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the first submit of speak_recognize

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CMakeLists.txt 0 → 100644
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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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build/speakrecognize 0 → 100755
No preview for this file type
eval.cpp 0 → 100644
This diff is collapsed. Click to expand it.
faceLandmarks.cpp 0 → 100644
#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;
}
include/faceLandmarks.h 0 → 100644
#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
include/retinaface.h 0 → 100644
#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
include/speak_detector.h 0 → 100644
#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
include/speakcls.h 0 → 100644
#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
lib/libspeakrecognize.so 0 → 100755
No preview for this file type
main.cpp 0 → 100644
#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;
}
model/cls_speak_v0.2.2.mnn 0 → 100644
No preview for this file type
model/det_face_retina_torch_1.4_v0.0.2.onnx 0 → 100644
No preview for this file type
model/det_landmarks_106_v0.0.1.onnx 0 → 100644
No preview for this file type
retinaface.cpp 0 → 100644
#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
speak_detector.cpp 0 → 100644
#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
speakcls.cpp 0 → 100644
#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
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