public override void Dispose()
{
if (grayMat != null)
{
grayMat.Dispose();
grayMat = null;
}
if (faces != null)
{
faces.Dispose();
}
if (debugMat != null)
{
debugMat.Dispose();
debugMat = null;
}
if (debugTexture != null)
{
Texture2D.Destroy(debugTexture);
debugTexture = null;
}
#if UNITY_WEBGL && !UNITY_EDITOR
if (getFilePath_Coroutine != null)
{
StopCoroutine(getFilePath_Coroutine);
((IDisposable)getFilePath_Coroutine).Dispose();
}
#endif
}
/// <summary>
/// Raises the destroy event.
/// </summary>
public override void Dispose()
{
if (grayMat != null)
{
grayMat.Dispose();
}
if (faces != null)
{
faces.Dispose();
}
if (texture != null)
{
Texture2D.Destroy(texture);
texture = null;
}
#if UNITY_WEBGL && !UNITY_EDITOR
foreach (var coroutine in coroutines)
{
StopCoroutine(coroutine);
((IDisposable)coroutine).Dispose();
}
#endif
}
/// <summary>
/// Raises the webcam texture to mat helper disposed event.
/// </summary>
public void OnWebCamTextureToMatHelperDisposed()
{
Debug.Log("OnWebCamTextureToMatHelperDisposed");
if (grayMat != null)
{
grayMat.Dispose();
}
if (effectsMat != null)
{
effectsMat.Dispose();
}
if (texture != null)
{
Texture2D.Destroy(texture);
texture = null;
}
if (faces != null)
{
faces.Dispose();
}
}
/// <summary>
/// Raises the web cam texture to mat helper disposed event.
/// </summary>
public void OnWebCamTextureToMatHelperDisposed()
{
Debug.Log("OnWebCamTextureToMatHelperDisposed");
processingAreaMat.Dispose();
grayMat.Dispose();
faces.Dispose();
}
/// <summary>
/// Raises the web cam texture to mat helper disposed event.
/// </summary>
public void OnWebCamTextureToMatHelperDisposed()
{
Debug.Log("OnWebCamTextureToMatHelperDisposed");
grayMat.Dispose();
cascade.Dispose();
faces.Dispose();
}
/// <summary>
/// Raises the web cam texture to mat helper disposed event.
/// </summary>
public void OnWebCamTextureToMatHelperDisposed()
{
Debug.Log("OnWebCamTextureToMatHelperDisposed");
if (grayMat != null)
{
grayMat.Dispose();
}
if (faces != null)
{
faces.Dispose();
}
}
/// <summary>
/// Raises the destroy event.
/// </summary>
void OnDestroy()
{
if (grayMat != null)
{
grayMat.Dispose();
}
if (faces != null)
{
faces.Dispose();
}
if (cascade != null)
{
cascade.Dispose();
}
}
public void OnWebCamTextureToMatHelperDisposed()
{
if (grayMat != null)
{
grayMat.Dispose();
}
if (texture != null)
{
Texture2D.Destroy(texture);
texture = null;
}
if (faces != null)
{
faces.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);
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, frame);
}
indices.Dispose();
boxes.Dispose();
confidences.Dispose();
}
private void Run()
{
//if true, The error log of the Native side OpenCV will be displayed on the Unity Editor Console.
Utils.setDebugMode(true);
Mat frame = Imgcodecs.imread(scenetext01_jpg_filepath);
#if !UNITY_WSA_10_0
if (frame.empty())
{
Debug.LogError("text/scenetext01.jpg is not loaded.Please copy from “OpenCVForUnity/StreamingAssets/text/” to “Assets/StreamingAssets/” folder. ");
}
#endif
Mat binaryMat = new Mat();
Mat maskMat = new Mat();
List <MatOfPoint> regions = new List <MatOfPoint> ();
ERFilter er_filter1 = Text.createERFilterNM1(trained_classifierNM1_xml_filepath, 8, 0.00015f, 0.13f, 0.2f, true, 0.1f);
ERFilter er_filter2 = Text.createERFilterNM2(trained_classifierNM2_xml_filepath, 0.5f);
Mat transition_p = new Mat(62, 62, CvType.CV_64FC1);
// string filename = "OCRHMM_transitions_table.xml";
// FileStorage fs(filename, FileStorage::READ);
// fs["transition_probabilities"] >> transition_p;
// fs.release();
//Load TransitionProbabilitiesData.
transition_p.put(0, 0, GetTransitionProbabilitiesData(OCRHMM_transitions_table_xml_filepath));
Mat emission_p = Mat.eye(62, 62, CvType.CV_64FC1);
string voc = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789";
OCRHMMDecoder decoder = OCRHMMDecoder.create(
OCRHMM_knn_model_data_xml_gz_filepath,
voc, transition_p, emission_p);
//Text Detection
Imgproc.cvtColor(frame, frame, Imgproc.COLOR_BGR2RGB);
Imgproc.cvtColor(frame, binaryMat, Imgproc.COLOR_RGB2GRAY);
Imgproc.threshold(binaryMat, binaryMat, 0, 255, Imgproc.THRESH_BINARY | Imgproc.THRESH_OTSU);
Core.absdiff(binaryMat, new Scalar(255), maskMat);
Text.detectRegions(binaryMat, er_filter1, er_filter2, regions);
Debug.Log("regions.Count " + regions.Count);
MatOfRect groups_rects = new MatOfRect();
List <OpenCVForUnity.Rect> rects = new List <OpenCVForUnity.Rect> ();
Text.erGrouping(frame, binaryMat, regions, groups_rects);
for (int i = 0; i < regions.Count; i++)
{
regions [i].Dispose();
}
regions.Clear();
rects.AddRange(groups_rects.toList());
groups_rects.Dispose();
//Text Recognition (OCR)
List <Mat> detections = new List <Mat> ();
for (int i = 0; i < (int)rects.Count; i++)
{
Mat group_img = new Mat();
maskMat.submat(rects [i]).copyTo(group_img);
Core.copyMakeBorder(group_img, group_img, 15, 15, 15, 15, Core.BORDER_CONSTANT, new Scalar(0));
detections.Add(group_img);
}
Debug.Log("detections.Count " + detections.Count);
//#Visualization
for (int i = 0; i < rects.Count; i++)
{
Imgproc.rectangle(frame, new Point(rects [i].x, rects [i].y), new Point(rects [i].x + rects [i].width, rects [i].y + rects [i].height), new Scalar(255, 0, 0), 2);
Imgproc.rectangle(frame, new Point(rects [i].x, rects [i].y), new Point(rects [i].x + rects [i].width, rects [i].y + rects [i].height), new Scalar(255, 255, 255), 1);
string output = decoder.run(detections [i], 0);
if (!string.IsNullOrEmpty(output))
{
Debug.Log("output " + output);
Imgproc.putText(frame, output, new Point(rects [i].x, rects [i].y), Core.FONT_HERSHEY_SIMPLEX, 0.5, new Scalar(0, 0, 255), 1, Imgproc.LINE_AA, false);
}
}
Texture2D texture = new Texture2D(frame.cols(), frame.rows(), TextureFormat.RGBA32, false);
Utils.matToTexture2D(frame, texture);
// Texture2D texture = new Texture2D (detections [0].cols (), detections [0].rows (), TextureFormat.RGBA32, false);
//
// Utils.matToTexture2D (detections [0], texture);
gameObject.GetComponent <Renderer> ().material.mainTexture = texture;
for (int i = 0; i < detections.Count; i++)
{
detections [i].Dispose();
}
binaryMat.Dispose();
maskMat.Dispose();
Utils.setDebugMode(false);
}
/// <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>()))
{
drawPred(vocOffset + classIdsList[idx], confidencesList[idx], box.x, box.y,
box.x + box.width, box.y + box.height, frame);
wordDisplay.text = classNames[classIdsList[idx]];
//Update the text summarizing the object encountered
if (!vocIDList.Contains(classIdsList[idx]))
{
//Update the vocabulary learned
vocIDList.Add(classIdsList[idx]);
EnglishText.text += "\n" + classNames[classIdsList[idx]] + "\n";
SpanishText.text += "\n" + classNames[80 + classIdsList[idx]] + "\n";
FrenchText.text += "\n" + classNames[160 + classIdsList[idx]] + "\n";
GermanText.text += "\n" + classNames[240 + classIdsList[idx]] + "\n";
ItalianText.text += "\n" + classNames[320 + classIdsList[idx]] + "\n";
}
}
}
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>
/// 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();
}
// Update is called once per frame
void Update()
{
if (webCamTextureToMatHelper.IsPlaying() && webCamTextureToMatHelper.DidUpdateThisFrame())
{
Mat rgbaMat = webCamTextureToMatHelper.GetMat();
Imgproc.cvtColor(rgbaMat, rgbMat, Imgproc.COLOR_RGBA2RGB);
/*Text Detection*/
Imgproc.cvtColor(rgbMat, binaryMat, Imgproc.COLOR_RGB2GRAY);
Imgproc.threshold(binaryMat, binaryMat, 0, 255, Imgproc.THRESH_BINARY | Imgproc.THRESH_OTSU);
Core.absdiff(binaryMat, new Scalar(255), maskMat);
List <MatOfPoint> regions = new List <MatOfPoint> ();
OpenCVForUnity.Text.detectRegions(binaryMat, er_filter1, er_filter2, regions);
// Debug.Log ("regions.Count " + regions.Count);
MatOfRect groups_rects = new MatOfRect();
List <OpenCVForUnity.Rect> rects = new List <OpenCVForUnity.Rect> ();
if (regions.Count > 0)
{
OpenCVForUnity.Text.erGrouping(rgbMat, binaryMat, regions, groups_rects);
}
for (int i = 0; i < regions.Count; i++)
{
regions [i].Dispose();
}
regions.Clear();
rects.AddRange(groups_rects.toList());
groups_rects.Dispose();
/*Text Recognition (OCR)*/
List <Mat> detections = new List <Mat> ();
for (int i = 0; i < (int)rects.Count; i++)
{
Mat group_img = new Mat();
maskMat.submat(rects [i]).copyTo(group_img);
Core.copyMakeBorder(group_img, group_img, 15, 15, 15, 15, Core.BORDER_CONSTANT, new Scalar(0));
detections.Add(group_img);
}
// Debug.Log ("detections.Count " + detections.Count);
// Debug.Log ("rects.Count " + rects.Count);
//#Visualization
for (int i = 0; i < rects.Count; i++)
{
Imgproc.rectangle(rgbaMat, new Point(rects [i].x, rects [i].y), new Point(rects [i].x + rects [i].width, rects [i].y + rects [i].height), new Scalar(255, 0, 0, 255), 2);
Imgproc.rectangle(rgbaMat, new Point(rects [i].x, rects [i].y), new Point(rects [i].x + rects [i].width, rects [i].y + rects [i].height), new Scalar(255, 255, 255, 255), 1);
Imgproc.putText(rgbaMat, "" + i, new Point(rects [i].x, rects [i].y), Core.FONT_HERSHEY_SIMPLEX, 0.5, new Scalar(255, 0, 0, 255), 1, Imgproc.LINE_AA, false);
}
for (int i = 0; i < detections.Count; i++)
{
string output = decoder.run(detections [i], 0);
Debug.Log("output " + output);
if (string.IsNullOrEmpty(output))
{
Debug.LogError("IsNullOrEmpty output " + output);
}
else
{
Imgproc.putText(rgbaMat, " " + output, new Point(rects [i].x, rects [i].y), Core.FONT_HERSHEY_SIMPLEX, 0.5, new Scalar(255, 0, 0, 255), 1, Imgproc.LINE_AA, false);
}
}
for (int i = 0; i < detections.Count; i++)
{
detections [i].Dispose();
}
detections.Clear();
Imgproc.putText(rgbaMat, "W:" + rgbaMat.width() + " H:" + rgbaMat.height() + " SO:" + Screen.orientation, new Point(5, rgbaMat.rows() - 10), Core.FONT_HERSHEY_SIMPLEX, 1.0, new Scalar(255, 255, 255, 255), 2, Imgproc.LINE_AA, false);
Utils.matToTexture2D(rgbaMat, texture, webCamTextureToMatHelper.GetBufferColors());
}
}