Fix some compiler warnings and avoid unnecessary conversions from std::string to char pointer

src/ccstruct/boxread.cpp

@@ -76,7 +76,7 @@ FILE *OpenBoxFile(const char *fname) {
 bool ReadAllBoxes(int target_page, bool skip_blanks, const char *filename, std::vector<TBOX> *boxes,
                   std::vector<std::string> *texts, std::vector<std::string> *box_texts,
                   std::vector<int> *pages) {
-  std::ifstream input(BoxFileName(filename).c_str(), std::ios::in | std::ios::binary);
+  std::ifstream input(BoxFileName(filename), std::ios::in | std::ios::binary);
   if (input.fail()) {
     tprintf("Cannot read box data from '%s'.\n", BoxFileName(filename).c_str());
     tprintf("Does it exists?\n");

src/textord/pithsync.cpp

@@ -316,9 +316,7 @@ double check_pitch_sync2(    // find segmentation
   TBOX this_box;                // bounding box
   TBOX next_box;                // box of next blob
   FPSEGPT *segpt;               // segment point
-  double best_cost;             // best path
   double mean_sum;              // computes result
-  FPCUTPT *best_end;            // end of best path
   int16_t best_fake;            // best fake level
   int16_t best_count;           // no of cuts
   BLOBNBOX_IT this_it;          // copy iterator
@@ -371,8 +369,6 @@ double check_pitch_sync2(    // find segmentation
   }
 
   this_it = *blob_it;
-  best_cost = FLT_MAX;
-  best_end = nullptr;
   this_box = box_next(&this_it); // first box
   next_box = box_next(&this_it); // second box
   blob_index = 1;
@@ -411,7 +407,8 @@ double check_pitch_sync2(    // find segmentation
   }
 
   best_fake = INT16_MAX;
-  best_cost = INT32_MAX;
+  // best path
+  double best_cost = INT32_MAX;
   best_count = INT16_MAX;
   while (x < right_edge + pitch) {
     offset = x < right_edge ? right_edge - x : 0;
@@ -438,7 +435,8 @@ double check_pitch_sync2(    // find segmentation
   }
   ASSERT_HOST(best_fake < INT16_MAX);
 
-  best_end = &cutpts[(best_left_x + best_right_x) / 2 - array_origin];
+  // end of best path
+  FPCUTPT *best_end = &cutpts[(best_left_x + best_right_x) / 2 - array_origin];
   if (this_box.right() == textord_test_x && this_box.top() == textord_test_y) {
     for (x = left_edge - pitch; x < right_edge + pitch; x++) {
       tprintf("x=%d, C=%g, s=%g, sq=%g, prev=%d\n", x, cutpts[x - array_origin].cost_function(),
@@ -509,9 +507,7 @@ double check_pitch_sync3(    // find segmentation
   FPSEGPT *segpt;               // segment point
   int minindex;                 // next input position
   int test_index;               // index to mins
-  double best_cost;             // best path
   double mean_sum;              // computes result
-  FPCUTPT *best_end;            // end of best path
   int16_t best_fake;            // best fake level
   int16_t best_count;           // no of cuts
   FPSEGPT_IT seg_it = seg_list; // output iterator
@@ -549,8 +545,6 @@ double check_pitch_sync3(    // find segmentation
                                    offset);
   }
 
-  best_cost = FLT_MAX;
-  best_end = nullptr;
   for (offset = -pitch_error, minindex = 0; offset < pitch_error; offset++, minindex++) {
     mins[minindex] = projection->local_min(x + offset);
   }
@@ -629,7 +623,8 @@ double check_pitch_sync3(    // find segmentation
   }
 
   best_fake = INT16_MAX;
-  best_cost = INT32_MAX;
+  // best path
+  double best_cost = INT32_MAX;
   best_count = INT16_MAX;
   while (x < right_edge + pitch) {
     offset = x < right_edge ? right_edge - x : 0;
@@ -656,7 +651,8 @@ double check_pitch_sync3(    // find segmentation
   }
   ASSERT_HOST(best_fake < INT16_MAX);
 
-  best_end = &cutpts[(best_left_x + best_right_x) / 2 - array_origin];
+  // end of best path
+  FPCUTPT *best_end = &cutpts[(best_left_x + best_right_x) / 2 - array_origin];
   //      for (x=left_edge-pitch;x<right_edge+pitch;x++)
   //      {
   //              tprintf("x=%d, C=%g, s=%g, sq=%g, prev=%d\n",

src/textord/tospace.cpp

@@ -283,10 +283,8 @@ void Textord::row_spacing_stats(TO_ROW *row, GAPMAP *gapmap, int16_t block_idx,
   int16_t gap_width;
   int16_t real_space_threshold = 0;
   int16_t max = 0;
-  int16_t index;
   int16_t large_gap_count = 0;
   bool suspected_table;
-  int32_t max_max_nonspace; // upper bound
   bool good_block_space_estimate = block_space_gap_width > 0;
   int32_t end_of_row;
   int32_t row_length = 0;
@@ -479,11 +477,12 @@ number of samples can be taken as certain kerns.
   below the threshold.
 */
 
-    max_max_nonspace = int32_t((row->space_threshold + row->kern_size) / 2);
+    // upper bound
+    int32_t max_max_nonspace = int32_t((row->space_threshold + row->kern_size) / 2);
 
     // default
     row->max_nonspace = max_max_nonspace;
-    for (index = 0; index <= max_max_nonspace; index++) {
+    for (int32_t index = 0; index <= max_max_nonspace; index++) {
       if (all_gap_stats.pile_count(index) > max) {
         max = all_gap_stats.pile_count(index);
       }

src/textord/underlin.cpp

@@ -211,13 +211,11 @@ void vertical_cunderline_projection( // project outlines
   ICOORD pos;               // current point
   ICOORD step;              // edge step
   int16_t lower_y, upper_y; // region limits
-  int32_t length;           // of outline
-  int16_t stepindex;        // current step
   C_OUTLINE_IT out_it = outline->child();
 
   pos = outline->start_pos();
-  length = outline->pathlength();
-  for (stepindex = 0; stepindex < length; stepindex++) {
+  int16_t length = outline->pathlength();
+  for (int16_t stepindex = 0; stepindex < length; stepindex++) {
     step = outline->step(stepindex);
     if (step.x() > 0) {
       lower_y = static_cast<int16_t>(floor(baseline->y(pos.x()) + baseline_offset + 0.5));

src/training/lstmtraining.cpp

@@ -92,7 +92,7 @@ int main(int argc, char **argv) {
   }
 
   // Check write permissions.
-  std::string test_file = FLAGS_model_output.c_str();
+  std::string test_file = FLAGS_model_output;
   test_file += "_wtest";
   FILE *f = fopen(test_file.c_str(), "wb");
   if (f != nullptr) {
@@ -107,10 +107,10 @@ int main(int argc, char **argv) {
   }
 
   // Setup the trainer.
-  std::string checkpoint_file = FLAGS_model_output.c_str();
+  std::string checkpoint_file = FLAGS_model_output;
   checkpoint_file += "_checkpoint";
   std::string checkpoint_bak = checkpoint_file + ".bak";
-  tesseract::LSTMTrainer trainer(FLAGS_model_output.c_str(), checkpoint_file.c_str(),
+  tesseract::LSTMTrainer trainer(FLAGS_model_output, checkpoint_file,
                                  FLAGS_debug_interval,
                                  static_cast<int64_t>(FLAGS_max_image_MB) * 1048576);
   if (!trainer.InitCharSet(FLAGS_traineddata.c_str())) {

src/training/unicharset/lstmtrainer.cpp

@@ -80,7 +80,7 @@ LSTMTrainer::LSTMTrainer()
   debug_interval_ = 0;
 }
 
-LSTMTrainer::LSTMTrainer(const char *model_base, const char *checkpoint_name,
+LSTMTrainer::LSTMTrainer(const std::string &model_base, const std::string &checkpoint_name,
                          int debug_interval, int64_t max_memory)
     : randomly_rotate_(false),
       training_data_(max_memory),

src/training/unicharset/lstmtrainer.h

@@ -84,7 +84,8 @@ using TestCallback = std::function<std::string(int, const double *,
 class TESS_UNICHARSET_TRAINING_API LSTMTrainer : public LSTMRecognizer {
 public:
   LSTMTrainer();
-  LSTMTrainer(const char *model_base, const char *checkpoint_name,
+  LSTMTrainer(const std::string &model_base,
+              const std::string &checkpoint_name,
               int debug_interval, int64_t max_memory);
   virtual ~LSTMTrainer();