bbox> detect_image(Mat image);
+};
+#endif
\ No newline at end of file

+#ifndef SPEAKCLS_DETECTOR
+#define SPEALCLS_DETECTOR
+#include "speakcls.h"
+#include "retinaface.h"
+#include "faceLandmarks.h"
+
+class SpeakDetector{
+    private:
+        RetinaFace face_det;
+        FaceLandmarks landm_det;
+        SpeakCls speak_cls;
+
+
+    public:
+        SpeakDetector(){};
+        void init_model(string face_det_model,string landm_det_model,string speak_cls_model);
+        float iou_compute(Bbox b1, Bbox b2);
+        vector<vector<cv::Mat>> mouth_process(vector<vector<vector<vector<float>>>> batch_landmarks, vector<cv::Mat> batch_images);
+        void image_reader(string file_path,int segment_num,vector<Mat> &bgr_frames,vector<vector<int>> &indices);
+
+        
+        void speak_recognize(string image_path);
+    
+};
+
+
+#endif
\ No newline at end of file

+#ifndef SPEAKCLS_H
+#define SPEALCLS_H
+#include<opencv2/opencv.hpp>
+#include<MNN/Interpreter.hpp>
+#include<MNN/ImageProcess.hpp>
+#include<typeinfo>
+using namespace std;
+using namespace cv;
+using namespace MNN;
+class SpeakCls{
+    private:
+        std::shared_ptr<MNN::Interpreter> net;
+        ScheduleConfig config;
+    public:
+        SpeakCls(){};
+        SpeakCls(string model_path){
+            
+            //"/home/situ/qfs/sdk_project/mnn_demo/cls_speak_v0.2.2.mnn"
+            net= std::shared_ptr<MNN::Interpreter>(MNN::Interpreter::createFromFile("/home/situ/qfs/sdk_project/mnn_demo/cls_speak_v0.2.2.mnn"));//创建解释器
+            config.numThread = 8;
+            config.type = MNN_FORWARD_CPU;
+
+        }
+
+    public:
+        cv::Mat standardize(cv::Mat image);
+        cv::Mat data_process(vector<Mat> images);
+        vector<double> softmax(vector<double> input);
+        bool detect_speak(vector<Mat> images);
+
+};
+#endif
\ No newline at end of file

+#include "speak_detector.h"
+
+int main(){
+
+    SpeakDetector speak = SpeakDetector();
+    speak.init_model("/home/situ/qfs/sdk_project/speak_det_c/model/det_face_retina_torch_1.4_v0.0.2.onnx","/home/situ/qfs/sdk_project/speak_det_c/model/det_landmarks_106_v0.0.1.onnx","/home/situ/qfs/sdk_project/speak_det_c/model/cls_speak_v0.2.2.mnn");
+    speak.speak_recognize("/data/speak/bank_test/speak/2327QUESTION_ANSWER");
+    return 0;
+}

+#include "retinaface.h"
+#include<iostream>
+// 生成anchors
+vector<vector<float>> RetinaFace::priorBox(vector<float> image_size){
+    vector<int> tmp1={16,32};
+    vector<int> tmp2={64,128};
+    vector<int> tmp3={256,512};
+    vector<vector<int>> min_sizes_;
+    min_sizes_.push_back(tmp1);
+    min_sizes_.push_back(tmp2);
+    min_sizes_.push_back(tmp3);
+    vector<int> steps={8,16,32};
+    vector<vector<int>> feature_maps;
+    vector<vector<float>> anchors;
+    for(int &step:steps){
+        vector<int> tmp(2,0);
+        tmp[0]=ceil(image_size[0]/step);
+        tmp[1]=ceil(image_size[1]/step);
+        feature_maps.push_back(tmp);
+    }
+    for(int k=0;k<feature_maps.size();k++){
+        vector<int> min_sizes=min_sizes_[k];
+        
+        for(int i=0;i<feature_maps[k][0];i++){
+            for(int j=0;j<feature_maps[k][1];j++){
+                for(int &min_size:min_sizes){
+                    float s_kx=float(min_size)/float(image_size[1]);
+                    float s_ky=float(min_size)/float(image_size[0]);
+                    float dense_cx=float((float(j)+float(0.5))*steps[k])/float(image_size[1]);
+                    float dense_cy=float((float(i)+float(0.5))*steps[k])/float(image_size[1]);
+                    vector<float> tmp_anchor={dense_cx,dense_cy,s_kx,s_ky};
+                    anchors.push_back(tmp_anchor);
+                }
+            }
+        }
+    }
+    return anchors;
+}
+
+// 解析bounding box 包含置信度
+vector<Bbox> RetinaFace::decode(Mat loc,Mat score,Mat pre,vector<vector<float>> priors,vector<float> variances){
+    vector<Bbox> boxes;
+    for(int i=0;i<priors.size();++i){
+        float b1=priors[i][0]+loc.at<float>(i,0)*variances[0]*priors[i][2];
+        float b2=priors[i][1]+loc.at<float>(i,1)*variances[0]*priors[i][3];
+        float b3=priors[i][2]*exp(loc.at<float>(i,2)*variances[1]);
+        float b4=priors[i][3]*exp(loc.at<float>(i,3)*variances[1]);
+        b1=b1-b3/float(2);
+        b2=b2-b4/float(2);
+        b3=b3+b1;
+        b4=b4+b2;
+        float l1=priors[i][0]+pre.at<float>(i,0)*variances[0]*priors[i][2];
+        float l2=priors[i][1]+pre.at<float>(i,1)*variances[0]*priors[i][3];
+        float l3=priors[i][0]+pre.at<float>(i,2)*variances[0]*priors[i][2];
+        float l4=priors[i][1]+pre.at<float>(i,3)*variances[0]*priors[i][3];
+        float l5=priors[i][0]+pre.at<float>(i,4)*variances[0]*priors[i][2];
+        float l6=priors[i][1]+pre.at<float>(i,5)*variances[0]*priors[i][3];
+        float l7=priors[i][0]+pre.at<float>(i,6)*variances[0]*priors[i][2];
+        float l8=priors[i][1]+pre.at<float>(i,7)*variances[0]*priors[i][3];
+        float l9=priors[i][0]+pre.at<float>(i,8)*variances[0]*priors[i][2];
+        float l10=priors[i][1]+pre.at<float>(i,9)*variances[0]*priors[i][3];
+        Bbox tmp_box={.xmin=b1*input_size[0]/resize_scale,.ymin=b2*input_size[1]/resize_scale,.xmax=b3*input_size[0]/resize_scale,.ymax=b4*input_size[1]/resize_scale,
+        .score=score.at<float>(i,0),.x1=(l1*input_size[0])/resize_scale,.y1=l2*input_size[1]/resize_scale,.x2=l3*input_size[0]/resize_scale,.y2=l4*input_size[1]/resize_scale,
+        .x3=l5*input_size[0]/resize_scale,.y3=l6*input_size[1]/resize_scale,.x4=l7*input_size[0]/resize_scale,.y4=l8*input_size[1]/resize_scale,.x5=l9*input_size[0]/resize_scale,.y5=l10*input_size[1]/resize_scale};
+        boxes.push_back(tmp_box);
+    }
+    return boxes;
+}
+
+// // 解析landmarks
+// vector<vector<float>> RetinaFace::decode_landm(vector<vector<float>> pre,vector<vector<float>> priors,vector<float> variances){
+//     vector<vector<float>> landms;
+//     for(int i=0;i<priors.size();i++){
+        
+//         vector<float> tmp_landm={l1,l2,l3,l4,l5,l6,l7,l8,l9,l10};
+//         landms.push_back(tmp_landm);
+//     }
+//     return landms;
+// }
+
+//NMS
+void RetinaFace::nms_cpu(std::vector<Bbox> &bboxes, float threshold){
+    if (bboxes.empty()){
+        return ;
+    }
+    // 1.之前需要按照score排序
+    std::sort(bboxes.begin(), bboxes.end(), [&](Bbox b1, Bbox b2){return b1.score>b2.score;});
+    // 2.先求出所有bbox自己的大小
+    std::vector<float> area(bboxes.size());
+    for (int i=0; i<bboxes.size(); ++i){
+        area[i] = (bboxes[i].xmax - bboxes[i].xmin + 1) * (bboxes[i].ymax - bboxes[i].ymin + 1);
+    }
+    // 3.循环
+    for (int i=0; i<bboxes.size(); ++i){
+        for (int j=i+1; j<bboxes.size(); ){
+            float left = std::max(bboxes[i].xmin, bboxes[j].xmin);
+            float right = std::min(bboxes[i].xmax, bboxes[j].xmax);
+            float top = std::max(bboxes[i].ymin, bboxes[j].ymin);
+            float bottom = std::min(bboxes[i].ymax, bboxes[j].ymax);
+            float width = std::max(right - left + 1, 0.f);
+            float height = std::max(bottom - top + 1, 0.f);
+            float u_area = height * width;
+            float iou = (u_area) / (area[i] + area[j] - u_area);
+            if (iou>=threshold){
+                bboxes.erase(bboxes.begin()+j);
+                area.erase(area.begin()+j);
+            }else{
+                ++j;
+            }
+        }
+    }
+}
+
+// 根据阈值筛选
+vector<Bbox> RetinaFace::select_score(vector<Bbox> bboxes,float threshold,float w_r,float h_r){
+    vector<Bbox> results;
+    for(Bbox &box:bboxes){
+        if (float(box.score)>=threshold){
+            box.xmin=box.xmin/w_r;
+            box.ymin=box.ymin/h_r;
+            box.xmax=box.xmax/w_r;
+            box.ymax=box.ymax/h_r;
+            box.x1=box.x1/w_r;
+            box.y1=box.y1/h_r;
+            box.x2=box.x2/w_r;
+            box.y2=box.y2/h_r;
+            box.x3=box.x3/w_r;
+            box.y3=box.y3/h_r;
+            box.x4=box.x4/w_r;
+            box.y4=box.y4/h_r;
+            box.x5=box.x5/w_r;
+            box.y5=box.y5/h_r;
+            results.push_back(box);
+        }
+    }
+    return results;
+}
+// vector转Bbox
+// vector<Bbox> RetinaFace::vec2Bbox(vector<vector<float>> bbox,float w_r,float h_r){
+//     vector<Bbox> result_bboxes;
+//     for(auto &c:bbox){
+//         Bbox tmp_box={.xmin=c[0]/w_r,.ymin=c[1]/h_r,.xmax=c[2]/w_r,.ymax=c[3]/h_r};
+//         result_bboxes.push_back(tmp_box);
+//     }
+//     return result_bboxes;
+// }
+// 数据后处理
+vector<Bbox> RetinaFace::bbox_process(bool is_bbox_process,vector<Bbox> bboxes,float frame_w,float frame_h){
+    vector<Bbox> result_bboxes;
+    if(is_bbox_process){
+        for(Bbox &bbox:bboxes){
+            Bbox new_bbox;
+            float face_w=bbox.xmax-bbox.xmin;
+            float face_h=bbox.ymax-bbox.ymin;
+            new_bbox.xmin=bbox.xmin-face_w*0.15;
+            new_bbox.xmax=bbox.xmax+face_w*0.15;
+            new_bbox.ymin=bbox.ymin;
+            new_bbox.ymax=bbox.ymax+face_h*0.15;
+            new_bbox.xmin=new_bbox.xmin>0?new_bbox.xmin:0;
+            new_bbox.ymin=new_bbox.ymin>0?new_bbox.ymin:0;
+            new_bbox.xmax=new_bbox.xmax>frame_w?frame_w:new_bbox.xmax;
+            new_bbox.ymax=new_bbox.ymax>frame_h?frame_h:new_bbox.ymax;
+            new_bbox.score=bbox.score;
+            // cout<<bbox.x1<<endl;
+            new_bbox.x1=bbox.x1>0?bbox.x1:0;
+            new_bbox.y1=bbox.y1>0?bbox.y1:0;
+            new_bbox.x2=bbox.x2>0?bbox.x2:0;
+            new_bbox.y2=bbox.y2>0?bbox.y2:0;
+            new_bbox.x3=bbox.x3>0?bbox.x3:0;
+            new_bbox.y3=bbox.y3>0?bbox.y3:0;
+            new_bbox.x4=bbox.x4>0?bbox.x4:0;
+            new_bbox.y4=bbox.y4>0?bbox.y4:0;
+            new_bbox.x5=bbox.x5>0?bbox.x5:0;
+            new_bbox.y5=bbox.y5>0?bbox.y5:0;
+            result_bboxes.push_back(new_bbox);
+            }
+    }else{
+        for(Bbox &bbox:bboxes){
+            Bbox new_bbox;
+            float face_w=bbox.xmax-bbox.xmin;
+            float face_h=bbox.ymax-bbox.ymin;
+            new_bbox.xmin=bbox.xmin-face_w;
+            new_bbox.xmax=bbox.xmax+face_w;
+            new_bbox.ymin=bbox.ymin;
+            new_bbox.ymax=bbox.ymax+face_h;
+            new_bbox.xmin=new_bbox.xmin>0?new_bbox.xmin:0;
+            new_bbox.ymin=new_bbox.ymin>0?new_bbox.ymin:0;
+            new_bbox.xmax=new_bbox.xmax>frame_w?frame_w:new_bbox.xmax;
+            new_bbox.ymax=new_bbox.ymax>frame_h?frame_h:new_bbox.ymax;
+            new_bbox.score=bbox.score;
+            new_bbox.x1=bbox.x1>0?bbox.x1:0;
+            new_bbox.y1=bbox.y1>0?bbox.y1:0;
+            new_bbox.x2=bbox.x2>0?bbox.x2:0;
+            new_bbox.y2=bbox.y2>0?bbox.y2:0;
+            new_bbox.x3=bbox.x3>0?bbox.x3:0;
+            new_bbox.y3=bbox.y3>0?bbox.y3:0;
+            new_bbox.x4=bbox.x4>0?bbox.x4:0;
+            new_bbox.y4=bbox.y4>0?bbox.y4:0;
+            new_bbox.x5=bbox.x5>0?bbox.x5:0;
+            new_bbox.y5=bbox.y5>0?bbox.y5:0;
+            result_bboxes.push_back(new_bbox);
+            }
+    }
+    return result_bboxes;
+}
+// 推理
+
+// vector<Bbox> RetinaFace::detect_image(Mat image){
+vector<Bbox> RetinaFace::detect_image(Mat image){
+    float w_r=float(640)/float(image.cols);
+    float h_r=float(640)/float(image.rows);
+    cv::Mat blob = cv::dnn::blobFromImage(image,resize_scale,Size(input_size[0],input_size[1]),Scalar(mean_data[0],mean_data[1],mean_data[2]));  // 由图片加载数据 这里还可以进行缩放、归一化等预处理
+    net.setInput(blob);  // 设置模型输入
+    std::vector<cv::Mat> netOutputImg;    
+    net.forward(netOutputImg,net.getUnconnectedOutLayersNames()); // 推理出结果
+
+    cv::Mat scores = netOutputImg[2].reshape(1,16800);
+    cv::Mat boxes = netOutputImg[0].reshape(1,16800);
+    cv::Mat landms = netOutputImg[1].reshape(1,16800);
+
+    vector<Bbox> result_boxes=decode(boxes,scores,landms,anchors,variances);
+    vector<Bbox> results=select_score(result_boxes,confidence_threshold,w_r,h_r);
+    
+    nms_cpu(results,nms_threshold);
+
+    if(is_bbox_process){
+        vector<Bbox> res_bboxes=bbox_process(true,results,image.cols,image.rows);
+        return res_bboxes;
+
+    }else{
+        vector<Bbox> res_bboxes=bbox_process(false,results,image.cols,image.rows);
+        return res_bboxes;
+    }
+}
\ No newline at end of file

+#include "speak_detector.h"
+
+void SpeakDetector::init_model(string face_det_model,string landm_det_model,string speak_cls_model){
+    face_det = RetinaFace(face_det_model);
+    landm_det = FaceLandmarks(landm_det_model);
+    speak_cls = SpeakCls(speak_cls_model);
+}
+
+float SpeakDetector::iou_compute(Bbox b1, Bbox b2)
+{   
+    float tmp_w=min(b1.xmax,b2.xmax) - max(b1.xmin, b2.xmin);
+    float tmp_h=min(b1.ymax, b2.ymax) - max(b1.ymin, b2.ymin);
+    float w = max(tmp_w, float(0));
+    float h = max(tmp_h, float(0));
+    return w*h / ((b1.xmax-b1.xmin)*(b1.ymax-b1.ymin) + (b2.xmax-b2.xmin)*(b2.ymax-b2.ymin) - w*h);
+}
+vector<vector<cv::Mat>> SpeakDetector::mouth_process(vector<vector<vector<vector<float>>>> batch_landmarks, vector<cv::Mat> batch_images){
+    int input_size=112;
+    vector<vector<cv::Mat>> align_mouths;
+    for(int i=0;i<batch_images.size();++i){
+        cv::Mat image = batch_images[i];
+        vector<cv::Mat> tmp_mouths;
+        for(int j=0;j<batch_landmarks[i].size();++j){
+            vector<float> mouth_xs; 
+            vector<float> mouth_ys;
+            for(int k=84;k<int(104);++k){
+                float x_q = round(batch_landmarks[i][j][k][0]);
+                float y_q = round(batch_landmarks[i][j][k][1]);
+                mouth_xs.push_back(x_q);
+                mouth_ys.push_back(y_q);
+            }
+            float mouth_width=*max_element(mouth_xs.begin(),mouth_xs.end())-*min_element(mouth_xs.begin(),mouth_xs.end());
+            float mouth_height=*max_element(mouth_ys.begin(),mouth_ys.end())-*min_element(mouth_ys.begin(),mouth_ys.end());
+            int mouth_min_x=ceil(*min_element(mouth_xs.begin(),mouth_xs.end())-mouth_width*0.2);
+            int mouth_min_y=ceil(*min_element(mouth_ys.begin(),mouth_ys.end())-mouth_height*0.1);
+            int mouth_max_x=ceil(*max_element(mouth_xs.begin(),mouth_xs.end())+mouth_width*0.2);
+            int mouth_max_y=ceil(*max_element(mouth_ys.begin(),mouth_ys.end())+mouth_height*0.1);
+            
+            mouth_min_x=mouth_min_x>0?mouth_min_x:0;
+            mouth_min_y=mouth_min_y>0?mouth_min_y:0;
+            cv::Rect mouth_rect = Rect(mouth_min_x,mouth_min_y,mouth_max_x-mouth_min_x,mouth_max_y-mouth_min_y);
+            cv::Mat mouth_crop = image(mouth_rect);
+            cv::Mat resize_mouth_crop;
+            cv::resize(mouth_crop,resize_mouth_crop,Size(input_size,input_size));
+            Point center=Point(input_size/2,input_size/2);
+            float dx = batch_landmarks[i][j][90][0]-batch_landmarks[i][j][84][0];
+            float dy = batch_landmarks[i][j][90][1]-batch_landmarks[i][j][84][1];
+            double angle = atan2(dy,dx)*180/float(M_PI);
+            cv::Mat rotate_matrix = cv::getRotationMatrix2D(center,double(angle),1);
+            cv::Mat rot_img;
+            cv::warpAffine(resize_mouth_crop,rot_img,rotate_matrix,Size(input_size,input_size));
+            tmp_mouths.push_back(rot_img);   
+        }
+        align_mouths.push_back(tmp_mouths);
+    }
+    return align_mouths;
+}
+//视频/图像数据切片
+//图像
+void SpeakDetector::image_reader(string file_path,int segment_num,vector<Mat> &bgr_frames,vector<vector<int>> &indices){
+    int new_length = 1;
+    vector<String> image_files;
+    glob(file_path, image_files, false);
+    int total_frames_num = (int)image_files.size();
+    float tick = float(total_frames_num - new_length + 1) / float(segment_num);
+    vector<int> indice;
+    for(int x=0;x<segment_num;++x){
+        indice.push_back(int(tick / 2.0 + tick * x));
+    }
+    indices.push_back(indice);
+    
+    for(auto im_file:image_files){
+        Mat bgr_img=cv::imread(im_file);
+        bgr_frames.push_back(bgr_img);
+    }
+}
+void SpeakDetector::speak_recognize(string image_path){
+    vector<Mat> all_bgr_images;
+    vector<vector<int>> total_split_indices;
+    image_reader(image_path,10,all_bgr_images,total_split_indices);
+    // vector<json> all_results;
+
+    bool is_talk=false;
+
+    for(int im_i=0;im_i<total_split_indices.size();++im_i){
+        vector<vector<cv::Mat>> face_list;
+        vector<vector<Bbox>> bbox_list;
+        vector<cv::Mat> rgb_frames;
+        vector<cv::Mat> bgr_frames;
+        int tmp_rows,tmp_cols;
+        for(int im_j=0;im_j<total_split_indices[im_i].size();++im_j){
+            Mat tmp_img=all_bgr_images[total_split_indices[im_i][im_j]];
+            if(im_j !=0){
+                if(tmp_img.rows!=tmp_rows&&tmp_img.cols!=tmp_cols){
+                    cv::resize(tmp_img,tmp_img,Size(int(tmp_img.cols),int(tmp_img.rows)));
+                }
+            }
+            tmp_rows=tmp_img.rows;
+            tmp_cols=tmp_img.cols;
+            Mat rgb_tmp_img;
+            cv::cvtColor(tmp_img,rgb_tmp_img,cv::COLOR_BGR2RGB);
+            bgr_frames.push_back(tmp_img);
+            rgb_frames.push_back(rgb_tmp_img);
+
+        }    
+        for(auto bgr_frame:bgr_frames){
+            vector<Bbox> boxes=face_det.detect_image(bgr_frame);
+            vector<cv::Mat> tmp_face_areas;
+            vector<Bbox> tmp_bbox_list;
+            for(auto box:boxes){
+                tmp_bbox_list.push_back(box);
+
+                // cout<<box.xmin<<" "<<box.ymin<<" "<<box.xmax-box.xmin<<" "<<box.ymax-box.ymin<<endl;
+                
+                Rect m_select = Rect(box.xmin,box.ymin,box.xmax-box.xmin,box.ymax-box.ymin);
+                
+                cv::Mat face_area=bgr_frame(m_select);
+                tmp_face_areas.push_back(face_area);
+                // cv::imshow("123",face_area);
+                // cv::waitKey(0);
+            }
+            face_list.push_back(tmp_face_areas);
+            bbox_list.push_back(tmp_bbox_list);
+        }
+
+        vector<vector<vector<vector<float>>>> landms_list;
+        for(int i=0;i<face_list.size();++i){
+            vector<vector<vector<float>>> tmp_landm_list;
+            for(int j=0;j<face_list[i].size();++j){
+                vector<vector<float>> tmp_landms=landm_det.detect_image_landmarks(face_list[i][j]);
+                for(int k=0;k<tmp_landms.size();++k){
+                    tmp_landms[k][0]=tmp_landms[k][0]+bbox_list[i][j].xmin;
+                    tmp_landms[k][1]=tmp_landms[k][1]+bbox_list[i][j].ymin;
+                }
+                tmp_landm_list.push_back(tmp_landms);
+            }
+            landms_list.push_back(tmp_landm_list);
+        }
+        vector<vector<cv::Mat>> mouth_list=mouth_process(landms_list,rgb_frames);
+
+        vector<vector<Bbox>> last_bboxes=bbox_list;
+        vector<Bbox> first_bboxes = bbox_list[0];
+        vector<vector<Bbox>>::iterator k = last_bboxes.begin();
+        last_bboxes.erase(k);
+        
+        vector<vector<Bbox>> all_track_bbox_list;
+        vector<vector<cv::Mat>> all_face_list,all_mouth_list;
+
+        for(int i=0;i<first_bboxes.size();++i){
+            Bbox first_bbox=first_bboxes[i];
+            vector<Bbox> track_bbox_list;
+            vector<cv::Mat> trace_face_list,trace_mouth_list;
+            track_bbox_list.push_back(first_bbox);
+            trace_face_list.push_back(face_list[0][i]);
+            trace_mouth_list.push_back(mouth_list[0][i]);
+            for(int j=0;j<last_bboxes.size();++j){
+                vector<Bbox> next_bboxes=last_bboxes[j];
+                for(int k=0;k<next_bboxes.size();++k){
+
+                    Bbox next_bbox = next_bboxes[k];
+                    float iou=iou_compute(first_bbox,next_bbox);
+                    if(iou>=0.4){
+                        track_bbox_list.push_back(next_bbox);
+                        trace_face_list.push_back(face_list[j+1][k]);
+                        trace_mouth_list.push_back(mouth_list[j+1][k]);
+                        break;
+                    }
+                }
+            }
+            all_track_bbox_list.push_back(track_bbox_list);
+            all_face_list.push_back(trace_face_list);
+            all_mouth_list.push_back(trace_mouth_list);
+        }
+        for(int j=0;j<all_mouth_list.size();j++){
+            vector<cv::Mat> select_mouth_list=all_mouth_list[j];
+
+            /**
+             * @brief 模型推理部分代码,返回result  0/1 ,其中1为说话,0为未说话
+             * 
+             */
+            bool result=speak_cls.detect_speak(select_mouth_list);
+            
+            // bool result=true;
+            if(result){
+                is_talk=true;
+                // speak_duration = (split_indices[0], split_indices[-1])
+                // Mat speaker = all_face_list[j][0];
+                // speaker_str = cv::imencode('.jpg', speaker)[1].tostring()
+                // speaker_str = base64.b64encode(speaker_str).decode()
+                // int position = j
+                // json cur_output={
+                    // "is_talk":true,
+                    // "speak_duration":[str(speak_duration[0]), str(speak_duration[1])],
+                    // "speaker":speaker_str,
+                    // "position":position
+                // }
+                // all_results.push_back(cur_output);
+                cout<<is_talk<<endl;
+            }else{
+                cout<<is_talk<<endl;
+            }
+
+        }
+        
+        // return 0;
+        }
+}
\ No newline at end of file

+#include "speakcls.h"
+
+cv::Mat SpeakCls::standardize(cv::Mat image){
+    cv::Mat image_f,dst;
+    image.convertTo(image_f, CV_32F);
+    Scalar max_pix = Scalar(255.0f,255.0f,255.0f);
+    Scalar mean = Scalar(0.485f, 0.456f, 0.406f);
+    Scalar std = Scalar(0.229f, 0.224f, 0.225f);
+    dst=image_f/max_pix;
+    dst = (dst-mean)/std;
+    return dst;
+}
+
+cv::Mat SpeakCls::data_process(vector<Mat> images){
+    std::vector<cv::Mat> all_image_channels;
+    for(auto f:images){
+        Mat tmp_image = standardize(f);
+        std::vector<cv::Mat> tmp_channels;
+        cv::split(tmp_image,tmp_channels);
+        all_image_channels.push_back(tmp_channels[0]);
+        all_image_channels.push_back(tmp_channels[1]);
+        all_image_channels.push_back(tmp_channels[2]);
+    }
+    Mat input_data;
+    cv::merge(all_image_channels,input_data);
+    return input_data;
+}
+
+vector<double> SpeakCls::softmax(vector<double> input){
+    double total=0;
+	for(auto x:input)
+	{
+		total+=exp(x);
+	}
+	vector<double> result;
+	for(auto x:input)
+	{
+		result.push_back(exp(x)/total);
+	}
+	return result;
+}
+
+bool SpeakCls::detect_speak(vector<Mat> images){
+    
+    auto session = net->createSession(config);//创建session 
+    Mat input_data=data_process(images);
+    // cout << _Tensor->elementSize() << endl;
+    std::vector<std::vector<cv::Mat>> nChannels;
+    std::vector<cv::Mat> rgbChannels(30);
+    cv::split(input_data, rgbChannels);
+    nChannels.push_back(rgbChannels); //  NHWC  转NCHW
+    auto *pvData = malloc(1 * 30 * 112 * 112 *sizeof(float));
+    int nPlaneSize = 112 * 112;
+    for (int c = 0; c < 30; ++c)
+    {
+    cv::Mat matPlane = nChannels[0][c];
+    memcpy((float *)(pvData) + c * nPlaneSize,\
+            matPlane.data, nPlaneSize * sizeof(float));
+    }
+    auto inTensor = net->getSessionInput(session, NULL);
+    auto nchwTensor = new Tensor(inTensor, Tensor::CAFFE);
+    ::memcpy(nchwTensor->host<float>(), pvData, nPlaneSize * 30 * sizeof(float));
+    
+
+    inTensor->copyFromHostTensor(nchwTensor);
+    //推理
+    net->runSession(session);
+    auto output= net->getSessionOutput(session, NULL);
+
+    MNN::Tensor feat_tensor(output, output->getDimensionType());
+    output->copyToHostTensor(&feat_tensor);
+    auto scores_dataPtr  = feat_tensor.host<float>();
+    // cout<<scores_dataPtr[0]<<" "<<scores_dataPtr[1]<<endl;
+    vector<double> outputs={scores_dataPtr[0],scores_dataPtr[1]};
+    // softmax
+    vector<double> result=softmax(outputs);
+    printf("output belong to class: %f  %f\n", result[0],result[1]);
+    if(result[0]>result[1]){
+        return false;
+    }else{
+        return true;
+    }
+}
\ No newline at end of file