/// <summary>
/// Recognizes the markers.
/// </summary>
/// <param name="grayscale">Grayscale.</param>
/// <param name="detectedMarkers">Detected markers.</param>
void recognizeMarkers(Mat grayscale, List <Marker> detectedMarkers)
{
List <Marker> goodMarkers = new List <Marker>();
// Identify the markers
for (int i = 0; i < detectedMarkers.Count; i++)
{
Marker marker = detectedMarkers[i];
// Find the perspective transformation that brings current marker to rectangular form
Mat markerTransform = Imgproc.getPerspectiveTransform(new MatOfPoint2f(marker.points.toArray()), m_markerCorners2d);
// Transform image to get a canonical marker image
Imgproc.warpPerspective(grayscale, canonicalMarkerImage, markerTransform, markerSize);
for (int p = 0; p < m_markerDesigns.Count; p++)
{
MatOfInt nRotations = new MatOfInt(0);
int id = Marker.getMarkerId(canonicalMarkerImage, nRotations, m_markerDesigns[p]);
if (id != -1)
{
marker.id = id;
//sort the points so that they are always in the same order no matter the camera orientation
List <Point> MarkerPointsList = marker.points.toList();
//std::rotate(marker.points.begin(), marker.points.begin() + 4 - nRotations, marker.points.end());
MarkerPointsList = MarkerPointsList.Skip(4 - nRotations.toArray()[0]).Concat(MarkerPointsList.Take(4 - nRotations.toArray()[0])).ToList();
marker.points.fromList(MarkerPointsList);
goodMarkers.Add(marker);
}
nRotations.Dispose();
}
}
// Refine marker corners using sub pixel accuracy
if (goodMarkers.Count > 0)
{
List <Point> preciseCornersPoint = new List <Point>(4 * goodMarkers.Count);
for (int i = 0; i < preciseCornersPoint.Capacity; i++)
{
preciseCornersPoint.Add(new Point(0, 0));
}
for (int i = 0; i < goodMarkers.Count; i++)
{
Marker marker = goodMarkers[i];
List <Point> markerPointsList = marker.points.toList();
for (int c = 0; c < 4; c++)
{
preciseCornersPoint[i * 4 + c] = markerPointsList[c];
}
}
MatOfPoint2f preciseCorners = new MatOfPoint2f(preciseCornersPoint.ToArray());
TermCriteria termCriteria = new TermCriteria(TermCriteria.MAX_ITER | TermCriteria.EPS, 30, 0.01);
Imgproc.cornerSubPix(grayscale, preciseCorners, new Size(5, 5), new Size(-1, -1), termCriteria);
preciseCornersPoint = preciseCorners.toList();
// Copy refined corners position back to markers
for (int i = 0; i < goodMarkers.Count; i++)
{
Marker marker = goodMarkers[i];
List <Point> markerPointsList = marker.points.toList();
for (int c = 0; c < 4; c++)
{
markerPointsList[c] = preciseCornersPoint[i * 4 + c];
}
}
preciseCorners.Dispose();
}
detectedMarkers.Clear();
detectedMarkers.AddRange(goodMarkers);
}
/// <summary>
/// Recognizes the markers.
/// </summary>
/// <param name="grayscale">Grayscale.</param>
/// <param name="detectedMarkers">Detected markers.</param>
void recognizeMarkers (Mat grayscale, List<Marker> detectedMarkers)
{
List<Marker> goodMarkers = new List<Marker> ();
// Identify the markers
for (int i=0; i<detectedMarkers.Count; i++) {
Marker marker = detectedMarkers [i];
// Find the perspective transformation that brings current marker to rectangular form
Mat markerTransform = Imgproc.getPerspectiveTransform (new MatOfPoint2f (marker.points.toArray ()), m_markerCorners2d);
// Transform image to get a canonical marker image
Imgproc.warpPerspective (grayscale, canonicalMarkerImage, markerTransform, markerSize);
MatOfInt nRotations = new MatOfInt (0);
int id = Marker.getMarkerId (canonicalMarkerImage, nRotations, m_markerDesign);
if (id != - 1) {
marker.id = id;
// Debug.Log ("id " + id);
//sort the points so that they are always in the same order no matter the camera orientation
List<Point> MarkerPointsList = marker.points.toList ();
// std::rotate(marker.points.begin(), marker.points.begin() + 4 - nRotations, marker.points.end());
MarkerPointsList = MarkerPointsList.Skip (4 - nRotations.toArray () [0]).Concat (MarkerPointsList.Take (4 - nRotations.toArray () [0])).ToList ();
marker.points.fromList (MarkerPointsList);
goodMarkers.Add (marker);
}
nRotations.Dispose ();
}
// Debug.Log ("goodMarkers " + goodMarkers.Count);
// Refine marker corners using sub pixel accuracy
if (goodMarkers.Count > 0) {
List<Point> preciseCornersPoint = new List<Point> (4 * goodMarkers.Count);
for (int i = 0; i < preciseCornersPoint.Capacity; i++) {
preciseCornersPoint.Add (new Point (0, 0));
}
for (int i=0; i<goodMarkers.Count; i++) {
Marker marker = goodMarkers [i];
List<Point> markerPointsList = marker.points.toList ();
for (int c = 0; c <4; c++) {
preciseCornersPoint [i * 4 + c] = markerPointsList [c];
}
}
MatOfPoint2f preciseCorners = new MatOfPoint2f (preciseCornersPoint.ToArray ());
TermCriteria termCriteria = new TermCriteria (TermCriteria.MAX_ITER | TermCriteria.EPS, 30, 0.01);
Imgproc.cornerSubPix (grayscale, preciseCorners, new Size (5, 5), new Size (-1, -1), termCriteria);
preciseCornersPoint = preciseCorners.toList ();
// Copy refined corners position back to markers
for (int i=0; i<goodMarkers.Count; i++) {
Marker marker = goodMarkers [i];
List<Point> markerPointsList = marker.points.toList ();
for (int c=0; c<4; c++) {
markerPointsList [c] = preciseCornersPoint [i * 4 + c];
}
}
preciseCorners.Dispose ();
}
detectedMarkers.Clear ();
detectedMarkers.AddRange (goodMarkers);
}
protected override void postprocess(Mat frame, List <Mat> outs, Net net)
{
List <int> classIdsList = new List <int>();
List <float> confidencesList = new List <float>();
List <Rect2d> boxesList = new List <Rect2d>();
List <Point[]> pointsList = new List <Point[]>();
if (outs.Count == 2)
{
// reshape mat : outs[0]:[1, x, 4] to [x, 4], outs[1]:[1, x, 2] to [x, 2]
Mat boxes_m = outs[0].reshape(1, new int[] { outs[0].size(1), outs[0].size(2) });
Mat scores_m = outs[1].reshape(1, new int[] { outs[1].size(1), outs[1].size(2) });
//Debug.Log("boxes_m: " + boxes_m);
//Debug.Log("scores_m: " + scores_m);
//Debug.Log("priors: " + priors);
convertLocationsToBoxes(boxes_m, priors, 0.1f, 0.2f);
centerFormToCornerForm(boxes_m);
Mat boxes_0_4 = new Mat(boxes_m, new Range(0, boxes_m.rows()), new Range(0, 4));
float[] boxes_arr = new float[boxes_0_4.rows() * boxes_0_4.cols()];
MatUtils.copyFromMat(boxes_0_4, boxes_arr);
Mat scores_1_2 = new Mat(scores_m, new Range(0, scores_m.rows()), new Range(1, 2));
float[] confidences_arr = new float[scores_1_2.rows()];
MatUtils.copyFromMat(scores_1_2, confidences_arr);
for (int i = 0; i < boxes_m.rows(); i++)
{
float confidence = confidences_arr[i];
if (confidence > confThreshold)
{
int boxes_index = i * 4;
float left = boxes_arr[boxes_index] * frame.cols();
float top = boxes_arr[boxes_index + 1] * frame.rows();
float right = boxes_arr[boxes_index + 2] * frame.cols();
float bottom = boxes_arr[boxes_index + 3] * frame.rows();
float width = right - left + 1f;
float height = bottom - top + 1f;
classIdsList.Add(0);
confidencesList.Add(confidence);
boxesList.Add(new Rect2d(left, top, width, height));
}
}
if (boxes_m.cols() > 4 && boxes_m.cols() % 2 == 0)
{
Mat points = new Mat(boxes_m, new Range(0, boxes_m.rows()), new Range(4, boxes_m.cols()));
float[] points_arr = new float[points.rows() * points.cols()];
MatUtils.copyFromMat(points, points_arr);
for (int i = 0; i < boxes_m.rows(); i++)
{
float confidence = confidences_arr[i];
if (confidence > confThreshold)
{
int points_index = i * points.cols();
Point[] p_arr = new Point[points.cols() / 2];
for (int index = 0; index < points.cols() / 2; index++)
{
float x = points_arr[points_index + index * 2] * frame.cols();
float y = points_arr[points_index + index * 2 + 1] * frame.rows();
p_arr[index] = new Point(x, y);
}
pointsList.Add(p_arr);
}
}
}
}
MatOfRect2d boxes = new MatOfRect2d();
boxes.fromList(boxesList);
MatOfFloat confidences = new MatOfFloat();
confidences.fromList(confidencesList);
MatOfInt indices = new MatOfInt();
Dnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold, indices);
//Debug.Log("indices.dump () " + indices.dump());
//Debug.Log ("indices.ToString () "+indices.ToString());
for (int i = 0; i < indices.total(); ++i)
{
int idx = (int)indices.get(i, 0)[0];
Rect2d box = boxesList[idx];
drawPred(classIdsList[idx], confidencesList[idx], box.x, box.y,
box.x + box.width, box.y + box.height, frame);
if (pointsList.Count > 0)
{
drawPredPoints(pointsList[idx], frame);
}
}
indices.Dispose();
boxes.Dispose();
confidences.Dispose();
}
/// <summary>
/// Postprocess the specified frame, outs and net.
/// </summary>
/// <param name="frame">Frame.</param>
/// <param name="outs">Outs.</param>
/// <param name="net">Net.</param>
private void postprocess(Mat frame, List <Mat> outs, Net net)
{
string outLayerType = outBlobTypes[0];
List <int> classIdsList = new List <int>();
List <float> confidencesList = new List <float>();
List <OpenCVForUnity.CoreModule.Rect> boxesList = new List <OpenCVForUnity.CoreModule.Rect>();
if (net.getLayer(new DictValue(0)).outputNameToIndex("im_info") != -1)
{
// Faster-RCNN or R-FCN
// Network produces output blob with a shape 1x1xNx7 where N is a number of
// detections and an every detection is a vector of values
// [batchId, classId, confidence, left, top, right, bottom]
if (outs.Count == 1)
{
outs[0] = outs[0].reshape(1, (int)outs[0].total() / 7);
//Debug.Log ("outs[i].ToString() " + outs [0].ToString ());
float[] data = new float[7];
for (int i = 0; i < outs[0].rows(); i++)
{
outs[0].get(i, 0, data);
float confidence = data[2];
if (confidence > confThreshold)
{
int class_id = (int)(data[1]);
int left = (int)(data[3] * frame.cols());
int top = (int)(data[4] * frame.rows());
int right = (int)(data[5] * frame.cols());
int bottom = (int)(data[6] * frame.rows());
int width = right - left + 1;
int height = bottom - top + 1;
classIdsList.Add((int)(class_id) - 0);
confidencesList.Add((float)confidence);
boxesList.Add(new OpenCVForUnity.CoreModule.Rect(left, top, width, height));
}
}
}
}
else if (outLayerType == "DetectionOutput")
{
// Network produces output blob with a shape 1x1xNx7 where N is a number of
// detections and an every detection is a vector of values
// [batchId, classId, confidence, left, top, right, bottom]
if (outs.Count == 1)
{
outs[0] = outs[0].reshape(1, (int)outs[0].total() / 7);
//Debug.Log ("outs[i].ToString() " + outs [0].ToString ());
float[] data = new float[7];
for (int i = 0; i < outs[0].rows(); i++)
{
outs[0].get(i, 0, data);
float confidence = data[2];
if (confidence > confThreshold)
{
int class_id = (int)(data[1]);
int left = (int)(data[3] * frame.cols());
int top = (int)(data[4] * frame.rows());
int right = (int)(data[5] * frame.cols());
int bottom = (int)(data[6] * frame.rows());
int width = right - left + 1;
int height = bottom - top + 1;
classIdsList.Add((int)(class_id) - 0);
confidencesList.Add((float)confidence);
boxesList.Add(new OpenCVForUnity.CoreModule.Rect(left, top, width, height));
}
}
}
}
else if (outLayerType == "Region")
{
for (int i = 0; i < outs.Count; ++i)
{
// Network produces output blob with a shape NxC where N is a number of
// detected objects and C is a number of classes + 4 where the first 4
// numbers are [center_x, center_y, width, height]
//Debug.Log ("outs[i].ToString() "+outs[i].ToString());
float[] positionData = new float[5];
float[] confidenceData = new float[outs[i].cols() - 5];
for (int p = 0; p < outs[i].rows(); p++)
{
outs[i].get(p, 0, positionData);
outs[i].get(p, 5, confidenceData);
int maxIdx = confidenceData.Select((val, idx) => new { V = val, I = idx }).Aggregate((max, working) => (max.V > working.V) ? max : working).I;
float confidence = confidenceData[maxIdx];
if (confidence > confThreshold)
{
int centerX = (int)(positionData[0] * frame.cols());
int centerY = (int)(positionData[1] * frame.rows());
int width = (int)(positionData[2] * frame.cols());
int height = (int)(positionData[3] * frame.rows());
int left = centerX - width / 2;
int top = centerY - height / 2;
classIdsList.Add(maxIdx);
confidencesList.Add((float)confidence);
boxesList.Add(new OpenCVForUnity.CoreModule.Rect(left, top, width, height));
}
}
}
}
else
{
Debug.Log("Unknown output layer type: " + outLayerType);
}
MatOfRect boxes = new MatOfRect();
boxes.fromList(boxesList);
detectionBoxes.AddRange(boxesList);
MatOfFloat confidences = new MatOfFloat();
confidences.fromList(confidencesList);
MatOfInt indices = new MatOfInt();
Dnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold, indices);
for (int i = 0; i < indices.total(); ++i)
{
int idx = (int)indices.get(i, 0)[0];
if (classNames != null)
{
modelOutput.Add(new KeyValuePair <string, float>(classNames[classIdsList[idx]], confidencesList[idx]));
}
if (gameObject.GetComponent <Renderer>() != null)
{
OpenCVForUnity.CoreModule.Rect box = boxesList[idx];
drawPred(classIdsList[idx], confidencesList[idx], box.x, box.y,
box.x + box.width, box.y + box.height, frame);
}
}
indices.Dispose();
boxes.Dispose();
confidences.Dispose();
}
/// <summary>
/// Process
/// </summary>
/// <returns></returns>
private async void Process()
{
float DOWNSCALE_RATIO = 1.0f;
while (true)
{
// Check TaskCancel
if (tokenSource.Token.IsCancellationRequested)
{
break;
}
rgbaMat = webCamTextureToMatHelper.GetMat();
// Debug.Log ("rgbaMat.ToString() " + rgbaMat.ToString ());
Mat downScaleRgbaMat = null;
DOWNSCALE_RATIO = 1.0f;
if (enableDownScale)
{
downScaleRgbaMat = imageOptimizationHelper.GetDownScaleMat(rgbaMat);
DOWNSCALE_RATIO = imageOptimizationHelper.downscaleRatio;
}
else
{
downScaleRgbaMat = rgbaMat;
DOWNSCALE_RATIO = 1.0f;
}
Imgproc.cvtColor(downScaleRgbaMat, bgrMat, Imgproc.COLOR_RGBA2BGR);
await Task.Run(() =>
{
// detect faces on the downscale image
if (!enableSkipFrame || !imageOptimizationHelper.IsCurrentFrameSkipped())
{
if (net == null)
{
Imgproc.putText(rgbaMat, "model file is not loaded.", new Point(5, rgbaMat.rows() - 30), Imgproc.FONT_HERSHEY_SIMPLEX, 0.7, new Scalar(255, 255, 255, 255), 2, Imgproc.LINE_AA, false);
Imgproc.putText(rgbaMat, "Please read console message.", new Point(5, rgbaMat.rows() - 10), Imgproc.FONT_HERSHEY_SIMPLEX, 0.7, new Scalar(255, 255, 255, 255), 2, Imgproc.LINE_AA, false);
}
else
{
// Create a 4D blob from a frame.
Size inpSize = new Size(inpWidth > 0 ? inpWidth : bgrMat.cols(),
inpHeight > 0 ? inpHeight : bgrMat.rows());
Mat blob = Dnn.blobFromImage(bgrMat, scale, inpSize, mean, swapRB, false);
// Run a model.
net.setInput(blob);
if (net.getLayer(new DictValue(0)).outputNameToIndex("im_info") != -1)
{ // Faster-RCNN or R-FCN
Imgproc.resize(bgrMat, bgrMat, inpSize);
Mat imInfo = new Mat(1, 3, CvType.CV_32FC1);
imInfo.put(0, 0, new float[] {
(float)inpSize.height,
(float)inpSize.width,
1.6f
});
net.setInput(imInfo, "im_info");
}
TickMeter tm = new TickMeter();
tm.start();
List <Mat> outs = new List <Mat>();
net.forward(outs, outBlobNames);
tm.stop();
// Debug.Log ("Inference time, ms: " + tm.getTimeMilli ());
postprocess(bgrMat, outs, net);
for (int i = 0; i < outs.Count; i++)
{
outs[i].Dispose();
}
blob.Dispose();
if (enableDownScale)
{
for (int i = 0; i < _boxesList.Count; ++i)
{
var rect = _boxesList[i];
_boxesList[i] = new OpenCVForUnity.CoreModule.Rect(
(int)(rect.x * DOWNSCALE_RATIO),
(int)(rect.y * DOWNSCALE_RATIO),
(int)(rect.width * DOWNSCALE_RATIO),
(int)(rect.height * DOWNSCALE_RATIO));
}
}
}
//Imgproc.rectangle(rgbaMat, new Point(0, 0), new Point(rgbaMat.width(), rgbaMat.height()), new Scalar(0, 0, 0, 0), -1);
MatOfRect boxes = new MatOfRect();
boxes.fromList(_boxesList);
MatOfFloat confidences = new MatOfFloat();
confidences.fromList(_confidencesList);
MatOfInt indices = new MatOfInt();
Dnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold, indices);
// Debug.Log ("indices.dump () "+indices.dump ());
// Debug.Log ("indices.ToString () "+indices.ToString());
for (int i = 0; i < indices.total(); ++i)
{
int idx = (int)indices.get(i, 0)[0];
OpenCVForUnity.CoreModule.Rect box = _boxesList[idx];
drawPred(_classIdsList[idx], _confidencesList[idx], box.x, box.y,
box.x + box.width, box.y + box.height, rgbaMat);
}
indices.Dispose();
boxes.Dispose();
confidences.Dispose();
}
});
Utils.fastMatToTexture2D(rgbaMat, texture);
Thread.Sleep(10);
}
}
/// <summary>
/// Postprocess the specified frame, outs and net.
/// </summary>
/// <param name="frame">Frame.</param>
/// <param name="outs">Outs.</param>
/// <param name="net">Net.</param>
private void postprocess(Mat frame, List <Mat> outs, Net net)
{
string outLayerType = outBlobTypes[0];
List <int> classIdsList = new List <int>();
List <float> confidencesList = new List <float>();
List <OpenCVForUnity.CoreModule.Rect> boxesList = new List <OpenCVForUnity.CoreModule.Rect>();
if (outLayerType == "Region")
{
for (int i = 0; i < outs.Count; ++i)
{
// Network produces output blob with a shape NxC where N is a number of
// detected objects and C is a number of classes + 4 where the first 4
// numbers are [center_x, center_y, width, height]
//Debug.Log("outs[i].ToString() " + outs[i].ToString());
float[] positionData = new float[5];
float[] confidenceData = new float[outs[i].cols() - 5];
for (int p = 0; p < outs[i].rows(); p++)
{
outs[i].get(p, 0, positionData);
outs[i].get(p, 5, confidenceData);
int maxIdx = confidenceData.Select((val, idx) => new { V = val, I = idx }).Aggregate((max, working) => (max.V > working.V) ? max : working).I;
float confidence = confidenceData[maxIdx];
if (confidence > confThreshold)
{
int centerX = (int)(positionData[0] * frame.cols());
int centerY = (int)(positionData[1] * frame.rows());
int width = (int)(positionData[2] * frame.cols());
int height = (int)(positionData[3] * frame.rows());
int left = centerX - width / 2;
int top = centerY - height / 2;
classIdsList.Add(maxIdx);
confidencesList.Add((float)confidence);
boxesList.Add(new OpenCVForUnity.CoreModule.Rect(left, top, width, height));
}
}
}
}
else
{
Debug.Log("Unknown output layer type: " + outLayerType);
}
MatOfRect boxes = new MatOfRect();
boxes.fromList(boxesList);
MatOfFloat confidences = new MatOfFloat();
confidences.fromList(confidencesList);
MatOfInt indices = new MatOfInt();
Dnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold, indices);
//Check the Language selected
switch (menuVariables.GetLanguage())
{
case "EN":
vocOffset = 0;
break;
case "ES":
vocOffset = 80;
break;
case "FR":
vocOffset = 160;
break;
case "DE":
vocOffset = 240;
break;
case "IT":
vocOffset = 320;
break;
default:
vocOffset = 0;
break;
}
//Draw the bouding box only if its in the center of the image (On Cursor)
for (int i = 0; i < indices.total(); ++i)
{
int idx = (int)indices.get(i, 0)[0];
OpenCVForUnity.CoreModule.Rect box = boxesList[idx];
if (isOnCursor(box, cursorObject.GetComponent <Cursor>()))
{
if (minigameList[wordFoundCounter] == classIdsList[idx])
{
drawPred(vocOffset + classIdsList[idx], confidencesList[idx], box.x, box.y,
box.x + box.width, box.y + box.height, frame);
//Update the text summarizing the object encountered
vocIDList.Add(classIdsList[idx]);
//vocLearn.text += classNames[classIdsList[idx]] + "\t" + classNames[240 + classIdsList[idx]] + "\t" + classNames[160 + classIdsList[idx]] + "\t" + classNames[320 + classIdsList[idx]] + "\n";
EnglishText.text += "\n" + classNames[classIdsList[idx]];
SpanishText.text += "\n" + classNames[80 + classIdsList[idx]];
FrenchText.text += "\n" + classNames[160 + classIdsList[idx]];
GermanText.text += "\n" + classNames[240 + classIdsList[idx]];
ItalianText.text += "\n" + classNames[320 + classIdsList[idx]];
wordFound = true;
Debug.Log("You found the" + classNames[classIdsList[idx]]);
}
}
}
indices.Dispose();
boxes.Dispose();
confidences.Dispose();
}
/// <summary>
/// Scanning the specified frame, outs and net.
/// </summary>
/// <param name="frame">Frame.</param>
/// <param name="outs">Outs.</param>
/// <param name="net">Net.</param>
private void postscan(Mat frame, List <Mat> outs, Net net)
{
string outLayerType = outBlobTypes[0];
List <int> classIdsList = new List <int>();
List <float> confidencesList = new List <float>();
List <OpenCVForUnity.CoreModule.Rect> boxesList = new List <OpenCVForUnity.CoreModule.Rect>();
if (outLayerType == "Region")
{
for (int i = 0; i < outs.Count; ++i)
{
// Network produces output blob with a shape NxC where N is a number of
// detected objects and C is a number of classes + 4 where the first 4
// numbers are [center_x, center_y, width, height]
//Debug.Log("outs[i].ToString() " + outs[i].ToString());
float[] positionData = new float[5];
float[] confidenceData = new float[outs[i].cols() - 5];
for (int p = 0; p < outs[i].rows(); p++)
{
outs[i].get(p, 0, positionData);
outs[i].get(p, 5, confidenceData);
int maxIdx = confidenceData.Select((val, idx) => new { V = val, I = idx }).Aggregate((max, working) => (max.V > working.V) ? max : working).I;
float confidence = confidenceData[maxIdx];
if (confidence > confThreshold)
{
int centerX = (int)(positionData[0] * frame.cols());
int centerY = (int)(positionData[1] * frame.rows());
int width = (int)(positionData[2] * frame.cols());
int height = (int)(positionData[3] * frame.rows());
int left = centerX - width / 2;
int top = centerY - height / 2;
classIdsList.Add(maxIdx);
confidencesList.Add((float)confidence);
boxesList.Add(new OpenCVForUnity.CoreModule.Rect(left, top, width, height));
}
}
}
}
else
{
Debug.Log("Unknown output layer type: " + outLayerType);
}
MatOfRect boxes = new MatOfRect();
boxes.fromList(boxesList);
MatOfFloat confidences = new MatOfFloat();
confidences.fromList(confidencesList);
MatOfInt indices = new MatOfInt();
Dnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold, indices);
//for-loop for the mini game - if a new class appears, add it to the
for (int i = 0; i < indices.total(); ++i)
{
int idx = (int)indices.get(i, 0)[0];
if (!minigameList.Contains(classIdsList[idx]))
{
Debug.Log(classNames[classIdsList[idx]]);
minigameList.Add(classIdsList[idx]);
if (minigameList.Count() > 1)
{
wordDisplay.text = minigameList.Count().ToString() + " words";
}
else
{
wordDisplay.text = minigameList.Count().ToString() + " word";
}
}
}
indices.Dispose();
boxes.Dispose();
confidences.Dispose();
}