def _onProcess(self, imageBuffer, startIndex, learningPhase,
XResult, yResult):
"""Overload"""
convertor = NumpyPILConvertor()
#Logging
self.setTask(len(imageBuffer),
"PixitCoordinator loop for each image")
#Init
counter = 0
index = startIndex * self.nbObjMultiplicator()
#Main loop
for image, label in imageBuffer:
image = convertor.numpyToPIL(image)
imgLs = self._multiSWExtractor.extract(image)
#Filling the X and y
for img in imgLs:
tmpRes = self._featureExtractor.extract(
convertor.pILToNumpy(img))
XResult[index] = self._rescaler(tmpRes)
yResult[index] = label
index += 1
#Logging progress
self.updateTaskProgress(counter)
counter += 1
def _onProcess(self, imageBuffer, startIndex, learningPhase, XResult,
yResult):
"""Overload"""
convertor = NumpyPILConvertor()
#Logging
self.setTask(len(imageBuffer), "PixitCoordinator loop for each image")
#Init
counter = 0
index = startIndex * self.nbObjMultiplicator()
#Main loop
for image, label in imageBuffer:
image = convertor.numpyToPIL(image)
imgLs = self._multiSWExtractor.extract(image)
#Filling the X and y
for img in imgLs:
tmpRes = self._featureExtractor.extract(
convertor.pILToNumpy(img))
XResult[index] = self._rescaler(tmpRes)
yResult[index] = label
index += 1
#Logging progress
self.updateTaskProgress(counter)
counter += 1
def _onProcess(self, imageBuffer, learningPhase):
"""Overload"""
ls = []
y = []
convertor = NumpyPILConvertor()
#Logging
counter = 0
self.setTask(len(imageBuffer),
"PixitCoordinator loop for each image")
for image, label in imageBuffer:
image = convertor.numpyToPIL(image)
imgLs = self._multiSWExtractor.extract(image)
for img in imgLs:
ls.append(
self._featureExtractor.extract(convertor.pILToNumpy(img)))
y = y + [label] * len(imgLs)
#Logging progress
self.updateTaskProgress(counter)
counter += 1
X = np.vstack((ls))
return X, y
def extract(self, image):
"""
Extract feature from the given image
Parameters
----------
image : :class:`PIL.Image` or preferably a numpy array
The image to process
Return
------
all_subwindow : a list of lists of subwindows
The element e[i][j] is a numpy array correspond to the ith
subwindow of the jth filter.
If the original image is included, it correspond to the first
(0th) filter.
"""
#Converting image in the right format
convertor = NumpyPILConvertor()
image = convertor.pILToNumpy(image)
filtered = []
#Including the original image if desired
if self._include_image:
pooledList = self._multiPooler.multipool(image)
for pooled in pooledList:
filtered.append(pooled)
#Applying the filters & Aggregating
for filt in self._finiteFilter:
#Filtering
npTmp = self._convolver(image, filt)
#Aggregating
pooledList = self._multiPooler.multipool(npTmp)
for pooled in pooledList:
filtered.append(pooled)
#Refreshing the boxes
shape = filtered[0].shape
self._swExtractor.refresh(shape[1], shape[0]) # width, height
#Extracting the subwindows
nbFilters = len(self._finiteFilter)
nbSubWindow = len(self._swExtractor)
nbPoolers = len(self._multiPooler)
nbImageFactor = nbFilters*nbPoolers
if self._include_image:
nbImageFactor += nbPoolers
allSubWindows = [[0] * nbImageFactor for i in xrange(nbSubWindow)]
for col, numpies in enumerate(filtered):
#converting image to the right format
img = convertor.numpyToPIL(numpies)
#Extracting the subwindows s.s.
subwindows = self._swExtractor.extract(img)
for row in xrange(nbSubWindow):
allSubWindows[row, col] = convertor.pILToNumpy(subwindows[row])
return allSubWindows
def extract(self, image):
"""
Extract feature from the given image
Parameters
----------
image : :class:`PIL.Image` or preferably a numpy array
The image to process
Return
------
all_subwindow : a list of lists of subwindows
The element e[i][j] is a numpy array correspond to the ith
subwindow of the jth filter.
If the original image is included, it correspond to the first
(0th) filter.
"""
#Converting image in the right format
convertor = NumpyPILConvertor()
image = convertor.pILToNumpy(image)
filtered = []
#Including the original image if desired
if self._include_image:
pooledList = self._multiPooler.multipool(image)
for pooled in pooledList:
filtered.append(pooled)
#Applying the filters & Aggregating
for filt in self._finiteFilter:
#Filtering
npTmp = self._convolver(image, filt)
#Aggregating
pooledList = self._multiPooler.multipool(npTmp)
for pooled in pooledList:
filtered.append(pooled)
#Refreshing the boxes
shape = filtered[0].shape
self._swExtractor.refresh(shape[1], shape[0]) # width, height
#Extracting the subwindows
nbFilters = len(self._finiteFilter)
nbSubWindow = len(self._swExtractor)
nbPoolers = len(self._multiPooler)
nbImageFactor = nbFilters * nbPoolers
if self._include_image:
nbImageFactor += nbPoolers
allSubWindows = [[0] * nbImageFactor for i in xrange(nbSubWindow)]
for col, numpies in enumerate(filtered):
#converting image to the right format
img = convertor.numpyToPIL(numpies)
#Extracting the subwindows s.s.
subwindows = self._swExtractor.extract(img)
for row in xrange(nbSubWindow):
allSubWindows[row, col] = convertor.pILToNumpy(subwindows[row])
return allSubWindows