tesseract/lstm/input.cpp

///////////////////////////////////////////////////////////////////////
// File:        input.cpp
// Description: Input layer class for neural network implementations.
// Author:      Ray Smith
// Created:     Thu Mar 13 09:10:34 PDT 2014
//
// (C) Copyright 2014, 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 "input.h"

#include "allheaders.h"
#include "imagedata.h"
#include "pageres.h"
#include "scrollview.h"

namespace tesseract {

// Max height for variable height inputs before scaling anyway.
const int kMaxInputHeight = 48;

Input::Input(const STRING& name, int ni, int no)
    : Network(NT_INPUT, name, ni, no), cached_x_scale_(1) {}
Input::Input(const STRING& name, const StaticShape& shape)
    : Network(NT_INPUT, name, shape.height(), shape.depth()),
      shape_(shape),
      cached_x_scale_(1) {
  if (shape.height() == 1) ni_ = shape.depth();
}

Input::~Input() {
}

// Writes to the given file. Returns false in case of error.
bool Input::Serialize(TFile* fp) const {
  if (!Network::Serialize(fp)) return false;
  if (fp->FWrite(&shape_, sizeof(shape_), 1) != 1) return false;
  return true;
}

// Reads from the given file. Returns false in case of error.
// If swap is true, assumes a big/little-endian swap is needed.
bool Input::DeSerialize(bool swap, TFile* fp) {
  if (fp->FRead(&shape_, sizeof(shape_), 1) != 1) return false;
  // TODO(rays) swaps!
  return true;
}

// Returns an integer reduction factor that the network applies to the
// time sequence. Assumes that any 2-d is already eliminated. Used for
// scaling bounding boxes of truth data.
int Input::XScaleFactor() const {
  return 1;
}

// Provides the (minimum) x scale factor to the network (of interest only to
// input units) so they can determine how to scale bounding boxes.
void Input::CacheXScaleFactor(int factor) {
  cached_x_scale_ = factor;
}

// Runs forward propagation of activations on the input line.
// See Network for a detailed discussion of the arguments.
void Input::Forward(bool debug, const NetworkIO& input,
                    const TransposedArray* input_transpose,
                    NetworkScratch* scratch, NetworkIO* output) {
  *output = input;
}

// Runs backward propagation of errors on the deltas line.
// See NetworkCpp for a detailed discussion of the arguments.
bool Input::Backward(bool debug, const NetworkIO& fwd_deltas,
                     NetworkScratch* scratch,
                     NetworkIO* back_deltas) {
  tprintf("Input::Backward should not be called!!\n");
  return false;
}

// Creates and returns a Pix of appropriate size for the network from the
// image_data. If non-null, *image_scale returns the image scale factor used.
// Returns nullptr on error.
/* static */
Pix* Input::PrepareLSTMInputs(const ImageData& image_data,
                              const Network* network, int min_width,
                              TRand* randomizer, float* image_scale) {
  // Note that NumInputs() is defined as input image height.
  int target_height = network->NumInputs();
  int width, height;
  Pix* pix = image_data.PreScale(target_height, kMaxInputHeight, image_scale,
                                 &width, &height, nullptr);
  if (pix == nullptr) {
    tprintf("Bad pix from ImageData!\n");
    return nullptr;
  }
  if (width <= min_width) {
    tprintf("Image too small to scale!! (%dx%d vs min width of %d)\n", width,
            height, min_width);
    pixDestroy(&pix);
    return nullptr;
  }
  return pix;
}

// Converts the given pix to a NetworkIO of height and depth appropriate to the
// given StaticShape:
// If depth == 3, convert to 24 bit color, otherwise normalized grey.
// Scale to target height, if the shape's height is > 1, or its depth if the
// height == 1. If height == 0 then no scaling.
// NOTE: It isn't safe for multiple threads to call this on the same pix.
/* static */
void Input::PreparePixInput(const StaticShape& shape, const Pix* pix,
                            TRand* randomizer, NetworkIO* input) {
  bool color = shape.depth() == 3;
  Pix* var_pix = const_cast<Pix*>(pix);
  int depth = pixGetDepth(var_pix);
  Pix* normed_pix = nullptr;
  // On input to BaseAPI, an image is forced to be 1, 8 or 24 bit, without
  // colormap, so we just have to deal with depth conversion here.
  if (color) {
    // Force RGB.
    if (depth == 32)
      normed_pix = pixClone(var_pix);
    else
      normed_pix = pixConvertTo32(var_pix);
  } else {
    // Convert non-8-bit images to 8 bit.
    if (depth == 8)
      normed_pix = pixClone(var_pix);
    else
      normed_pix = pixConvertTo8(var_pix, false);
  }
  int width = pixGetWidth(normed_pix);
  int height = pixGetHeight(normed_pix);
  int target_height = shape.height();
  if (target_height == 1) target_height = shape.depth();
  if (target_height == 0) target_height = height;
  float im_factor = static_cast<float>(target_height) / height;
  if (im_factor != 1.0f) {
    // Get the scaled image.
    Pix* scaled_pix = pixScale(normed_pix, im_factor, im_factor);
    pixDestroy(&normed_pix);
    normed_pix = scaled_pix;
  }
  input->FromPix(shape, normed_pix, randomizer);
  pixDestroy(&normed_pix);
}

}  // namespace tesseract.