public static Image DrawImageDetectionResult(MultiObjectLocalizationAndLabelingResult res, string image_url, List <string> Categories)
{
Image originalImage = ImageUtilities.getImageFromURI(image_url);
List <Rectangle> rectangles = new List <Rectangle>();
List <Color> colors = new List <Color>();
List <string> ids = new List <string>();
List <bool> dashed = new List <bool>();
for (int j = 0; j < res.objects.Count; j++)
{
MultiObjectLocalizationAndLabelingResultSingleEntry box = res.objects[j];
int x = box.boundingBox.tlx;
int y = box.boundingBox.tly;
int width = box.boundingBox.brx - box.boundingBox.tlx;
int height = box.boundingBox.bry - box.boundingBox.tly;
Rectangle r = new Rectangle(x, y, width, height);
rectangles.Add(r);
string category = box.Category;
int colorIndex = Categories.IndexOf(category);
colors.Add(DrawingBoxesAndLinesOnImages.Colors[colorIndex]);
string id = j + "-" + category;
ids.Add(id);
dashed.Add(false);
}
Image imageWithBoxes = DrawingBoxesAndLinesOnImages.addRectanglesToImage(originalImage, rectangles, colors, ids, dashed);
return(imageWithBoxes);
}
public static MultiObjectLocalizationAndLabelingResult GetImageDetectionResult(string image_url, List <string> Categories)
{
MultiObjectLocalizationAndLabelingResult ret = new MultiObjectLocalizationAndLabelingResult();
int height = 0;
int width = 0;
PredictResponse res = ImageDetectionRequest(image_url, out height, out width);
//float score_threshold =
int class_counts = res.Outputs["detection_classes"].FloatVal.Count;
float[] classes = new float[class_counts];
res.Outputs["detection_classes"].FloatVal.CopyTo(classes, 0);
int box_counts = res.Outputs["detection_boxes"].FloatVal.Count;
float[] boxes = new float[box_counts];
res.Outputs["detection_boxes"].FloatVal.CopyTo(boxes, 0);
int score_counts = res.Outputs["detection_scores"].FloatVal.Count;
float[] scores = new float[score_counts];
res.Outputs["detection_scores"].FloatVal.CopyTo(scores, 0);
double score_threshold = 0.5;
for (int i = 0; i < score_counts; i++)
{
float s = scores[i];
if (s == 0)
{
break;
}
if (s < score_threshold)
{
continue;
}
int c = (int)classes[i] - 1;
// comes in as ymin, xmin, ymax, xmax
int tlx = (int)(boxes[4 * i + 1] * width);
int tly = (int)(boxes[4 * i] * height);
int brx = (int)(boxes[4 * i + 3] * width);
int bry = (int)(boxes[4 * i + 2] * height);
MultiObjectLocalizationAndLabelingResultSingleEntry e = new MultiObjectLocalizationAndLabelingResultSingleEntry();
e.boundingBox = new BoundingBox(tlx, tly, brx, bry);
e.Category = Categories[c];
ret.objects.Add(e);
}
ret.imageHeight = height;
ret.imageWidth = width;
return(ret);
}
private static void ValidateSatyamKITTIObjectCountingAggregationResult(List <SatyamAggregatedResultsTableEntry> aggResultEntries,
//bool saveImage = false,
out double totalError,
out double totalGroundTruth,
int MinHeight = TaskConstants.OBJECT_COUNTING_VALIDATION_MIN_HEIGHT,
int MaxOcclusion = TaskConstants.OBJECT_COUNTING_VALIDATION_MAX_OCCLUSION,
double Max_Truncation = TaskConstants.OBJECT_COUNTING_VALIDATION_MIN_TRUNCATION)
{
List <double> aggResultCounts = new List <double>();
List <int> GroundTruthCounts = new List <int>();
List <double> errors = new List <double>();
foreach (SatyamAggregatedResultsTableEntry aggResultEntry in aggResultEntries)
{
SatyamSaveAggregatedDataSatyam data = new SatyamSaveAggregatedDataSatyam(aggResultEntry);
string fileName = URIUtilities.filenameFromURINoExtension(data.SatyamURI);
KITTIDetectionGroundTruth GroundTruthObjects = new KITTIDetectionGroundTruth(KITTIDetectionResultValidation.GroundTruthLabelDirectory + fileName + ".txt", MinHeight, MaxOcclusion, Max_Truncation);
string jobGUID = aggResultEntry.JobGUID;
int taskID = aggResultEntry.SatyamTaskTableEntryID;
String resultString = data.AggregatedResultString;
ObjectCountingAggregatedResult result = JSonUtils.ConvertJSonToObject <ObjectCountingAggregatedResult>(resultString);
int GroundtruthCount = 0;
for (int i = 0; i < GroundTruthObjects.objects.Count; i++)
{
MultiObjectLocalizationAndLabelingResultSingleEntry obj = GroundTruthObjects.objects[i];
//if (obj.Category == "Car" || obj.Category == "Van" || obj.Category == "DontCare")
if (obj.Category == "Car" || obj.Category == "Van")
{
//if (!GroundTruthObjects.BlackListed[i])
//{
GroundtruthCount++;
//}
}
}
aggResultCounts.Add(result.Count);
GroundTruthCounts.Add(GroundtruthCount);
errors.Add(Math.Abs(GroundtruthCount - result.Count));
}
totalError = errors.Sum();
totalGroundTruth = GroundTruthCounts.Sum();
double totalErrorRatio = totalError / totalGroundTruth;
Console.WriteLine("Error: {0} / {1} = {2}", totalError, totalGroundTruth, totalErrorRatio);
Console.WriteLine("Total Aggregated {0}", aggResultEntries.Count);
}
public static void testJSONString()
{
MultiObjectLocalizationAndLabelingResult res = new MultiObjectLocalizationAndLabelingResult();
res.objects = new List <MultiObjectLocalizationAndLabelingResultSingleEntry>();
MultiObjectLocalizationAndLabelingResultSingleEntry entry = new MultiObjectLocalizationAndLabelingResultSingleEntry();
entry.boundingBox = new BoundingBox(5, 15, 25, 35);
entry.Category = "Car";
res.objects.Add(entry);
entry = new MultiObjectLocalizationAndLabelingResultSingleEntry();
entry.boundingBox = new BoundingBox(45, 55, 65, 75);
entry.Category = "Bus";
res.objects.Add(entry);
string jsonString = JSonUtils.ConvertObjectToJSon <MultiObjectLocalizationAndLabelingResult>(res);
}
public static MultiObjectLocalizationAndLabelingAggregatedResult getAggregatedResult(List <MultiObjectLocalizationAndLabelingResult> results,
int MinResults = TaskConstants.MULTI_OBJECT_LOCALIZATION_AND_LABLING_MTURK_MIN_RESULTS_TO_AGGREGATE,
int MaxResults = TaskConstants.MULTI_OBJECT_LOCALIZATION_AND_LABLING_MTURK_MAX_RESULTS_TO_AGGREGATE,
double CategoryMajorityThreshold = TaskConstants.MULTI_OBJECT_LOCALIZATION_AND_LABLING_MTURK_MAJORITY_CATEGORY_THRESHOLD,
double ObjectsCoverageThreshold = TaskConstants.MULTI_OBJECT_LOCALIZATION_AND_LABLING_MTURK_OBJECT_COVERAGE_THRESHOLD_FOR_AGGREGATION_TERMINATION,
double DeviationPixelThreshold = TaskConstants.MULTI_OBJECT_LOCALIZATION_AND_LABLING_MTURK_DEVIATION_THRESHOLD
)
{
if (results.Count < MinResults)
{
return(null);
}
//if the image was scaled down during display, errors get scaled up
//so we need to scale our deviation thresholds
double minboxdimension_threshold_x = MIN_BOX_DIMENSION_FOR_CONSIDERATION * results[0].displayScaleReductionX;
double minboxdimension_threshold_y = MIN_BOX_DIMENSION_FOR_CONSIDERATION * results[0].displayScaleReductionY;
//first use multipartitie wieghted matching to associated the boxes disregarding the labels since
//people might make mistake with lables but boxes are usually right
List <List <BoundingBox> > allboxes = new List <List <BoundingBox> >();
List <int> noBoxesPerResult = new List <int>(); //how many boxes did each person draw?
List <List <bool> > largeBoxes = new List <List <bool> >(); //was this box was bigger than MIN_BOX_DIMENSION_FOR_CONSIDERATION?
foreach (MultiObjectLocalizationAndLabelingResult res in results)
{
allboxes.Add(new List <BoundingBox>());
largeBoxes.Add(new List <bool>());
if (res == null)
{
noBoxesPerResult.Add(0);
continue;
}
List <BoundingBox> boxList = allboxes[allboxes.Count - 1];
List <bool> largeBoxList = largeBoxes[largeBoxes.Count - 1];
foreach (MultiObjectLocalizationAndLabelingResultSingleEntry entry in res.objects)
{
boxList.Add(entry.boundingBox);
if (entry.boundingBox.getWidth() < minboxdimension_threshold_x && entry.boundingBox.getHeight() < minboxdimension_threshold_y)
{
largeBoxList.Add(false);
}
else
{
largeBoxList.Add(true);
}
}
noBoxesPerResult.Add(boxList.Count);
}
//now associate boxes across the various results
List <MultipartiteWeightedMatch> boxAssociation = BoundingBoxAssociation.computeBoundingBoxAssociations(allboxes);
int noBoxes = boxAssociation.Count;
int noAssociatedBoxes = 0;
int noIgnorable = 0;
//how many of the drawn boxes for each result were actually associated by two or more people for each user?
SortedDictionary <int, int> noMultipleAssociatedBoxesPerResult = new SortedDictionary <int, int>();
SortedDictionary <int, int> noSmallIgnorableBoxesPerResult = new SortedDictionary <int, int>();
for (int i = 0; i < results.Count; i++)
{
noMultipleAssociatedBoxesPerResult.Add(i, 0);
noSmallIgnorableBoxesPerResult.Add(i, 0);
}
foreach (MultipartiteWeightedMatch match in boxAssociation)
{
if (match.elementList.Count > 1) //this has been corroborated by two people
{
noAssociatedBoxes++;
foreach (KeyValuePair <int, int> entry in match.elementList)
{
noMultipleAssociatedBoxesPerResult[entry.Key]++;
}
}
else
{
List <int> keys = match.elementList.Keys.ToList();
if (largeBoxes[keys[0]][match.elementList[keys[0]]] == false) //the box was a small box can be ignored
{
noIgnorable++;
noSmallIgnorableBoxesPerResult[keys[0]]++;
}
}
}
//count how many people have a high association ratio
int noHighAssociationRatio = 0;
for (int i = 0; i < results.Count; i++)
{
if (noBoxesPerResult[i] == 0)
{
continue;
}
double ratio = ((double)noMultipleAssociatedBoxesPerResult[i] + (double)noSmallIgnorableBoxesPerResult[i]) / (double)noBoxesPerResult[i];
if (ratio > ObjectsCoverageThreshold)
{
noHighAssociationRatio++;
}
}
if (noHighAssociationRatio < MinResults) //at least three people should have all their boxes highly corroborated by one other person
{
return(null);
}
//now we have to find out if the boxes were drawn well and only take the boxes that are within
//a reasonable deviation of each other.
//if the image was scaled down during display, errors get scaled up
//so we need to scale our deviation thresholds
double deviation_threshold_x = DeviationPixelThreshold * results[0].displayScaleReductionX;
double deviation_threshold_y = DeviationPixelThreshold * results[0].displayScaleReductionY;
int noAcceptedBoxes = 0;
int noSmallIgnore = 0;
List <List <BoundingBoxGroup> > allBoxGroups = new List <List <BoundingBoxGroup> >();
List <BoundingBox> finalBoxes = new List <BoundingBox>(); //stores the aggregated boxes
SortedDictionary <int, List <int> > HighQualityAssociatedBoxPerResult = new SortedDictionary <int, List <int> >();
foreach (MultipartiteWeightedMatch match in boxAssociation)
{
List <BoundingBox> boxList = new List <BoundingBox>();
List <string> identifiers = new List <string>();
foreach (KeyValuePair <int, int> entry in match.elementList)
{
boxList.Add(allboxes[entry.Key][entry.Value]);
identifiers.Add(entry.Key + "_" + entry.Value);
}
//List<BoundingBoxGroup> boxGroups = MergeAndGroupBoundingBoxes.GreedyMeanHierarchicalMergeByPixelDeviation(boxList, identifiers, DEVIATION_THRESHOLD);
List <BoundingBoxGroup> boxGroups = MergeAndGroupBoundingBoxes.GreedyMeanHierarchicalMergeByPixelDeviation(boxList, identifiers, deviation_threshold_x, deviation_threshold_y);
allBoxGroups.Add(boxGroups);
int maxCount = 0;
int index = -1;
getMaxCountGroup(boxGroups, out maxCount, out index);
////find the boxgroup with the maxCount
//int maxCount = 0;
//int index = -1;
//for(int i=0;i<boxGroups.Count;i++)
//{
// if(boxGroups[i].boundingBoxList.Count > maxCount) // there are two boxes within acceptance range
// {
// maxCount = boxGroups[i].boundingBoxList.Count;
// index = i;
// }
//}
if (maxCount > 1)
{
noAcceptedBoxes++;
finalBoxes.Add(boxGroups[index].mergedBoundingBox);
// count the majority group high associations
foreach (string id in boxGroups[index].identifiers)
{
string[] fields = id.Split('_');
int partId = Convert.ToInt32(fields[0]);
int boxId = Convert.ToInt32(fields[1]);
if (!HighQualityAssociatedBoxPerResult.ContainsKey(partId))
{
HighQualityAssociatedBoxPerResult.Add(partId, new List <int>());
}
HighQualityAssociatedBoxPerResult[partId].Add(boxId);
}
}
else
{
finalBoxes.Add(BoundingBox.NullBoundingBox);
//were all the boxes drawn too small?
bool small = true;
for (int i = 0; i < boxGroups.Count; i++)
{
string[] parts = boxGroups[i].identifiers[0].Split('_');
int resultNo = Convert.ToInt32(parts[0]);
int boxNo = Convert.ToInt32(parts[1]);
if (largeBoxes[resultNo][boxNo] == true)
{
small = false;
break;
}
}
if (small)
{
noSmallIgnore++;
}
}
}
if (((double)noAcceptedBoxes + (double)noSmallIgnore) / (double)noAssociatedBoxes < ObjectsCoverageThreshold &&
results.Count < MaxResults) //this is acceptable
{
return(null);
}
//count how many people have a high "quality" association ratio
int noResultsWithHighQualityObjectCoverage = 0;
for (int i = 0; i < results.Count; i++)
{
if (noBoxesPerResult[i] == 0)
{
continue;
}
if (!HighQualityAssociatedBoxPerResult.ContainsKey(i))
{
continue;
}
double ratio = ((double)HighQualityAssociatedBoxPerResult[i].Count) / (double)noBoxesPerResult[i];
if (ratio > ObjectsCoverageThreshold)
{
noResultsWithHighQualityObjectCoverage++;
}
}
if (noResultsWithHighQualityObjectCoverage < MinResults && results.Count < MaxResults) //at least three people should have most of their boxes highly corroborated by one other person
{
return(null);
}
//now we need to see if the categores have a supermajority
List <string> finalCategories = new List <string>(); //stores the aggregated categories
int cntr = -1;
foreach (MultipartiteWeightedMatch match in boxAssociation)
{
cntr++;
if (BoundingBox.IsNullBoundingBox(finalBoxes[cntr])) //this is not an acceptable box
{
finalCategories.Add("");
continue;
}
Dictionary <string, int> categoryNames = new Dictionary <string, int>();
int totalCount = match.elementList.Count;
int maxCount = 0;
string maxCategory = "";
foreach (KeyValuePair <int, int> entry in match.elementList)
{
string category = results[entry.Key].objects[entry.Value].Category;
if (!categoryNames.ContainsKey(category))
{
categoryNames.Add(category, 0);
}
categoryNames[category]++;
if (maxCount < categoryNames[category])
{
maxCount = categoryNames[category];
maxCategory = category;
}
}
double probability = ((double)maxCount + 1) / ((double)totalCount + 2);
if (probability < CategoryMajorityThreshold && results.Count < MaxResults) //this is not a valid category need more work
{
return(null);
}
else
{
finalCategories.Add(maxCategory);
}
}
//now we have enough information to create the aggregate results
MultiObjectLocalizationAndLabelingAggregatedResult aggResult = new MultiObjectLocalizationAndLabelingAggregatedResult();
aggResult.metaData = new MultiObjectLocalizationAndLabelingAggregatedResultMetaData();
aggResult.metaData.TotalCount = results.Count;
aggResult.boxesAndCategories = new MultiObjectLocalizationAndLabelingResult();
aggResult.boxesAndCategories.objects = new List <MultiObjectLocalizationAndLabelingResultSingleEntry>();
cntr = -1;
foreach (MultipartiteWeightedMatch match in boxAssociation)
{
cntr++;
if (BoundingBox.IsNullBoundingBox(finalBoxes[cntr]))
{
continue;
}
MultiObjectLocalizationAndLabelingResultSingleEntry entry = new MultiObjectLocalizationAndLabelingResultSingleEntry();
entry.boundingBox = finalBoxes[cntr];
entry.Category = finalCategories[cntr];
aggResult.boxesAndCategories.objects.Add(entry);
}
aggResult.boxesAndCategories.displayScaleReductionX = results[0].displayScaleReductionX;
aggResult.boxesAndCategories.displayScaleReductionY = results[0].displayScaleReductionY;
aggResult.boxesAndCategories.imageHeight = results[0].imageHeight;
aggResult.boxesAndCategories.imageWidth = results[0].imageWidth;
return(aggResult);
}
public static void SaveResultImagesLocally(List <SatyamResultsTableEntry> entries, string directoryName)
{
if (!Directory.Exists(directoryName))
{
Directory.CreateDirectory(directoryName);
}
directoryName = directoryName + "\\Raw";
if (!Directory.Exists(directoryName))
{
Directory.CreateDirectory(directoryName);
}
for (int i = 0; i < entries.Count; i++)
{
SatyamResultsTableEntry entry = entries[i];
SatyamResult satyamResult = JSonUtils.ConvertJSonToObject <SatyamResult>(entry.ResultString);
SatyamTask task = JSonUtils.ConvertJSonToObject <SatyamTask>(satyamResult.TaskParametersString);
SatyamJob job = task.jobEntry;
MultiObjectLocalizationAndLabelingResult res = JSonUtils.ConvertJSonToObject <MultiObjectLocalizationAndLabelingResult>(satyamResult.TaskResult);
string ofilename = URIUtilities.filenameFromURI(task.SatyamURI);
string[] fields = ofilename.Split('.');
string fileName = fields[0];
Image originalImage = ImageUtilities.getImageFromURI(task.SatyamURI);
MultiObjectLocalizationAndLabelingSubmittedJob jobDefinition = JSonUtils.ConvertJSonToObject <MultiObjectLocalizationAndLabelingSubmittedJob>(job.JobParameters);
Image imageWithBoundary = DrawingBoxesAndLinesOnImages.addLinesToImage(originalImage, jobDefinition.BoundaryLines, Color.Red, true);
List <Rectangle> rectangles = new List <Rectangle>();
List <Color> colors = new List <Color>();
List <string> ids = new List <string>();
List <bool> dashed = new List <bool>();
for (int j = 0; j < res.objects.Count; j++)
{
MultiObjectLocalizationAndLabelingResultSingleEntry box = res.objects[j];
int x = box.boundingBox.tlx;
int y = box.boundingBox.tly;
int width = box.boundingBox.brx - box.boundingBox.tlx;
int height = box.boundingBox.bry - box.boundingBox.tly;
Rectangle r = new Rectangle(x, y, width, height);
rectangles.Add(r);
string category = box.Category;
int colorIndex = jobDefinition.Categories.IndexOf(category);
colors.Add(DrawingBoxesAndLinesOnImages.Colors[colorIndex]);
string id = j + "-" + category;
ids.Add(id);
dashed.Add(false);
}
Image imageWithBoxesAndBoundary = DrawingBoxesAndLinesOnImages.addRectanglesToImage(imageWithBoundary, rectangles, colors, ids, dashed);
fileName = fileName + "-Result";
if (satyamResult.amazonInfo.AssignmentID != "")
{
//fileName = fileName + "-" + satyamResult.amazonInfo.AssignmentID + "_" + entry.ID;
fileName = fileName + "-" + entry.ID + "_" + satyamResult.PrevResultID;
}
ImageUtilities.saveImage(imageWithBoxesAndBoundary, directoryName, fileName);
}
}
