training: Add missing static attributes

That fixes several warnings from clang++ like the following one:

    src/training/combine_lang_model.cpp:36:1: warning: no previous extern declaration for non-static variable 'FLAGS_lang_is_rtl' [-Wmissing-variable-declarations]

Signed-off-by: Stefan Weil <sw@weilnetz.de>

src/training/combine_lang_model.cpp

@@ -22,22 +22,22 @@
 #include "tprintf.h"
 #include "unicharset_training_utils.h"
 
-STRING_PARAM_FLAG(input_unicharset, "",
-                  "Filename with unicharset to complete and use in encoding");
-STRING_PARAM_FLAG(script_dir, "",
-                  "Directory name for input script unicharsets");
-STRING_PARAM_FLAG(words, "",
-                  "File listing words to use for the system dictionary");
-STRING_PARAM_FLAG(puncs, "", "File listing punctuation patterns");
-STRING_PARAM_FLAG(numbers, "", "File listing number patterns");
-STRING_PARAM_FLAG(output_dir, "", "Root directory for output files");
-STRING_PARAM_FLAG(version_str, "", "Version string to add to traineddata file");
-STRING_PARAM_FLAG(lang, "", "Name of language being processed");
-BOOL_PARAM_FLAG(lang_is_rtl, false,
-                "True if lang being processed is written right-to-left");
-BOOL_PARAM_FLAG(pass_through_recoder, false,
-                "If true, the recoder is a simple pass-through of the"
-                " unicharset. Otherwise, potentially a compression of it");
+static STRING_PARAM_FLAG(input_unicharset, "",
+                         "Filename with unicharset to complete and use in encoding");
+static STRING_PARAM_FLAG(script_dir, "",
+                         "Directory name for input script unicharsets");
+static STRING_PARAM_FLAG(words, "",
+                         "File listing words to use for the system dictionary");
+static STRING_PARAM_FLAG(puncs, "", "File listing punctuation patterns");
+static STRING_PARAM_FLAG(numbers, "", "File listing number patterns");
+static STRING_PARAM_FLAG(output_dir, "", "Root directory for output files");
+static STRING_PARAM_FLAG(version_str, "", "Version string to add to traineddata file");
+static STRING_PARAM_FLAG(lang, "", "Name of language being processed");
+static BOOL_PARAM_FLAG(lang_is_rtl, false,
+                       "True if lang being processed is written right-to-left");
+static BOOL_PARAM_FLAG(pass_through_recoder, false,
+                       "If true, the recoder is a simple pass-through of the "
+                       "unicharset. Otherwise, potentially a compression of it");
 
 int main(int argc, char** argv) {
   // Sets properties on the input unicharset file, and writes:

src/training/commontraining.cpp

@@ -87,27 +87,27 @@ using tesseract::ShapeTable;
 // -M 0.625   -B 0.05   -I 1.0   -C 1e-6.
 CLUSTERCONFIG Config = { elliptical, 0.625, 0.05, 1.0, 1e-6, 0 };
 FEATURE_DEFS_STRUCT feature_defs;
-CCUtil ccutil;
+static CCUtil ccutil;
 
 INT_PARAM_FLAG(debug_level, 0, "Level of Trainer debugging");
-INT_PARAM_FLAG(load_images, 0, "Load images with tr files");
-STRING_PARAM_FLAG(configfile, "", "File to load more configs from");
+static INT_PARAM_FLAG(load_images, 0, "Load images with tr files");
+static STRING_PARAM_FLAG(configfile, "", "File to load more configs from");
 STRING_PARAM_FLAG(D, "", "Directory to write output files to");
-STRING_PARAM_FLAG(F, "font_properties", "File listing font properties");
+static STRING_PARAM_FLAG(F, "font_properties", "File listing font properties");
 STRING_PARAM_FLAG(X, "", "File listing font xheights");
 STRING_PARAM_FLAG(U, "unicharset", "File to load unicharset from");
 STRING_PARAM_FLAG(O, "", "File to write unicharset to");
-STRING_PARAM_FLAG(output_trainer, "", "File to write trainer to");
+static STRING_PARAM_FLAG(output_trainer, "", "File to write trainer to");
 STRING_PARAM_FLAG(test_ch, "", "UTF8 test character string");
-DOUBLE_PARAM_FLAG(clusterconfig_min_samples_fraction, Config.MinSamples,
-                  "Min number of samples per proto as % of total");
-DOUBLE_PARAM_FLAG(clusterconfig_max_illegal, Config.MaxIllegal,
-                  "Max percentage of samples in a cluster which have more"
-                  " than 1 feature in that cluster");
-DOUBLE_PARAM_FLAG(clusterconfig_independence, Config.Independence,
-                  "Desired independence between dimensions");
-DOUBLE_PARAM_FLAG(clusterconfig_confidence, Config.Confidence,
-                  "Desired confidence in prototypes created");
+static DOUBLE_PARAM_FLAG(clusterconfig_min_samples_fraction, Config.MinSamples,
+                         "Min number of samples per proto as % of total");
+static DOUBLE_PARAM_FLAG(clusterconfig_max_illegal, Config.MaxIllegal,
+                         "Max percentage of samples in a cluster which have more"
+                         " than 1 feature in that cluster");
+static DOUBLE_PARAM_FLAG(clusterconfig_independence, Config.Independence,
+                         "Desired independence between dimensions");
+static DOUBLE_PARAM_FLAG(clusterconfig_confidence, Config.Confidence,
+                         "Desired confidence in prototypes created");
 
 /**
  * This routine parses the command line arguments that were

src/training/lstmeval.cpp

@@ -2,7 +2,6 @@
 // File:        lstmeval.cpp
 // Description: Evaluation program for LSTM-based networks.
 // Author:      Ray Smith
-// Created:     Wed Nov 23 12:20:06 PST 2016
 //
 // (C) Copyright 2016, Google Inc.
 // Licensed under the Apache License, Version 2.0 (the "License");
@@ -25,15 +24,15 @@
 #include "strngs.h"
 #include "tprintf.h"
 
-STRING_PARAM_FLAG(model, "", "Name of model file (training or recognition)");
-STRING_PARAM_FLAG(traineddata, "",
-                  "If model is a training checkpoint, then traineddata must "
-                  "be the traineddata file that was given to the trainer");
-STRING_PARAM_FLAG(eval_listfile, "",
-                  "File listing sample files in lstmf training format.");
-INT_PARAM_FLAG(max_image_MB, 2000, "Max memory to use for images.");
-INT_PARAM_FLAG(verbosity, 1,
-               "Amount of diagnosting information to output (0-2).");
+static STRING_PARAM_FLAG(model, "", "Name of model file (training or recognition)");
+static STRING_PARAM_FLAG(traineddata, "",
+                         "If model is a training checkpoint, then traineddata must "
+                         "be the traineddata file that was given to the trainer");
+static STRING_PARAM_FLAG(eval_listfile, "",
+                         "File listing sample files in lstmf training format.");
+static INT_PARAM_FLAG(max_image_MB, 2000, "Max memory to use for images.");
+static INT_PARAM_FLAG(verbosity, 1,
+                      "Amount of diagnosting information to output (0-2).");
 
 int main(int argc, char **argv) {
   tesseract::CheckSharedLibraryVersion();

src/training/lstmtraining.cpp

@@ -2,7 +2,6 @@
 // File:        lstmtraining.cpp
 // Description: Training program for LSTM-based networks.
 // Author:      Ray Smith
-// Created:     Fri May 03 11:05:06 PST 2013
 //
 // (C) Copyright 2013, Google Inc.
 // Licensed under the Apache License, Version 2.0 (the "License");
@@ -28,41 +27,41 @@
 #include "tprintf.h"
 #include "unicharset_training_utils.h"
 
-INT_PARAM_FLAG(debug_interval, 0, "How often to display the alignment.");
-STRING_PARAM_FLAG(net_spec, "", "Network specification");
-INT_PARAM_FLAG(net_mode, 192, "Controls network behavior.");
-INT_PARAM_FLAG(perfect_sample_delay, 0,
-               "How many imperfect samples between perfect ones.");
-DOUBLE_PARAM_FLAG(target_error_rate, 0.01, "Final error rate in percent.");
-DOUBLE_PARAM_FLAG(weight_range, 0.1, "Range of initial random weights.");
-DOUBLE_PARAM_FLAG(learning_rate, 10.0e-4, "Weight factor for new deltas.");
-DOUBLE_PARAM_FLAG(momentum, 0.5, "Decay factor for repeating deltas.");
-DOUBLE_PARAM_FLAG(adam_beta, 0.999, "Decay factor for repeating deltas.");
-INT_PARAM_FLAG(max_image_MB, 6000, "Max memory to use for images.");
-STRING_PARAM_FLAG(continue_from, "", "Existing model to extend");
-STRING_PARAM_FLAG(model_output, "lstmtrain", "Basename for output models");
-STRING_PARAM_FLAG(train_listfile, "",
-                  "File listing training files in lstmf training format.");
-STRING_PARAM_FLAG(eval_listfile, "",
-                  "File listing eval files in lstmf training format.");
-BOOL_PARAM_FLAG(stop_training, false,
-               "Just convert the training model to a runtime model.");
-BOOL_PARAM_FLAG(convert_to_int, false,
-                "Convert the recognition model to an integer model.");
-BOOL_PARAM_FLAG(sequential_training, false,
-                "Use the training files sequentially instead of round-robin.");
-INT_PARAM_FLAG(append_index, -1, "Index in continue_from Network at which to"
-               " attach the new network defined by net_spec");
-BOOL_PARAM_FLAG(debug_network, false,
-                "Get info on distribution of weight values");
-INT_PARAM_FLAG(max_iterations, 0, "If set, exit after this many iterations");
-STRING_PARAM_FLAG(traineddata, "",
-                  "Combined Dawgs/Unicharset/Recoder for language model");
-STRING_PARAM_FLAG(old_traineddata, "",
-                  "When changing the character set, this specifies the old"
-                  " character set that is to be replaced");
-BOOL_PARAM_FLAG(randomly_rotate, false,
-                "Train OSD and randomly turn training samples upside-down");
+static INT_PARAM_FLAG(debug_interval, 0, "How often to display the alignment.");
+static STRING_PARAM_FLAG(net_spec, "", "Network specification");
+static INT_PARAM_FLAG(net_mode, 192, "Controls network behavior.");
+static INT_PARAM_FLAG(perfect_sample_delay, 0,
+                      "How many imperfect samples between perfect ones.");
+static DOUBLE_PARAM_FLAG(target_error_rate, 0.01, "Final error rate in percent.");
+static DOUBLE_PARAM_FLAG(weight_range, 0.1, "Range of initial random weights.");
+static DOUBLE_PARAM_FLAG(learning_rate, 10.0e-4, "Weight factor for new deltas.");
+static DOUBLE_PARAM_FLAG(momentum, 0.5, "Decay factor for repeating deltas.");
+static DOUBLE_PARAM_FLAG(adam_beta, 0.999, "Decay factor for repeating deltas.");
+static INT_PARAM_FLAG(max_image_MB, 6000, "Max memory to use for images.");
+static STRING_PARAM_FLAG(continue_from, "", "Existing model to extend");
+static STRING_PARAM_FLAG(model_output, "lstmtrain", "Basename for output models");
+static STRING_PARAM_FLAG(train_listfile, "",
+                         "File listing training files in lstmf training format.");
+static STRING_PARAM_FLAG(eval_listfile, "",
+                         "File listing eval files in lstmf training format.");
+static BOOL_PARAM_FLAG(stop_training, false,
+                       "Just convert the training model to a runtime model.");
+static BOOL_PARAM_FLAG(convert_to_int, false,
+                       "Convert the recognition model to an integer model.");
+static BOOL_PARAM_FLAG(sequential_training, false,
+                       "Use the training files sequentially instead of round-robin.");
+static INT_PARAM_FLAG(append_index, -1, "Index in continue_from Network at which to"
+                      " attach the new network defined by net_spec");
+static BOOL_PARAM_FLAG(debug_network, false,
+                       "Get info on distribution of weight values");
+static INT_PARAM_FLAG(max_iterations, 0, "If set, exit after this many iterations");
+static STRING_PARAM_FLAG(traineddata, "",
+                         "Combined Dawgs/Unicharset/Recoder for language model");
+static STRING_PARAM_FLAG(old_traineddata, "",
+                         "When changing the character set, this specifies the old"
+                         " character set that is to be replaced");
+static BOOL_PARAM_FLAG(randomly_rotate, false,
+                       "Train OSD and randomly turn training samples upside-down");
 
 // Number of training images to train between calls to MaintainCheckpoints.
 const int kNumPagesPerBatch = 100;

src/training/mergenf.cpp

@@ -33,18 +33,18 @@
 #include "params.h"
 
 /*-------------------once in subfeat---------------------------------*/
-double_VAR(training_angle_match_scale, 1.0, "Angle Match Scale ...");
+static double_VAR(training_angle_match_scale, 1.0, "Angle Match Scale ...");
 
-double_VAR(training_similarity_midpoint, 0.0075, "Similarity Midpoint ...");
+static double_VAR(training_similarity_midpoint, 0.0075, "Similarity Midpoint ...");
 
-double_VAR(training_similarity_curl, 2.0, "Similarity Curl ...");
+static double_VAR(training_similarity_curl, 2.0, "Similarity Curl ...");
 
 /*-----------------------------once in fasttrain----------------------------------*/
-double_VAR(training_tangent_bbox_pad, 0.5, "Tangent bounding box pad ...");
+static double_VAR(training_tangent_bbox_pad, 0.5, "Tangent bounding box pad ...");
 
-double_VAR(training_orthogonal_bbox_pad, 2.5, "Orthogonal bounding box pad ...");
+static double_VAR(training_orthogonal_bbox_pad, 2.5, "Orthogonal bounding box pad ...");
 
-double_VAR(training_angle_pad, 45.0, "Angle pad ...");
+static double_VAR(training_angle_pad, 45.0, "Angle pad ...");
 
 /**
  * Compare protos p1 and p2 and return an estimate of the

src/training/set_unicharset_properties.cpp

@@ -18,8 +18,8 @@
 #include "unicharset_training_utils.h"
 
 // The directory that is searched for universal script unicharsets.
-STRING_PARAM_FLAG(script_dir, "",
-                  "Directory name for input script unicharsets/xheights");
+static STRING_PARAM_FLAG(script_dir, "",
+                         "Directory name for input script unicharsets/xheights");
 
 int main(int argc, char** argv) {
   tesseract::CheckSharedLibraryVersion();

src/training/shapeclustering.cpp

@@ -28,12 +28,12 @@
 #include "params.h"
 #include "strngs.h"
 
-INT_PARAM_FLAG(display_cloud_font, -1,
-               "Display cloud of this font, canonical_class1");
-INT_PARAM_FLAG(display_canonical_font, -1,
-               "Display canonical sample of this font, canonical_class2");
-STRING_PARAM_FLAG(canonical_class1, "", "Class to show ambigs for");
-STRING_PARAM_FLAG(canonical_class2, "", "Class to show ambigs for");
+static INT_PARAM_FLAG(display_cloud_font, -1,
+                      "Display cloud of this font, canonical_class1");
+static INT_PARAM_FLAG(display_canonical_font, -1,
+                      "Display canonical sample of this font, canonical_class2");
+static STRING_PARAM_FLAG(canonical_class1, "", "Class to show ambigs for");
+static STRING_PARAM_FLAG(canonical_class2, "", "Class to show ambigs for");
 
 // Loads training data, if requested displays debug information, otherwise
 // creates the master shape table by shape clustering and writes it to a file.