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https://github.com/tesseract-ocr/tesseract.git
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9e66fb918f
It was formatted with clang-format-7 -i unittest/*.{c*,h}.
Signed-off-by: Stefan Weil <sw@weilnetz.de>
92 lines
3.9 KiB
C++
92 lines
3.9 KiB
C++
#include "leptonica/include/allheaders.h"
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#include "tesseract/api/baseapi.h"
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#include "tesseract/unittest/lstm_test.h"
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namespace tesseract {
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namespace {
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TEST_F(LSTMTrainerTest, EncodesEng) {
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TestEncodeDecodeBoth("eng",
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"The quick brown 'fox' jumps over: the lazy dog!");
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}
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TEST_F(LSTMTrainerTest, EncodesKan) {
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TestEncodeDecodeBoth("kan", "ಫ್ರಬ್ರವರಿ ತತ್ವಾಂಶಗಳೆಂದರೆ ಮತ್ತು ಜೊತೆಗೆ ಕ್ರಮವನ್ನು");
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}
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TEST_F(LSTMTrainerTest, EncodesKor) {
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TestEncodeDecodeBoth("kor",
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"이는 것으로 다시 넣을 수는 있지만 선택의 의미는");
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}
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TEST_F(LSTMTrainerTest, MapCoder) {
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LSTMTrainer fra_trainer;
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fra_trainer.InitCharSet(TestDataNameToPath("fra.traineddata"));
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LSTMTrainer deu_trainer;
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deu_trainer.InitCharSet(TestDataNameToPath("deu.traineddata"));
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// A string that uses characters common to French and German.
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string kTestStr = "The quick brown 'fox' jumps over: the lazy dog!";
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GenericVector<int> deu_labels;
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EXPECT_TRUE(deu_trainer.EncodeString(kTestStr.c_str(), &deu_labels));
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// The french trainer cannot decode them correctly.
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STRING badly_decoded = fra_trainer.DecodeLabels(deu_labels);
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string bad_str(&badly_decoded[0], badly_decoded.length());
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EXPECT_NE(kTestStr, bad_str);
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// Encode the string as fra.
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GenericVector<int> fra_labels;
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EXPECT_TRUE(fra_trainer.EncodeString(kTestStr.c_str(), &fra_labels));
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// Use the mapper to compute what the labels are as deu.
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std::vector<int> mapping = fra_trainer.MapRecoder(deu_trainer.GetUnicharset(),
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deu_trainer.GetRecoder());
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GenericVector<int> mapped_fra_labels(fra_labels.size(), -1);
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for (int i = 0; i < fra_labels.size(); ++i) {
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mapped_fra_labels[i] = mapping[fra_labels[i]];
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EXPECT_NE(-1, mapped_fra_labels[i]) << "i=" << i << ", ch=" << kTestStr[i];
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EXPECT_EQ(mapped_fra_labels[i], deu_labels[i])
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<< "i=" << i << ", ch=" << kTestStr[i]
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<< " has deu label=" << deu_labels[i] << ", but mapped to "
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<< mapped_fra_labels[i];
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}
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// The german trainer can now decode them correctly.
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STRING decoded = deu_trainer.DecodeLabels(mapped_fra_labels);
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string ok_str(&decoded[0], decoded.length());
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EXPECT_EQ(kTestStr, ok_str);
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}
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// Tests that the actual fra model can be converted to the deu character set
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// and still read an eng image with 100% accuracy.
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TEST_F(LSTMTrainerTest, ConvertModel) {
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// Setup a trainer with a deu charset.
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LSTMTrainer deu_trainer;
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deu_trainer.InitCharSet(TestDataNameToPath("deu.traineddata"));
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// Load the fra traineddata, strip out the model, and save to a tmp file.
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TessdataManager mgr;
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string fra_data =
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file::JoinPath(FLAGS_test_srcdir, "tessdata_best", "fra.traineddata");
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CHECK(mgr.Init(fra_data.c_str())) << "Failed to load " << fra_data;
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string model_path = file::JoinPath(FLAGS_test_tmpdir, "fra.lstm");
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CHECK(mgr.ExtractToFile(model_path.c_str()));
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// Load the fra model into the deu_trainer, and save the converted model.
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CHECK(deu_trainer.TryLoadingCheckpoint(model_path.c_str(), fra_data.c_str()))
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<< "Failed checkpoint load for " << model_path << " and " << fra_data;
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string deu_data = file::JoinPath(FLAGS_test_tmpdir, "deu.traineddata");
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CHECK(deu_trainer.SaveTraineddata(deu_data.c_str()));
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// Now run the saved model on phototest. (See BasicTesseractTest in
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// baseapi_test.cc).
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TessBaseAPI api;
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api.Init(FLAGS_test_tmpdir.c_str(), "deu", tesseract::OEM_LSTM_ONLY);
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Pix* src_pix = pixRead(TestDataNameToPath("phototest.tif").c_str());
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CHECK(src_pix);
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api.SetImage(src_pix);
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std::unique_ptr<char[]> result(api.GetUTF8Text());
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string truth_text;
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CHECK_OK(file::GetContents(TestDataNameToPath("phototest.gold.txt"),
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&truth_text, file::Defaults()));
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EXPECT_STREQ(truth_text.c_str(), result.get());
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pixDestroy(&src_pix);
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}
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} // namespace
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} // namespace tesseract
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