* new training process for Chinese gray images.v1.6alpha
liuruoze
8 years ago
parent
1e604768db
commit
a366a0b950
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////////////////////////////////////
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EasyPR Option:
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1. 测试;
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2. 批量测试;
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2. 批量测试(推荐);
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3. SVM训练;
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4. ANN训练;
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5. GDTS生成;
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5. 中文字符训练;
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6. 开发团队;
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7. 感谢名单;
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8. 退出;
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#ifndef EASYPR_TRAIN_ANNCHTRAIN_H_
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#define EASYPR_TRAIN_ANNCHTRAIN_H_
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#include "easypr/train/train.h"
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#include "easypr/util/kv.h"
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#include "easypr/core/feature.h"
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#include <memory>
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namespace easypr {
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class AnnChTrain : public ITrain {
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public:
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explicit AnnChTrain(const char* chars_folder, const char* xml);
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virtual void train();
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virtual void test();
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std::pair<std::string, std::string> identifyChinese(cv::Mat input);
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cv::Mat AnnChTrain::generateGraySyntheticImage(const cv::Mat& image);
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private:
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virtual cv::Ptr<cv::ml::TrainData> tdata();
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void AnnChTrain::trainVal(size_t number_for_count = 100);
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cv::Ptr<cv::ml::ANN_MLP> ann_;
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const char* ann_xml_;
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const char* chars_folder_;
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std::shared_ptr<Kv> kv_;
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int type;
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annCallback extractFeature;
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};
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}
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#endif // EASYPR_TRAIN_ANNCHTRAIN_H_
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#ifndef EASYPR_CREATE_DATA_H_
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#define EASYPR_CREATE_DATA_H_
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#include "opencv2/opencv.hpp"
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#include "easypr/config.h"
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using namespace cv;
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using namespace std;
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/*! \namespace easypr
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Namespace where all the C++ EasyPR functionality resides
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*/
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namespace easypr {
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// shift an image
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Mat translateImg(Mat img, int offsetx, int offsety);
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// rotate an image
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Mat rotateImg(Mat source, float angle);
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// crop the image
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Mat cropImg(Mat src, int x, int y, int shift);
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Mat generateSyntheticImage(const Mat& image, int use_swap = 1);
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} /*! \namespace easypr*/
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#endif // EASYPR_CREATE_DATA_H_
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#include <numeric>
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#include <ctime>
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#include "easypr/train/annCh_train.h"
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#include "easypr/config.h"
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#include "easypr/core/chars_identify.h"
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#include "easypr/core/feature.h"
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#include "easypr/core/core_func.h"
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#include "easypr/util/util.h"
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#include "easypr/train/create_data.h"
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namespace easypr {
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AnnChTrain::AnnChTrain(const char* chars_folder, const char* xml)
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: chars_folder_(chars_folder), ann_xml_(xml) {
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ann_ = cv::ml::ANN_MLP::create();
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type = 1;
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kv_ = std::shared_ptr<Kv>(new Kv);
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kv_->load("etc/province_mapping");
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extractFeature = getGrayCharFeatures;
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}
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void AnnChTrain::train() {
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int classNumber = 0;
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int input_number = 0;
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int hidden_number = 0;
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int output_number = 0;
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classNumber = kChineseNumber;
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input_number = kGrayCharHeight * kGrayCharWidth;
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hidden_number = 64;
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output_number = classNumber;
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cv::Mat layers;
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layers.create(1, 3, CV_32SC1);
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layers.at<int>(0) = input_number;
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layers.at<int>(1) = hidden_number;
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layers.at<int>(2) = output_number;
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ann_->setLayerSizes(layers);
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ann_->setActivationFunction(cv::ml::ANN_MLP::SIGMOID_SYM, 1, 1);
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ann_->setTrainMethod(cv::ml::ANN_MLP::TrainingMethods::RPROP);
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ann_->setTermCriteria(cvTermCriteria(CV_TERMCRIT_ITER, 30000, 0.0001));
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ann_->setBackpropWeightScale(0.05);
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ann_->setBackpropMomentumScale(0.9);
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// using raw data or raw + synthic data.
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trainVal(350);
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}
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std::pair<std::string, std::string> AnnChTrain::identifyChinese(cv::Mat input) {
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Mat feature;
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extractFeature(input, feature);
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float maxVal = -2;
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int result = -1;
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cv::Mat output(1, kChineseNumber, CV_32FC1);
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ann_->predict(feature, output);
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for (int j = 0; j < kChineseNumber; j++) {
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float val = output.at<float>(j);
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// std::cout << "j:" << j << "val:" << val << std::endl;
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if (val > maxVal) {
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maxVal = val;
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result = j;
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}
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}
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auto index = result + kCharsTotalNumber - kChineseNumber;
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const char* key = kChars[index];
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std::string s = key;
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std::string province = kv_->get(s);
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return std::make_pair(s, province);
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}
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void AnnChTrain::test() {
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//TODO
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}
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cv::Mat AnnChTrain::generateGraySyntheticImage(const Mat& image) {
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int rand_type = rand();
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Mat result = image.clone();
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if (rand_type % 2 == 0) {
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int ran_x = rand() % 5 - 2;
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int ran_y = rand() % 5 - 2;
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result = translateImg(result, ran_x, ran_y);
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}
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else if (rand_type % 2 != 0) {
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float angle = float(rand() % 15 - 7);
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result = rotateImg(result, angle);
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}
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return result;
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}
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void AnnChTrain::trainVal(size_t number_for_count) {
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assert(chars_folder_);
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cv::Mat train_samples;
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std::vector<cv::Mat> val_images;
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std::vector<int> train_label, val_labels;
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float percentage = 0.7f;
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int classNumber = kChineseNumber;
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for (int i = 0; i < classNumber; ++i) {
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auto char_key = kChars[i + kCharsTotalNumber - classNumber];
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char sub_folder[512] = { 0 };
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sprintf(sub_folder, "%s/%s", chars_folder_, char_key);
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fprintf(stdout, ">> Testing characters %s in %s \n", char_key, sub_folder);
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auto chars_files = utils::getFiles(sub_folder);
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size_t char_size = chars_files.size();
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fprintf(stdout, ">> Characters count: %d \n", char_size);
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std::vector<cv::Mat> matVec;
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matVec.reserve(number_for_count);
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for (auto file : chars_files) {
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auto img = cv::imread(file, IMREAD_GRAYSCALE); // a grayscale image
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matVec.push_back(img);
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}
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// genrate the synthetic images
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/*for (int t = 0; t < (int)number_for_count - (int)char_size; t++) {
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int rand_range = char_size + t;
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int ran_num = rand() % rand_range;
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auto img = matVec.at(ran_num);
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auto simg = generateGraySyntheticImage(img);
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matVec.push_back(simg);
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}*/
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fprintf(stdout, ">> Characters count: %d \n", matVec.size());
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// random sort the mat;
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srand(unsigned(time(NULL)));
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//random_shuffle(matVec.begin(), matVec.end());
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size_t split_index = size_t(matVec.size() * percentage);
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for (size_t i = 0; i < matVec.size(); ++i) {
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Mat img = matVec.at(i);
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Mat feature;
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extractFeature(img, feature);
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if (i <= split_index) {
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train_samples.push_back(feature);
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train_label.push_back(i);
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}
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else {
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val_images.push_back(img);
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val_labels.push_back(i);
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}
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}
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}
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// generate train data
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train_samples.convertTo(train_samples, CV_32F);
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cv::Mat train_classes = cv::Mat::zeros((int)train_label.size(), classNumber, CV_32F);
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for (int i = 0; i < train_classes.rows; ++i)
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train_classes.at<float>(i, train_label[i]) = 1.f;
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auto train_data = cv::ml::TrainData::create(train_samples, cv::ml::SampleTypes::ROW_SAMPLE, train_classes);
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// train the data, calculate the cost time
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std::cout << "Training ANN chinese model, please wait..." << std::endl;
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long start = utils::getTimestamp();
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ann_->train(train_data);
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long end = utils::getTimestamp();
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ann_->save(ann_xml_);
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std::cout << "Your ANN Model was saved to " << ann_xml_ << std::endl;
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std::cout << "Training done. Time elapse: " << (end - start) / (1000 * 60) << "minute" << std::endl;
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// test the accuracy_rate
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int corrects_all = 0, sum_all = val_images.size();
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for (size_t i = 0; i < val_images.size(); ++i) {
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cv::Mat img = val_images.at(i);
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int label = val_labels.at(i);
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auto char_key = kChars[i + kCharsTotalNumber - classNumber];
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std::pair<std::string, std::string> ch = identifyChinese(img);
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if (ch.first == char_key)
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corrects_all++;
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}
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float accuracy_rate = (float)corrects_all / (float)sum_all;
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std::cout << "Accuracy_rate: " << accuracy_rate << std::endl;
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}
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cv::Ptr<cv::ml::TrainData> AnnChTrain::tdata() {
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assert(chars_folder_);
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cv::Mat samples;
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std::vector<int> labels;
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std::cout << "Collecting chars in " << chars_folder_ << std::endl;
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int classNumber = 0;
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if (type == 0) classNumber = kCharsTotalNumber;
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if (type == 1) classNumber = kChineseNumber;
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for (int i = 0; i < classNumber; ++i) {
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auto char_key = kChars[i + kCharsTotalNumber - classNumber];
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char sub_folder[512] = {0};
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sprintf(sub_folder, "%s/%s", chars_folder_, char_key);
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std::cout << " >> Featuring characters " << char_key << " in "
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<< sub_folder << std::endl;
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auto chars_files = utils::getFiles(sub_folder);
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for (auto file : chars_files) {
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auto img = cv::imread(file, 0); // a grayscale image
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auto fps = charFeatures2(img, kPredictSize);
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samples.push_back(fps);
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labels.push_back(i);
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}
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}
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cv::Mat samples_;
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samples.convertTo(samples_, CV_32F);
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cv::Mat train_classes =
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cv::Mat::zeros((int)labels.size(), classNumber, CV_32F);
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for (int i = 0; i < train_classes.rows; ++i) {
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train_classes.at<float>(i, labels[i]) = 1.f;
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}
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return cv::ml::TrainData::create(samples_, cv::ml::SampleTypes::ROW_SAMPLE,
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train_classes);
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}
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}
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#include "easypr/train/create_data.h"
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namespace easypr {
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// shift an image
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Mat translateImg(Mat img, int offsetx, int offsety){
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Mat dst;
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Mat trans_mat = (Mat_<double>(2, 3) << 1, 0, offsetx, 0, 1, offsety);
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warpAffine(img, dst, trans_mat, img.size(), 1, 0, Scalar(255));
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return dst;
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}
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// rotate an image
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Mat rotateImg(Mat source, float angle){
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Point2f src_center(source.cols / 2.0F, source.rows / 2.0F);
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Mat rot_mat = getRotationMatrix2D(src_center, angle, 1.0);
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Mat dst;
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warpAffine(source, dst, rot_mat, source.size(), 1, 0, Scalar(255));
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return dst;
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}
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// crop the image
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Mat cropImg(Mat src, int x, int y, int shift){
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int width = kGrayCharWidth;
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int height = kGrayCharHeight;
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int crop_width = width - shift;
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int crop_height = height - shift;
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int x_shift = shift;
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int y_shift = shift;
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x = x < x_shift ? x : x_shift;
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y = y < y_shift ? y : y_shift;
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Rect rect = Rect(x, y, crop_width, crop_height);
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Mat dst = src(rect);
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resize(dst, dst, Size(width, height));
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return dst;
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}
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Mat generateSyntheticImage(const Mat& image, int use_swap) {
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int rd = rand();
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Mat result = image.clone();
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if (rd >> 0 & 1) {
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int shift = 4;
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int ran_x = rand() % shift;
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int ran_y = rand() % shift;
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result = cropImg(result, ran_x, ran_y, shift);
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}
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if (rd >> 1 & 1) {
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}
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if (rd >> 2 & 1) {
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}
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if (rd >> 3 & 1) {
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int ran_x = rand() % 6 - 3;
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int ran_y = rand() % 6 - 3;
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result = translateImg(result, ran_x, ran_y);
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}
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if (rd >> 4 & 1) {
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float angle = float(rand() % 20 - 10);
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result = rotateImg(result, angle);
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}
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return result;
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}
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}
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