m53297601
/
EasyPR
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Finished ANN training for opencv3.0.

Browse Source
v1.6alpha
Micooz 10 years ago
parent 530676df52
commit 66adeedf5d
1 changed files with 113 additions and 79 deletions
Show Stats Download Patch File Download Diff File

192
src/train/ann_train.cpp
Unescape View File

@ -1,88 +1,122 @@
#include "easypr/train/ann_train.h"
#include "easypr/train/ann_train.h"
#include "easypr/core/core_func.h"
#include "easypr/core/chars_identify.h"
#include "easypr/util/util.h"
#include "easypr/config.h"
namespace easypr{
AnnTrain::AnnTrain(const char* chars_folder, const char* zhchars_folder, const char* xml)
:chars_folder_(chars_folder), zhchars_folder_(zhchars_folder), ann_xml_(xml){
}
void AnnTrain::train(const int & neurons /* = 40 */){
this->getTrainData();
cv::Mat layers = {
train_data_->getNSamples(),// the input layer
neurons, // the neurons
sizeof(kChinese) + sizeof(kCharacters) // the output layer
};
ann_->setLayerSizes(layers);
ann_->setTrainMethod(cv::ml::ANN_MLP::TrainingMethods::BACKPROP);
ann_->setBackpropWeightScale(0.1);
ann_->setBackpropMomentumScale(0.1);
std::cout << "Training ANN model, please wait..." << std::endl;
long start = utils::getTimestamp();
ann_->train(train_data_);
long end = utils::getTimestamp();
std::cout << "Training done. Elapse: " << (end - start) / 1000 << std::endl;
ann_->save(ann_xml_);
std::cout << "Your ANN Model was saved to " << ann_xml_ << std::endl;
}
void AnnTrain::getTrainData(){
assert(chars_folder_);
assert(zhchars_folder_);
// create new
cv::Mat samples;
cv::Mat responses;
std::cout << "Collecting chars in " << chars_folder_ << std::endl;
for (auto i = 0; i < sizeof(kCharacters); ++i){
char c = kCharacters[i];
char sub_folder[512] = { 0 };
sprintf(sub_folder, "%s/%c", chars_folder_, c);
std::cout << " >> Featuring characters " << c << " in " << sub_folder << std::endl;
auto chars_files = utils::getFiles(sub_folder);
for (auto file : chars_files){
auto img = cv::imread(file);
auto fps = features(img, kPredictSize);
samples.push_back(fps);
responses.push_back(i);
}
}
std::cout << "Collecting zh-chars in " << zhchars_folder_ << std::endl;
namespace easypr {
AnnTrain::AnnTrain(const char* chars_folder,
const char* xml)
: chars_folder_(chars_folder),
ann_xml_(xml) {
ann_ = cv::ml::ANN_MLP::create();
}
void AnnTrain::train(const int& neurons /* = 40 */) {
cv::Mat layers(1, 3, CV_32SC1);
layers.at<int>(0) = 120; // the input layer
layers.at<int>(1) = neurons; // the neurons
layers.at<int>(2) = kCharsTotalNumber; // the output layer
ann_->setLayerSizes(layers);
ann_->setActivationFunction(cv::ml::ANN_MLP::SIGMOID_SYM, 1, 1);
ann_->setTrainMethod(cv::ml::ANN_MLP::TrainingMethods::BACKPROP);
ann_->setBackpropWeightScale(0.1);
ann_->setBackpropMomentumScale(0.1);
auto traindata = train_data();
std::cout << "Training ANN model, please wait..." << std::endl;
long start = utils::getTimestamp();
ann_->train(traindata);
long end = utils::getTimestamp();
std::cout << "Training done. Time elapse: " << (end - start) << "ms" << std::endl;
ann_->save(ann_xml_);
std::cout << "Your ANN Model was saved to " << ann_xml_ << std::endl;
}
cv::Ptr<cv::ml::TrainData> AnnTrain::train_data() {
assert(chars_folder_);
for (auto i = 0; i < sizeof(kChinese); ++i){
const char *zhc = kChinese[i];
char sub_folder[512] = { 0 };
cv::Mat samples;
std::vector<int> labels;
sprintf(sub_folder, "%s/%s", zhchars_folder_, zhc);
std::cout << " >> Featuring zh-characters " << zhc << " in " << sub_folder << std::endl;
std::cout << "Collecting chars in " << chars_folder_ << std::endl;
auto chars_files = utils::getFiles(sub_folder);
for (auto file : chars_files){
auto img = cv::imread(file);
auto fps = features(img, kPredictSize);
for (int i = 0; i < kCharsTotalNumber; ++i) {
auto char_key = kChars[i];
char sub_folder[512] = {0};
samples.push_back(fps);
responses.push_back(i + sizeof(kCharacters));
sprintf(sub_folder, "%s/%s", chars_folder_, char_key);
std::cout << " >> Featuring characters " << char_key << " in " << sub_folder << std::endl;
auto chars_files = utils::getFiles(sub_folder);
for (auto file : chars_files) {
auto img = cv::imread(file);
auto fps = features(img, kPredictSize);
samples.push_back(fps);
labels.push_back(i);
}
}
cv::Mat samples_;
samples.convertTo(samples_, CV_32F);
cv::Mat train_classes = cv::Mat::zeros((int) labels.size(), kCharsTotalNumber,
CV_32F);
for (int i = 0; i < train_classes.rows; ++i) {
train_classes.at<float>(i, labels[i]) = 1.f;
}
return cv::ml::TrainData::create(samples_,
cv::ml::SampleTypes::ROW_SAMPLE,
train_classes);
}
void AnnTrain::test() {
assert(chars_folder_);
for (int i = 0; i < kCharsTotalNumber; ++i) {
auto char_key = kChars[i];
char sub_folder[512] = {0};
sprintf(sub_folder, "%s/%s", chars_folder_, char_key);
fprintf(stdout, " >> Testing characters %s in %s \n",
char_key, sub_folder);
auto chars_files = utils::getFiles(sub_folder);
int corrects = 0, sum = 0;
std::vector<std::string> error_files;
for (auto file : chars_files) {
auto img = cv::imread(file);
std::pair<std::string, std::string> ch = CharsIdentify::instance()->identify(img);
if (ch.first == char_key) {
++corrects;
} else {
error_files.push_back(utils::getFileName(file));
}
++sum;
}
//
cv::Mat samples_row;
cv::Mat responses_row;
samples.convertTo(samples_row, CV_32FC1);
responses.convertTo(responses_row, CV_32FC1);
fprintf(stdout, " >> [sum: %d, correct: %d, rate: %.2f]\n",
sum, corrects, (float)corrects / (sum == 0 ? 1 : sum));
std::string error_string;
auto end = error_files.end();
if (error_files.size() >= 10) {
end -= error_files.size() * (1 - 0.1);
}
for(auto i = error_files.begin(); i != end; ++i) {
error_string.append(*i);
if (i != end - 1) {
error_string.append(", ");
} else {
error_string.append(" ...");
}
}
fprintf(stdout, " >> [%s]\n", error_string.c_str());
}
}
train_data_ = cv::ml::TrainData::create(samples_row, ml::SampleTypes::ROW_SAMPLE, responses_row);
}
}
}

Write Preview
Loading…
Cancel
Save