mirror of
https://github.com/tesseract-ocr/tesseract.git
synced 2024-11-30 23:49:05 +08:00
1ac76d8825
Add more subtests to langmodel_test Add more subtests to langmodel_test fix and enable lstmtrainer_test fix and enable some subtests from recodebeam_test partial fix for resultiterator_test fix typo removing the terminating linefeed. fix typo changes
108 lines
4.7 KiB
C++
108 lines
4.7 KiB
C++
// (C) Copyright 2017, Google Inc.
|
|
// Licensed under the Apache License, Version 2.0 (the "License");
|
|
// you may not use this file except in compliance with the License.
|
|
// You may obtain a copy of the License at
|
|
// http://www.apache.org/licenses/LICENSE-2.0
|
|
// Unless required by applicable law or agreed to in writing, software
|
|
// distributed under the License is distributed on an "AS IS" BASIS,
|
|
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
// See the License for the specific language governing permissions and
|
|
// limitations under the License.
|
|
|
|
#include "allheaders.h"
|
|
#include "baseapi.h"
|
|
#include "lstm_test.h"
|
|
|
|
namespace tesseract {
|
|
namespace {
|
|
|
|
TEST_F(LSTMTrainerTest, EncodesEng) {
|
|
TestEncodeDecodeBoth("eng",
|
|
"The quick brown 'fox' jumps over: the lazy dog!");
|
|
}
|
|
|
|
TEST_F(LSTMTrainerTest, EncodesKan) {
|
|
TestEncodeDecodeBoth("kan", "ಫ್ರಬ್ರವರಿ ತತ್ವಾಂಶಗಳೆಂದರೆ ಮತ್ತು ಜೊತೆಗೆ ಕ್ರಮವನ್ನು");
|
|
}
|
|
|
|
TEST_F(LSTMTrainerTest, EncodesKor) {
|
|
TestEncodeDecodeBoth("kor",
|
|
"이는 것으로 다시 넣을 수는 있지만 선택의 의미는");
|
|
}
|
|
|
|
TEST_F(LSTMTrainerTest, MapCoder) {
|
|
LSTMTrainer fra_trainer;
|
|
fra_trainer.InitCharSet(TestDataNameToPath("fra/fra.traineddata"));
|
|
LSTMTrainer deu_trainer;
|
|
deu_trainer.InitCharSet(TestDataNameToPath("deu/deu.traineddata"));
|
|
// A string that uses characters common to French and German.
|
|
std::string kTestStr = "The quick brown 'fox' jumps over: the lazy dog!";
|
|
GenericVector<int> deu_labels;
|
|
EXPECT_TRUE(deu_trainer.EncodeString(kTestStr.c_str(), &deu_labels));
|
|
// The french trainer cannot decode them correctly.
|
|
STRING badly_decoded = fra_trainer.DecodeLabels(deu_labels);
|
|
std::string bad_str(&badly_decoded[0], badly_decoded.length());
|
|
LOG(INFO) << "bad_str fra=" << bad_str << "\n";
|
|
EXPECT_NE(kTestStr, bad_str);
|
|
// Encode the string as fra.
|
|
GenericVector<int> fra_labels;
|
|
EXPECT_TRUE(fra_trainer.EncodeString(kTestStr.c_str(), &fra_labels));
|
|
// Use the mapper to compute what the labels are as deu.
|
|
std::vector<int> mapping = fra_trainer.MapRecoder(deu_trainer.GetUnicharset(),
|
|
deu_trainer.GetRecoder());
|
|
GenericVector<int> mapped_fra_labels(fra_labels.size(), -1);
|
|
for (int i = 0; i < fra_labels.size(); ++i) {
|
|
mapped_fra_labels[i] = mapping[fra_labels[i]];
|
|
EXPECT_NE(-1, mapped_fra_labels[i]) << "i=" << i << ", ch=" << kTestStr[i];
|
|
EXPECT_EQ(mapped_fra_labels[i], deu_labels[i])
|
|
<< "i=" << i << ", ch=" << kTestStr[i]
|
|
<< " has deu label=" << deu_labels[i] << ", but mapped to "
|
|
<< mapped_fra_labels[i];
|
|
}
|
|
// The german trainer can now decode them correctly.
|
|
STRING decoded = deu_trainer.DecodeLabels(mapped_fra_labels);
|
|
std::string ok_str(&decoded[0], decoded.length());
|
|
LOG(INFO) << "ok_str deu=" << ok_str << "\n";
|
|
EXPECT_EQ(kTestStr, ok_str);
|
|
}
|
|
|
|
// Tests that the actual fra model can be converted to the deu character set
|
|
// and still read an eng image with 100% accuracy.
|
|
TEST_F(LSTMTrainerTest, ConvertModel) {
|
|
// Setup a trainer with a deu charset.
|
|
LSTMTrainer deu_trainer;
|
|
deu_trainer.InitCharSet(TestDataNameToPath("deu/deu.traineddata"));
|
|
// Load the fra traineddata, strip out the model, and save to a tmp file.
|
|
TessdataManager mgr;
|
|
std::string fra_data =
|
|
file::JoinPath(TESSDATA_BEST_DIR, "fra.traineddata");
|
|
CHECK(mgr.Init(fra_data.c_str()));
|
|
LOG(INFO) << "Load " << fra_data << "\n";
|
|
std::string model_path = file::JoinPath(FLAGS_test_tmpdir, "fra.lstm");
|
|
CHECK(mgr.ExtractToFile(model_path.c_str()));
|
|
LOG(INFO) << "Extract " << model_path << "\n";
|
|
// Load the fra model into the deu_trainer, and save the converted model.
|
|
CHECK(deu_trainer.TryLoadingCheckpoint(model_path.c_str(), fra_data.c_str()));
|
|
LOG(INFO) << "Checkpoint load for " << model_path << " and " << fra_data << "\n";
|
|
std::string deu_data = file::JoinPath(FLAGS_test_tmpdir, "deu.traineddata");
|
|
CHECK(deu_trainer.SaveTraineddata(deu_data.c_str()));
|
|
LOG(INFO) << "Save " << deu_data << "\n";
|
|
// Now run the saved model on phototest. (See BasicTesseractTest in
|
|
// baseapi_test.cc).
|
|
TessBaseAPI api;
|
|
api.Init(FLAGS_test_tmpdir, "deu", tesseract::OEM_LSTM_ONLY);
|
|
Pix* src_pix = pixRead(TestingNameToPath("phototest.tif").c_str());
|
|
CHECK(src_pix);
|
|
api.SetImage(src_pix);
|
|
std::unique_ptr<char[]> result(api.GetUTF8Text());
|
|
std::string truth_text;
|
|
CHECK_OK(file::GetContents(TestingNameToPath("phototest.gold.txt"),
|
|
&truth_text, file::Defaults()));
|
|
|
|
EXPECT_STREQ(truth_text.c_str(), result.get());
|
|
pixDestroy(&src_pix);
|
|
}
|
|
|
|
} // namespace
|
|
} // namespace tesseract
|