liuruoze
10 years ago
parent
480df262b6
commit
e1df2fff3a
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Before Width: | Height: | Size: 184 KiB After Width: | Height: | Size: 84 KiB |
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#include "easypr/core/chars_identify.h"
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#include "easypr/core/character.hpp"
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#include "easypr/config.h"
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#include "easypr/core/core_func.h"
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namespace easypr {
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CharsIdentify* CharsIdentify::instance_ = nullptr;
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CharsIdentify* CharsIdentify::instance() {
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if (!instance_) {
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instance_ = new CharsIdentify;
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}
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return instance_;
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}
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CharsIdentify::CharsIdentify() {
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ann_ = ml::ANN_MLP::load<ml::ANN_MLP>(kDefaultAnnPath);
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kv_ = std::shared_ptr<Kv>(new Kv);
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kv_->load("etc/province_mapping");
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}
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void CharsIdentify::LoadModel(std::string path) {
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ann_->clear();
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ann_->ml::ANN_MLP::load<ml::ANN_MLP>(path);
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}
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void CharsIdentify::classify(cv::Mat featureRows, std::vector<int>& out_maxIndexs,
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std::vector<float>& out_maxVals, std::vector<bool> isChineseVec){
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int rowNum = featureRows.rows;
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cv::Mat output(rowNum, kCharsTotalNumber, CV_32FC1);
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ann_->predict(featureRows, output);
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for (int output_index = 0; output_index < rowNum; output_index++) {
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int result = -1;
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float maxVal = -2.f;
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bool isChinses = isChineseVec[output_index];
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if (!isChinses) {
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result = 0;
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for (int j = 0; j < kCharactersNumber; 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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}
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else {
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result = kCharactersNumber;
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for (int j = kCharactersNumber; j < kCharsTotalNumber; 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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}
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out_maxIndexs[output_index] = result;
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out_maxVals[output_index] = maxVal;
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}
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}
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void CharsIdentify::classify(std::vector<CCharacter>& charVec){
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size_t charVec_size = charVec.size();
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Mat featureRows;
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for (size_t index = 0; index < charVec_size; index++) {
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Mat charInput = charVec[index].getCharacterMat();
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Mat feature = charFeatures(charInput, kPredictSize);
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featureRows.push_back(feature);
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}
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int rowNum = featureRows.rows;
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cv::Mat output(rowNum, kCharsTotalNumber, CV_32FC1);
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ann_->predict(featureRows, output);
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for (int output_index = 0; output_index < rowNum; output_index++) {
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CCharacter& character = charVec[output_index];
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int result = -1;
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float maxVal = -2.f;
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string label = "";
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bool isChinses = character.getIsChinese();
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if (!isChinses) {
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result = 0;
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for (int j = 0; j < kCharactersNumber; 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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}
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else {
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result = kCharactersNumber;
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for (int j = kCharactersNumber; j < kCharsTotalNumber; 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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}
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character.setCharacterScore(maxVal);
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character.setCharacterStr(result);
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}
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}
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int CharsIdentify::classify(cv::Mat f, float& maxVal, bool isChinses){
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int result = -1;
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cv::Mat output(1, kCharsTotalNumber, CV_32FC1);
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ann_->predict(f, output);
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maxVal = -2.f;
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if (!isChinses) {
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result = 0;
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for (int j = 0; j < kCharactersNumber; 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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}
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else {
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result = kCharactersNumber;
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for (int j = kCharactersNumber; j < kCharsTotalNumber; 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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}
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//std::cout << "maxVal:" << maxVal << std::endl;
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return result;
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}
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bool CharsIdentify::isCharacter(cv::Mat input, std::string& label, float& maxVal, bool isChinese) {
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cv::Mat feature = charFeatures(input, kPredictSize);
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auto index = static_cast<int>(classify(feature, maxVal, isChinese));
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if (isChinese)
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std::cout << "maxVal:" << maxVal << std::endl;
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if (maxVal >= 0.9) {
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if (index < kCharactersNumber) {
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label = std::make_pair(kChars[index], kChars[index]).second;
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}
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else {
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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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label = std::make_pair(s, province).second;
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}
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return true;
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}
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else
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return false;
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}
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/*bool CharsIdentify::charsJudge(std::vector<CCharacter>& charVec) {
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cv::Mat feature = charFeatures(input, kPredictSize);
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auto index = static_cast<int>(classify(feature, maxVal, isChinese));
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if (isChinese)
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std::cout << "maxVal:" << maxVal << std::endl;
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if (maxVal >= 0.9) {
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if (index < kCharactersNumber) {
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label = std::make_pair(kChars[index], kChars[index]).second;
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}
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else {
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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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label = std::make_pair(s, province).second;
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}
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return true;
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}
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else
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return false;
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}*/
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std::pair<std::string, std::string> CharsIdentify::identify(cv::Mat input, bool isChinese) {
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cv::Mat feature = charFeatures(input, kPredictSize);
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float maxVal = -2;
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auto index = static_cast<int>(classify(feature, maxVal, isChinese));
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if (index < kCharactersNumber) {
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return std::make_pair(kChars[index], kChars[index]);
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}
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else {
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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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}
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int CharsIdentify::identify(std::vector<cv::Mat> inputs, std::vector<std::pair<std::string, std::string>>& outputs,
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std::vector<bool> isChineseVec) {
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Mat featureRows;
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size_t input_size = inputs.size();
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for (size_t i = 0; i < input_size; i++) {
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Mat input = inputs[i];
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cv::Mat feature = charFeatures(input, kPredictSize);
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featureRows.push_back(feature);
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}
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std::vector<int> maxIndexs;
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std::vector<float> maxVals;
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classify(featureRows, maxIndexs, maxVals, isChineseVec);
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for (size_t row_index = 0; row_index < input_size; row_index++) {
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int index = maxIndexs[row_index];
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if (index < kCharactersNumber) {
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outputs[row_index] = std::make_pair(kChars[index], kChars[index]);
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}
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else {
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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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outputs[row_index] = std::make_pair(s, province);
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}
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}
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return 0;
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}
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}
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