def createNewFDVT(self):
fn = QtGui.QFileDialog.getOpenFileName(self, "Select FDVT", '.',
'*.csv')
anno = A.Annotation()
anno.loadFromFile(fn[0])
# filterTuples = anno.extractAllFilterTuples()
annotationSelector = SD.AnnotationSelector()
annotationSelector.setAnnotationFile(fn[0])
OD.OverlayDialogWidgetBase.getUserInput(self, annotationSelector)
# annotationFilters = AS.AnnotationSelecter.getAnnotationSelection(self,
# anno)
annotationSelection = annotationSelector.getUserSelection()
annotationFilters = []
colors = []
for a, b, c in annotationSelection:
annotationFilters += [A.AnnotationFilter(None, [a], [b])]
colors += [c]
filteredAnno = anno.filterFrameListMultiple(annotationFilters,
exactMatch=False)
import tempfile
tmpDir = tempfile.mkdtemp()
fdvt = FDV.FrameDataVisualizationTreeBehaviour(tmpDir)
fdvt.importAnnotation(filteredAnno, annoFilters=annotationFilters)
self.addFDVT(fdvt, colors)
print annotationFilters
def classify(self):
"""
Applies the classifier on all data
:return:
"""
print("classifying!")
if len(self.videoListRel) == 1:
progressSteps = VE.videoExplorer.retrieveVideoLength(
self.rel2absFile(self.videoListRel[0]), 100000)
self.fdvt = FDV.FrameDataVisualizationTreeBehaviour(
os.path.join(self.rootFolder, 'viz', 'color-diff'))
self.fdvt.meta['singleFileMode'] = True
else:
progressSteps = len(self.videoListRel)
self.fdvt = FDV.FrameDataVisualizationTreeBehaviour(
os.path.join(self.rootFolder, 'viz', 'color-diff'))
self.fdvt.meta['singleFileMode'] = False
self.setStatus("classifying...", progressSteps)
filters = []
# filt = A.AnnotationFilter(annotators=["simpleFlyShowCase"],
# behaviours=['negative'],
# vials=self.annotationFilters[0].vials)
# self.fdvt.addNewClass(filt)
# filters += [filt]
for af in self.annotationFilters:
filt = A.AnnotationFilter(annotators=["SimpleHistogramPlugin"],
behaviours=af.behaviours,
vials=af.vials)
self.fdvt.addNewClass(filt)
filters += [filt]
for i, videoPathRel in enumerate(self.videoListRel):
feat = np.hstack(
(np.load(self.generateColorFeatureSavePath(videoPathRel)),
np.load(self.generatediffImgFeatureSavePath(videoPathRel))))
dv = [] # deltaVector
for k in range(0, feat.shape[0], 100):
f = feat[k:k + 100]
prediction = self.classifier.predict(f)
for i, p in enumerate(prediction):
# p-1 because tehre is no negative class!!!
dv += [
self.fdvt.getDeltaValue(videoPathRel, k + i,
filters[int(p - 1)])
]
if len(self.videoListRel) == 1:
self.incrementStatus(100)
self.fdvt.insertDeltaVector(dv)
if len(self.videoListRel) > 1:
self.incrementStatus()
self.fdvt.save()
def addAnnotation(self, annotation, key, addAllAtOnce=True):
"""
adds an annotation to a scene
"""
cfg.log.info("beginning")
if self.vialNo is None:
filt = Annotation.AnnotationFilter([0], self.annotator,
self.behaviourName)
else:
filt = Annotation.AnnotationFilter(self.vialNo, self.annotator,
self.behaviourName)
self.annotationDict[key] = annotation.filterFrameList(filt, exactMatch=False)
cfg.log.info("end")
return
def prepareAnnotationFilters(self):
self.annotations = []
self.annotations += [[\
Annotation.AnnotationFilter([self.vial],["peter"],["falling"]),
Annotation.AnnotationFilter([self.vial],["peter"],["dropping"])\
]]
self.annotations += [[\
Annotation.AnnotationFilter([self.vial],["peter"],["struggling"])
]]
self.annoIdces = []
for i in range(len(self.annotations)):
self.annoIdces += [[]]
for filt in self.annotations[i]:
print filt
self.annoIdces[i] += [
self.queryFDVT.getAnnotationFilterCode(filt)
]
self.annoIdces.insert(0, [0])
def classify(self):
"""
Applies the classifier on all data
:return:
"""
print("classifying!")
self.setStatus("classifying..", len(self.posListRel))
filters = []
self.fdvt = FDV.FrameDataVisualizationTreeBehaviour()
self.fdvt.meta['singleFileMode'] = False
filt = A.AnnotationFilter(annotators=["simpleFlyShowCase"],
behaviours=['negative'],
vials=self.annotationFilters[0].vials)
self.fdvt.addNewClass(filt)
filters += [filt]
for af in self.annotationFilters:
filt = A.AnnotationFilter(annotators=["simpleFlyShowCase"],
behaviours=af.behaviours,
vials=af.vials)
self.fdvt.addNewClass(filt)
filters += [filt]
for i, posPath in enumerate(self.posListRel):
feat = np.load(self.generateFeatureSavePath(posPath))
predictions = self.classifier.predict(feat)
dv = [] # deltaVector
for k, p in enumerate(predictions):
dv += [self.fdvt.getDeltaValue(posPath, k, filters[int(p)])]
self.fdvt.insertDeltaVector(dv)
self.incrementStatus()
self.fdvt.save("/media/peter/Seagate Backup Plus Drive/tmp/stackFdvt.npy")
def setAnnotation(self, anno):
self.anno = anno
self.filterTuples = anno.extractAllFilterTuples()
self.AnnotationFilterCheckboxes = []
for af in self.filterTuples:
alw = AnnotationFilterCheckbox()
annoFilter = A.AnnotationFilter(vials=None,
annotators=[af[0]],
behaviours=[af[1]])
alw.setAnnotationFilter(annoFilter)
self.AnnotationFilterCheckboxes += [alw]
self.annotationLayout.addWidget(alw)
def classify(self):
"""
Applies the classifier on all data
:return:
"""
print("classifying!")
if len(self.videoListRel) == 1:
progressSteps = VE.videoExplorer.retrieveVideoLength(
self.rel2absFile(self.videoListRel[0]), 100000)
# self.fdvt = FDV.FrameDataVisualizationTreeBehaviour()
# self.fdvt.meta['singleFileMode'] = True
else:
progressSteps = len(self.videoListRel)
# self.fdvt = FDV.FrameDataVisualizationTreeBehaviour()
# self.fdvt.meta['singleFileMode'] = False
self.setStatus("classifying...", progressSteps)
filters = []
# filt = A.AnnotationFilter(annotators=["simpleFlyShowCase"],
# behaviours=['negative'],
# vials=self.annotationFilters[0].vials)
# self.fdvt.addNewClass(filt)
# filters += [filt]
clfy_res = []
for af in self.annotationFilters:
filt = A.AnnotationFilter(annotators=["SimpleHistogramPlugin"],
behaviours=af.behaviours,
vials=af.vials)
# self.fdvt.addNewClass(filt)
filters += [filt]
clfy_res += [{'frames': [], 'meta': {}}]
for i, videoPathRel in enumerate(self.videoListRel):
feat = np.load(self.generateFeatureSavePath(videoPathRel))
dv = [] # deltaVector
for k in range(0, feat.shape[0], 100):
f = feat[k:k + 100]
prediction = self.classifier.predict(f)
for j, p in enumerate(prediction):
# p-1 because tehre is no negative class!!!
# dv += [self.fdvt.getDeltaValue(videoPathRel, k + j,
# filters[int(p-1)])]
# UPDATE ANNOTATIONS
clfy_res[p]['frames'] += [k + j]
clfy_res[p]['meta'][k + j] = {
"confidence": 1,
"boundingBox": [None, None, None, None]
}
if len(self.videoListRel) == 1:
self.incrementStatus(100)
# SAVE RESULT IN ANNOTATIONS
resAnno = A.Annotation(frameNo=feat.shape[0])
for i, af in enumerate(self.annotationFilters):
annotator = af.annotators[0]
label = af.behaviours[0]
if clfy_res[i]['frames']:
resAnno.addAnnotation(vial=None,
frames=clfy_res[i]['frames'],
annotator='SimpleHistogramPlugin',
behaviour=label,
metadata=clfy_res[i]['meta'])
# self.fdvt.insertDeltaVector(dv)
resAnno.saveToFile(
self.generateFeatureSavePath(videoPathRel) + 'hist.csv')
if len(self.videoListRel) > 1:
self.incrementStatus()