//returns score & score after removing erroenously small rows and cols (null if segmentation algorithm doesn't support this)
private static Tuple<double, double?> EvaluateByNumRowsAndCols(List<WordsearchImage> wordsearchImages, SegmentationAlgorithm segAlgorithm)
{
DefaultLog.Info("Evaluating Wordsearch Image Segmentation by number of rows and cols returned . . .");
int numCorrect = 0;
int numCorrectRemoveSmallRowsAndCols = 0;
//Test the algorithm on each Wordsearch Image
foreach(WordsearchImage wordsearchImage in wordsearchImages)
{
//Register an interest in the Bitmap of the Wordsearch Image
wordsearchImage.RegisterInterestInBitmap();
Segmentation proposedSegmentation = segAlgorithm.Segment(wordsearchImage.Bitmap);
//Keep track of the number of correct
if(proposedSegmentation.NumRows == wordsearchImage.Rows &&
proposedSegmentation.NumCols == wordsearchImage.Cols)
{
numCorrect++;
}
//If this segmentation algorithm performs its segmentation with start & end character indices & can therefore have
// small rows and cols removed, do it
if(segAlgorithm is SegmentationAlgorithmByStartEndIndices)
{
Segmentation segRemoveSmallRowsAndCols = proposedSegmentation.RemoveSmallRowsAndCols();
if(segRemoveSmallRowsAndCols.NumRows == wordsearchImage.Rows &&
segRemoveSmallRowsAndCols.NumCols == wordsearchImage.Cols)
{
numCorrectRemoveSmallRowsAndCols++;
}
}
//Clean Up
wordsearchImage.DeregisterInterestInBitmap();
}
DefaultLog.Info("Returned {0}/{1} Wordsearch Segmentations Correctly", numCorrect, wordsearchImages.Count);
double score = (double)numCorrect / wordsearchImages.Count;
double? scoreRemoveSmallRowsAndCols = null;
if(segAlgorithm is SegmentationAlgorithmByStartEndIndices)
{
scoreRemoveSmallRowsAndCols = (double)numCorrectRemoveSmallRowsAndCols / wordsearchImages.Count;
DefaultLog.Info("Returned {0}/{1} Wordsearch Segmentations Correctly after removing small rows and cols",
numCorrectRemoveSmallRowsAndCols, wordsearchImages.Count);
}
DefaultLog.Info("Wordsearch Image Segmentation Evaluation Completed");
return Tuple.Create(score, scoreRemoveSmallRowsAndCols);
}
//Constructors
public AlgorithmCombination(SegmentationAlgorithm detectionSegmentationAlgorithm,
bool detectionSegmentationRemoveSmallRowsAndCols, SegmentationAlgorithm segmentationAlgorithm,
bool segmentationRemoveSmallRowsAndCols, EvaluateFullSystem.SegmentationMethod segmentationMethod,
Classifier probabilisticRotationCorrectionClassifier, Classifier classifier,
Solver wordsearchSolver)
{
this.DetectionSegmentationAlgorithm = detectionSegmentationAlgorithm;
this.DetectionSegmentationRemoveSmallRowsAndCols = detectionSegmentationRemoveSmallRowsAndCols;
this.SegmentationAlgorithm = segmentationAlgorithm;
this.SegmentationRemoveSmallRowsAndCols = segmentationRemoveSmallRowsAndCols;
this.SegmentationMethod = segmentationMethod;
this.ProbabilisticRotationCorrectionClassifier = probabilisticRotationCorrectionClassifier;
this.Classifier = classifier;
this.WordsearchSolver = wordsearchSolver;
}
private static double EvaluateReturnsWordsearch(List<Image> images, SegmentationAlgorithm segAlgorithm, bool removeSmallRowsAndCols)
{
DefaultLog.Info("Evaluating Wordsearch Detection by best wordsearch returned . . .");
int numCorrect = 0;
//Test the algorithm on each Image
foreach (Image image in images)
{
//Register an interest in the Bitmap of the Image
image.RegisterInterestInBitmap();
Tuple<List<IntPoint>, Bitmap> bestCandidate = DetectionAlgorithm.ExtractBestWordsearch(image.Bitmap, segAlgorithm, removeSmallRowsAndCols);
//If we found a valid best candidate
if (bestCandidate != null)
{
//Check if the best candidate is a wordsearch in this image
List<IntPoint> points = bestCandidate.Item1;
if(IsWordsearch(points, image))
{
numCorrect++;
}
else
{
//Console.WriteLine("Incorrect"); //Debug Point (for viewing incorrect Bitmaps)
}
}
else //Otherwise we couldn't find anything that resembeled a quadrilateral (and could therefore be a wordsearch)
{
}
//Clean up
bestCandidate.Item2.Dispose();
image.DeregisterInterestInBitmap();
}
DefaultLog.Info("Found a Wordsearch for {0} / {1} Images correctly", numCorrect, images.Count);
DefaultLog.Info("Wordsearch Detection Evaluation Completed");
return (double)numCorrect / images.Count;
}
internal static WordsearchSolutionEvaluator EvaluateWordsearchBitmap(Bitmap wordsearchBitmap, string[] wordsToFind,
Dictionary<string, List<WordPosition>> correctSolutions, SegmentationAlgorithm segmentationAlgorithm,
bool segmentationRemoveSmallRowsAndCols, SegmentationMethod segmentationMethod,
Classifier probabilisticRotationCorrectionClassifier, Classifier classifier, Solver wordsearchSolver)
{
/*
* Wordsearch Segmentation
*/
Segmentation segmentation = segmentationAlgorithm.Segment(wordsearchBitmap);
//Remove erroneously small rows and columns from the segmentation if that option is specified
if(segmentationRemoveSmallRowsAndCols)
{
segmentation = segmentation.RemoveSmallRowsAndCols();
}
/*
* Wordsearch Rotation Correction
*/
WordsearchRotation originalRotation;
//If we're using fixed row & col width
if (segmentationMethod == SegmentationMethod.FixedWidth)
{
originalRotation = new WordsearchRotation(wordsearchBitmap, segmentation.NumRows, segmentation.NumCols);
}
else //Otherwise we're using varied row/col width segmentation, use the Segmentation object
{
originalRotation = new WordsearchRotation(wordsearchBitmap, segmentation);
}
WordsearchRotation rotatedWordsearch = WordsearchRotationCorrection.CorrectOrientation(originalRotation, probabilisticRotationCorrectionClassifier);
Bitmap rotatedImage = rotatedWordsearch.Bitmap;
//If the wordsearch has been rotated
if (rotatedImage != wordsearchBitmap)
{
//Update the segmentation
//If the wordsearch rotation won't have been passed a segmentation
if (segmentationMethod == SegmentationMethod.FixedWidth)
{
//Make a new fixed width segmentation from the WordsearchRotation
segmentation = new Segmentation(rotatedWordsearch.Rows, rotatedWordsearch.Cols,
rotatedImage.Width, rotatedImage.Height);
}
else
{
//Use the rotated segmentation
segmentation = rotatedWordsearch.Segmentation;
}
}
/*
* Classification
*/
//Split image up into individual characters
Bitmap[,] rawCharImgs = null;
//If we're using fixed row & col width
if (segmentationMethod == SegmentationMethod.FixedWidth)
{
ResizeBicubic resize = new ResizeBicubic(Constants.CHAR_WITH_WHITESPACE_WIDTH * segmentation.NumCols,
Constants.CHAR_WITH_WHITESPACE_HEIGHT * segmentation.NumRows);
Bitmap resizedImage = resize.Apply(rotatedImage);
rawCharImgs = SplitImage.Grid(resizedImage, segmentation.NumRows, segmentation.NumCols);
//Resized image no longer required
resizedImage.Dispose();
}
else //Otherwise we're using varied row/col width segmentation
{
rawCharImgs = SplitImage.Segment(rotatedImage, segmentation);
//If the Segmentation Method is to resize the raw char imgs, resize them
if (segmentationMethod == SegmentationMethod.VariedWidthWithResize)
{
ResizeBicubic resize = new ResizeBicubic(Constants.CHAR_WITH_WHITESPACE_WIDTH, Constants.CHAR_WITH_WHITESPACE_HEIGHT);
for (int i = 0; i < rawCharImgs.GetLength(0); i++)
{
for (int j = 0; j < rawCharImgs.GetLength(1); j++)
{
//Only do the resize if it isn't already that size
if (rawCharImgs[i, j].Width != Constants.CHAR_WITH_WHITESPACE_WIDTH
|| rawCharImgs[i, j].Height != Constants.CHAR_WITH_WHITESPACE_HEIGHT)
{
Bitmap orig = rawCharImgs[i, j];
rawCharImgs[i, j] = resize.Apply(orig);
//Remove the now unnecessary original/not resized image
orig.Dispose();
}
}
}
}
}
//Full sized rotated image no longer required
rotatedImage.Dispose();
//Get the part of the image that actually contains the character (without any whitespace)
Bitmap[,] charImgs = CharImgExtractor.ExtractAll(rawCharImgs);
//Raw char img's are no longer required
rawCharImgs.ToSingleDimension().DisposeAll();
//Perform the classification on all of the images (returns probabilities for each possible class)
double[][][] classifierOutput = classifier.Classify(charImgs);
//Actual images of the characters are no longer required
charImgs.ToSingleDimension().DisposeAll();
/*
* Solve Wordsearch
*/
Solution solution = wordsearchSolver.Solve(classifierOutput, wordsToFind);
/*
* Evaluate the Proposed Solution
*/
WordsearchSolutionEvaluator evaluator = new WordsearchSolutionEvaluator(solution, correctSolutions);
return evaluator;
}
private static double Evaluate(List<Image> images, SegmentationAlgorithm detectionSegmentationAlgorithm,
bool detectionSegmentationRemoveSmallRowsAndCols, SegmentationAlgorithm segmentationAlgorithm,
bool segmentationRemoveSmallRowsAndCols, SegmentationMethod segmentationMethod,
Classifier probabilisticRotationCorrectionClassifier, Classifier classifier, Solver wordsearchSolver)
{
DefaultLog.Info("Evaluating Full System . . .");
int numCorrect = 0;
List<WordsearchSolutionEvaluator> evaluators = new List<WordsearchSolutionEvaluator>();
foreach(Image image in images)
{
//Register an interest in the Bitmap of the image
image.RegisterInterestInBitmap();
/*
* Wordsearch Detection
*/
Tuple<List<IntPoint>, Bitmap> wordsearchImageTuple = DetectionAlgorithm.ExtractBestWordsearch(image.Bitmap, detectionSegmentationAlgorithm, detectionSegmentationRemoveSmallRowsAndCols);
//Original wordsearch image is no longer required
image.DeregisterInterestInBitmap();
//If the system failed to find anything remotely resembling a wordsearch, fail now
if(wordsearchImageTuple == null)
{
continue;
}
//Get the words to look for later from this image & the correct solutions
string[] wordsToFind = null; //Requires default, but won't even get used
Dictionary<string, List<WordPosition>> correctSolutions = null;
//If the image contains more than one wordsearch, we need to work out which one has been found
if(image.WordsearchImages.Length > 1)
{
List<IntPoint> coordinates = wordsearchImageTuple.Item1;
bool found = false;
//Select the wordsearch found using the algorithm for checking if the returned wordsearch is correct in EvaluateWordsearchDetection
foreach(WordsearchImage wordsearchImage in image.WordsearchImages)
{
//If it's this wordsearch
if(EvaluateWordsearchDetection.IsWordsearch(coordinates, wordsearchImage))
{
wordsToFind = wordsearchImage.Wordsearch.Words;
correctSolutions = wordsearchImage.Wordsearch.Solutions;
found = true;
break;
}
}
//If this isn't one of the wordsearches in the image, then fail now
if(!found)
{
//Clean up
wordsearchImageTuple.Item2.Dispose();
continue;
}
}
else //Otherwise just use the one wordsearch that's in the image
{
wordsToFind = image.WordsearchImages[0].Wordsearch.Words;
correctSolutions = image.WordsearchImages[0].Wordsearch.Solutions;
}
Bitmap extractedImage = wordsearchImageTuple.Item2;
/*
* Image Segmentation onwards happen in EvaluateWordsearchBitmap
*/
WordsearchSolutionEvaluator evaluator = EvaluateWordsearchBitmap(extractedImage, wordsToFind, correctSolutions,
segmentationAlgorithm, segmentationRemoveSmallRowsAndCols, segmentationMethod,
probabilisticRotationCorrectionClassifier, classifier, wordsearchSolver);
//Clean up
extractedImage.Dispose();
//Log Evaluation
evaluators.Add(evaluator);
DefaultLog.Info(evaluator.ToString());
if(evaluator.Correct)
{
numCorrect++;
}
}
DefaultLog.Info("System found all words correctly for {0} / {1} Images correctly", numCorrect, images.Count);
//Calculate some extra statistics
int numWordsearchesNoWordsFound = 0;
int numDidntReachEvaluation = images.Count - evaluators.Count;
double fMeasureSum = 0;
int numValidFMeasures = 0;
foreach (WordsearchSolutionEvaluator evaluator in evaluators)
{
//If no words were found correctly
if(evaluator.TruePositive == 0)
{
numWordsearchesNoWordsFound++;
}
//If there was a valid F-Measure
if(!double.IsNaN(evaluator.FMeasure))
{
fMeasureSum += evaluator.FMeasure;
numValidFMeasures++;
}
}
DefaultLog.Info("In {0} wordsearches no words were found correctly at all", numWordsearchesNoWordsFound);
DefaultLog.Info("{0} wordsearch images got discarded before reaching the evaluation stage", numDidntReachEvaluation);
DefaultLog.Info("Average F-Measure (when not NaN): {0}", fMeasureSum / numValidFMeasures);
DefaultLog.Info("Full System Evaluation Completed");
return (double)numCorrect / images.Count;
}
//Method to extract a Bitmap of the best match for a wordsearch in an image using a specified Wordsearch Segmentation Algorithm
//returns null if no Wordsearch candidate could be found in the image
public static Tuple<List<IntPoint>, Bitmap> ExtractBestWordsearch(Bitmap image, SegmentationAlgorithm segAlg, bool removeSmallRowsAndCols)
{
double blobMinDimensionDbl = BLOB_MIN_DIMENSION_PERCENTAGE / 100;
int minWidth = (int)Math.Ceiling(image.Width * blobMinDimensionDbl); //Round up, so that the integer comaprison minimum will always be correct
int minHeight = (int)Math.Ceiling(image.Height * blobMinDimensionDbl);
List<List<IntPoint>> quads = ShapeFinder.Quadrilaterals(image, minWidth, minHeight);
//Check that there are some quads found to search through for the best wordsearch candidate
if(quads.Count != 0)
{
double bestScore = double.NegativeInfinity;
List<IntPoint> bestCoords = null;
Bitmap bestBitmap = null;
//Search for the Bitmap that yields the best score
foreach (List<IntPoint> quad in quads)
{
//Extract the Bitmap of this quad
QuadrilateralTransformation quadTransform = new QuadrilateralTransformation(quad);
Bitmap quadBitmap = quadTransform.Apply(image);
//Score this wordsearch candidate
double score;
//If an InvalidRowsAndCols Exception gets throw by the segmentation
// (due to there being 0 rows/cols in the returned segmentation)
// this is obviously not a good wordsearch candidate
try
{
Segmentation segmentation = segAlg.Segment(quadBitmap);
//If removing erroneously small rows and cols before scoring the segmentation, do so now
if(removeSmallRowsAndCols)
{
segmentation = segmentation.RemoveSmallRowsAndCols();
}
CandidateScorer scorer = new CandidateScorer(segmentation);
score = scorer.WordsearchRecognitionScore;
}
catch(InvalidRowsAndColsException)
{
//This is slightly better than the default score of Negative Infinity as any candidate
// (even one with no rows or cols found in it) is better than no candidate whatsoever
score = double.MinValue;
}
//If this score is better than the previous best (don't
// override equal scores as the list is size ordered and
// we'll default to the biggest wordsearch as being better)
if(score > bestScore)
{
bestScore = score;
bestCoords = quad;
//Dispose of the previously best Bitmap resource
if(bestBitmap != null)
{
bestBitmap.Dispose();
}
//Update the ptr to the new one
bestBitmap = quadBitmap;
}
else
{
//Clean up
quadBitmap.Dispose();
}
}
return Tuple.Create(bestCoords, bestBitmap);
}
else //Otherwise there are no quads to search through
{
return null;
}
}
public static Tuple<List<IntPoint>, Bitmap> ExtractBestWordsearch(Bitmap image, SegmentationAlgorithm segAlg)
{
return ExtractBestWordsearch(image, segAlg, false); //Don't remove the erroneously small rows and cols by default
}