/**********************************************************************
 * File:        fixxht.cpp  (Formerly fixxht.c)
 * Description: Improve x_ht and look out for case inconsistencies
 * Author:		Phil Cheatle
 * Created:		Thu Aug  5 14:11:08 BST 1993
 *
 * (C) Copyright 1992, Hewlett-Packard Ltd.
 ** 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          <string.h>
#include          <ctype.h>
#include          "params.h"
#include          "float2int.h"
#include          "tesseractclass.h"

namespace tesseract {

	// Fixxht overview.
	// Premise: Initial estimate of x-height is adequate most of the time, but
	// occasionally it is incorrect. Most notable causes of failure are:
	// 1. Small caps, where the top of the caps is the same as the body text
	// xheight. For small caps words the xheight needs to be reduced to correctly
	// recognize the caps in the small caps word.
	// 2. All xheight lines, such as summer. Here the initial estimate will have
	// guessed that the blob tops are caps and will have placed the xheight too low.
	// 3. Noise/logos beside words, or changes in font size on a line. Such
	// things can blow the statistics and cause an incorrect estimate.
	// 4. Incorrect baseline. Can happen when 2 columns are incorrectly merged.
	// In this case the x-height is often still correct.
	//
	// Algorithm.
	// Compare the vertical position (top only) of alphnumerics in a word with
	// the range of positions in training data (in the unicharset).
	// See CountMisfitTops. If any characters disagree sufficiently with the
	// initial xheight estimate, then recalculate the xheight, re-run OCR on
	// the word, and if the number of vertical misfits goes down, along with
	// either the word rating or certainty, then keep the new xheight.
	// The new xheight is calculated as follows:ComputeCompatibleXHeight
	// For each alphanumeric character that has a vertically misplaced top
	// (a misfit), yet its bottom is within the acceptable range (ie it is not
	// likely a sub-or super-script) calculate the range of acceptable xheight
	// positions from its range of tops, and give each value in the range a
	// number of votes equal to the distance of its top from its acceptance range.
	// The x-height position with the median of the votes becomes the new
	// x-height. This assumes that most characters will be correctly recognized
	// even if the x-height is incorrect. This is not a terrible assumption, but
	// it is not great. An improvement would be to use a classifier that does
	// not care about vertical position or scaling at all.
	// Separately collect stats on shifted baselines and apply the same logic to
	// computing a best-fit shift to fix the error. If the baseline needs to be
	// shifted, but the x-height is OK, returns the original x-height along with
	// the baseline shift to indicate that recognition needs to re-run.

	// If the max-min top of a unicharset char is bigger than kMaxCharTopRange
	// then the char top cannot be used to judge misfits or suggest a new top.
	const int kMaxCharTopRange = 48;

	// Returns the number of misfit blob tops in this word.
	int Tesseract::CountMisfitTops(WERD_RES *word_res) {
		int bad_blobs = 0;
		int num_blobs = word_res->rebuild_word->NumBlobs();
		for (int blob_id = 0; blob_id < num_blobs; ++blob_id) {
			TBLOB* blob = word_res->rebuild_word->blobs[blob_id];
			UNICHAR_ID class_id = word_res->best_choice->unichar_id(blob_id);
			if (unicharset.get_isalpha(class_id) || unicharset.get_isdigit(class_id)) {
				int top = blob->bounding_box().top();
				if (top >= INT_FEAT_RANGE)
					top = INT_FEAT_RANGE - 1;
				int min_bottom, max_bottom, min_top, max_top;
				unicharset.get_top_bottom(class_id, &min_bottom, &max_bottom,
					&min_top, &max_top);
				if (max_top - min_top > kMaxCharTopRange)
					continue;
				bool bad = top < min_top - x_ht_acceptance_tolerance ||
					top > max_top + x_ht_acceptance_tolerance;
				if (bad)
					++bad_blobs;
				if (debug_x_ht_level >= 1) {
					tprintf("Class %s is %s with top %d vs limits of %d->%d, +/-%d\n",
						unicharset.id_to_unichar(class_id),
						bad ? "Misfit" : "OK", top, min_top, max_top,
						static_cast<int>(x_ht_acceptance_tolerance));
				}
			}
		}
		return bad_blobs;
	}

	// Returns a new x-height maximally compatible with the result in word_res.
	// See comment above for overall algorithm.
	float Tesseract::ComputeCompatibleXheight(WERD_RES *word_res,
		float* baseline_shift) {
		STATS top_stats(0, MAX_UINT8);
		STATS shift_stats(-MAX_UINT8, MAX_UINT8);
		int bottom_shift = 0;
		int num_blobs = word_res->rebuild_word->NumBlobs();
		do {
			top_stats.clear();
			shift_stats.clear();
			for (int blob_id = 0; blob_id < num_blobs; ++blob_id) {
				TBLOB* blob = word_res->rebuild_word->blobs[blob_id];
				UNICHAR_ID class_id = word_res->best_choice->unichar_id(blob_id);
				if (unicharset.get_isalpha(class_id) ||
					unicharset.get_isdigit(class_id)) {
					int top = blob->bounding_box().top() + bottom_shift;
					// Clip the top to the limit of normalized feature space.
					if (top >= INT_FEAT_RANGE)
						top = INT_FEAT_RANGE - 1;
					int bottom = blob->bounding_box().bottom() + bottom_shift;
					int min_bottom, max_bottom, min_top, max_top;
					unicharset.get_top_bottom(class_id, &min_bottom, &max_bottom,
						&min_top, &max_top);
					// Chars with a wild top range would mess up the result so ignore them.
					if (max_top - min_top > kMaxCharTopRange)
						continue;
					int misfit_dist = MAX((min_top - x_ht_acceptance_tolerance) - top,
						top - (max_top + x_ht_acceptance_tolerance));
					int height = top - kBlnBaselineOffset;
					if (debug_x_ht_level >= 2) {
						tprintf("Class %s: height=%d, bottom=%d,%d top=%d,%d, actual=%d,%d: ",
							unicharset.id_to_unichar(class_id),
							height, min_bottom, max_bottom, min_top, max_top,
							bottom, top);
					}
					// Use only chars that fit in the expected bottom range, and where
					// the range of tops is sensibly near the xheight.
					if (min_bottom <= bottom + x_ht_acceptance_tolerance &&
						bottom - x_ht_acceptance_tolerance <= max_bottom &&
						min_top > kBlnBaselineOffset &&
						max_top - kBlnBaselineOffset >= kBlnXHeight &&
						misfit_dist > 0) {
						// Compute the x-height position using proportionality between the
						// actual height and expected height.
						int min_xht = DivRounded(height * kBlnXHeight,
							max_top - kBlnBaselineOffset);
						int max_xht = DivRounded(height * kBlnXHeight,
							min_top - kBlnBaselineOffset);
						if (debug_x_ht_level >= 2) {
							tprintf(" xht range min=%d, max=%d\n", min_xht, max_xht);
						}
						// The range of expected heights gets a vote equal to the distance
						// of the actual top from the expected top.
						for (int y = min_xht; y <= max_xht; ++y)
							top_stats.add(y, misfit_dist);
					}
					else if ((min_bottom > bottom + x_ht_acceptance_tolerance ||
						bottom - x_ht_acceptance_tolerance > max_bottom) &&
						bottom_shift == 0) {
						// Get the range of required bottom shift.
						int min_shift = min_bottom - bottom;
						int max_shift = max_bottom - bottom;
						if (debug_x_ht_level >= 2) {
							tprintf(" bottom shift min=%d, max=%d\n", min_shift, max_shift);
						}
						// The range of expected shifts gets a vote equal to the min distance
						// of the actual bottom from the expected bottom, spread over the
						// range of its acceptance.
						int misfit_weight = abs(min_shift);
						if (max_shift > min_shift)
							misfit_weight /= max_shift - min_shift;
						for (int y = min_shift; y <= max_shift; ++y)
							shift_stats.add(y, misfit_weight);
					}
					else {
						if (bottom_shift == 0) {
							// Things with bottoms that are already ok need to say so, on the
							// 1st iteration only.
							shift_stats.add(0, kBlnBaselineOffset);
						}
						if (debug_x_ht_level >= 2) {
							tprintf(" already OK\n");
						}
					}
				}
			}
			if (shift_stats.get_total() > top_stats.get_total()) {
				bottom_shift = IntCastRounded(shift_stats.median());
				if (debug_x_ht_level >= 2) {
					tprintf("Applying bottom shift=%d\n", bottom_shift);
				}
			}
		} while (bottom_shift != 0 &&
			top_stats.get_total() < shift_stats.get_total());
		// Baseline shift is opposite sign to the bottom shift.
		*baseline_shift = -bottom_shift / word_res->denorm.y_scale();
		if (debug_x_ht_level >= 2) {
			tprintf("baseline shift=%g\n", *baseline_shift);
		}
		if (top_stats.get_total() == 0)
			return bottom_shift != 0 ? word_res->x_height : 0.0f;
		// The new xheight is just the median vote, which is then scaled out
		// of BLN space back to pixel space to get the x-height in pixel space.
		float new_xht = top_stats.median();
		if (debug_x_ht_level >= 2) {
			tprintf("Median xht=%f\n", new_xht);
			tprintf("Mode20:A: New x-height = %f (norm), %f (orig)\n",
				new_xht, new_xht / word_res->denorm.y_scale());
		}
		// The xheight must change by at least x_ht_min_change to be used.
		if (fabs(new_xht - kBlnXHeight) >= x_ht_min_change)
			return new_xht / word_res->denorm.y_scale();
		else
			return bottom_shift != 0 ? word_res->x_height : 0.0f;
	}

}  // namespace tesseract