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https://github.com/tesseract-ocr/tesseract.git
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83 lines
3.1 KiB
C++
83 lines
3.1 KiB
C++
///////////////////////////////////////////////////////////////////////
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// File: lstmeval.cpp
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// Description: Evaluation program for LSTM-based networks.
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// Author: Ray Smith
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// Created: Wed Nov 23 12:20:06 PST 2016
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//
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// (C) Copyright 2016, 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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///////////////////////////////////////////////////////////////////////
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#ifdef GOOGLE_TESSERACT
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#include "base/commandlineflags.h"
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#endif
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#include "commontraining.h"
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#include "genericvector.h"
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#include "lstmtester.h"
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#include "strngs.h"
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#include "tprintf.h"
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STRING_PARAM_FLAG(model, "", "Name of model file (training or recognition)");
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STRING_PARAM_FLAG(traineddata, "",
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"If model is a training checkpoint, then traineddata must "
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"be the traineddata file that was given to the trainer");
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STRING_PARAM_FLAG(eval_listfile, "",
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"File listing sample files in lstmf training format.");
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INT_PARAM_FLAG(max_image_MB, 2000, "Max memory to use for images.");
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INT_PARAM_FLAG(verbosity, 1,
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"Amount of diagnosting information to output (0-2).");
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int main(int argc, char **argv) {
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tesseract::CheckSharedLibraryVersion();
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ParseArguments(&argc, &argv);
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if (FLAGS_model.empty()) {
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tprintf("Must provide a --model!\n");
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return 1;
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}
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if (FLAGS_eval_listfile.empty()) {
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tprintf("Must provide a --eval_listfile!\n");
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return 1;
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}
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tesseract::TessdataManager mgr;
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if (!mgr.Init(FLAGS_model.c_str())) {
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if (FLAGS_traineddata.empty()) {
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tprintf("Must supply --traineddata to eval a training checkpoint!\n");
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return 1;
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}
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tprintf("%s is not a recognition model, trying training checkpoint...\n",
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FLAGS_model.c_str());
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if (!mgr.Init(FLAGS_traineddata.c_str())) {
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tprintf("Failed to load language model from %s!\n",
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FLAGS_traineddata.c_str());
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return 1;
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}
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GenericVector<char> model_data;
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if (!tesseract::LoadDataFromFile(FLAGS_model.c_str(), &model_data)) {
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tprintf("Failed to load model from: %s\n", FLAGS_model.c_str());
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return 1;
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}
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mgr.OverwriteEntry(tesseract::TESSDATA_LSTM, &model_data[0],
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model_data.size());
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}
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tesseract::LSTMTester tester(static_cast<int64_t>(FLAGS_max_image_MB) *
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1048576);
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if (!tester.LoadAllEvalData(FLAGS_eval_listfile.c_str())) {
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tprintf("Failed to load eval data from: %s\n", FLAGS_eval_listfile.c_str());
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return 1;
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}
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double errs = 0.0;
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STRING result =
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tester.RunEvalSync(0, &errs, mgr,
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/*training_stage (irrelevant)*/ 0, FLAGS_verbosity);
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tprintf("%s\n", result.string());
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return 0;
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} /* main */
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