diff --git a/libClassification/ICNNPredict.h b/libClassification/ICNNPredict.h new file mode 100644 index 0000000000000000000000000000000000000000..a4e603369532a11c71b1b0537334e2b1ece79d89 --- /dev/null +++ b/libClassification/ICNNPredict.h @@ -0,0 +1,65 @@ + +#pragma once + + +#ifndef interface +#define CINTERFACE +#define interface struct +#endif + +//------------------------------------------------------------------------------------------------- +#ifndef IMPORT +#define IMPORT __declspec(dllimport) +#endif + + +//------------------------------------------------------------------------------------------------- +#ifndef EXPORT +#define EXPORT __declspec(dllexport) +#endif + +#include +#include + +typedef std::pair Prediction; +typedef std::pair PredictionIdx; + +#include +#include +#include + + + + + interface ICNNPredict +{ + virtual bool IsCPUMode() = 0; + virtual std::vector Classify(const std::string& file, int N = 5) = 0; + virtual std::vector Classify(const unsigned char* pJPGBuffer, int len, int N = 5) = 0; + virtual std::vector Classify(const cv::Mat& img, int N = 5) = 0; + virtual std::vector > BatchClassify(const std::vector& imgs, int N = 5) = 0; + virtual std::vector > BatchPredict(const std::vector& img)=0; + virtual std::vector CropClassify(const cv::Mat& img, int std_size, int crop_num = 1, int N = 5) = 0; + virtual std::vector ClassifyRtnIdx(const cv::Mat& img, int N = 5) = 0; + + //virtual std::vector ExtractFeature(const cv::Mat& img, const std::string& strLayerName = "") = 0; + virtual std::vector GetLayerFeatureMaps(const std::string& strLayerName, std::vector& outshape) = 0; + virtual int GetFeatureDim() = 0; + virtual std::vector< std::vector > GetLastBlockFeature(const cv::Mat& img) = 0; + virtual std::vector GetOutputFeatureMap(const cv::Mat& img, std::vector& outshape) = 0; + + virtual std::vector GetLabels() = 0; + + virtual void SetMean(const std::string& mean_file) = 0; + + virtual std::vector Predict(const cv::Mat& img) = 0; + virtual void GetInputImageSize(int &w, int &h) = 0; + + //advanced operations + virtual float Pruning(float weight_t, const char* saveas_name=0)=0; + virtual cv::Mat EstimateReceptiveField(const cv::Mat& img, const std::string& layerName, int x, int y, int idxNeuron = -1,bool islstm=false,int* width_parts=0) = 0; + virtual void GetLayerFeatureMapSize(int w, int h, const std::string& layerName,int& w1, int& h1)=0; + virtual void Release()=0; +}; + + extern "C" EXPORT ICNNPredict* CreatePredictInstance(const char* model_folder, bool use_gpu); diff --git a/libClassification/MTMain.cpp b/libClassification/MTMain.cpp new file mode 100644 index 0000000000000000000000000000000000000000..584b95c1af3bc98e5747f2b52cc08f04fd090c1c Binary files /dev/null and b/libClassification/MTMain.cpp differ diff --git a/libClassification/ReadMe.txt b/libClassification/ReadMe.txt new file mode 100644 index 0000000000000000000000000000000000000000..e9decb3ee381b793910f62751ab4c3959645ac8a --- /dev/null +++ b/libClassification/ReadMe.txt @@ -0,0 +1,53 @@ +======================================================================== + DYNAMIC LINK LIBRARY : libClassification Project Overview +======================================================================== + +AppWizard has created this libClassification DLL for you. + +This file contains a summary of what you will find in each of the files that +make up your libClassification application. + + +libClassification.vcxproj + This is the main project file for VC++ projects generated using an Application Wizard. + It contains information about the version of Visual C++ that generated the file, and + information about the platforms, configurations, and project features selected with the + Application Wizard. + +libClassification.vcxproj.filters + This is the filters file for VC++ projects generated using an Application Wizard. + It contains information about the association between the files in your project + and the filters. This association is used in the IDE to show grouping of files with + similar extensions under a specific node (for e.g. ".cpp" files are associated with the + "Source Files" filter). + +libClassification.cpp + This is the main DLL source file. + + When created, this DLL does not export any symbols. As a result, it + will not produce a .lib file when it is built. If you wish this project + to be a project dependency of some other project, you will either need to + add code to export some symbols from the DLL so that an export library + will be produced, or you can set the Ignore Input Library property to Yes + on the General propert page of the Linker folder in the project's Property + Pages dialog box. + +///////////////////////////////////////////////////////////////////////////// +Other standard files: + +StdAfx.h, StdAfx.cpp + These files are used to build a precompiled header (PCH) file + named libClassification.pch and a precompiled types file named StdAfx.obj. + +///////////////////////////////////////////////////////////////////////////// +Other notes: + +AppWizard uses "TODO:" comments to indicate parts of the source code you +should add to or customize. + +///////////////////////////////////////////////////////////////////////////// +No such lib: +libjpeg.lib +libpng.lib +zlib.lib +//////////////////////////////////////////////////////////////////////// diff --git a/libClassification/caffe-gpu.h b/libClassification/caffe-gpu.h new file mode 100644 index 0000000000000000000000000000000000000000..21c0992e5fd85fef75eaa0b0380b9f604700dbed --- /dev/null +++ b/libClassification/caffe-gpu.h @@ -0,0 +1,103 @@ +#ifndef CAFFE_GPU_H_ +#define CAFFE_GPU_H_ +#include + +// c++ +#include +#include +#include +#include "tform_fcn.h" +// opencv +#include +// boost +//#include "boost/make_shared.hpp" + + +//#define CPU_ONLY +#define INTER_FAST +using namespace caffe; + +typedef struct FaceRect { + float x1; + float y1; + float x2; + float y2; + float score; /**< Larger score should mean higher confidence. */ +} FaceRect; + +typedef struct FacePts { + float x[5], y[5]; +} FacePts; + +typedef struct FaceInfo { + FaceRect bbox; + cv::Vec4f regression; + FacePts facePts; + double roll; + double pitch; + double yaw; +} FaceInfo; + + + +class _declspec(dllexport) MTCNN { + public: + MTCNN(const string& proto_model_dir); + void Detect(const cv::Mat& img, std::vector &faceInfo, int minSize, double* threshold, double factor); + + private: + bool CvMatToDatumSignalChannel(const cv::Mat& cv_mat, Datum* datum); + void Preprocess(const cv::Mat& img, + std::vector* input_channels); + void WrapInputLayer(std::vector* input_channels,Blob* input_layer, + const int height,const int width); + /*void SetMean();*/ + void GenerateBoundingBox( Blob* confidence,Blob* reg, + float scale,float thresh,int image_width,int image_height); + void ClassifyFace(const std::vector& regressed_rects,cv::Mat &sample_single, + boost::shared_ptr >& net,double thresh,char netName); + void ClassifyFace_MulImage(const std::vector ®ressed_rects, cv::Mat &sample_single, + boost::shared_ptr >& net, double thresh, char netName); + std::vector NonMaximumSuppression(std::vector& bboxes,float thresh,char methodType); + void Bbox2Square(std::vector& bboxes); + void Padding(int img_w, int img_h); + std::vector BoxRegress(std::vector &faceInfo_, int stage); + void RegressPoint(const std::vector& faceInfo); + + private: + boost::shared_ptr > PNet_; + boost::shared_ptr > RNet_; + boost::shared_ptr > ONet_; + + // x1,y1,x2,t2 and score + std::vector condidate_rects_; + std::vector total_boxes_; + std::vector regressed_rects_; + std::vector regressed_pading_; + + std::vector crop_img_; + int curr_feature_map_w_; + int curr_feature_map_h_; + int num_channels_; +}; + + + + +//extern int outnum; +//extern real_t face_dev; +//_declspec(dllexport) void setoutnum(int); +//_declspec(dllexport) void set_facedev(float num); +// +////std::vector recface(cv::Mat mat, caffe::Net& net, int nummtcnn = 0); +////_declspec(dllexport) std::vector recface(cv::Mat mat, caffe::Net& net); +//_declspec(dllexport) std::vector recface(cv::Mat mat, caffe::Net* net, MTCNN *detector); +////_declspec(dllexport) std::vector return_face_rect(cv::Mat mat); +//_declspec(dllexport) std::vector return_face_rect(cv::Mat mat, MTCNN *detector); +// +//// 夹角余弦 +//_declspec(dllexport) double cosine(const std::vector& v1, const std::vector& v2); +//_declspec(dllexport) int test(cv::Mat image, vector& faceinfo_, vector& result_); + + +#endif \ No newline at end of file diff --git a/libClassification/classification.cpp b/libClassification/classification.cpp new file mode 100644 index 0000000000000000000000000000000000000000..b77fbc9af840bf3bfe9582f92b71b7214d4174b8 --- /dev/null +++ b/libClassification/classification.cpp @@ -0,0 +1,999 @@ +#ifdef WIN32 +#include +#endif + +#include "classification.hpp" + +//#include "cblas.h" + +#include "glog/logging.h" + +/* +For Windows +value mode +00 Existence only +02 Write-only +04 Read-only +06 Read and write + +For linux +F_OK Existence only +R_OK Read-only +W_OK Write-only +X_OK Executable +*/ +bool CheckFileExist(const char* szFile) +{ +#ifdef WIN32 + return _access(szFile, 0) != -1; +#else + return access(szFile, F_OK) != -1; +#endif + +} + + +extern "C" EXPORT ICNNPredict* CreatePredictInstance(const char* model_folder, bool use_gpu) +{ + Classifier* p = new Classifier(); + if (!p->Init(model_folder, use_gpu)) + { + delete p; + p = NULL; + } + return p; +} + +Classifier::Classifier(){ } + +bool Classifier::Init(const string& model_path, bool gpu_mode) { + + + const string trained_file = model_path + "/model.dll"; + const string model_file = model_path + "/deploy.dll"; + string mean_file = model_path + "/mean.binaryproto"; + const string mean_value_file = model_path + "/mean_values.txt"; + const string label_file = model_path + "/label.dll"; + + if (!CheckFileExist(mean_file.c_str())) + mean_file = mean_value_file; + + if (!gpu_mode) + Caffe::set_mode(Caffe::CPU); + else + Caffe::set_mode(Caffe::GPU); + + /* Load the network. */ + net_.reset(new Net(model_file, TEST)); + net_->CopyTrainedLayersFrom(trained_file); + //net_->set_debug_info(true); + + CHECK_EQ(net_->num_inputs(), 1) << "Network should have exactly one input."; + CHECK_EQ(net_->num_outputs(), 1) << "Network should have exactly one output."; + + Blob* input_layer = net_->input_blobs()[0]; + num_channels_ = input_layer->channels(); + CHECK(num_channels_ == 3 || num_channels_ == 1) + << "Input layer should have 1 or 3 channels."; + input_geometry_ = cv::Size(input_layer->width(), input_layer->height()); + + /* Load the binaryproto mean file. */ + SetMean(mean_file); + + /* Load labels. */ + if (label_file.size() > 0) + { + std::ifstream labels(label_file.c_str()); + CHECK(labels) << "Unable to open labels file " << label_file; + string line; + while (std::getline(labels, line)) + labels_.push_back(string(line)); + } + else + { + Blob* output_layer = net_->output_blobs()[0]; + char szlabel[100]; + printf("output ch=%d\n", output_layer->channels()); + for (int i = 0; i < output_layer->channels(); i++) + { + sprintf(szlabel, "%d", i); + labels_.push_back(szlabel); + } + + } + + +// Blob* output_layer = net_->output_blobs()[0]; +// CHECK_EQ(labels_.size(), output_layer->channels()) +// << "Number of labels is different from the output layer dimension."; + + return true; +} +bool Classifier::Init(const string& trained_file, const string& model_file, + const string&mean_file, const string&label_file, + bool gpu_mode) +{ + if (!gpu_mode) + Caffe::set_mode(Caffe::CPU); + else + Caffe::set_mode(Caffe::GPU); + + + /* Load the network. */ + net_.reset(new Net(model_file, TEST)); + net_->CopyTrainedLayersFrom(trained_file); + + CHECK_EQ(net_->num_inputs(), 1) << "Network should have exactly one input."; + CHECK_EQ(net_->num_outputs(), 1) << "Network should have exactly one output."; + + Blob* input_layer = net_->input_blobs()[0]; + num_channels_ = input_layer->channels(); + CHECK(num_channels_ == 3 || num_channels_ == 1) + << "Input layer should have 1 or 3 channels."; + input_geometry_ = cv::Size(input_layer->width(), input_layer->height()); + + /* Load the binaryproto mean file. */ + SetMean(mean_file); + + /* Load labels. */ + if (label_file.size() > 0) + { + std::ifstream labels(label_file.c_str()); + CHECK(labels) << "Unable to open labels file " << label_file; + string line; + while (std::getline(labels, line)) + labels_.push_back(string(line)); + } + else + { + Blob* output_layer = net_->output_blobs()[0]; + char szlabel[100]; + printf("output ch=%d\n", output_layer->channels()); + for (int i = 0; i < output_layer->channels(); i++) + { + sprintf(szlabel, "%d", i); + labels_.push_back(szlabel); + } + + } + + +// Blob* output_layer = net_->output_blobs()[0]; +// CHECK_EQ(labels_.size(), output_layer->channels()) +// << "Number of labels is different from the output layer dimension."; + + return true; +} + +int Classifier::FindMaxChannelLayer() +{ + const vector > >&blobs = net_->blobs(); + int maxchannels = 0; + int idx = -1; + for (int i = (int)blobs.size() - 1; i >= 0; i--) + { + if (blobs[i]->channels() > maxchannels) + { + maxchannels = blobs[i]->channels(); + idx = i; + } + } + + printf("max channel layer=%d, channels=%d\n", idx, maxchannels); + + return idx; +} + +int Classifier::FindLayerIndex(const string& strLayerName) +{ + const vector& vLayers = net_->layer_names(); + + int idx = -1; + for (int i = (int)vLayers.size() - 1; i >= 0; i--) + { + if (vLayers[i] == strLayerName) + { + idx = i; + break; + } + } + return idx; +} + +static bool PairCompare(const std::pair& lhs, + const std::pair& rhs) { + return lhs.first > rhs.first; +} + +/* Return the indices of the top N values of vector v. */ +static std::vector Argmax(const std::vector& v, int N) { + std::vector > pairs; + for (size_t i = 0; i < v.size(); ++i) + pairs.push_back(std::make_pair(v[i], (int)i)); + std::partial_sort(pairs.begin(), pairs.begin() + N, pairs.end(), PairCompare); + + std::vector result; + for (int i = 0; i < N; ++i) + result.push_back(pairs[i].second); + return result; +} +/* Return the top Na predictions. */ +std::vector Classifier::Classify(const string& file, int N) { + + cv::Mat img = cv::imread(file, CV_LOAD_IMAGE_COLOR); + return Classify(img, N); +} + +std::vector Classifier::Classify(const unsigned char* pJPGBuffer, int len, int N /*= 5*/) +{ + vector jpg(len); + memcpy(&jpg[0], pJPGBuffer, len); + + cv::Mat img = cv::imdecode(jpg, CV_LOAD_IMAGE_COLOR); + + return Classify(img, N); +} + +std::vector Classifier::Classify(const cv::Mat& img, int N /*= 5*/) +{ + std::vector predictions; + + if (img.empty()) + { + std::cout << "img is empty" << std::endl; + return predictions; + } + + std::vector output = Predict(img); + +#if 0 + bool softmax = false; + float sum = 0; + float maxv=output.size()>0 ? output[0]:0; + for (size_t i = 0; i < output.size(); i++) + { + sum += output[i]; + if (sum>1.01f) + softmax = true; + if(output[i]>maxv) + maxv=output[i]; + } + if (softmax) + { + sum = 0; + std::vector expout(output.size()); + for (size_t i = 0; i < output.size(); i++) + { + expout[i] = exp(output[i]-maxv); + sum += expout[i]; + } + if (sum>0) + { + for (size_t i = 0; i < output.size(); i++) + output[i] = expout[i] / sum; + } + } +#endif + if ((int)output.size() < N) + N = (int)output.size(); + std::vector maxN = Argmax(output, N); + + for (int i = 0; i < N; ++i) + { + int idx = maxN[i]; + predictions.push_back(std::make_pair(labels_[idx], output[idx])); + //std::cout << "make pair:" << idx << "," << labels_[idx] << "," << output[idx] < > Classifier::BatchClassify(const std::vector& imgs, int N /*= 5*/) +{ + std::vector > predictions; + if (imgs.size() == 0) + return predictions; + + std::vector< std::vector > outputs = BatchPredict(imgs); + + predictions.resize(outputs.size()); + for (size_t k = 0; k < outputs.size();k++) + { + std::vector maxN = Argmax(outputs[k], N); + for (int i = 0; i < N; ++i) + { + int idx = maxN[i]; + predictions[k].push_back(std::make_pair(idx, outputs[k][idx])); + } + } + + return predictions; +} + + + +std::vector Classifier::CropClassify(const cv::Mat& img, int std_size, int crop_num, int N /*= 5*/) +{ + //resize shorter edge to std_size + int w0 = img.cols, h0 = img.rows; + int w1 = w0, h1 = w0; + if (w0 <= h0) + { + w1 = std_size; + h1 = w1*h0 / w0; + } + else + { + h1 = std_size; + w1 = h1*w0 / h0; + } + + cv::Mat imgresize = img; + if (w0 != w1 || h0 != h1) + resize(img, imgresize, cv::Size(w1, h1)); + + //crop + cv::Mat imgcrop = imgresize; + if (crop_num == 1)//center crop + { + cv::Rect roi = { (w1 - input_geometry_.width) / 2 - 1, (h1 - input_geometry_.height) / 2 - 1, + input_geometry_.width, input_geometry_.height }; + if (roi.x < 0) roi.x = 0; + if (roi.y < 0) roi.y = 0; + + imgcrop = imgresize(roi); + } + + return Classify(imgcrop, N); +} + +std::vector Classifier::ClassifyRtnIdx(const cv::Mat& img, int N /*= 5*/) +{ + std::vector predictions; + + if (img.empty()) + { + std::cout << "img is empty" << std::endl; + return predictions; + } + + std::vector output = Predict(img); + + std::vector maxN = Argmax(output, N); + + for (int i = 0; i < N; ++i) + { + int idx = maxN[i]; + predictions.push_back(std::make_pair(idx, output[idx])); + } + + return predictions; +} + +std::vector< std::vector > Classifier::GetLastBlockFeature(const cv::Mat& img) +{ + Blob* input_layer = net_->input_blobs()[0]; + input_layer->Reshape(1, num_channels_, + input_geometry_.height, input_geometry_.width); + /* Forward dimension change to all layers. */ + net_->Reshape(); + + std::vector input_channels; + WrapInputLayer(&input_channels); + + Preprocess(img, &input_channels); + + net_->Forward(); + + const vector > >&blobs = net_->blobs(); + + int idx = blobs.size() - 1; + + const float* begin = blobs[idx]->cpu_data(); + int dim1 = blobs[idx]->channels(); + int dim2 = blobs[idx]->height(); + vector< vector > vFeatures(dim1); + for (int i = 0; i < dim1;i++) + { + vFeatures[i].resize(dim2); + memcpy(&vFeatures[i][0], begin + i*dim2, dim2*sizeof(float)); + } + + return vFeatures; +} + + + +/* Load the mean file in binaryproto format. */ +void Classifier::SetMean(const string& mean_file) +{ + if (mean_file.rfind(".txt") != string::npos) + { + vector chmeans; + string line; + ifstream fs(mean_file); + while (getline(fs, line)) + { + if (line.size() == 0) + continue; + chmeans.push_back((float)atof(line.c_str())); + } + if (chmeans.size() != 1 && chmeans.size() != 3) + { + printf("wrong mean value format\n"); + return; + } + //int meantype = chmeans.size() == 1 ? CV_32FC1 : CV_32FC3; + //cv::Scalar channel_mean; + channel_mean_ = chmeans; + + return; + } + BlobProto blob_proto; + ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto); + + /* Convert from BlobProto to Blob */ + Blob mean_blob; + mean_blob.FromProto(blob_proto); + CHECK_EQ(mean_blob.channels(), num_channels_) + << "Number of channels of mean file doesn't match input layer."; + + /* The format of the mean file is planar 32-bit float BGR or grayscale. */ + std::vector channels; + float* data = mean_blob.mutable_cpu_data(); + for (int i = 0; i < num_channels_; ++i) { + /* Extract an individual channel. */ + cv::Mat channel(mean_blob.height(), mean_blob.width(), CV_32FC1, data); + channels.push_back(channel); + data += mean_blob.height() * mean_blob.width(); + } + + /* Merge the separate channels into a single image. */ + cv::Mat mean; + cv::merge(channels, mean); + +#if 0 + /* Compute the global mean pixel value and create a mean image + * filled with this value. */ + cv::Scalar channel_mean = cv::mean(mean); + mean_ = cv::Mat(input_geometry_, mean.type(), channel_mean); +#else + mean.convertTo(mean_, CV_32FC3); +#endif +} + +std::vector Classifier::Predict(const cv::Mat& img) { + Blob* input_layer = net_->input_blobs()[0]; + if (input_geometry_.width != input_layer->shape(3) || input_geometry_.height != input_layer->shape(2)) + { + input_layer->Reshape(1, num_channels_, + input_geometry_.height, input_geometry_.width); + /* Forward dimension change to all layers. */ + net_->Reshape(); + } + + std::vector input_channels; + WrapInputLayer(&input_channels); + + Preprocess(img, &input_channels); + + net_->Forward(); + + /* Copy the output layer to a std::vector */ + Blob* output_layer = net_->output_blobs()[0]; + const float* begin = output_layer->cpu_data(); + const float* end = begin + output_layer->channels(); + return std::vector(begin, end); +} + +std::vector< std::vector > Classifier::BatchPredict(const std::vector& imgs) +{ + PrepareBatchInputs(imgs); + + net_->Forward(); + + /* Copy the output layer to a std::vector */ + Blob* output_layer = net_->output_blobs()[0]; + + const float* begin = output_layer->cpu_data(); + std::vector< std::vector > outs(imgs.size()); + int labelnum = output_layer->channels(); + for (size_t i = 0; i < imgs.size();i++) + { + outs[i] = std::vector(begin, begin+ labelnum); + begin += labelnum; + } + + return outs; +} +std::vector Classifier::GetLayerFeatureMaps(const string& strLayerName, std::vector& outshape) +{ + std::vector v; + + const shared_ptr >& blob = net_->blob_by_name(strLayerName); + + if (!blob) + return v; + + const float* begin = blob->cpu_data(); + const float* end = begin + blob->count(); + outshape = blob->shape(); + return std::vector(begin, end); +} +// std::vector Classifier::ExtractFeature(const cv::Mat& img, const string& strLayerName) { +// Blob* input_layer = net_->input_blobs()[0]; +// input_layer->Reshape(1, num_channels_, +// input_geometry_.height, input_geometry_.width); +// /* Forward dimension change to all layers. */ +// net_->Reshape(); +// +// std::vector input_channels; +// WrapInputLayer(&input_channels); +// +// Preprocess(img, &input_channels); +// +// net_->Forward(); +// +// +// return GetLayerFeature(strLayerName); +// } + + +int Classifier::GetFeatureDim() +{ + const vector > >&blobs = net_->blobs(); + for (int i = (int)blobs.size() - 1; i >= 0; i--) + { + if (blobs[i]->channels() > 1000) + { + blobs[i]->channels(); + return blobs[i]->channels(); + } + } + + return -1; +} + + +/* Wrap the input layer of the network in separate cv::Mat objects + * (one per channel). This way we save one memcpy operation and we + * don't need to rely on cudaMemcpy2D. The last preprocessing + * operation will write the separate channels directly to the input + * layer. */ +void Classifier::WrapInputLayer(std::vector* input_channels) { + Blob* input_layer = net_->input_blobs()[0]; + + int width = input_layer->width(); + int height = input_layer->height(); + float* input_data = input_layer->mutable_cpu_data(); + int ch = input_layer->channels(); + for (int k=0;knum();k++) + { + for (int i = 0; i < ch; ++i) { + cv::Mat channel(height, width, CV_32FC1, input_data); + input_channels->push_back(channel); + input_data += width * height; + } + } + +} + +void Classifier::Preprocess(const cv::Mat& img, + std::vector* input_channels, bool resize_img) { + /* Convert the input image to the input image format of the network. */ + cv::Mat sample; + if (img.channels() == 3 && num_channels_ == 1) + cv::cvtColor(img, sample, CV_BGR2GRAY); + else if (img.channels() == 4 && num_channels_ == 1) + cv::cvtColor(img, sample, CV_BGRA2GRAY); + else if (img.channels() == 4 && num_channels_ == 3) + cv::cvtColor(img, sample, CV_BGRA2BGR); + else if (img.channels() == 1 && num_channels_ == 3) + cv::cvtColor(img, sample, CV_GRAY2BGR); + else + sample = img; + + cv::Mat sample_resized; + if (resize_img && (sample.size() != input_geometry_)) + cv::resize(sample, sample_resized, input_geometry_); + else + sample_resized = sample; + + cv::Mat sample_float; + if (num_channels_ == 3) + sample_resized.convertTo(sample_float, CV_32FC3); + else + sample_resized.convertTo(sample_float, CV_32FC1); + + cv::Mat sample_normalized; + + if (!mean_.empty()) + { + cv::subtract(sample_float, mean_, sample_normalized); + } + else + { + cv::Scalar channel_mean; + for (size_t i = 0; i < channel_mean_.size(); i++) + { + channel_mean[i] = channel_mean_[i]; + } + + int imgtype = num_channels_ == 3 ? CV_32FC3 : CV_32FC1; + cv::Mat curmean = cv::Mat(cv::Size(img.cols, img.rows), imgtype, channel_mean); + cv::subtract(sample_float, curmean, sample_normalized); + } + + cv::split(sample_normalized, *input_channels); + + } + +void Classifier::GetInputImageSize(int &w, int &h) +{ + + w = input_geometry_.width; + h = input_geometry_.height; + +} + + + +float Classifier::Pruning(float weight_t, const char* saveas_name) +{ + const vector > >&layers = net_->layers(); +#if 0 + int scale = 1000; + vector hist(scale*2+2,0); + int num1 = 0, num2 = 0; + for (size_t i = 0; i < layers.size();i++) + { + if(layers[i]->blobs().size()==0) + continue; + float* weights = layers[i]->blobs()[0]->mutable_cpu_data(); + int num = layers[i]->blobs()[0]->count(); + for (int j = 0; j < num;j++) + { + if (weights[j] < 0) + num1++; + else if (weights[j] > 0) + num2++; + + int nw = (int)(fabs(weights[j]) * scale+0.5f); + if (nw < 0)nw = 0; + if (nw >= scale) + nw = scale-1; + hist[nw]++; + } + } + for (size_t i = 0; i < hist.size();i++) + { + if(hist[i]) + printf("%d ", hist[i]); + } +#endif + + uint64_t sum = 0, pruned = 0; + for (size_t i = 0; i < layers.size(); i++) + { + if (layers[i]->blobs().size() == 0) + continue; + float* weights = layers[i]->blobs()[0]->mutable_cpu_data(); + int num = layers[i]->blobs()[0]->count(); + + sum += (uint64_t)num; + for (int j = 0; j < num; j++) + { + if (fabs(weights[j])ToProto(&net_param, false); + WriteProtoToBinaryFile(net_param, saveas_name); + } + + return sum ? float(pruned) / sum:0; +} + +//感受野估计，需要事先加载模型 +//img：输入图像 +//layerName:哪一层的感觉野 +//x,y：坐标 +//idxNeuron:神经元索引，-1时会合并所有神经元的感觉野， +cv::Mat Classifier::EstimateReceptiveField(const cv::Mat& img, const string& layerName, int xo, int yo, int idxNeuron, bool islstm, int* width_parts) +{ + //通过对全图像素做修改，看指定层feature map的变化情况，来确定指定层指定神经元的感觉野 + Forward(img, layerName); + const shared_ptr >& blob = net_->blob_by_name(layerName); + const float* begin = blob->cpu_data(); + const float* end = begin + blob->count(); + vector outshape = blob->shape();//BxCxHxW, or WxBxC (lstm) + std::vector origResponse(begin, end); + + int w1 = 0, h1 = 0; + int num_feature_maps=0; + if (!islstm) + { + w1 = outshape[3]; + h1 = outshape[2]; + num_feature_maps = outshape[1]; + } + else + { + w1 = outshape[0]; + h1 = 1; + } + + if (islstm) + yo = 0; + + if (xo < 0 || xo >= w1 || yo < 0 || yo >= h1) + return cv::Mat(); + + if (width_parts) + *width_parts = w1; + + int w = img.cols, h = img.rows; + cv::Mat matRF(cv::Size(w, h), CV_32FC1); + memset(matRF.data, 0, h*matRF.step1()*sizeof(float)); + + int ch = img.channels(); + int ws = img.step1(); + int dim_feature = w1*h1; + if (islstm) + dim_feature = outshape[2]; + + if (h1 > 1)//高度不是1 + { + const int batch_size = std::min(32,w*h); + vector vImages(batch_size); + vector vRects(batch_size); + const int step_x = 2, step_y = 2; + vector vRectModifieds; + int nx = (w + step_x - 1) / step_x + 1, ny = (h + step_y - 1) / step_y + 1; + int num = 0; + for (int i=0;i= batch_size || (i==ny-1&&j==nx-1)) + { + BatchForward(vImages, layerName); + const float* pout = blob->cpu_data(); + outshape = blob->shape(); + int channels = outshape[1]; + int nfeatures = channels * outshape[0]; + w1 = outshape[3]; + h1 = outshape[2]; + + int dim = w1*h1; + int offsetxoyo = w1*yo + xo; + float sumdiff = 0; + const float* pf0 = origResponse.data(); + for (int k=0;k(y, x) = sumdiff; + } + } + } + + num = 0; + } + + } + } + } + else + { + cv::Mat matTemp = img.clone(); + vector vDiffSum(w); + + for (int x = 0; x < w; x++) + { + //modify image + memcpy(matTemp.data, img.data, h*img.step1()); + uchar* pdst = matTemp.data + x*matTemp.channels(); + //const uchar* psrc = img.data + x*matTemp.channels(); + for (int y=0;y= 0 && idxNeuron < num_feature_maps) + { + const float* pf0 = origResponse.data() + idxNeuron*num_feature_maps + xo; + const float* pf1 = blob->cpu_data() + idxNeuron*num_feature_maps + xo; + float diff = fabs(*pf1 - *pf0); + if (diff < 1e-6) + diff = 0; + vDiffSum[x] += diff; + } + else + { + float sum = 0; + for (int j = 0; j < num_feature_maps; j++) + { + const float* pf0 = origResponse.data() + j*dim_feature + xo; + const float* pf1 = blob->cpu_data() + j*dim_feature + xo; + float diff = fabs(*pf1 - *pf0); + if (diff < 1e-6) + diff = 0; + sum += diff; + } + vDiffSum[x] += sum / num_feature_maps; + } + } + else + { + int T = w1, t = xo; + float sum = 0; + const float* pf0 = origResponse.data() + t*dim_feature; + const float* pf1 = blob->cpu_data() + t*dim_feature; + for (int i = 0; i < dim_feature; i++) + { + float diff = fabs(pf1[i] - pf0[i]); + if (diff < 1e-6) + diff = 0; + sum += diff; + } + vDiffSum[x] += sum / dim_feature; + } + } + + for (int x = 0; x < w; x++) + { + for (int y = 0; y < h; y++) + { + matRF.at(y,x) = vDiffSum[x]; + } + } + + } + + return matRF; +} + +void Classifier::GetLayerFeatureMapSize(int w, int h, const std::string& layerName, int& w1, int& h1) +{ + Blob* input_layer = net_->input_blobs()[0]; + if (w!= input_layer->shape(3) || h != input_layer->shape(2)) + { + input_layer->Reshape(input_layer->shape(0), input_layer->shape(1),h,w); + /* Forward dimension change to all layers. */ + net_->Reshape(); + } + + + const shared_ptr >& blob = net_->blob_by_name(layerName); + + if (blob->shape().size() == 4) + { + w1 = blob->shape(3); + h1 = blob->shape(2); + } + else if (blob->shape().size() == 3)//lstm (TxBxC) + { + w1 = blob->shape(0); + h1 = 1; + } +} + +bool Classifier::IsCPUMode() +{ + return (Caffe::mode() == Caffe::CPU); +} + +void Classifier::Forward(const cv::Mat& img, const string& lastLayerName) +{ + vector imgs; + imgs.push_back(img); + BatchForward(imgs, lastLayerName); +} + + + +void Classifier::BatchForward(const vector& imgs, const string& lastLayerName) +{ + if (!net_->has_layer(lastLayerName)) + return; + PrepareBatchInputs(imgs); + net_->ForwardFromTo(0, net_->layer_index_by_name(lastLayerName)); +} + +void Classifier::PrepareInput(const cv::Mat& img) +{ + vector imgs; + imgs.push_back(img); + PrepareBatchInputs(imgs); +} + +void Classifier::PrepareBatchInputs(const vector& imgs) +{ + if (imgs.size() == 0) + return; + Blob* input_layer = net_->input_blobs()[0]; + if ((int)imgs.size() != input_layer->shape(0)//image num + || imgs[0].cols != input_layer->shape(3) //width + || imgs[0].rows != input_layer->shape(2)//height + ) + { + input_layer->Reshape(imgs.size(), num_channels_, + imgs[0].rows, imgs[0].cols); + /* Forward dimension change to all layers. */ + net_->Reshape(); + } + + std::vector input_channels; + WrapInputLayer(&input_channels); + + + for (size_t i = 0; i < imgs.size(); i++) + { + vector vChannels; + Preprocess(imgs[i], &vChannels,false);//减均值图、浮点化、分通道 + for (int j = 0; j < num_channels_; j++) + vChannels[j].copyTo(input_channels[i*num_channels_ + j]);//必须用copyTo，赋值操作是内存交换，赋值不会修改input_layer的内容 + } +} + +std::vector Classifier::GetOutputFeatureMap(const cv::Mat& img, std::vector& outshape) +{ + PrepareInput(img); + + net_->Forward(); + + Blob* output_layer = net_->output_blobs()[0]; + const float* begin = output_layer->cpu_data(); + const float* end = begin + output_layer->count(); + + outshape = output_layer->shape(); + + return std::vector(begin, end); +} + \ No newline at end of file diff --git a/libClassification/classification.hpp b/libClassification/classification.hpp new file mode 100644 index 0000000000000000000000000000000000000000..09e8e364ee9ca4a6799344779503d8db34e89b3f --- /dev/null +++ b/libClassification/classification.hpp @@ -0,0 +1,91 @@ +#ifndef __CLASSIFICATION__ +#define __CLASSIFICATION__ + + + +#include +// #include +// #include +// #include + +#include "ICNNPredict.h" + + +using namespace caffe; // NOLINT(build/namespaces) +using std::string; +using std::ifstream; + + + +class EXPORT Classifier : public ICNNPredict +{ +public: + Classifier(); + + bool Init(const string& model_path, bool gpu_mode = true); + bool Init(const string& trained_file, const string& model_file, + const string&mean_file, const string&label_file, + bool gpu_mode); + void Release() { delete this; } + + bool IsCPUMode(); + + std::vector Classify(const string& file, int N = 5); + std::vector Classify(const unsigned char* pJPGBuffer, int len, int N = 5); + + std::vector Classify(const cv::Mat& img, int N = 5); + std::vector< std::vector > BatchClassify(const std::vector& imgs, int N = 5); + std::vector CropClassify(const cv::Mat& img, int std_size, int crop_num = 1, int N = 5); + + std::vector ClassifyRtnIdx(const cv::Mat& img, int N = 5); + + + //std::vector ExtractFeature(const cv::Mat& img,const string& strLayerName=""); + std::vector GetLayerFeatureMaps(const string& strLayerName, std::vector& outshape); + int GetFeatureDim(); + std::vector GetLabels() { return labels_; } + std::vector< std::vector > GetLastBlockFeature(const cv::Mat& img); + std::vector GetOutputFeatureMap(const cv::Mat& img, std::vector& outshape); + + + void SetMean(const string& mean_file); + + std::vector Predict(const cv::Mat& img); + std::vector > BatchPredict(const std::vector& img); + + void WrapInputLayer(std::vector* input_channels); + + void Preprocess(const cv::Mat& img, + std::vector* input_channels,bool resize_img=true); + + void GetInputImageSize(int &w, int &h); + + //advanced operations + float Pruning(float weight_t, const char* saveas_name = 0); + + //感受野估计 + cv::Mat EstimateReceptiveField(const cv::Mat& img, const string& layerName,int x,int y, int idxNeuron = -1, bool islstm = false, int* width_parts = 0); + void GetLayerFeatureMapSize(int w, int h, const std::string& layerName, int& w1, int& h1); +private: + void Forward(const cv::Mat& img, const string& lastLayerName); + void BatchForward(const vector& imgs, const string& lastLayerName); + void PrepareInput(const cv::Mat& img); + void PrepareBatchInputs(const vector& imgs); +private: + std::shared_ptr > net_; + cv::Size input_geometry_; + int num_channels_; + cv::Mat mean_; + std::vector channel_mean_; + std::vector labels_; + + std::vector< std::set > synsetwords; + + int FindMaxChannelLayer(); + int FindLayerIndex(const string& strLayerName); +}; + + + + +#endif diff --git a/libClassification/classifierCaffe.cpp b/libClassification/classifierCaffe.cpp new file mode 100644 index 0000000000000000000000000000000000000000..6681bf601c145e557aa0ad9112bf536a50f7564a --- /dev/null +++ b/libClassification/classifierCaffe.cpp @@ -0,0 +1,620 @@ +锘#include "classifierCaffe.h" + + +/////////////////////////////// +#include +#include +#include +#include /*鐢ㄥ埌浜唗ime鍑芥暟锛屾墍浠ヨ鏈夎繖涓ご鏂囦欢*/ +#include +#include +#include +#include +#include + +#include +#ifdef USE_OPENCV +#include +#include +#include +#endif // USE_OPENCV +#include +#include +#include +#include +#include +#include + + + +#ifdef USE_OPENCV +using namespace caffe; // NOLINT(build/namespaces) +using std::string; + +extern void initTrainImage(); +extern vector imgNames; +// +//#define showSteps 0 +// +using namespace std; +//char * configFile = "config.txt"; +// +////璇诲彇config鏂囦欢閲岀殑鍐呭-- +//char* trainSetPosPath = (char *)malloc(200 * sizeof(char)); +//char* templateName = (char *)malloc(200 * sizeof(char)); +//int frame_width = 640; +//int frame_height = 480; +//char *model_file = (char *)malloc(200 * sizeof(char)); //model_file = "LightenedCNN_C_deploy.prototxt"; +//char *trained_file = (char *)malloc(200 * sizeof(char)); //trained_file = "LightenedCNN_C.caffemodel"; +//int label_file = 256; +//void readConfig(char* configFile, char* trainSetPosPath){ +// fstream f; +// char cstring[1000]; +// int readS = 0; +// f.open(configFile, fstream::in); +// char param1[200]; strcpy(param1, ""); +// char param2[200]; strcpy(param2, ""); +// char param3[200]; strcpy(param3, ""); +// +// //--璇诲彇绗竴琛岋細-- +// f.getline(cstring, sizeof(cstring)); +// readS = sscanf(cstring, "%s %s %s", param1, param2, param3); +// strcpy(trainSetPosPath, param3); +// +// //--璇诲彇绗2琛岋細-- 瀵规瘮浜鸿劯 +// f.getline(cstring, sizeof(cstring)); +// readS = sscanf(cstring, "%s %s %s", param1, param2, param3); +// strcpy(templateName, param3); +// +// //--璇诲彇绗3琛岋細-- 鐩告満瀹 +// f.getline(cstring, sizeof(cstring)); +// readS = sscanf(cstring, "%s %s %d", param1, param2, &frame_width); +// +// //--璇诲彇绗4琛岋細-- 鐩告満楂 +// f.getline(cstring, sizeof(cstring)); +// readS = sscanf(cstring, "%s %s %d", param1, param2, &frame_height); +// +// //--璇诲彇绗5琛岋細-- 璁粌妯″瀷 +// f.getline(cstring, sizeof(cstring)); +// readS = sscanf(cstring, "%s %s %s", param1, param2, param3); +// strcpy(model_file, param3); +// +// //--璇诲彇绗6琛岋細-- 璁粌鏉冮噸 +// f.getline(cstring, sizeof(cstring)); +// readS = sscanf(cstring, "%s %s %s", param1, param2, param3); +// strcpy(trained_file, param3); +// +// //--璇诲彇绗6琛岋細-- 鐗瑰緛鏁伴噺 +// f.getline(cstring, sizeof(cstring)); +// readS = sscanf(cstring, "%s %s %d", param1, param2, &label_file); +//} +// +////閬嶅巻config.txt閲岀殑鏍圭洰褰曚笅鐨勬墍鏈夌殑鏂囦欢锛屽寘鎷瓙鐩綍銆-- +//// 鍏朵腑瀛愮洰褰曠殑鍚嶅瓧灏辨槸label锛屽瓙鐩綍閲岀殑鏂囦欢涓簂abel瀵逛簬鐨勮缁冩祴璇曟牱鏈--- +//vector imgNames; +//vector imgLists; +//vector imgLabels; +//int labelTemp = 0; +// +//void dfsFolder(string folderPath){ +// _finddata_t FileInfo; +// string strfind = folderPath + "\\*"; +// long long Handle = _findfirst(strfind.c_str(), &FileInfo); +// if (Handle == -1L) +// { +// cerr << "can not match the folder path" << endl; +// exit(-1); +// } +// do{ +// //鍒ゆ柇鏄惁鏈夊瓙鐩綍-- +// if (FileInfo.attrib & _A_SUBDIR) { +// // cout<> str; +// +// string finalList = FileInfo.name; +// imgLists.push_back(finalList); +// +// } +// } while (_findnext(Handle, &FileInfo) == 0); +// _findclose(Handle); +// +//} +// +//void initTrainImage(){ +// readConfig(configFile, trainSetPosPath); +// +// string folderPath = trainSetPosPath; +// // string folderPath = "H:\\char\\poker_rec_char_equalist_test"; +// // string folderPath = "C:\\planeSample\\recSampleData\\rec"; +// // string folderPath = "C:\\Users\\Administrator\\Desktop\\LPR\\hu\\"; +// dfsFolder(folderPath); +// +//} +//////////////////////////////////////////// + + +Classifier::Classifier(const string& model_file, + const string& trained_file, + const string& mean_file, + const int& label_file) { +#ifdef CPU_ONLY + caffe::Caffe::set_mode(caffe::Caffe::CPU); +#else + Caffe::set_mode(Caffe::GPU); +#endif + + /* Load the network. */ + net_.reset(new caffe::Net(model_file, caffe::TEST)); + net_->CopyTrainedLayersFrom(trained_file); + + CHECK_EQ(net_->num_inputs(), 1) << "Network should have exactly one input."; + //CHECK_EQ(net_->num_outputs(), 1) << "Network should have exactly one output."; + + caffe::Blob* input_layer = net_->input_blobs()[0]; + num_channels_ = input_layer->channels(); + CHECK(num_channels_ == 3 || num_channels_ == 1) + << "Input layer should have 1 or 3 channels."; + input_geometry_ = cv::Size(input_layer->width(), input_layer->height()); + + /* Load the binaryproto mean file. */ + if (mean_file != ""){ + SetMean(mean_file); + } + + + + vector label_array; + for (int j = 0; j < label_file; j++) + { + label_array.push_back("0"); + } + + caffe::Blob* output_layer = net_->output_blobs()[0]; + CHECK_EQ(label_array.size(), output_layer->channels()) + << "Number of labels is different from the output layer dimension."; + labels_.push_back(label_array); + +} + +static bool PairCompare(const std::pair& lhs, + const std::pair& rhs) { + return lhs.first > rhs.first; +} + +/* Return the indices of the top N values of vector v. */ +static std::vector Argmax(const std::vector& v, int N) { + std::vector > pairs; + for (size_t i = 0; i < v.size(); ++i) + pairs.push_back(std::make_pair(v[i], static_cast(i))); + std::partial_sort(pairs.begin(), pairs.begin() + N, pairs.end(), PairCompare); + + std::vector result; + for (int i = 0; i < N; ++i) + result.push_back(pairs[i].second); + return result; +} + +/* Return the top N predictions. */ +std::vector Classifier::Classify(const cv::Mat& img) { + auto output = Predict(img); + + vector v1; + for (int i = 0; i < output.size(); i++){ + v1.push_back((float)output[i]); + } + + return v1; +} + +/* Load the mean file in binaryproto format. */ +void Classifier::SetMean(const string& mean_file) { + caffe::BlobProto blob_proto; + caffe::ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto); + + /* Convert from BlobProto to Blob */ + caffe::Blob mean_blob; + mean_blob.FromProto(blob_proto); + CHECK_EQ(mean_blob.channels(), num_channels_) + << "Number of channels of mean file doesn't match input layer."; + + /* The format of the mean file is planar 32-bit float BGR or grayscale. */ + std::vector channels; + float* data = mean_blob.mutable_cpu_data(); + for (int i = 0; i < num_channels_; ++i) { + /* Extract an individual channel. */ + cv::Mat channel(mean_blob.height(), mean_blob.width(), CV_32FC1, data); + channels.push_back(channel); + data += mean_blob.height() * mean_blob.width(); + } + + /* Merge the separate channels into a single image. */ + cv::Mat mean; + cv::merge(channels, mean); + + /* Compute the global mean pixel value and create a mean image + * filled with this value. */ + cv::Scalar channel_mean = cv::mean(mean); + mean_ = cv::Mat(input_geometry_, mean.type(), channel_mean); +} + +std::vector Classifier::Predict(const cv::Mat& img) { + caffe::Blob* input_layer = net_->input_blobs()[0]; + input_layer->Reshape(1, num_channels_, + input_geometry_.height, input_geometry_.width); + /* Forward dimension change to all layers. */ + net_->Reshape(); + + std::vector input_channels; + WrapInputLayer(&input_channels); + + Preprocess(img, &input_channels); + + net_->ForwardPrefilled(); + + /* Copy the output layer to a std::vector */ + caffe::Blob* output_layer1 = net_->output_blobs()[0]; + + + const float* begin1 = output_layer1->cpu_data(); + const float* end1 = begin1 + output_layer1->channels(); + + + std::vector prob1(begin1, end1); + + return prob1; +} + +/* Wrap the input layer of the network in separate cv::Mat objects +* (one per channel). This way we save one memcpy operation and we +* don't need to rely on cudaMemcpy2D. The last preprocessing +* operation will write the separate channels directly to the input +* layer. */ +void Classifier::WrapInputLayer(std::vector* input_channels) { + caffe::Blob* input_layer = net_->input_blobs()[0]; + + int width = input_layer->width(); + int height = input_layer->height(); + float* input_data = input_layer->mutable_cpu_data(); + for (int i = 0; i < input_layer->channels(); ++i) { + cv::Mat channel(height, width, CV_32FC1, input_data); + input_channels->push_back(channel); + input_data += width * height; + } +} + +void Classifier::Preprocess(const cv::Mat& img, + std::vector* input_channels) { + /* Convert the input image to the input image format of the network. */ + cv::Mat sample; + if (img.channels() == 3 && num_channels_ == 1) + cv::cvtColor(img, sample, cv::COLOR_BGR2GRAY); + else if (img.channels() == 4 && num_channels_ == 1) + cv::cvtColor(img, sample, cv::COLOR_BGRA2GRAY); + else if (img.channels() == 4 && num_channels_ == 3) + cv::cvtColor(img, sample, cv::COLOR_BGRA2BGR); + else if (img.channels() == 1 && num_channels_ == 3) + cv::cvtColor(img, sample, cv::COLOR_GRAY2BGR); + else + sample = img; + + cv::Mat sample_resized; + if (sample.size() != input_geometry_) + cv::resize(sample, sample_resized, input_geometry_); + else + sample_resized = sample; + + cv::Mat sample_float; + if (num_channels_ == 3) + sample_resized.convertTo(sample_float, CV_32FC3); + else + sample_resized.convertTo(sample_float, CV_32FC1); + + + cv::Mat sample_normalized; + if (mean_.data != NULL) + cv::subtract(sample_float, mean_, sample_normalized); + else { + sample_normalized = sample_float; + + int channels = sample_normalized.channels(); + int nRows = sample_normalized.rows; + int nCols = sample_normalized.cols* channels; + if (sample_normalized.isContinuous()) + { + nCols *= nRows; + nRows = 1; + } + int i, j; + float* p; + for (i = 0; i < nRows; ++i) + { + p = sample_normalized.ptr(i); + for (j = 0; j < nCols; ++j) + { + //if (label_file == 256) + p[j] *= 0.00390625; + //else if (label_file == 512){ + // p[j] -= 128; + // p[j] *= 0.0078125; + //} + // cout << p[j] << " "; + } + } + } + + /* This operation will write the separate BGR planes directly to the + * input layer of the network because it is wrapped by the cv::Mat + * objects in input_channels. */ + cv::split(sample_normalized, *input_channels); + + CHECK(reinterpret_cast(input_channels->at(0).data) + == net_->input_blobs()[0]->cpu_data()) + << "Input channels are not wrapping the input layer of the network."; +} + + +double dotProduct(const vector& v1, const vector& v2) +{ + assert(v1.size() == v2.size()); + float ret = 0.0; + for (vector::size_type i = 0; i != v1.size(); ++i) + { + ret += v1[i] * v2[i]; + } + return ret; +} + +double module(const vector& v) +{ + float ret = 0.0; + for (vector::size_type i = 0; i != v.size(); ++i) + { + ret += v[i] * v[i]; + } + return sqrt(ret); +} + +// 澶硅浣欏鸡 +double cosine(const vector& v1, const vector& v2) +{ + assert(v1.size() == v2.size()); + return dotProduct(v1, v2) / (module(v1) * module(v2)); +} + + +//float CalcSimilarity(float const fc1, +// float const fc2, +// long dim) { +// if (dim == -1) { +// dim = 2048; +// } +// return simd_dot(fc1, fc2, dim)/ ( +// sqrt(simd_dot(fc1, fc1, dim))* +// sqrt(simd_dot(fc2, fc2, dim)) +// ); +//} +// +// +//float simd_dot(const float* x, const float* y, const long& len) { +// float inner_prod = 0.0f; +// __m128 X, Y; // 128-bit values +// __m128 acc = _mm_setzero_ps(); // set to (0, 0, 0, 0) +// float temp[4]; +// +// long i; +// for (i = 0; i + 4 < len; i += 4) { +// X = _mm_loadu_ps(x + i); // load chunk of 4 floats +// Y = _mm_loadu_ps(y + i); +// acc = _mm_add_ps(acc, _mm_mul_ps(X, Y)); +// } +// _mm_storeu_ps(&temp[0], acc); // store acc into an array +// inner_prod = temp[0] + temp[1] + temp[2] + temp[3]; +// +// // add the remaining values +// for (; i < len; ++i) { +// inner_prod += x[i] * y[i]; +// } +// return inner_prod; +//} + +int main0(int argc, char** argv) { + ::google::InitGoogleLogging(argv[0]); + initTrainImage(); + + string model_file = "face_deploy_mirror_normalize.prototxt"; + string trained_file = "face_train_test_iter_530000.caffemodel"; //7锛6 + string mean_file = ""; + int label_file = 512; + //string model_file = "LightenedCNN_C_deploy.prototxt"; + //string trained_file = "LightenedCNN_C.caffemodel"; //7锛6 + //string mean_file = ""; + //int label_file = 256; + + std::cout << "the labels' channel:" << label_file << std::endl; + Classifier classifier(model_file, trained_file, mean_file, label_file); + + string file = ".\\face_align\\1.jpg"; + cv::Mat img = cv::imread(file, 1); + CHECK(!img.empty()) << "Unable to decode image " << file; + std::cout << "---------- Prediction for "<< file << " ----------" << std::endl; + vector predictions = classifier.Classify(img); + + int imgNum = imgNames.size(); + for (int iNum = 0; iNum < imgNum; iNum++){ + + cout << endl << iNum << " " << imgNames[iNum].c_str() << endl; + //string file2 = "F:\\MTCNN-master\\vs2013_caffe_BN_multi_label\\water_meter_caffe_old\\face\\3.jpg"; + cv::Mat img2 = cv::imread(imgNames[iNum].c_str(), 1); + //CHECK(!img2.empty()) << "Unable to decode image " << imgNames[iNum].c_str(); + + if (img2.empty()) continue; + //cv::normalize(img2,img2,255,0,CV_MINMAX); + + + vector predictions2 = classifier.Classify(img2); + + /* Print the top N predictions. */ + for (size_t i = 0; i < predictions.size(); ++i) { + std::cout << predictions[i] << " "; + } + std::cout << std::endl << std::endl; + + for (size_t i = 0; i < predictions2.size(); ++i) { + std::cout << predictions2[i] << " "; + } + + double sim2 = cosine(predictions, predictions2); + cout << "鐩镐技搴︿负2 :" << sim2 << endl; + } + return 1; +} + + +Classifier * init() { + + initTrainImage(); + + string model_file = "face_deploy_mirror_normalize.prototxt"; + string trained_file = "face_train_test_iter_530000.caffemodel"; //7锛6 + string mean_file = ""; + int label_file = 512; + //string model_file = "LightenedCNN_C_deploy.prototxt"; + //string trained_file = "LightenedCNN_C.caffemodel"; //7锛6 + //string mean_file = ""; + //int label_file = 256; + + std::cout << "the labels' channel:" << label_file << std::endl; + //Classifier classifier(model_file, trained_file, mean_file, label_file); + Classifier *classifier; + classifier = new Classifier(model_file, trained_file, mean_file, label_file); + + return classifier; +} + + +void test_(Classifier *classify) { + Classifier *classifier = classify; + + string file = ".\\face_align\\1.jpg"; + cv::Mat img = cv::imread(file, 1); + CHECK(!img.empty()) << "Unable to decode image " << file; + //std::cout << "---------- Prediction for " << file << " ----------" << std::endl; + vector predictions = classifier->Classify(img); + + int imgNum = imgNames.size(); + for (int iNum = 0; iNum < imgNum; iNum++) { + + //cout << endl << iNum << " " << imgNames[iNum].c_str() << endl; + //string file2 = "F:\\MTCNN-master\\vs2013_caffe_BN_multi_label\\water_meter_caffe_old\\face\\3.jpg"; + cv::Mat img2 = cv::imread(imgNames[iNum].c_str(), 1); + //CHECK(!img2.empty()) << "Unable to decode image " << imgNames[iNum].c_str(); + + if (img2.empty()) continue; + //cv::normalize(img2,img2,255,0,CV_MINMAX); + + /*while (1) */{ + vector predictions2 = classifier->Classify(img2); + + /* Print the top N predictions. */ + /*for (size_t i = 0; i < predictions.size(); ++i) { + std::cout << predictions[i] << " "; + } + std::cout << std::endl << std::endl; + + for (size_t i = 0; i < predictions2.size(); ++i) { + std::cout << predictions2[i] << " "; + }*/ + + double sim2 = cosine(predictions, predictions2); + //cout << "鐩镐技搴︿负2 :" << sim2 << endl; + } + } +} + +int main00(int argc, char **argv) { + ::google::InitGoogleLogging(argv[0]); + Classifier *classifier = init(); + Classifier *classifier2 = init(); + //init_rec(); + //init_detect(); + + for (int i = 0; i < 10; i++) + { + cout << i << endl; + std::thread first(test_, classifier); + std::thread second(test_, classifier2); + //std::thread third(test_); + + + first.join(); + second.join(); + //third.join(); + } + + return 1; +} + +int main000(int argc, char **argv) { + Phase phase = TEST; + int gpuid = 0; + +#ifdef CPU_ONLY + Caffe::set_mode(Caffe::CPU); +#else + Caffe::set_mode(Caffe::GPU); + Caffe::SetDevice(gpuid); +#endif + + boost::shared_ptr >feature_net; + boost::shared_ptr >feature_net1; + + //boost::thread_specific_ptr >feature_net; + + string protopath = "face_deploy_mirror_normalize.prototxt"; + string modelpath = "face_train_test_iter_530000.caffemodel"; //7锛6 + + feature_net.reset(new Net(protopath, phase)); + feature_net->CopyTrainedLayersFrom(modelpath); + + feature_net1.reset(new Net(protopath, phase)); + feature_net1->CopyTrainedLayersFrom(modelpath); + + const int num = 2; + /*std::thread * thrcall = new thread[num]; + thrcall[0].set_param(0, feature_net); + thrcall[0].start(); + + thrcall[1].set_param(1, feature_net1); + thrcall[1].start(); + + thrcall[0].join(); + thrcall[1].join();*/ + + return 1; +} + +#endif // USE_OPENCV \ No newline at end of file diff --git a/libClassification/classifierCaffe.h b/libClassification/classifierCaffe.h new file mode 100644 index 0000000000000000000000000000000000000000..3a5c3f4667ce053fca21454daa57cff368717702 --- /dev/null +++ b/libClassification/classifierCaffe.h @@ -0,0 +1,62 @@ +#ifndef _CLASSIFIERCAFFE_H_ +#define _CLASSIFIERCAFFE_H_ + + + +#include + +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include + +#include +#include "boost/make_shared.hpp" + +//using namespace caffe; // NOLINT(build/namespaces) +using std::string; +using namespace std; +double cosine(const vector& v1, const vector& v2); + +/* Pair (label, confidence) representing a prediction. */ +// change: 2 classify — (label1, confidence1) (label2, confidence2) +typedef std::pair Prediction; +class _declspec(dllexport) Classifier { +public: + Classifier(const string& model_file, + const string& trained_file, + const string& mean_file, + const int& label_files); + + std::vector Classifier::Classify(const cv::Mat& img); + + cv::Size input_geometry_; +private: + void SetMean(const string& mean_file); + + std::vector Predict(const cv::Mat& img); + + void WrapInputLayer(std::vector* input_channels); + + void Preprocess(const cv::Mat& img, + std::vector* input_channels); + +private: + std::shared_ptr > net_; + + int num_channels_; + cv::Mat mean_; + std::vector> labels_; //multi +}; + + + +#endif // _CLASSIFIERCAFFE_H_ \ No newline at end of file diff --git a/libClassification/config.txt b/libClassification/config.txt new file mode 100644 index 0000000000000000000000000000000000000000..1787660ce2a1f2c4a9e5b65e7a76b3d4c10173cf --- /dev/null +++ b/libClassification/config.txt @@ -0,0 +1,2 @@ +path = testImg +startNum = 0 diff --git a/libClassification/debugTime.h b/libClassification/debugTime.h new file mode 100644 index 0000000000000000000000000000000000000000..85f5666b16c64a83e30e93375a2474ab3a2b6f7f --- /dev/null +++ b/libClassification/debugTime.h @@ -0,0 +1,42 @@ +#include +#include +#include +#include +LARGE_INTEGER m_liPerfFreq; +LARGE_INTEGER m_liPerfStart; +LARGE_INTEGER liPerfNow; +double dfTim; +void getStartTime() +{ + QueryPerformanceFrequency(&m_liPerfFreq); + QueryPerformanceCounter(&m_liPerfStart); +} + +void getEndTime() +{ + QueryPerformanceCounter(&liPerfNow); + dfTim=( ((liPerfNow.QuadPart - m_liPerfStart.QuadPart) * 1000.0f)/m_liPerfFreq.QuadPart); +} + +void test() +{ + static long num_steps = 1000000; + double step, pi; + double x, sum=0.0; + int i=0; + step = 1.0/(double)num_steps; + getStartTime(); + +//#pragma omp parallel for private(x) reduction(+:sum) + for (i=0; i + + + + Debug + Win32 + + + Debug + x64 + + + Release + Win32 + + + Release + x64 + + + + {55468200-4311-4568-BF06-E417E19025B2} + Win32Proj + libClassification + 8.1 + + + + DynamicLibrary + true + 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Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\include\thrust/system/detail/adl/equal.h +C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\include\thrust/system/detail/sequential/equal.h +C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\include\thrust/system/cpp/detail/equal.h +C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\include\thrust/system/cuda/detail/equal.h +C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\include\thrust/detail/device_vector.inl +C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\include\thrust/host_vector.h +C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\include\thrust/detail/host_vector.inl +C:\Program Files (x86)\Windows Kits\10\Include\10.0.10240.0\ucrt\assert.h +C:\Program Files (x86)\Windows Kits\10\Include\10.0.10240.0\ucrt\assert.h +C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\include\algorithm +d:\ocr\plate_card_blstm\caffe_ocr-master_cudn5.1_cuda8.0_vs2014\3rdparty\include\gflags\gflags_declare.h +d:\ocr\plate_card_blstm\caffe_ocr-master_cudn5.1_cuda8.0_vs2014\3rdparty\include\gflags\gflags_gflags.h +C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\include\sstream +d:\ocr\plate_card_blstm\caffe_ocr-master_cudn5.1_cuda8.0_vs2014\3rdparty\include\glog\glog/log_severity.h +C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\include\fstream +C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\include\map +C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\include\xtree +C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\include\set +C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\include\cublas_v2.h +c:\program files\nvidia gpu computing toolkit\cuda\v8.0\include\cublas_api.h +c:\program files\nvidia gpu computing toolkit\cuda\v8.0\include\driver_types.h +c:\program files\nvidia gpu computing toolkit\cuda\v8.0\include\cuComplex.h +C:\Program Files (x86)\Windows Kits\10\Include\10.0.10240.0\ucrt\math.h +c:\program files\nvidia gpu computing toolkit\cuda\v8.0\include\vector_types.h +c:\program files\nvidia gpu computing toolkit\cuda\v8.0\include\cuda_fp16.h +c:\program files\nvidia gpu computing toolkit\cuda\v8.0\include\library_types.h +C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\include\cuda_runtime.h +C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\include\curand.h +C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\include\cuda_runtime.h +C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\include\driver_types.h +C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\include\driver_types.h +C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\include\cuda_runtime.h +C:\Program Files (x86)\Windows Kits\10\Include\10.0.10240.0\ucrt\assert.h +C:\Program Files (x86)\Windows Kits\10\Include\10.0.10240.0\ucrt\assert.h +C:\Program Files (x86)\Windows Kits\10\Include\10.0.10240.0\ucrt\assert.h +d:\ocr\plate_card_blstm\caffe_ocr-master_cudn5.1_cuda8.0_vs2014\3rdparty\include\openblas\openblas_config.h +C:\Program Files (x86)\Windows Kits\10\Include\10.0.10240.0\ucrt\math.h diff --git a/libClassification/stdafx.cpp b/libClassification/stdafx.cpp new file mode 100644 index 0000000000000000000000000000000000000000..81f6269a2b62f62a84831acdfff32aaba4dae045 --- /dev/null +++ b/libClassification/stdafx.cpp @@ -0,0 +1,8 @@ +// stdafx.cpp : source file that includes just the standard includes +// libClassification.pch will be the pre-compiled header +// stdafx.obj will contain the pre-compiled type information + +#include "stdafx.h" + +// TODO: reference any additional headers you need in STDAFX.H +// and not in this file diff --git a/libClassification/stdafx.h b/libClassification/stdafx.h new file mode 100644 index 0000000000000000000000000000000000000000..f3a07375c7662343c2e2ae68dbed61ed9363d902 --- /dev/null +++ b/libClassification/stdafx.h @@ -0,0 +1,16 @@ +// stdafx.h : include file for standard system include files, +// or project specific include files that are used frequently, but +// are changed infrequently +// + +#pragma once + +#include "targetver.h" + +#define WIN32_LEAN_AND_MEAN // Exclude rarely-used stuff from Windows headers +// Windows Header Files: +#include + + + +// TODO: reference additional headers your program requires here diff --git a/libClassification/targetver.h b/libClassification/targetver.h new file mode 100644 index 0000000000000000000000000000000000000000..87c0086de751bac3d47208b77d76b82d78f26702 --- /dev/null +++ b/libClassification/targetver.h @@ -0,0 +1,8 @@ +#pragma once + +// Including SDKDDKVer.h defines the highest available Windows platform. + +// If you wish to build your application for a previous Windows platform, include WinSDKVer.h and +// set the _WIN32_WINNT macro to the platform you wish to support before including SDKDDKVer.h. + +#include diff --git a/libClassification/tform_fcn.cpp b/libClassification/tform_fcn.cpp new file mode 100644 index 0000000000000000000000000000000000000000..128adf09ed85bc20ea8614fc5f1748f0256aed4e --- /dev/null +++ b/libClassification/tform_fcn.cpp @@ -0,0 +1,395 @@ +#include "tform_fcn.h" +cv::Mat min(cv::Mat minrow){ + cv::Mat ret(1, minrow.cols, CV_32FC1); + for (int col = 0; col < minrow.cols; col++){ + float minnum = minrow.at(0, col); + for (int row = 0; row < minrow.rows; row++){ + if (minrow.at(row, col) < minnum) + minnum = minrow.at(row, col); + } + ret.at(0, col) = minnum; + } + return ret; +} + +cv::Mat max(cv::Mat maxrow){ + cv::Mat ret(1, maxrow.cols, CV_32FC1); + for (int col = 0; col < maxrow.cols; col++){ + float maxnum = maxrow.at(0, col); + for (int row = 0; row < maxrow.rows; row++){ + if (maxrow.at(row, col) > maxnum) + maxnum = maxrow.at(row, col); + } + ret.at(0, col) = maxnum; + } + return ret; +} + +cv::Mat getshift(cv::Mat mat){//二 + float tol = 1000.0f; + cv::Mat minPoints = min(mat); + cv::Mat maxPoints = max(mat); + cv::Mat center = (minPoints + maxPoints) / 2; + cv::Mat span = maxPoints - minPoints; + if ((span.at(0, 0) > 0 && abs(center.at(0, 0)) / span.at(0, 0) > tol) || + (span.at(0, 1) > 0 && abs(center.at(0, 1)) / span.at(0, 1) > tol)){ + return center; + }else{ + center.at(0, 0) = 0; + center.at(0, 1) = 0; + return center; + } +} +int rank(cv::Mat src){//求秩 + //cv::Mat S = cv::Mat(src.rows, 1, CV_32FC1); + cv::SVD thissvd(src, cv::SVD::FULL_UV);//矩阵奇异分解 + cv::Mat S = thissvd.w; + int sum = 0; + for (int row= 0; row < S.rows; row++){ + if (S.at(row, 0)>0.00001) + sum++; + } + return sum; +} + + +Tfm findNonreflectiveSimilarity(cv::Mat src, cv::Mat dst, options opt){//四 + int k = opt.K; + cv::Mat X(dst.rows * 2, 4, CV_32FC1); + for (int col = 0; col < X.cols; col++){ + for (int row = 0; row < X.rows;row++){ + if (col == 0){ + X.at(row, col) = dst.at(row%dst.rows, row / dst.rows); + } + else if (col == 1){ + X.at(row, col) = (-2 *( row / dst.rows)+1)*dst.at(row%dst.rows, 1 - row / dst.rows); + } + else if (col == 2){ + if (row < X.rows / 2) + X.at(row, col) = 1.0f; + else + X.at(row, col) = 0; + } + else if (col == 3){ + if (row < X.rows / 2) + X.at(row, col) = 0; + else + X.at(row, col) = 1.0f; + } + } + } + cv::Mat U(src.rows * 2, 1, CV_32FC1); + for (int col = 0; col < U.cols; col++){ + for (int row = 0; row < U.rows; row++){ + U.at(row, col) = src.at(row%src.rows, row / src.rows); + } + } + if (rank(X) >= k * 2){ + cv::MatExpr x = X.inv(cv::DECOMP_SVD); + cv::Mat xx = x.operator cv::Mat(); + cv::Mat r(xx.rows,U.cols,CV_32FC1);//r = X \U ==》 xx*U + for (int row = 0; row < r.rows; row++){ + r.at(row, 0) = 0; + for (int num = 0; num < U.rows; num++){ + r.at(row, 0) += xx.at(row,num) * U.at(num,0); + } + } + float sc = r.at(0, 0); + float ss = r.at(1, 0); + float tx = r.at(2, 0); + float ty = r.at(3, 0); + cv::Mat Tinv(3, 3, CV_32FC1); + Tinv.at(0, 0) = sc; Tinv.at(0, 1) = -ss; Tinv.at(0, 2) = 0; + Tinv.at(1, 0) = ss; Tinv.at(1, 1) = sc; Tinv.at(1, 2) = 0; + Tinv.at(2, 0) = tx; Tinv.at(2, 1) = ty; Tinv.at(2, 2) = 1; + cv::Mat T = Tinv.inv(cv::DECOMP_SVD).operator cv::Mat(); + T.at(0, 2) = 0; T.at(1, 2) = 0; T.at(2, 2) = 1; + Tfm retfm; + retfm.forword = T; + retfm.inv = Tinv; + return retfm; + } + else { + throw matlabexception(); + } +} + +Tfm affine(cv::Mat multrTY){ + Tfm ret; + ret.forword = multrTY; + ret.inv = multrTY.inv().operator cv::Mat(); + ret.inv.at(0, 2) = 0; ret.inv.at(1, 2) = 0; ret.inv.at(2, 2) = 1; + return ret; +} + +cv::Mat tformfwd(Tfm trans1, cv::Mat U){ + cv::Mat M = trans1.forword; + cv::Mat X1(U.rows, U.cols + 1, CV_32FC1); + for (int col = 0; col < X1.cols;col++) + for (int row = 0; row < X1.rows; row++){ + if (col == 2) + X1.at(row, col) = 1; + else + X1.at(row, col) = U.at(row, col); + } + cv::Mat U1(X1.rows,M.cols,CV_32FC1);//X1 * M + for (int col = 0; col < U1.cols; col++){ + for (int row = 0; row < U1.rows; row++){ + U1.at(row, col) = 0; + for (int num = 0; num < X1.cols; num++){ + U1.at(row, col) += X1.at(row,num)*M.at(num,col); + } + } + } + U1.adjustROI(0,0,0,-1); + return U1; +} + +Tfm findT_fcn(cv::Mat src, cv::Mat dst, options opt){//三 + opt.K = 2; + Tfm train1 = findNonreflectiveSimilarity(src, dst, opt); + cv::Mat dst2; + dst.convertTo(dst2,CV_32FC1); + for (int row = 0; row < dst2.rows; row++){ + dst2.at(row, 0) = -1 * dst2.at(row, 0); + //std::cout << "dst2: " << dst2.at(row, 0) << std::endl; + } + Tfm train2r = findNonreflectiveSimilarity(src, dst2, opt); + cv::Mat TreflectY(3,3,CV_32FC1); + TreflectY.at(0, 0) = -1; TreflectY.at(0, 1) = 0; TreflectY.at(0,2) = 0; + TreflectY.at(1, 0) = 0; TreflectY.at(1, 1) = 1; TreflectY.at(1, 2) = 0; + TreflectY.at(2, 0) = 0; TreflectY.at(2, 1) = 0; TreflectY.at(2, 2) = 1; + cv::Mat multrTY(train2r.forword.rows, TreflectY.cols, CV_32FC1);//trani2r.forword * TreflectY + for (int col = 0; col < multrTY.cols; col++){ + for (int row = 0; row < multrTY.rows; row++){ + multrTY.at(row, col) = 0; + for (int tr_col = 0; tr_col < train2r.forword.cols; tr_col++){ + multrTY.at(row, col) += train2r.forword.at(row, tr_col) * TreflectY.at( tr_col,col); + } + //std::cout << multrTY.at(row, col) << std::endl; + } + } + Tfm train2 = affine(multrTY); + cv::Mat xy1 = tformfwd(train1, src); + cv::Mat sub1; + cv::subtract(xy1,dst,sub1);//sub1 = xy1 -dst + double norm1 = cv::norm(sub1, cv::NORM_L2); + /*std::cout << sub1.at(0, 0) << " " << sub1.at(0, 1) << std::endl; + std::cout << sub1.at(1, 0) << " " << sub1.at(1, 1) << std::endl; + std::cout << sub1.at(2, 0) << " " << sub1.at(2, 1) << std::endl; + std::cout << sub1.at(3, 0) << " " << sub1.at(3, 1) << std::endl; + std::cout << sub1.at(4, 0) << " " << sub1.at(4, 1) << std::endl;*/ + + cv::Mat xy2 = tformfwd(train2, src); + cv::Mat sub2; + cv::subtract(xy2, dst, sub2);//sub2 = xy2 -dst + double norm2 = cv::norm(sub2, cv::NORM_L2); + /*std::cout << sub2.at(0, 0) << " " << sub2.at(0, 1) << std::endl; + std::cout << sub2.at(1, 0) << " " << sub2.at(1, 1) << std::endl; + std::cout << sub2.at(2, 0) << " " << sub2.at(2, 1) << std::endl; + std::cout << sub2.at(3, 0) << " " << sub2.at(3, 1) << std::endl; + std::cout << sub2.at(4, 0) << " " << sub2.at(4, 1) << std::endl;*/ + if (norm1 <= norm2) + return train1; + else + return train2; +} + +Tfm cp2tform_similarity(cv::Mat src, cv::Mat dst){ // 一 + Tfm ret; + options opt; + opt.order = 3; opt.K = 3; + int M = src.rows; + if (M < opt.K) + throw matlabexception(); + cv::Mat srcshift = getshift(src); + cv::Mat dstshift = getshift(dst); + int needToShift = 0; + for (int row = 0; row < srcshift.rows; row++){ + for (int col = 0; col < srcshift.cols; col++){ + if (srcshift.at(row, col) < -0.00001 || srcshift.at(row, col) > 0.00001) + needToShift = 1; + } + } + for (int row = 0; row < dstshift.rows; row++){ + for (int col = 0; col < dstshift.cols; col++){ + if (dstshift.at(row, col) < -0.00001 || dstshift.at(row, col) > 0.00001) + needToShift = 1; + } + } + if (!needToShift) + ret = findT_fcn(src, dst, opt); + else + throw matlabexception(); + return ret; +} + + +cv::Mat tform_inv(cv::Mat G, /*Tfm reg_b,*/ Tfm tm/*, Tfm reg_a*/){ + cv::Mat inv = tm.inv; + cv::Mat G1(G.rows, G.cols, CV_32FC2); //G1 = G*inv + + for (int n = 0; n < 2;n++) + for (int col = 0; col < G.cols; col++){ + for (int row = 0; row < G.rows; row++){ + G1.at(row, col)[n] = 0; + for (int num = 0; num < inv.rows; num++){ + + if (num < 2) + G1.at(row, col)[n] += G.at(row, col)[num] * inv.at(num, n);//x*costh+y*sinth x*sinth+y*costh + else + G1.at(row, col)[n] += inv.at(num, n);//+inv * 1 + } + + } + } + return G1; +} +cv::Mat itransform(cv::Mat img, Tfm tm, int rows, int cols){ + /*Tfm reg_b; + reg_b.forword = cv::Mat(1, 2, CV_32FC1); + reg_b.forword.at(0, 0) = 1; reg_b.forword.at(0, 1) = 1; + reg_b.inv = cv::Mat(1, 2, CV_32FC1); + reg_b.inv.at(0, 0) =0; reg_b.inv.at(0, 1) = 0; + + Tfm reg_a; + reg_a.forword = cv::Mat(1, 2, CV_32FC1); + reg_a.forword.at(0, 0) = 1; reg_a.forword.at(0, 1) = 1; + reg_a.inv = cv::Mat(1, 2, CV_32FC1); + reg_a.inv.at(0, 0) = 0; reg_a.inv.at(0, 1) = 0;*/ + + cv::Mat G(cols, rows, CV_32FC2); + + for (int col = 0; col < G.cols; col++){ + for (int row = 0; row < G.rows; row++){ + G.at(row, col)[0] = row/*+1*/;//存坐标 + G.at(row, col)[1] = col /*+ 1*/; + } + } + + cv::Mat M = tform_inv(G,/* reg_b, */tm/*, reg_a*/); + + cv::Mat MT = M.t().operator cv::Mat();//MT 112*96*2; + /*for (int n = 0; n < 2;n++) + for (int row = 0; row < M.rows; row++){ + for (int col = 0; col < M.cols; col++){ + std::cout << M.at(row, col)[n] << " " << MT.at(col, row)[n] << std::endl; + } + }*/ + cv::Mat dst(rows, cols, img.type()); + + for (int row = 0; row < MT.rows; row++){ + for (int col = 0; col < MT.cols; col++){ + int x1 = (int)MT.at(row, col)[0]; + if (x1 < 0) x1 = 0; + else if (x1 >= img.cols - 1) x1 = img.cols - 2; + int x2 = x1 + 1; + int y1 = (int)MT.at(row, col)[1]; + if (y1 < 0) y1 = 0; + else if (y1 >= img.rows - 1) y1 = img.rows - 2; + int y2 = y1 + 1; + + float x1_f = x2 - MT.at(row, col)[0]; + float x2_f =1- x1_f; + float y1_f = y2 - MT.at(row, col)[1]; + float y2_f = 1 - y1_f; + for (int n = 0; n < dst.channels(); n++){ + dst.at(row, col)[n] = int(img.at(y1, x1)[n] * y1_f*x1_f + img.at(y2, x2)[n] * y2_f*x2_f + img.at(y2, x1)[n] * y2_f*x1_f + img.at(y1, x2)[n] * y1_f*x2_f); + } + } + } + return dst; +} + + +//旋转图像内容不变，尺寸相应变大loose +cv::Mat rotateImage1(cv::Mat img, double degree) +{ + double angle = degree * CV_PI / 180.; // 弧度 + double a = sin(angle), b = cos(angle); + int width = img.cols; + int height = img.rows; + int width_rotate = int(height * fabs(a) + width * fabs(b)); + int height_rotate = int(width * fabs(a) + height * fabs(b)); + + // 旋转中心 + cv::Point center = cv::Point(width / 2, height / 2); + cv::Mat map_Matrix = cv::getRotationMatrix2D(center, degree, 1.0); + + map_Matrix.at(0, 2) += (width_rotate - width) / 2; + map_Matrix.at(1, 2) += (height_rotate - height) / 2; + + cv::Mat img_rotate; + // INTER_NEARST,INTER_LINEAR ,CV_INTER_CUBIC + cv::warpAffine(img, img_rotate, map_Matrix, { width_rotate, height_rotate }, cv::INTER_CUBIC | CV_WARP_FILL_OUTLIERS, cv::BORDER_CONSTANT, cv::Scalar()); + return img_rotate; +} +//face h:w == 1:1 +cv::Mat onetone(cv::Mat img, cv::Mat point, int size, int emh, int eh){ + if (img.empty() || point.empty()) throw(matlabexception()); + cv::Mat F3; + img.convertTo(F3, CV_32FC3); + double ang_tan = 1.0*(point.at(0, 1) - point.at(1, 1)) / (point.at(0, 0) - point.at(1, 0));//dy/dx + float ang = atan(ang_tan) / CV_PI * 180;//角度 + cv::Mat img_rot = rotateImage1(F3, ang);//旋转 + //int imgh = img.rows; + //int imgw = img.cols; + //eye_ceneter + double eye_center_x = 1.0*(point.at(0, 0) + point.at(1, 0)) / 2; + double eye_center_y = 1.0*(point.at(0, 1) + point.at(1, 1)) / 2; + ang = -ang / 180 * CV_PI;//弧度 + + double x0 = eye_center_x - 1.0*img.cols / 2; + double y0 = eye_center_y - 1.0*img.rows / 2; + + int eye_center_xx = round(x0*cos(ang) - y0*sin(ang) + 1.0*img_rot.cols / 2); + int eye_center_yy = round(x0*sin(ang) + y0*cos(ang) + 1.0*img_rot.rows / 2);//eye center + + double mth_center_x = 1.0*(point.at(3, 0) + point.at(4, 0)) / 2; + double mth_center_y = 1.0*(point.at(3, 1) + point.at(4, 1)) / 2; + + double x00 = mth_center_x - 1.0*img.cols / 2; + double y00 = mth_center_y - 1.0*img.rows / 2; + + int mth_center_xx = round(x00*cos(ang) - y00*sin(ang) + 1.0*img_rot.cols / 2); + int mth_center_yy = round(x00*sin(ang) + y00*cos(ang) + 1.0*img_rot.rows / 2);//mouth center + + double re_scal = 1.0* emh / (mth_center_yy - eye_center_yy); + int imgh = img_rot.rows; + int imgw = img_rot.cols; + cv::Mat img_rot_resize; + cv::resize(img_rot, img_rot_resize, cv::Size(img_rot.cols*re_scal, img_rot.rows*re_scal)); + + + int eye_new_x = round((eye_center_xx - 1.0*imgw / 2)*re_scal + 1.0*img_rot_resize.cols / 2); + int eye_new_y = round((eye_center_yy - 1.0*imgh / 2)*re_scal + 1.0*img_rot_resize.rows / 2); + + cv::Mat img_crop(cv::Size(size, size), img_rot_resize.type()); + + int crop_y = eye_new_y - eh; + int crop_y_end = crop_y + size - 1; + int cropy_x = eye_new_x - floor(1.0*size / 2); + int cropy_x_end = cropy_x + size - 1; + + int box_x = cropy_x, box_x_end = cropy_x_end, box_y = crop_y, box_y_end = crop_y_end; + if (cropy_x < 1) box_x = 1; + if (cropy_x > img_rot_resize.cols) box_x = img_rot_resize.cols; + if (cropy_x_end < 1) box_x_end = 1; + if (cropy_x_end > img_rot_resize.cols) box_x_end = img_rot_resize.cols; + + if (crop_y < 1) box_y = 1; + if (crop_y > img_rot_resize.rows) box_y = img_rot_resize.rows; + if (crop_y_end < 1) box_y_end = 1; + if (crop_y_end > img_rot_resize.rows) box_y_end = img_rot_resize.rows; + + for (int h = box_y - crop_y; h < box_y_end - crop_y; h++){ + for (int w = box_x - cropy_x; w < box_x_end - cropy_x; w++){ + img_crop.at(h, w)[0] = img_rot_resize.at(h + crop_y, w + cropy_x)[0]; + img_crop.at(h, w)[1] = img_rot_resize.at(h + crop_y, w + cropy_x)[1]; + img_crop.at(h, w)[2] = img_rot_resize.at(h + crop_y, w + cropy_x)[2]; + } + } + //cv::Mat ret; + //img_crop.convertTo(ret, CV_8UC3); + return img_crop; +} \ No newline at end of file diff --git a/libClassification/tform_fcn.h b/libClassification/tform_fcn.h new file mode 100644 index 0000000000000000000000000000000000000000..0059d6768073ca1390fe50bb517623561a030da7 --- /dev/null +++ b/libClassification/tform_fcn.h @@ -0,0 +1,20 @@ +#ifndef TFORM_FCN_H +#define TFORM_FCN_H +#include +class matlabexception{}; +typedef struct tfm +{ + cv::Mat forword;//向前映射 + cv::Mat inv;//反向映射 +} Tfm; + +typedef struct opt +{ + int order;//向前映射 + int K;//反向映射 +} options; +Tfm cp2tform_similarity(cv::Mat src, cv::Mat dst); //src源图 坐标 变形后的图坐标 +cv::Mat itransform(cv::Mat img,Tfm tm,int rows,int cols); + +cv::Mat onetone(cv::Mat img, cv::Mat point, int size, int emh, int eh); +#endif \ No newline at end of file diff --git a/libClassification/vc140.pdb b/libClassification/vc140.pdb new file mode 100644 index 0000000000000000000000000000000000000000..db0bc8af4221712d914438a5d0f8a5af24519815 Binary files /dev/null and b/libClassification/vc140.pdb differ