def main():
folder='./Results/'
wu2013GroundTruth = "/Users/Ivan/Files/Data/wu2013"
saveResultsFolder=list()
bookChapter="/Users/Ivan/Documents/Papers/My_papers/Tracking_book_chapter/images"
paper="/Users/Ivan/Documents/Papers/My_papers/CVPR_2016_Robust_tracking/images"
#saveResultsFolder.append(bookChapter)
saveResultsFolder.append(paper)
save=True
datasetType = 'wu2013'
dataset = Dataset(wu2013GroundTruth, datasetType)
baseLineRun= folder+'upd=1_hogANDhist_int_f1'+".p"
paperPlot=PaperPlots(dataset,folder)
plotComparisonToOtherTrackers(dataset,saveResultsFolder,save)
#plotFeatureKernel(paperPlot,saveResultsFolder,save)
plotSensitivity(paperPlot, baseLineRun,saveResultsFolder,save)
def createSavedEvaluations(wildcard):
wu2013results = "/Users/Ivan/Files/Results/Tracking/wu2013"
wu2013GroundTruth = "/Users/Ivan/Files/Data/wu2013"
vot2014Results = "/Users/Ivan/Files/Results/Tracking/vot2014"
vot2014GrounTruth = "/Users/Ivan/Files/Data/vot2014"
datasetType = 'wu2013'
dataset = Dataset(wu2013GroundTruth, datasetType)
#runsNames =['lambda_gray_s0_e0.4', 'lambda_gray_s0.2_e0.4',
# 'lambda_gray_s0.3_e0.3', 'lambda_gray_s0.4_e0.4', 'lambda_gray_s0.5_e0.3']
runsNames = list()
for i in range(0, len(runsNames)):
runsNames[i] = './Runs/' + runsNames[i] + ".p"
runs = list()
runsNames = glob.glob('./Runs/' + wildcard + '*.p')
#
names = list()
for runName in runsNames:
run = loadPickle(runName)
names.append(runName)
print runName
runs.append(run)
evaluator = EvaluatorAllExperiments(dataset, runs, names)
strSave = './Results/'
evaluator.calculateMetricsAndSave(strSave)
def customParameter():
folder='./Results/'
wu2013GroundTruth = "/Users/Ivan/Files/Data/wu2013"
saveResultsFolder=list()
bookChapter="/Users/Ivan/Documents/Papers/My_papers/Tracking_book_chapter/images"
paper="/Users/Ivan/Documents/Papers/My_papers/Tracking_with_Robust_Kalman/images"
saveResultsFolder.append(bookChapter)
saveResultsFolder.append(paper)
save=False
datasetType = 'wu2013'
dataset = Dataset(wu2013GroundTruth, datasetType)
baseLineRun= folder+'upd=3_hogANDhist_int_f1'+".p"
paperPlot=PaperPlots(dataset,folder)
#plotComparisonToOtherTrackers(dataset,saveResultsFolder,save)
#plotFeatureKernel(paperPlot,saveResultsFolder,save)
plotParameter(paperPlot, baseLineRun,saveResultsFolder,save, parameter='lambda')
def compareDefaultPlots(wildcard="lambda_SE"):
wu2013GroundTruth = "/Users/Ivan/Files/Data/wu2013"
datasetType = 'wu2013'
dataset = Dataset(wu2013GroundTruth, datasetType)
runsNames = glob.glob("./Runs/" + wildcard + "*.p")
formatSave='pdf'
regexp= re.compile("(.*\/)(.+)(.p)")
#s0_e0.2
#s0_e0.5
#s0.1_e0.3
#s0.2_e0.2
#s0.3_e0.4
d=dict()
runs=list()
for runName in runsNames:
m=re.match(regexp,runName)
name=m.group(2)
print name
run = loadPickle(runName)
run.trackerLabel=runName
run.data['TRE'].data=[]
run.data['SRE'].data=[]
d[runName] = run
runs.append(run)
evaluator = Evaluator(dataset, runs)
evaluator.evaluateSingleTracker(runs[0])
def generateAllVizualiations():
wu2013GroundTruth = "/Users/Ivan/Files/Data/wu2013"
folderForGraphs='./Visualizations/'
datasetType = 'wu2013'
dataset = Dataset(wu2013GroundTruth, datasetType)
runsNames = glob.glob('./Runs/upd=3*.p')
runsNames =['lambda_nonorm_s0_e0.5', 'lambda_nonorm_s0.2_e0.2',
'lambda_nonorm_s0.3_e0.4', 'lambda_nonorm_s0.4_e0.4', 'lambda_nonorm_s0.5_e0.3']
#runsNames =['lambda_SE_s0_e0.2', 'lambda_SE_s0_e0.5',
# 'lambda_SE_s0.1_e0.3', 'lambda_SE_s0.2_e0.2', 'lambda_SE_s0.3_e0.4']
for i in range(0,len(runsNames)):
runsNames[i]="./Runs/"+runsNames[i]+".p"
formatSave='pdf'
regexp= re.compile("(.*\/)(.+)(.p)")
for runName in runsNames:
m=re.match(regexp,runName)
name=m.group(2)
print name
run = loadPickle(runName)
viz = VisualizeAllExperiments(dataset, run)
viz.barplot(savefile=folderForGraphs+name+"."+ formatSave)
def main(argv=None):
if argv is None:
argv = sys.argv
wu2013GroundTruth = "/Users/Ivan/Files/Data/wu2013"
datasetType = 'wu2013'
runName = './Runs/lambda=0.1_hogANDhist_int_f1.p'
run=loadPickle(runName)
#print run
experimenType='default'
run=run.data[experimenType]
#
vidName='jogging-2'
dataset = Dataset(wu2013GroundTruth, datasetType)
viz=VisualizeExperiment(dataset,run)
#viz.show(vidName, experimenType)
#viz.barplot()
viz.precisionAndSuccessPlot(vidName)
def robustKalman():
savefilename = "/Users/Ivan/Documents/Papers/My_papers/CVPR_2016_Robust_tracking/images/"
format = 'png'
folder = './Results/'
wu2013GroundTruth = "/Users/Ivan/Files/Data/wu2013"
saveResultsFolder = list()
bookChapter = "/Users/Ivan/Documents/Papers/My_papers/Tracking_book_chapter/images"
paper = "/Users/Ivan/Documents/Papers/My_papers/CVPR_2016_Robust_tracking/images"
#saveResultsFolder.append(bookChapter)
saveResultsFolder.append(paper)
save = True
datasetType = 'wu2013'
dataset = Dataset(wu2013GroundTruth, datasetType)
paperPlot = PaperPlots(dataset, folder)
# this has to changeop
baseLineRun = folder + 'Rob_Struck_filter_f0' + ".p"
plotOPE_SRE_TRE_Robust(saveFigureToFolder=savefilename, format='pdf')
plotSensitivity(paperPlot, baseLineRun, saveResultsFolder, save)
def plot_OPE_comparison():
wu2013GroundTruth = "/Users/Ivan/Files/Data/wu2013"
datasetType = 'wu2013'
dataset = Dataset(wu2013GroundTruth, datasetType)
(runsNames, corrected_names) = getRunsForCVPR2016()
runs = list()
names = list()
for runName in runsNames:
run = loadPickle(runName)
names.append(runName)
runs.append(run)
plotMetricsDict = dict()
completeMetricDict = dict()
alternativeNames = corrected_names
if len(alternativeNames) == 0:
for r in runs:
plotMetricsDict[r.name] = r.plotMetricsDictEntry
completeMetricDict[r.name] = r.completeMetricDictEntry
else:
for r, name in zip(runs, alternativeNames):
plotMetricsDict[name] = r.plotMetricsDictEntry
completeMetricDict[name] = r.completeMetricDictEntry
success = list()
precision = list()
names = list()
import re
for name, value in plotMetricsDict.iteritems():
s = value['default'][5]
p = value['default'][4]
success.append(s)
precision.append(p)
if "ObjStruck with " in name:
short_name = re.sub("ObjStruck with ", '', name)
else:
short_name = name
names.append(short_name)
df = pd.DataFrame({
'name': names,
'success': success,
'precision': precision
})
print df
sns.set_style("whitegrid")
plt.subplots_adjust(bottom=0.4)
ax = sns.barplot(x="name", y="success", data=df, palette="Blues_d")
locs, labels = plt.xticks()
plt.setp(labels, rotation=-45)
plt.show()
def main():
wu2013GroundTruth = "/Users/Ivan/Files/Data/Tracking_benchmark"
datasetType = 'wu2013'
dataset = Dataset(wu2013GroundTruth, datasetType)
d = dataset.dictData
maxCpus = 4
obj = ObjectnessBoxesEvaluator(dataset, cpus=maxCpus)
obj.evaluate()
def plotOPE_SRE_TRE_Robust(
saveFigureToFolder='/Users/Ivan/Code/Tracking/Antrack/doc/technical_reports/images/',
format='png'):
wu2013results = "/Users/Ivan/Files/Results/Tracking/wu2013"
wu2013GroundTruth = "/Users/Ivan/Files/Data/wu2013"
vot2014Results = "/Users/Ivan/Files/Results/Tracking/vot2014"
vot2014GrounTruth = "/Users/Ivan/Files/Data/vot2014"
datasetType = 'wu2013'
dataset = Dataset(wu2013GroundTruth, datasetType)
(runsNames, corrected_names) = getRunsForCVPR2016_robust()
runs = list()
names = list()
for runName in runsNames:
run = loadPickle(runName)
names.append(runName)
#run = run.data[experimentType]
runs.append(run)
# run=loadPickle('./Runs/TLD.p')
# runs.append(run)
# names.append('./Runs/TLD.p')
save = True
evaluator = EvaluatorAllExperiments(dataset, list(), names)
successAndPrecision = 'cvpr2016_SuccessAndPrecision_wu2013'
histograms = 'cvpr2016_histogram_wu2013'
i = format
if save:
evaluator.evaluateFromSave(
runs,
successAndPrecisionPlotName=saveFigureToFolder +
successAndPrecision + '.' + i,
histogramPlot=saveFigureToFolder + histograms + '.' + i,
alternativeNames=corrected_names)
else:
evaluator.evaluateFromSave(runs, alternativeNames=corrected_names)
def generateDefaultVizualiations(wildcard):
wu2013GroundTruth = "/Users/Ivan/Files/Data/wu2013"
folderForGraphs='./Visualizations/'
datasetType = 'wu2013'
dataset = Dataset(wu2013GroundTruth, datasetType)
runsNames = glob.glob("./Runs/" + wildcard + "*.p")
#runsNames = glob.glob('./Runs/upd=3_hogANDhist_int*.p')
formatSave='pdf'
regexp= re.compile("(.*\/)(.+)(.p)")
for runName in runsNames:
m=re.match(regexp,runName)
name=m.group(2)
print name
run = loadPickle(runName)
run.data['TRE'].data=[]
run.data['SRE'].data=[]
viz = VisualizeAllExperiments(dataset, run)
viz.barplotDefault(savefile=folderForGraphs+name+"."+ formatSave)
def main(argv=None):
if argv is None:
argv = sys.argv
wu2013GroundTruth = "/Users/Ivan/Files/Data/Tracking_benchmark"
datasetType = 'wu2013'
experimentType='default'
runName = './Runs/a28_hist_int_f1.p'
run = loadPickle(runName)
# print run
vidName = 'basketball'
dataset = Dataset(wu2013GroundTruth, datasetType)
viz = VisualizeAllExperiments(dataset, run)
#viz.precisionAndSuccessPlot()
# exp1=run.data['default']
#
# vizOne=VisualizeExperiment(dataset,exp1)
#
# vizOne.precisionAndSuccessPlot(vidName)
#viz.show(vidName,experimentRunNumber=0,experiment='default')
#viz.show(vidName,100)
runsNames = glob.glob('./Runs/*.p')
for r in runsNames:
print r
createSavedEvaluations(wildcard)
wu2013results = "/Users/Ivan/Files/Results/Tracking/wu2013"
wu2013GroundTruth = "/Users/Ivan/Files/Data/wu2013"
vot2014Results = "/Users/Ivan/Files/Results/Tracking/vot2014"
vot2014GrounTruth = "/Users/Ivan/Files/Data/vot2014"
datasetType = 'wu2013'
# Note: in wu2013 i
# trackerLabel="STR+f_hog"
# wildcard = sys.argv[1]
dataset = Dataset(wu2013GroundTruth, datasetType)
runsNames = glob.glob('./Results/' + wildcard + '*.p')
runsNames = getRunsForCVPR2016()
#runsNames = ['SAMF', 'Kernelized_filter', 'fk_hist_int_f0', 'fk_hist_int_f1','TLD']
runs = list()
#
names = list()
for runName in runsNames:
#runName= './Results/' + runName + '.p'
run = loadPickle(runName)
names.append(runName)
print runName
#run = run.data[experimentType]
def plotStraddelingEdgeOPE(wildcard, savefilename='', format='pdf'):
wu2013GroundTruth = "/Users/Ivan/Files/Data/wu2013"
folderForGraphs = './Visualizations/'
datasetType = 'wu2013'
dataset = Dataset(wu2013GroundTruth, datasetType)
runsNames = glob.glob("./Results/" + wildcard + "*.p")
#runsNames = glob.glob('./Runs/upd=3_hogANDhist_int*.p')
formatSave = 'pdf'
regexp = re.compile("(.*\/)(.+)(.p)")
experimentType = 'default'
d = dict()
for runName in runsNames:
m = re.match(regexp, runName)
name = m.group(2)
run = loadPickle(runName)
results = run.plotMetricsDictEntry['default']
d[name] = results[4:6]
(df1, df2) = reshuffleStraddlingEdgeOPEData(d)
save = False
# Initialize a grid of plots with an Axes for each walk
grid = sns.FacetGrid(df1,
col="$\lambda_e$",
hue="$\lambda_e$",
col_wrap=3,
size=3)
# Draw a horizontal line to show the starting point
grid.map(plt.axhline, y=0.749, ls=":", c=".5")
# Draw a line plot to show the trajectory of each random walk
grid.map(plt.plot, "$\lambda_s$", "Precision", marker="o", ms=4)
grid.set(xticks=np.arange(6) / float(10),
yticks=[0.6, 0.65, 0.7, 0.75, 0.8, 0.85],
xlim=(0, 0.5),
ylim=(0.6, 0.85))
grid.fig.tight_layout(w_pad=1)
# Adjust the tick positions and labels
if savefilename == '':
plt.show()
else:
plt.savefig(savefilename + "precision." + format)
grid = sns.FacetGrid(df2,
col="$\lambda_e$",
hue="$\lambda_e$",
col_wrap=3,
size=3)
grid.map(plt.axhline, y=0.59, ls=":", c=".5")
grid.map(plt.plot, "$\lambda_s$", "Success", marker="o", ms=4)
grid.set(xticks=np.arange(6) / float(10),
yticks=[0.5, 0.55, 0.6, 0.65, 0.7],
xlim=(0, 0.5),
ylim=(0.5, 0.7))
grid.fig.tight_layout(w_pad=1)
if savefilename == '':
plt.show()
else:
plt.savefig(savefilename + "success." + format)