mirror of
https://github.com/tesseract-ocr/tesseract.git
synced 2024-12-12 07:29:07 +08:00
f93fb9de74
The test currently has subtests which fail because of missing files. Signed-off-by: Stefan Weil <sw@weilnetz.de>
115 lines
4.9 KiB
C++
115 lines
4.9 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 <string> // for std::string
|
|
|
|
#include "absl/strings/str_cat.h"
|
|
|
|
#include "gmock/gmock.h" // for testing::ElementsAreArray
|
|
|
|
#include "include_gunit.h"
|
|
#include "lang_model_helpers.h"
|
|
#include "log.h" // for LOG
|
|
#include "lstmtrainer.h"
|
|
#include "unicharset_training_utils.h"
|
|
|
|
namespace tesseract {
|
|
namespace {
|
|
|
|
std::string TestDataNameToPath(const std::string& name) {
|
|
return file::JoinPath(TESTING_DIR, name);
|
|
}
|
|
|
|
// This is an integration test that verifies that CombineLangModel works to
|
|
// the extent that an LSTMTrainer can be initialized with the result, and it
|
|
// can encode strings. More importantly, the test verifies that adding an extra
|
|
// character to the unicharset does not change the encoding of strings.
|
|
TEST(LangModelTest, AddACharacter) {
|
|
constexpr char kTestString[] = "Simple ASCII string to encode !@#$%&";
|
|
constexpr char kTestStringRupees[] = "ASCII string with Rupee symbol ₹";
|
|
// Setup the arguments.
|
|
std::string script_dir = LANGDATA_DIR;
|
|
std::string eng_dir = file::JoinPath(script_dir, "eng");
|
|
std::string unicharset_path = TestDataNameToPath("eng_beam.unicharset");
|
|
UNICHARSET unicharset;
|
|
EXPECT_TRUE(unicharset.load_from_file(unicharset_path.c_str()));
|
|
std::string version_str = "TestVersion";
|
|
std::string output_dir = FLAGS_test_tmpdir;
|
|
LOG(INFO) << "Output dir=" << output_dir;
|
|
std::string lang1 = "eng";
|
|
bool pass_through_recoder = false;
|
|
GenericVector<STRING> words, puncs, numbers;
|
|
// If these reads fail, we get a warning message and an empty list of words.
|
|
ReadFile(file::JoinPath(eng_dir, "eng.wordlist"), nullptr)
|
|
.split('\n', &words);
|
|
EXPECT_GT(words.size(), 0);
|
|
ReadFile(file::JoinPath(eng_dir, "eng.punc"), nullptr).split('\n', &puncs);
|
|
EXPECT_GT(puncs.size(), 0);
|
|
ReadFile(file::JoinPath(eng_dir, "eng.numbers"), nullptr)
|
|
.split('\n', &numbers);
|
|
EXPECT_GT(numbers.size(), 0);
|
|
bool lang_is_rtl = false;
|
|
// Generate the traineddata file.
|
|
EXPECT_EQ(0, CombineLangModel(unicharset, script_dir, version_str, output_dir,
|
|
lang1, pass_through_recoder, words, puncs,
|
|
numbers, lang_is_rtl, nullptr, nullptr));
|
|
// Init a trainer with it, and encode a string.
|
|
std::string traineddata1 =
|
|
file::JoinPath(output_dir, lang1, absl::StrCat(lang1, ".traineddata"));
|
|
LSTMTrainer trainer1;
|
|
trainer1.InitCharSet(traineddata1);
|
|
GenericVector<int> labels1;
|
|
EXPECT_TRUE(trainer1.EncodeString(kTestString, &labels1));
|
|
|
|
// Add a new character to the unicharset and try again.
|
|
int size_before = unicharset.size();
|
|
unicharset.unichar_insert("₹");
|
|
SetupBasicProperties(/*report_errors*/ true, /*decompose (NFD)*/ false,
|
|
&unicharset);
|
|
EXPECT_EQ(size_before + 1, unicharset.size());
|
|
// Generate the traineddata file.
|
|
std::string lang2 = "extended";
|
|
EXPECT_EQ(EXIT_SUCCESS,
|
|
CombineLangModel(unicharset, script_dir, version_str, output_dir,
|
|
lang2, pass_through_recoder, words, puncs, numbers,
|
|
lang_is_rtl, nullptr, nullptr));
|
|
// Init a trainer with it, and encode a string.
|
|
std::string traineddata2 =
|
|
file::JoinPath(output_dir, lang2, absl::StrCat(lang2, ".traineddata"));
|
|
LSTMTrainer trainer2;
|
|
trainer2.InitCharSet(traineddata2);
|
|
GenericVector<int> labels2;
|
|
EXPECT_TRUE(trainer2.EncodeString(kTestString, &labels2));
|
|
// Copy labels1 to a std::vector, renumbering the null char to match trainer2.
|
|
// Since Tensor Flow's CTC implementation insists on having the null be the
|
|
// last label, and we want to be compatible, null has to be renumbered when
|
|
// we add a class.
|
|
int null1 = trainer1.null_char();
|
|
int null2 = trainer2.null_char();
|
|
EXPECT_EQ(null1 + 1, null2);
|
|
std::vector<int> labels1_v(labels1.size());
|
|
for (int i = 0; i < labels1.size(); ++i) {
|
|
if (labels1[i] == null1)
|
|
labels1_v[i] = null2;
|
|
else
|
|
labels1_v[i] = labels1[i];
|
|
}
|
|
EXPECT_THAT(labels1_v,
|
|
testing::ElementsAreArray(&labels2[0], labels2.size()));
|
|
// To make sure we we are not cheating somehow, we can now encode the Rupee
|
|
// symbol, which we could not do before.
|
|
EXPECT_FALSE(trainer1.EncodeString(kTestStringRupees, &labels1));
|
|
EXPECT_TRUE(trainer2.EncodeString(kTestStringRupees, &labels2));
|
|
}
|
|
|
|
} // namespace
|
|
} // namespace tesseract
|