public TrackedObject(OpenCVForUnity.CoreModule.Rect rect)
{
lastPositions = new PositionsVector();
numDetectedFrames = 1;
numFramesNotDetected = 0;
lastPositions.Add(rect.clone());
_id = GetNextId();
id = _id;
}
/// <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();
}
