public static Results EvaluateSessionWindowed(DeviceType device, int subject_id)
{
configuartion_parameters_t parameneters = new configuartion_parameters_t(device);
// Load subject dataset
Dataset ds = Global.load_subject_dataset(device, subject_id);
List <Sample> train_set = Global.GetTrainSet(ds, 1);
// Covert the dataset to format accepted by Jackknife
List <Jackknife.Sample> jk_train_set = JackknifeConnector.GetJKTrainSet(train_set);
// Load subject session
List <Frame> frames = new List <Frame>();
Global.load_session(device, subject_id, frames, ds);
// Load ground truth
List <GestureCommand> cmds = new List <GestureCommand>();
GestureCommand.GetAllCommands(cmds, ds, device, subject_id);
// Train the recognizer
JackknifeBlades blades = new JackknifeBlades();
blades.SetIPDefaults();
blades.ResampleCnt = 20;
Jackknife.Jackknife jk = new Jackknife.Jackknife(blades);
foreach (Jackknife.Sample s in jk_train_set)
{
jk.AddTemplate(s);
}
// Set between 2.0 and 10.0 in steps of .25
// to find the best result
jk.SetRejectionThresholds(5.25f);
// Set up filter for session points
ExponentialMovingAverage ema_filter = new ExponentialMovingAverage(frames[0].pt);
Vector pt;
WindowSegmentor windowSegmentor = new WindowSegmentor(jk);
//List<RecognitionResult> rresults = new List<RecognitionResult>();
List <ContinuousResult> continuous_results = new List <ContinuousResult>();
// Go through session
for (int session_pt = 0; session_pt < frames.Count; session_pt++)
{
long ts1 = DateTime.Now.Ticks / TimeSpan.TicksPerMillisecond; // at beginning
pt = ema_filter.Filter(frames[session_pt].pt, 1 / (double)parameneters.fps);
long ts2 = DateTime.Now.Ticks / TimeSpan.TicksPerMillisecond; // after filter
Jackknife.Vector jkpt = JackknifeConnector.ToJKVector(pt);
windowSegmentor.Update(jkpt);
windowSegmentor.Segment(continuous_results);
if (session_pt % 2000 == 0)
{
Debug.Log(string.Format("{0}% Done", (double)session_pt / (double)frames.Count * 100.0));
}
}
foreach (ContinuousResult cr in continuous_results)
{
Debug.Log(string.Format("st {0}, en {1}, gid {2}", cr.startFrameNo, cr.endFrameNo, cr.gid));
}
// Per gesture confusion matrix
List <ConfisionMatrix> cm = new List <ConfisionMatrix>();
for (int ii = 0; ii < ds.Gestures.Count; ii++)
{
cm.Add(new ConfisionMatrix());
}
for (int ii = 0; ii < continuous_results.Count; ii++)
{
ContinuousResult result = continuous_results[ii];
bool found = false;
int cidx = 0;
for (cidx = 0; cidx < cmds.Count; cidx++)
{
found = cmds[cidx].Hit(result);
if (found == true)
{
break;
}
}
if (found == true)
{
// true positive
if (cmds[cidx].detected == false)
{
cmds[cidx].detected = true;
cm[result.gid].tp += 1.0f;
}
}
else
{
bool bad = GestureCommand.IsBadCommand(
frames,
result);
if (bad == true)
{
continue;
}
// false positive
cm[result.gid].fp += 1.0f;
}
}
// false negatives
for (int cidx = 0; cidx < cmds.Count; cidx++)
{
if (cmds[cidx].detected == true)
{
continue;
}
cm[cmds[cidx].gid].fn += 1.0;
}
Results ret = new Results();
for (int ii = 0; ii < cm.Count; ii++)
{
ret.AppendResults(cm[ii]);
}
ret.PrintF();
return(ret);
}
public static Results EvaluateSession(DeviceType device, int subject_id)
{
configuartion_parameters_t parameneters = new configuartion_parameters_t(device);
// Load subject dataset
Dataset ds = Global.load_subject_dataset(device, subject_id);
List <Sample> train_set = Global.GetTrainSet(ds, 1);
// Covert the dataset to format accepted by Jackknife
List <Jackknife.Sample> jk_train_set = JackknifeConnector.GetJKTrainSet(train_set);
// Load subject session
List <Frame> frames = new List <Frame>();
Global.load_session(device, subject_id, frames, ds);
//Debug.Log("Frame_cnt = " + frames.Count());
// Load ground truth
List <GestureCommand> cmds = new List <GestureCommand>();
GestureCommand.GetAllCommands(cmds, ds, device, subject_id);
// Train the segmentor
ContinuousResultOptions cr_options = new ContinuousResultOptions();
//COREY FIX latency framecount
cr_options.latencyFrameCount = 1;
Machete yeah = new Machete(device, cr_options);
foreach (Sample s in train_set)
{
yeah.AddSample(s, filtered: true);
}
//PrintYeahStats(yeah);
// Train the recognizer
JackknifeBlades blades = new JackknifeBlades();
blades.SetIPDefaults();
//COREY FIX RESAMPLECNT
blades.ResampleCnt = 20;
blades.LowerBound = false;
Jackknife.Jackknife jk = new Jackknife.Jackknife(blades);
foreach (Jackknife.Sample s in jk_train_set)
{
jk.AddTemplate(s);
}
// Set between 2.0 and 10.0 in steps of .25
// to find the best result
// Best at 7.5 with around 66% :/
jk.SetRejectionThresholds(7.0);
// Set up filter for session points
ExponentialMovingAverage ema_filter = new ExponentialMovingAverage(frames[0].pt, 5.0);
Vector pt;
List <Vector> video = new List <Vector>();
List <RecognitionResult> rresults = new List <RecognitionResult>();
int triggered_count = 0;
// Go through session
for (int session_pt = 0; session_pt < frames.Count; session_pt++)
{
long ts1 = DateTime.Now.Ticks / TimeSpan.TicksPerMillisecond; // at beginning
List <ContinuousResult> continuous_results = new List <ContinuousResult>();
pt = ema_filter.Filter(frames[session_pt].pt, 1 / (double)parameneters.fps);
long ts2 = DateTime.Now.Ticks / TimeSpan.TicksPerMillisecond; // after filter
video.Add(pt);
//Debug.Log(string.Format("Pt: {0} {1} {2}", pt.Data[0], pt.Data[1], pt.Data[2]));
yeah.ProcessFrame(pt, session_pt, continuous_results);
long ts3 = DateTime.Now.Ticks / TimeSpan.TicksPerMillisecond; // after processing frame
bool cancel_if_better_score = false;
ContinuousResult result = ContinuousResult.SelectResult(
continuous_results,
cancel_if_better_score);
long ts4 = DateTime.Now.Ticks / TimeSpan.TicksPerMillisecond; // after looking for result
//Debug.Log(string.Format("{0} {1} {2}", ts2 - ts1, ts3 - ts2, ts4 - ts3));
//Debug.Log(string.Format("FRAME NO: {0}", frame_no));
if (result == null)
{
continue;
}
//COREY FIX For comparing against the original code
//if (result.sample.GestureId == 0)
// Debug.Log(string.Format("start {0}, end {1}", result.startFrameNo, result.endFrameNo + 1));
triggered_count += 1;
//Debug.Log(string.Format("Frame: {3} Result: {0}, Sample: {1}, Score: {2}", result.gid, result.sample.GestureName, result.score, frame_no));
//Debug.Log(string.Format("Best result as of: {0} {1}", ii, yeah.bestScore));
// Run recognizer on segmented result
double recognizer_d = 0.0f;
bool match = false;
// Save a buffer to pass to recognizer
List <Jackknife.Vector> jkbuffer = JackknifeConnector.GetJKBufferFromVideo(
video,
result.startFrameNo,
result.endFrameNo);
long ts5 = DateTime.Now.Ticks / TimeSpan.TicksPerMillisecond; // before passing to recognizer
match = jk.IsMatch(jkbuffer, result.sample.GestureId, out recognizer_d);
//COREY Fix print out scores
if (result.sample.GestureId == 0)
{
//Debug.Log(string.Format("start {0}, end {1} ", result.startFrameNo, result.endFrameNo + 1) + string.Format("Is match = {0}, score = {1}",
// match,
// recognizer_d));
}
// Matched to template with this gid
if (match == false)
{
continue;
}
long ts6 = DateTime.Now.Ticks / TimeSpan.TicksPerMillisecond; // after classifying
// Gesture was accepted
RecognitionResult rresult = new RecognitionResult();
rresult.gid = result.gid;
rresult.start = result.startFrameNo;
rresult.end = result.endFrameNo;
rresult.score = recognizer_d;
match = false;
for (int ii = 0; ii < rresults.Count; ii++)
{
if (rresults[ii].Update(rresult) == true)
{
match = true;
break;
}
}
// if some result was updated for better, continue
if (match == true)
{
continue;
}
rresults.Add(rresult);
}
// Per gesture confusion matrix
List <ConfisionMatrix> cm = new List <ConfisionMatrix>();
for (int ii = 0; ii < ds.Gestures.Count; ii++)
{
cm.Add(new ConfisionMatrix());
}
for (int ii = 0; ii < rresults.Count; ii++)
{
RecognitionResult result = rresults[ii];
bool found = false;
int cidx = 0;
for (cidx = 0; cidx < cmds.Count; cidx++)
{
found = cmds[cidx].Hit(result);
if (found == true)
{
break;
}
}
if (found == true)
{
// true positive
if (cmds[cidx].detected == false)
{
cmds[cidx].detected = true;
cm[result.gid].tp += 1.0f;
}
}
else
{
bool bad = GestureCommand.IsBadCommand(
frames,
result);
if (bad == true)
{
continue;
}
// false positive
cm[result.gid].fp += 1.0f;
}
}
// false negatives
for (int cidx = 0; cidx < cmds.Count; cidx++)
{
if (cmds[cidx].detected == true)
{
continue;
}
cm[cmds[cidx].gid].fn += 1.0;
}
Results ret = new Results();
for (int ii = 0; ii < cm.Count; ii++)
{
ret.AppendResults(cm[ii]);
//temp += string.Format("{5}:\t A: {0:N6}, E: {1:N6}, P: {2:N6}, R: {3:N6}, F1: {4:N6}\n", ret.accuracy/ret.total, ret.error / ret.total, ret.precision / ret.total, ret.recall / ret.total, ret.f1_0 / ret.total, ii);
}
//Debug.Log(temp);
return(ret);
}