mirror of
https://github.com/tesseract-ocr/tesseract.git
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202 lines
9.7 KiB
C++
202 lines
9.7 KiB
C++
// (C) Copyright 2017, Google Inc.
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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// http://www.apache.org/licenses/LICENSE-2.0
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include <string> // for std::string
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#include "gmock/gmock.h" // for testing::ElementsAreArray
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#include "include_gunit.h"
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#include "lang_model_helpers.h"
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#include "log.h" // for LOG
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#include "lstmtrainer.h"
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#include "unicharset_training_utils.h"
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namespace tesseract {
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std::string TestDataNameToPath(const std::string &name) {
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return file::JoinPath(TESTING_DIR, name);
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}
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// This is an integration test that verifies that CombineLangModel works to
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// the extent that an LSTMTrainer can be initialized with the result, and it
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// can encode strings. More importantly, the test verifies that adding an extra
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// character to the unicharset does not change the encoding of strings.
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TEST(LangModelTest, AddACharacter) {
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constexpr char kTestString[] = "Simple ASCII string to encode !@#$%&";
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constexpr char kTestStringRupees[] = "ASCII string with Rupee symbol ₹";
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// Setup the arguments.
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std::string script_dir = LANGDATA_DIR;
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std::string eng_dir = file::JoinPath(script_dir, "eng");
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std::string unicharset_path = TestDataNameToPath("eng_beam.unicharset");
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UNICHARSET unicharset;
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EXPECT_TRUE(unicharset.load_from_file(unicharset_path.c_str()));
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std::string version_str = "TestVersion";
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file::MakeTmpdir();
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std::string output_dir = FLAGS_test_tmpdir;
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LOG(INFO) << "Output dir=" << output_dir << "\n";
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std::string lang1 = "eng";
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bool pass_through_recoder = false;
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// If these reads fail, we get a warning message and an empty list of words.
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std::vector<std::string> words = split(ReadFile(file::JoinPath(eng_dir, "eng.wordlist")), '\n');
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EXPECT_GT(words.size(), 0);
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std::vector<std::string> puncs = split(ReadFile(file::JoinPath(eng_dir, "eng.punc")), '\n');
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EXPECT_GT(puncs.size(), 0);
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std::vector<std::string> numbers = split(ReadFile(file::JoinPath(eng_dir, "eng.numbers")), '\n');
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EXPECT_GT(numbers.size(), 0);
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bool lang_is_rtl = false;
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// Generate the traineddata file.
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EXPECT_EQ(0, CombineLangModel(unicharset, script_dir, version_str, output_dir, lang1,
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pass_through_recoder, words, puncs, numbers, lang_is_rtl, nullptr,
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nullptr));
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// Init a trainer with it, and encode kTestString.
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std::string traineddata1 = file::JoinPath(output_dir, lang1, lang1) + ".traineddata";
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LSTMTrainer trainer1;
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trainer1.InitCharSet(traineddata1);
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std::vector<int> labels1;
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EXPECT_TRUE(trainer1.EncodeString(kTestString, &labels1));
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std::string test1_decoded = trainer1.DecodeLabels(labels1);
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std::string test1_str(&test1_decoded[0], test1_decoded.length());
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LOG(INFO) << "Labels1=" << test1_str << "\n";
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// Add a new character to the unicharset and try again.
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int size_before = unicharset.size();
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unicharset.unichar_insert("₹");
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SetupBasicProperties(/*report_errors*/ true, /*decompose (NFD)*/ false, &unicharset);
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EXPECT_EQ(size_before + 1, unicharset.size());
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// Generate the traineddata file.
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std::string lang2 = "extended";
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EXPECT_EQ(EXIT_SUCCESS, CombineLangModel(unicharset, script_dir, version_str, output_dir, lang2,
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pass_through_recoder, words, puncs, numbers, lang_is_rtl,
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nullptr, nullptr));
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// Init a trainer with it, and encode kTestString.
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std::string traineddata2 = file::JoinPath(output_dir, lang2, lang2) + ".traineddata";
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LSTMTrainer trainer2;
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trainer2.InitCharSet(traineddata2);
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std::vector<int> labels2;
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EXPECT_TRUE(trainer2.EncodeString(kTestString, &labels2));
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std::string test2_decoded = trainer2.DecodeLabels(labels2);
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std::string test2_str(&test2_decoded[0], test2_decoded.length());
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LOG(INFO) << "Labels2=" << test2_str << "\n";
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// encode kTestStringRupees.
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std::vector<int> labels3;
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EXPECT_TRUE(trainer2.EncodeString(kTestStringRupees, &labels3));
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std::string test3_decoded = trainer2.DecodeLabels(labels3);
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std::string test3_str(&test3_decoded[0], test3_decoded.length());
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LOG(INFO) << "labels3=" << test3_str << "\n";
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// Copy labels1 to a std::vector, renumbering the null char to match trainer2.
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// Since Tensor Flow's CTC implementation insists on having the null be the
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// last label, and we want to be compatible, null has to be renumbered when
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// we add a class.
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int null1 = trainer1.null_char();
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int null2 = trainer2.null_char();
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EXPECT_EQ(null1 + 1, null2);
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std::vector<int> labels1_v(labels1.size());
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for (unsigned i = 0; i < labels1.size(); ++i) {
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if (labels1[i] == null1) {
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labels1_v[i] = null2;
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} else {
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labels1_v[i] = labels1[i];
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}
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}
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EXPECT_THAT(labels1_v, testing::ElementsAreArray(&labels2[0], labels2.size()));
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// To make sure we we are not cheating somehow, we can now encode the Rupee
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// symbol, which we could not do before.
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EXPECT_FALSE(trainer1.EncodeString(kTestStringRupees, &labels1));
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EXPECT_TRUE(trainer2.EncodeString(kTestStringRupees, &labels2));
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}
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// Same as above test, for hin instead of eng
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TEST(LangModelTest, AddACharacterHindi) {
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constexpr char kTestString[] = "हिन्दी में एक लाइन लिखें";
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constexpr char kTestStringRupees[] = "हिंदी में रूपये का चिन्ह प्रयोग करें ₹१००.००";
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// Setup the arguments.
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std::string script_dir = LANGDATA_DIR;
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std::string hin_dir = file::JoinPath(script_dir, "hin");
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std::string unicharset_path = TestDataNameToPath("hin_beam.unicharset");
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UNICHARSET unicharset;
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EXPECT_TRUE(unicharset.load_from_file(unicharset_path.c_str()));
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std::string version_str = "TestVersion";
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file::MakeTmpdir();
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std::string output_dir = FLAGS_test_tmpdir;
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LOG(INFO) << "Output dir=" << output_dir << "\n";
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std::string lang1 = "hin";
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bool pass_through_recoder = false;
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// If these reads fail, we get a warning message and an empty list of words.
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std::vector<std::string> words = split(ReadFile(file::JoinPath(hin_dir, "hin.wordlist")), '\n');
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EXPECT_GT(words.size(), 0);
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std::vector<std::string> puncs = split(ReadFile(file::JoinPath(hin_dir, "hin.punc")), '\n');
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EXPECT_GT(puncs.size(), 0);
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std::vector<std::string> numbers = split(ReadFile(file::JoinPath(hin_dir, "hin.numbers")), '\n');
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EXPECT_GT(numbers.size(), 0);
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bool lang_is_rtl = false;
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// Generate the traineddata file.
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EXPECT_EQ(0, CombineLangModel(unicharset, script_dir, version_str, output_dir, lang1,
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pass_through_recoder, words, puncs, numbers, lang_is_rtl, nullptr,
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nullptr));
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// Init a trainer with it, and encode kTestString.
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std::string traineddata1 = file::JoinPath(output_dir, lang1, lang1) + ".traineddata";
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LSTMTrainer trainer1;
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trainer1.InitCharSet(traineddata1);
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std::vector<int> labels1;
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EXPECT_TRUE(trainer1.EncodeString(kTestString, &labels1));
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std::string test1_decoded = trainer1.DecodeLabels(labels1);
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std::string test1_str(&test1_decoded[0], test1_decoded.length());
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LOG(INFO) << "Labels1=" << test1_str << "\n";
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// Add a new character to the unicharset and try again.
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int size_before = unicharset.size();
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unicharset.unichar_insert("₹");
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SetupBasicProperties(/*report_errors*/ true, /*decompose (NFD)*/ false, &unicharset);
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EXPECT_EQ(size_before + 1, unicharset.size());
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// Generate the traineddata file.
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std::string lang2 = "extendedhin";
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EXPECT_EQ(EXIT_SUCCESS, CombineLangModel(unicharset, script_dir, version_str, output_dir, lang2,
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pass_through_recoder, words, puncs, numbers, lang_is_rtl,
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nullptr, nullptr));
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// Init a trainer with it, and encode kTestString.
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std::string traineddata2 = file::JoinPath(output_dir, lang2, lang2) + ".traineddata";
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LSTMTrainer trainer2;
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trainer2.InitCharSet(traineddata2);
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std::vector<int> labels2;
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EXPECT_TRUE(trainer2.EncodeString(kTestString, &labels2));
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std::string test2_decoded = trainer2.DecodeLabels(labels2);
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std::string test2_str(&test2_decoded[0], test2_decoded.length());
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LOG(INFO) << "Labels2=" << test2_str << "\n";
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// encode kTestStringRupees.
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std::vector<int> labels3;
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EXPECT_TRUE(trainer2.EncodeString(kTestStringRupees, &labels3));
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std::string test3_decoded = trainer2.DecodeLabels(labels3);
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std::string test3_str(&test3_decoded[0], test3_decoded.length());
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LOG(INFO) << "labels3=" << test3_str << "\n";
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// Copy labels1 to a std::vector, renumbering the null char to match trainer2.
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// Since Tensor Flow's CTC implementation insists on having the null be the
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// last label, and we want to be compatible, null has to be renumbered when
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// we add a class.
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int null1 = trainer1.null_char();
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int null2 = trainer2.null_char();
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EXPECT_EQ(null1 + 1, null2);
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std::vector<int> labels1_v(labels1.size());
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for (unsigned i = 0; i < labels1.size(); ++i) {
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if (labels1[i] == null1) {
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labels1_v[i] = null2;
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} else {
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labels1_v[i] = labels1[i];
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}
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}
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EXPECT_THAT(labels1_v, testing::ElementsAreArray(&labels2[0], labels2.size()));
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// To make sure we we are not cheating somehow, we can now encode the Rupee
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// symbol, which we could not do before.
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EXPECT_FALSE(trainer1.EncodeString(kTestStringRupees, &labels1));
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EXPECT_TRUE(trainer2.EncodeString(kTestStringRupees, &labels2));
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}
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} // namespace tesseract
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