mirror of
https://github.com/tesseract-ocr/tesseract.git
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230 lines
8.0 KiB
C++
230 lines
8.0 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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// Unit test to run Tesseract instances in parallel threads and verify
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// the OCR result.
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// Note that success of running this test as-is does NOT verify
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// thread-safety. For that, you need to run this binary under TSAN using the
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// associated baseapi_thread_test_with_tsan.sh script.
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//
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// The tests are partitioned by instance to allow running Tesseract/Cube/both
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// and by stage to run initialization/recognition/both. See flag descriptions
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// for details.
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#include <functional>
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#include <memory>
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#include <string>
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#ifdef INCLUDE_TENSORFLOW
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# include <tensorflow/core/lib/core/threadpool.h>
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#endif
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#include <allheaders.h>
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#include <tesseract/baseapi.h>
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#include "absl/strings/ascii.h" // for absl::StripAsciiWhitespace
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#include "commandlineflags.h"
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#include "include_gunit.h"
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#include "log.h"
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// Run with Tesseract instances.
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BOOL_PARAM_FLAG(test_tesseract, true, "Test tesseract instances");
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// Run with Cube instances.
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// Note that with TSAN, Cube typically takes much longer to test. Ignoring
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// std::string operations using the associated tess_tsan.ignore file when
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// testing Cube significantly reduces testing time.
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BOOL_PARAM_FLAG(test_cube, true, "Test Cube instances");
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// When used with TSAN, having more repetitions can help in finding hidden
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// thread-safety violations at the expense of increased testing time.
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INT_PARAM_FLAG(reps, 1, "Num of parallel test repetitions to run.");
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INT_PARAM_FLAG(max_concurrent_instances, 0,
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"Maximum number of instances to run in parallel at any given "
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"instant. The number of concurrent instances cannot exceed "
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"reps * number_of_langs_tested, which is also the default value.");
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namespace tesseract {
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static const char *kTessLangs[] = {"eng", "vie", nullptr};
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static const char *kTessImages[] = {"HelloGoogle.tif", "viet.tif", nullptr};
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static const char *kTessTruthText[] = {"Hello Google", "\x74\x69\xe1\xba\xbf\x6e\x67", nullptr};
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static const char *kCubeLangs[] = {"hin", "ara", nullptr};
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static const char *kCubeImages[] = {"raaj.tif", "arabic.tif", nullptr};
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static const char *kCubeTruthText[] = {"\xe0\xa4\xb0\xe0\xa4\xbe\xe0\xa4\x9c",
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"\xd8\xa7\xd9\x84\xd8\xb9\xd8\xb1\xd8\xa8\xd9\x8a", nullptr};
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class BaseapiThreadTest : public ::testing::Test {
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protected:
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static void SetUpTestCase() {
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CHECK(FLAGS_test_tesseract || FLAGS_test_cube)
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<< "Need to test at least one of Tesseract/Cube!";
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// Form a list of langs/gt_text/image_files we will work with.
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std::vector<std::string> image_files;
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if (FLAGS_test_tesseract) {
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int i = 0;
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while (kTessLangs[i] && kTessTruthText[i] && kTessImages[i]) {
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langs_.push_back(kTessLangs[i]);
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gt_text_.push_back(kTessTruthText[i]);
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image_files.push_back(kTessImages[i]);
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++i;
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}
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LOG(INFO) << "Testing Tesseract on " << i << " languages.";
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}
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if (FLAGS_test_cube) {
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int i = 0;
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while (kCubeLangs[i] && kCubeTruthText[i] && kCubeImages[i]) {
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langs_.push_back(kCubeLangs[i]);
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gt_text_.push_back(kCubeTruthText[i]);
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image_files.push_back(kCubeImages[i]);
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++i;
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}
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LOG(INFO) << "Testing Cube on " << i << " languages.";
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}
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num_langs_ = langs_.size();
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// Pre-load the images into an array. We will be making multiple copies of
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// an image here if FLAGS_reps > 1 and that is intentional. In this test, we
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// wish to not make any assumptions about the thread-safety of Pix objects,
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// and so entirely disallow concurrent access of a Pix instance.
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const int n = num_langs_ * FLAGS_reps;
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for (int i = 0; i < n; ++i) {
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std::string path = TESTING_DIR "/" + image_files[i % num_langs_];
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Pix *new_pix = pixRead(path.c_str());
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QCHECK(new_pix != nullptr) << "Could not read " << path;
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pix_.push_back(new_pix);
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}
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#ifdef INCLUDE_TENSORFLOW
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pool_size_ = (FLAGS_max_concurrent_instances < 1) ? num_langs_ * FLAGS_reps
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: FLAGS_max_concurrent_instances;
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#endif
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}
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static void TearDownTestCase() {
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for (auto &pix : pix_) {
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pixDestroy(&pix);
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}
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}
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#ifdef INCLUDE_TENSORFLOW
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void ResetPool() {
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pool_.reset(
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new tensorflow::thread::ThreadPool(tensorflow::Env::Default(), "tessthread", pool_size_));
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}
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void WaitForPoolWorkers() {
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pool_.reset(nullptr);
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}
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std::unique_ptr<tensorflow::thread::ThreadPool> pool_;
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static int pool_size_;
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#endif
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static std::vector<Pix *> pix_;
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static std::vector<std::string> langs_;
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static std::vector<std::string> gt_text_;
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static int num_langs_;
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};
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// static member variable declarations.
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#ifdef INCLUDE_TENSORFLOW
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int BaseapiThreadTest::pool_size_;
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#endif
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std::vector<Pix *> BaseapiThreadTest::pix_;
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std::vector<std::string> BaseapiThreadTest::langs_;
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std::vector<std::string> BaseapiThreadTest::gt_text_;
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int BaseapiThreadTest::num_langs_;
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static void InitTessInstance(TessBaseAPI *tess, const std::string &lang) {
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CHECK(tess != nullptr);
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EXPECT_EQ(0, tess->Init(TESSDATA_DIR, lang.c_str()));
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}
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static void GetCleanedText(TessBaseAPI *tess, Pix *pix, std::string *ocr_text) {
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tess->SetImage(pix);
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char *result = tess->GetUTF8Text();
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*ocr_text = result;
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delete[] result;
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absl::StripAsciiWhitespace(ocr_text);
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}
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static void VerifyTextResult(TessBaseAPI *tess, Pix *pix, const std::string &lang,
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const std::string &expected_text) {
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TessBaseAPI *tess_local = nullptr;
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if (tess) {
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tess_local = tess;
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} else {
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tess_local = new TessBaseAPI;
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InitTessInstance(tess_local, lang);
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}
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std::string ocr_text;
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GetCleanedText(tess_local, pix, &ocr_text);
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EXPECT_STREQ(expected_text.c_str(), ocr_text.c_str());
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if (tess_local != tess)
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delete tess_local;
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}
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// Check that Tesseract/Cube produce the correct results in single-threaded
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// operation. If not, it is pointless to run the real multi-threaded tests.
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TEST_F(BaseapiThreadTest, TestBasicSanity) {
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for (int i = 0; i < num_langs_; ++i) {
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TessBaseAPI tess;
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InitTessInstance(&tess, langs_[i]);
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std::string ocr_text;
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GetCleanedText(&tess, pix_[i], &ocr_text);
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CHECK(strcmp(gt_text_[i].c_str(), ocr_text.c_str()) == 0) << "Failed with lang = " << langs_[i];
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}
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}
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// Test concurrent instance initialization.
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TEST_F(BaseapiThreadTest, TestInit) {
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#ifdef INCLUDE_TENSORFLOW
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const int n = num_langs_ * FLAGS_reps;
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ResetPool();
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std::vector<TessBaseAPI> tess(n);
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for (int i = 0; i < n; ++i) {
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pool_->Schedule(std::bind(InitTessInstance, &tess[i], langs_[i % num_langs_]));
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}
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WaitForPoolWorkers();
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#endif
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}
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// Test concurrent recognition.
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TEST_F(BaseapiThreadTest, TestRecognition) {
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#ifdef INCLUDE_TENSORFLOW
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const int n = num_langs_ * FLAGS_reps;
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std::vector<TessBaseAPI> tess(n);
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// Initialize api instances in a single thread.
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for (int i = 0; i < n; ++i) {
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InitTessInstance(&tess[i], langs_[i % num_langs_]);
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}
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ResetPool();
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for (int i = 0; i < n; ++i) {
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pool_->Schedule(std::bind(VerifyTextResult, &tess[i], pix_[i], langs_[i % num_langs_],
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gt_text_[i % num_langs_]));
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}
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WaitForPoolWorkers();
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#endif
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}
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TEST_F(BaseapiThreadTest, TestAll) {
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#ifdef INCLUDE_TENSORFLOW
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const int n = num_langs_ * FLAGS_reps;
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ResetPool();
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for (int i = 0; i < n; ++i) {
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pool_->Schedule(std::bind(VerifyTextResult, nullptr, pix_[i], langs_[i % num_langs_],
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gt_text_[i % num_langs_]));
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}
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WaitForPoolWorkers();
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#endif
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}
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} // namespace tesseract
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