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yuxue 5 years ago
parent 129fe13a27
commit 0f468282e4
1 changed files with 16 additions and 37 deletions
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53
src/main/resources/easypr/ann_train.cpp
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@ -9,8 +9,6 @@
#include "easypr/train/create_data.h"
#include "easypr/util/util.h"
// 原版C++语言 训练代码
namespace easypr {
@ -88,7 +86,7 @@ void AnnTrain::train() {
test();
}
// 识别中文
// 识别 中文
std::pair<std::string, std::string> AnnTrain::identifyChinese(cv::Mat input) {
cv::Mat feature = charFeatures2(input, kPredictSize);
float maxVal = -2;
@ -113,19 +111,16 @@ std::pair<std::string, std::string> AnnTrain::identifyChinese(cv::Mat input) {
return std::make_pair(s, province);
}
// 识别 字符
std::pair<std::string, std::string> AnnTrain::identify(cv::Mat input) {
cv::Mat feature = charFeatures2(input, kPredictSize);
float maxVal = -2;
int result = 0;
//std::cout << feature << std::endl;
cv::Mat output(1, kCharsTotalNumber, CV_32FC1);
ann_->predict(feature, output);
//std::cout << output << std::endl;
for (int j = 0; j < kCharsTotalNumber; j++) {
float val = output.at<float>(j);
//std::cout << "j:" << j << "val:" << val << std::endl;
if (val > maxVal) {
maxVal = val;
result = j;
@ -144,6 +139,7 @@ std::pair<std::string, std::string> AnnTrain::identify(cv::Mat input) {
}
}
// 测试并计算准确率
void AnnTrain::test() {
assert(chars_folder_);
@ -165,9 +161,8 @@ void AnnTrain::test() {
std::vector<std::pair<std::string, std::string>> error_files;
for (auto file : chars_files) {
auto img = cv::imread(file, 0); // a grayscale image
auto img = cv::imread(file, 0); // 读取灰度图像
if (!img.data) {
//cout << "Null pointer!" << endl;
continue;
}
std::pair<std::string, std::string> ch;
@ -185,7 +180,6 @@ void AnnTrain::test() {
++sum_all;
}
float rate = (float)corrects / (sum == 0 ? 1 : sum);
fprintf(stdout, ">> [sum: %d, correct: %d, rate: %.2f]\n", sum, corrects, rate);
rate_list.push_back(rate);
std::string error_string;
@ -195,8 +189,7 @@ void AnnTrain::test() {
}
for (auto k = error_files.begin(); k != end; ++k) {
auto kv = *k;
error_string.append(" ").append(kv.first).append(": ").append(
kv.second);
error_string.append(" ").append(kv.first).append(": ").append(kv.second);
if (k != end - 1) {
error_string.append(",\n");
} else {
@ -205,8 +198,7 @@ void AnnTrain::test() {
}
fprintf(stdout, ">> [\n%s\n ]\n", error_string.c_str());
}
fprintf(stdout, ">> [sum_all: %d, correct_all: %d, rate: %.4f]\n", sum_all, corrects_all,
(float)corrects_all / (sum_all == 0 ? 1 : sum_all));
fprintf(stdout, ">> [sum_all: %d, correct_all: %d, rate: %.4f]\n", sum_all, corrects_all, (float)corrects_all / (sum_all == 0 ? 1 : sum_all));
double rate_sum = std::accumulate(rate_list.begin(), rate_list.end(), 0.0);
double rate_mean = rate_sum / (rate_list.size() == 0 ? 1 : rate_list.size());
@ -214,6 +206,7 @@ void AnnTrain::test() {
fprintf(stdout, ">> [classNumber: %d, avg_rate: %.4f]\n", classNumber, rate_mean);
}
// 获取合成图像
cv::Mat getSyntheticImage(const Mat& image) {
int rand_type = rand();
Mat result = image.clone();
@ -223,16 +216,15 @@ cv::Mat getSyntheticImage(const Mat& image) {
int ran_y = rand() % 5 - 2;
result = translateImg(result, ran_x, ran_y);
}
else if (rand_type % 2 != 0) {
} else if (rand_type % 2 != 0) {
float angle = float(rand() % 15 - 7);
result = rotateImg(result, angle);
}
return result;
}
cv::Ptr<cv::ml::TrainData> AnnTrain::sdata(size_t number_for_count) {
assert(chars_folder_);
@ -269,18 +261,10 @@ cv::Ptr<cv::ml::TrainData> AnnTrain::sdata(size_t number_for_count) {
auto img = matVec.at(ran_num);
auto simg = getSyntheticImage(img);
matVec.push_back(simg);
if (1) {
std::stringstream ss(std::stringstream::in | std::stringstream::out);
ss << sub_folder << "/" << i << "_" << t << "_" << ran_num << ".jpg";
imwrite(ss.str(), simg);
}
}
fprintf(stdout, ">> Characters count: %d \n", (int)matVec.size());
for (auto img : matVec) {
auto fps = charFeatures2(img, kPredictSize);
samples.push_back(fps);
labels.push_back(i);
}
@ -288,15 +272,13 @@ cv::Ptr<cv::ml::TrainData> AnnTrain::sdata(size_t number_for_count) {
cv::Mat samples_;
samples.convertTo(samples_, CV_32F);
cv::Mat train_classes =
cv::Mat::zeros((int)labels.size(), classNumber, CV_32F);
cv::Mat train_classes = cv::Mat::zeros((int)labels.size(), classNumber, 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);
return cv::ml::TrainData::create(samples_, cv::ml::SampleTypes::ROW_SAMPLE, train_classes);
}
cv::Ptr<cv::ml::TrainData> AnnTrain::tdata() {
@ -316,14 +298,12 @@ cv::Ptr<cv::ml::TrainData> AnnTrain::tdata() {
char sub_folder[512] = {0};
sprintf(sub_folder, "%s/%s", chars_folder_, char_key);
std::cout << " >> Featuring characters " << char_key << " in "
<< sub_folder << std::endl;
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, 0); // a grayscale image
auto img = cv::imread(file, 0); // 读取灰度图像
auto fps = charFeatures2(img, kPredictSize);
samples.push_back(fps);
labels.push_back(i);
}
@ -331,14 +311,13 @@ cv::Ptr<cv::ml::TrainData> AnnTrain::tdata() {
cv::Mat samples_;
samples.convertTo(samples_, CV_32F);
cv::Mat train_classes =
cv::Mat::zeros((int)labels.size(), classNumber, CV_32F);
cv::Mat train_classes = cv::Mat::zeros((int)labels.size(), classNumber, 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);
return cv::ml::TrainData::create(samples_, cv::ml::SampleTypes::ROW_SAMPLE, train_classes);
}
}

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