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#include <numeric>
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#include <ctime>
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#include "easypr/train/ann_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/train/create_data.h"
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#include "easypr/util/util.h"
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// 原版C++语言 训练代码
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namespace easypr {
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AnnTrain::AnnTrain(const char* chars_folder, const char* xml): chars_folder_(chars_folder), ann_xml_(xml) {
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ann_ = cv::ml::ANN_MLP::create();
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type = 0; // type=0, 所有字符；type=1, 只有中文字符
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kv_ = std::shared_ptr<Kv>(new Kv);
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kv_->load("resources/text/province_mapping"); // zh_cuan 川 zh_gan1 甘
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}
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void AnnTrain::train() {
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int classNumber = 0;
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cv::Mat layers;
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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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if (type == 0) {
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classNumber = kCharsTotalNumber;
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input_number = kAnnInput;
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hidden_number = kNeurons;
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output_number = classNumber;
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} else if (type == 1) {
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classNumber = kChineseNumber;
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input_number = kAnnInput;
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hidden_number = kNeurons;
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output_number = classNumber;
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}
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int N = input_number;
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int m = output_number;
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int first_hidden_neurons = int(std::sqrt((m + 2) * N) + 2 * std::sqrt(N / (m + 2)));
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int second_hidden_neurons = int(m * std::sqrt(N / (m + 2)));
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bool useTLFN = false;
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if (!useTLFN) {
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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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} else {
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// 两层神经网络很难训练，所以不要尝试
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fprintf(stdout, ">> Use two-layers neural networks,\n");
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layers.create(1, 4, CV_32SC1);
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layers.at<int>(0) = input_number;
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layers.at<int>(1) = first_hidden_neurons;
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layers.at<int>(2) = second_hidden_neurons;
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layers.at<int>(3) = output_number;
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}
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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::BACKPROP);
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ann_->setTermCriteria(cvTermCriteria(CV_TERMCRIT_ITER, 30000, 0.0001));
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ann_->setBackpropWeightScale(0.1);
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ann_->setBackpropMomentumScale(0.1);
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auto files = Utils::getFiles(chars_folder_);
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if (files.size() == 0) {
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fprintf(stdout, "No file found in the train folder!\n");
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return;
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}
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// 使用原始数据 或者原始数据+合成数据
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auto traindata = sdata(350);
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ann_->train(traindata);
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ann_->save(ann_xml_);
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test();
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}
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// 识别 中文
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std::pair<std::string, std::string> AnnTrain::identifyChinese(cv::Mat input) {
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cv::Mat feature = charFeatures2(input, kPredictSize);
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float maxVal = -2;
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int result = 0;
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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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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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// 识别 字符
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std::pair<std::string, std::string> AnnTrain::identify(cv::Mat input) {
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cv::Mat feature = charFeatures2(input, kPredictSize);
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float maxVal = -2;
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int result = 0;
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cv::Mat output(1, kCharsTotalNumber, CV_32FC1);
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ann_->predict(feature, output);
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for (int j = 0; j < kCharsTotalNumber; j++) {
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float val = output.at<float>(j);
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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;
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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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// 测试并计算准确率
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void AnnTrain::test() {
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assert(chars_folder_);
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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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int corrects_all = 0, sum_all = 0;
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std::vector<float> rate_list;
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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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int corrects = 0, sum = 0;
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std::vector<std::pair<std::string, std::string>> error_files;
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for (auto file : chars_files) {
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auto img = cv::imread(file, 0); // 读取灰度图像
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if (!img.data) {
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continue;
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}
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std::pair<std::string, std::string> ch;
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if (type == 0) ch = identify(img);
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if (type == 1) ch = identifyChinese(img);
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if (ch.first == char_key) {
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++corrects;
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++corrects_all;
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} else {
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error_files.push_back(std::make_pair(utils::getFileName(file), ch.second));
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}
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++sum;
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++sum_all;
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}
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float rate = (float)corrects / (sum == 0 ? 1 : sum);
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rate_list.push_back(rate);
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std::string error_string;
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auto end = error_files.end();
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if (error_files.size() >= 10) {
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end -= static_cast<size_t>(error_files.size() * (1 - 0.1));
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}
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for (auto k = error_files.begin(); k != end; ++k) {
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auto kv = *k;
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error_string.append(" ").append(kv.first).append(": ").append(kv.second);
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if (k != end - 1) {
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error_string.append(",\n");
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} else {
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error_string.append("\n ...");
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}
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}
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fprintf(stdout, ">> [\n%s\n ]\n", error_string.c_str());
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}
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fprintf(stdout, ">> [sum_all: %d, correct_all: %d, rate: %.4f]\n", sum_all, corrects_all, (float)corrects_all / (sum_all == 0 ? 1 : sum_all));
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double rate_sum = std::accumulate(rate_list.begin(), rate_list.end(), 0.0);
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double rate_mean = rate_sum / (rate_list.size() == 0 ? 1 : rate_list.size());
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fprintf(stdout, ">> [classNumber: %d, avg_rate: %.4f]\n", classNumber, rate_mean);
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}
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// 获取合成图像
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cv::Mat getSyntheticImage(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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} 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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// 处理训练文件
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cv::Ptr<cv::ml::TrainData> AnnTrain::sdata(size_t number_for_count) {
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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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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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srand((unsigned)time(0));
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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", int(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, 0); // a grayscale image
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matVec.push_back(img);
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}
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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 = getSyntheticImage(img);
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matVec.push_back(simg);
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}
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for (auto img : matVec) {
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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 = 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, train_classes);
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}
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cv::Ptr<cv::ml::TrainData> AnnTrain::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 " << 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); // 读取灰度图像
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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 = 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, train_classes);
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}
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}
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