private static double scoreBitmap(WordsearchRotation rotation, Classifier classifier)
{
//Extract each charcater in this wordsearch, then run them through the classifier and sum the liklihoods of
// the most probable class to determine an overall score for the image
Bitmap[,] chars = null;
//If using number of rows & cols for a fixed row/col width/height
if(rotation.Segmentation == null)
{
//Use standardised width & height for characters (do this by first resizing the image)
int wordsearchWidth = Constants.CHAR_WITH_WHITESPACE_WIDTH * rotation.Cols;
int wordsearchHeight = Constants.CHAR_WITH_WHITESPACE_HEIGHT * rotation.Rows;
ResizeBicubic resize = new ResizeBicubic(wordsearchWidth, wordsearchHeight);
Bitmap resizedImg = resize.Apply(rotation.Bitmap);
//Split the bitmap up into a 2D array of bitmaps
chars = SplitImage.Grid(resizedImg, rotation.Rows, rotation.Cols);
//If the image got resized, dispose of the resized copy
if(resizedImg != rotation.Bitmap)
{
resizedImg.Dispose();
}
}
else //Otherwise we have a Segmentation object to use
{
chars = SplitImage.Segment(rotation.Bitmap, rotation.Segmentation);
}
double score = 0;
foreach(Bitmap charImg in chars)
{
//Remove all of the whitespace etc... returning an image that can be used for classification
Bitmap extractedCharImg = CharImgExtractor.Extract(charImg);
//Classify this bitmap
double[] charResult = classifier.Classify(extractedCharImg);
//Get the largest probability from the classifier output and add it to the overall score
double largest = charResult[0];
for(int i = 1; i < charResult.Length; i++)
{
if(charResult[i] > largest)
{
largest = charResult[i];
}
}
score += largest;
//Clean up
extractedCharImg.Dispose();
charImg.Dispose();
}
return score;
}
internal static double Evaluate(List<WordsearchImage> wordsearchImages, Classifier classifier)
{
DefaultLog.Info("Evaluating Wordsearch Image Rotation Correction . . .");
int numCorrect = 0;
int numTotal = 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();
//Test the system on each posisble rotation of the wordsearch image
int[] angles = new int[] { 0, 90, 180, 270 };
Parallel.ForEach(angles, angle =>
{
WordsearchRotation rotation = new WordsearchRotation(wordsearchImage.Bitmap.DeepCopy(), (int)wordsearchImage.Rows, (int)wordsearchImage.Cols);
rotation.Rotate(angle);
//Rotate the image for the wordsearch back to the correct orientation so that we know the correct answer
WordsearchRotation correctRotation = rotation.DeepCopy();
correctRotation.Rotate((360 - angle) % 360); //Angles must be EXACTLY the same as the ones used in the correction in order to yield the same result (i.e. 0, 90, 180, 270)
WordsearchRotation proposedRotation = WordsearchRotationCorrection.CorrectOrientation(rotation, classifier);
//Keep track of the number correct & total number
if (proposedRotation.Bitmap.DataEquals(correctRotation.Bitmap))
{
numCorrect++;
}
numTotal++;
//Clean up
rotation.Bitmap.Dispose();
correctRotation.Bitmap.Dispose();
proposedRotation.Bitmap.Dispose();
});
//Clean up
wordsearchImage.DeregisterInterestInBitmap();
}
DefaultLog.Info("Returned {0}/{1} Wordsearch Rotations Correctly", numCorrect, numTotal);
DefaultLog.Info("Wordsearch Image Rotation Evaluation Completed");
return (double)numCorrect / numTotal;
}
//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;
}
public static WordsearchRotation CorrectOrientation(WordsearchRotation wordsearchRotation, Classifier classifier) //Classifier MUST be probablistic
{
//Rotate the Bitmap through the 4 possible rotations
WordsearchRotation[] rotations = new WordsearchRotation[4];
for(int i = 0; i < rotations.Length; i++)
{
int angle = i * 90;
rotations[i] = wordsearchRotation.DeepCopy();
rotations[i].Rotate(angle);
}
//Calculate how likely wach rotation is to be correct
double bestScore = double.MinValue;
int bestIdx = -1;
for(int i = 0; i < rotations.Length; i++)
{
double score = scoreBitmap(rotations[i], classifier);
if(score > bestScore)
{
bestScore = score;
bestIdx = i;
}
}
//Dispose of the Wordsearch Rotations that weren't the best
for(int i = 0; i < rotations.Length; i++)
{
if(i != bestIdx)
{
rotations[i].Bitmap.Dispose();
}
}
return rotations[bestIdx];
}
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;
}
