本文整理汇总了Python中pyworkflow.em.convert.ImageHandler.read方法的典型用法代码示例。如果您正苦于以下问题:Python ImageHandler.read方法的具体用法?Python ImageHandler.read怎么用?Python ImageHandler.read使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pyworkflow.em.convert.ImageHandler
的用法示例。
在下文中一共展示了ImageHandler.read方法的5个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _buildDendrogram
# 需要导入模块: from pyworkflow.em.convert import ImageHandler [as 别名]
# 或者: from pyworkflow.em.convert.ImageHandler import read [as 别名]
def _buildDendrogram(self, leftIndex, rightIndex, index, writeAverages=False, level=0):
""" This function is recursively called to create the dendogram graph(binary tree)
and also to write the average image files.
Params:
leftIndex, rightIndex: the indinxes within the list where to search.
index: the index of the class average.
writeImages: flag to select when to write averages.
From self:
self.dendroValues: the list with the heights of each node
self.dendroImages: image stack filename to read particles
self.dendroAverages: stack name where to write averages
It will search for the max in values list (between minIndex and maxIndex).
Nodes to the left of the max are left childs and the other right childs.
"""
maxValue = self.dendroValues[leftIndex]
maxIndex = 0
for i, v in enumerate(self.dendroValues[leftIndex+1:rightIndex]):
if v > maxValue:
maxValue = v
maxIndex = i+1
m = maxIndex + leftIndex
node = DendroNode(index, maxValue)
ih = ImageHandler()
particleNumber = self.dendroIndexes[m+1]
node.imageList = [particleNumber]
if writeAverages:
node.image = ih.read((particleNumber, self.dendroImages))
def addChildNode(left, right, index):
if right > left:
child = self._buildDendrogram(left, right, index, writeAverages, level+1)
node.addChild(child)
node.length += child.length
node.imageList += child.imageList
if writeAverages:
node.image += child.image
del child.image # Allow to free child image memory
if rightIndex > leftIndex + 1 and level < self.dendroMaxLevel:
addChildNode(leftIndex, m, 2*index)
addChildNode(m+1, rightIndex, 2*index+1)
node.avgCount = self.dendroAverageCount + 1
self.dendroAverageCount += 1
node.path = '%[email protected]%s' % (node.avgCount, self.dendroAverages)
if writeAverages:
#TODO: node['image'] /= float(node['length'])
#node.image.inplaceDivide(float(node.length)) #FIXME: not working, noisy images
avgImage = node.image / float(node.length)
ih.write(avgImage, (node.avgCount, self.dendroAverages))
fn = self._getTmpPath('doc_class%03d.stk' % index)
doc = SpiderDocFile(fn, 'w+')
for i in node.imageList:
doc.writeValues(i)
doc.close()
return node
示例2: generateReportImages
# 需要导入模块: from pyworkflow.em.convert import ImageHandler [as 别名]
# 或者: from pyworkflow.em.convert.ImageHandler import read [as 别名]
def generateReportImages(self, firstThumbIndex=0, micScaleFactor=6):
""" Function to generate thumbnails for the report. Uses data from
self.thumbPaths.
===== Params =====
- firstThumbIndex: index from which we start generating thumbnails
- micScaleFactor: how much to reduce in size the micrographs.
"""
ih = ImageHandler()
numMics = len(self.thumbPaths[MIC_PATH])
for i in range(firstThumbIndex, numMics):
print('Generating images for mic %d' % (i+1))
# mic thumbnails
dstImgPath = join(self.reportDir, self.thumbPaths[MIC_THUMBS][i])
if not exists(dstImgPath):
if self.micThumbSymlinks:
pwutils.createAbsLink(self.thumbPaths[MIC_PATH][i], dstImgPath)
else:
ih.computeThumbnail(self.thumbPaths[MIC_PATH][i],
dstImgPath, scaleFactor=micScaleFactor,
flipOnY=True)
# shift plots
if SHIFT_THUMBS in self.thumbPaths:
dstImgPath = join(self.reportDir, self.thumbPaths[SHIFT_THUMBS][i])
if not exists(dstImgPath):
pwutils.createAbsLink(self.thumbPaths[SHIFT_PATH][i], dstImgPath)
# Psd thumbnails
# If there ARE thumbnail for the PSD (no ctf protocol and
# moviealignment hasn't computed it
if PSD_THUMBS in self.thumbPaths:
if self.ctfProtocol is None:
srcImgPath = self.thumbPaths[PSD_PATH][i]
dstImgPath = join(self.reportDir, self.thumbPaths[PSD_THUMBS][i])
if not exists(dstImgPath) and srcImgPath is not None:
if srcImgPath.endswith('psd'):
psdImg1 = ih.read(srcImgPath)
psdImg1.convertPSD()
psdImg1.write(dstImgPath)
ih.computeThumbnail(dstImgPath, dstImgPath,
scaleFactor=1, flipOnY=True)
else:
pwutils.createAbsLink(srcImgPath, dstImgPath)
else:
dstImgPath = join(self.reportDir, self.thumbPaths[PSD_THUMBS][i])
if not exists(dstImgPath):
ih.computeThumbnail(self.thumbPaths[PSD_PATH][i],
dstImgPath, scaleFactor=1, flipOnY=True)
return
示例3: _computeRightPreview
# 需要导入模块: from pyworkflow.em.convert import ImageHandler [as 别名]
# 或者: from pyworkflow.em.convert.ImageHandler import read [as 别名]
def _computeRightPreview(self):
""" This function should compute the right preview
using the self.lastObj that was selected
"""
from pyworkflow.em.packages.xmipp3 import locationToXmipp
# Copy image to filter to Tmp project folder
outputName = os.path.join("Tmp", "filtered_particle")
outputPath = outputName + ".spi"
cleanPath(outputPath)
outputLoc = (1, outputPath)
ih = ImageHandler()
ih.convert(self.lastObj.getLocation(), outputLoc)
outputLocSpiStr = locationToSpider(1, outputName)
pars = {}
pars["filterType"] = self.protocolParent.filterType.get()
pars["filterMode"] = self.protocolParent.filterMode.get()
pars["usePadding"] = self.protocolParent.usePadding.get()
pars["op"] = "FQ"
if self.protocolParent.filterType <= FILTER_SPACE_REAL:
pars['filterRadius'] = self.getRadius()
else:
pars['lowFreq'] = self.getLowFreq()
pars['highFreq'] = self.getHighFreq()
if self.protocolParent.filterType == FILTER_FERMI:
pars['temperature'] = self.getTemperature()
filter_spider(outputLocSpiStr, outputLocSpiStr, **pars)
# Get output image and update filtered image
img = ImageHandler()._img
locXmippStr = locationToXmipp(1, outputPath)
img.read(locXmippStr)
self.rightImage = img
self.updateFilteredImage()
示例4: SpiderProtClassifyCluster
# 需要导入模块: from pyworkflow.em.convert import ImageHandler [as 别名]
# 或者: from pyworkflow.em.convert.ImageHandler import read [as 别名]
class SpiderProtClassifyCluster(SpiderProtClassify):
""" Base for Clustering Spider classification protocols.
"""
def __init__(self, script, classDir, **kwargs):
SpiderProtClassify.__init__(self, script, classDir, **kwargs)
#--------------------------- STEPS functions ------------------------------
def createOutputStep(self):
self.buildDendrogram(True)
#--------------------------- UTILS functions ------------------------------
def _fillClassesFromNodes(self, classes2D, nodeList):
""" Create the SetOfClasses2D from the images of each node
in the dendrogram.
"""
particles = classes2D.getImages()
sampling = classes2D.getSamplingRate()
# We need to first create a map between the particles index and
# the assigned class number
classDict = {}
nodeDict = {}
classCount = 0
for node in nodeList:
if node.path:
classCount += 1
node.classId = classCount
nodeDict[classCount] = node
for i in node.imageList:
classDict[int(i)] = classCount
def updateItem(p, i):
classId = classDict.get(i, None)
if classId is None:
p._appendItem = False
else:
p.setClassId(classId)
def updateClass(cls):
node = nodeDict[cls.getObjId()]
rep = cls.getRepresentative()
rep.setSamplingRate(sampling)
rep.setLocation(node.avgCount, self.dendroAverages)
particlesRange = range(1, particles.getSize()+1)
classes2D.classifyItems(updateItemCallback=updateItem,
updateClassCallback=updateClass,
itemDataIterator=iter(particlesRange))
def _fillParticlesFromNodes(self, inputParts, outputParts, nodeList):
""" Create the SetOfClasses2D from the images of each node
in the dendrogram.
"""
allImages = set()
for node in nodeList:
if node.path:
for i in node.imageList:
allImages.add(i)
def updateItem(item, index):
item._appendItem = index in allImages
particlesRange = range(1, inputParts.getSize()+1)
outputParts.copyItems(inputParts,
updateItemCallback=updateItem,
itemDataIterator=iter(particlesRange))
def buildDendrogram(self, writeAverages=False):
""" Parse Spider docfile with the information to build the dendrogram.
Params:
writeAverages: whether to write class averages or not.
"""
dendroFile = self._getFileName('dendroDoc')
# Dendrofile is a docfile with at least 3 data colums (class, height, id)
doc = SpiderDocFile(dendroFile)
values = []
indexes = []
for _, h, i in doc.iterValues():
indexes.append(i)
values.append(h)
doc.close()
self.dendroValues = values
self.dendroIndexes = indexes
self.dendroImages = self._getFileName('particles')
self.dendroAverages = self._getFileName('averages')
self.dendroAverageCount = 0 # Write only the number of needed averages
self.dendroMaxLevel = 10 # FIXME: remove hard coding if working the levels
self.ih = ImageHandler()
return self._buildDendrogram(0, len(values)-1, 1, writeAverages)
def getImage(self, particleNumber):
return self.ih.read((particleNumber, self.dendroImages))
def addChildNode(self, node, leftIndex, rightIndex, index,
#.........这里部分代码省略.........
示例5: SpiderProtClassifyCluster
# 需要导入模块: from pyworkflow.em.convert import ImageHandler [as 别名]
# 或者: from pyworkflow.em.convert.ImageHandler import read [as 别名]
class SpiderProtClassifyCluster(SpiderProtClassify):
""" Base for Clustering Spider classification protocols.
"""
def __init__(self, script, classDir, **kwargs):
SpiderProtClassify.__init__(self, script, classDir, **kwargs)
#--------------------------- STEPS functions --------------------------------------------
def createOutputStep(self):
self.buildDendrogram(True)
#--------------------------- UTILS functions --------------------------------------------
def _fillClassesFromNodes(self, classes, nodeList):
""" Create the SetOfClasses2D from the images of each node
in the dendogram.
"""
img = Particle()
sampling = classes.getSamplingRate()
for node in nodeList:
if node.path:
#print "node.path: ", node.path
class2D = Class2D()
avg = Particle()
#avg.copyObjId(class2D)
avg.setLocation(node.avgCount, self.dendroAverages)
avg.setSamplingRate(sampling)
class2D.setRepresentative(avg)
class2D.setSamplingRate(sampling)
classes.append(class2D)
#print "class2D.id: ", class2D.getObjId()
for i in node.imageList:
#img.setObjId(i) # FIXME: this is wrong if the id is different from index
img.cleanObjId()
img.setLocation(int(i), self.dendroImages)
class2D.append(img)
classes.update(class2D)
def _fillParticlesFromNodes(self, particles, nodeList):
""" Create the SetOfClasses2D from the images of each node
in the dendogram.
"""
img = Particle()
for node in nodeList:
if node.path:
for i in node.imageList:
#img.setObjId(i) # FIXME: this is wrong if the id is different from index
img.cleanObjId()
img.setLocation(int(i), self.dendroImages)
particles.append(img)
def buildDendrogram(self, writeAverages=False):
""" Parse Spider docfile with the information to build the dendogram.
Params:
dendroFile: docfile with a row per image.
Each row contains the image id and the height.
"""
dendroFile = self._getFileName('dendroDoc')
# Dendrofile is a docfile with at least 3 data colums (class, height, id)
doc = SpiderDocFile(dendroFile)
values = []
indexes = []
for c, h, _ in doc.iterValues():
indexes.append(c)
values.append(h)
doc.close()
self.dendroValues = values
self.dendroIndexes = indexes
self.dendroImages = self._getFileName('particles')
self.dendroAverages = self._getFileName('averages')
self.dendroAverageCount = 0 # Write only the number of needed averages
self.dendroMaxLevel = 10 # FIXME: remove hard coding if working the levels
self.ih = ImageHandler()
return self._buildDendrogram(0, len(values)-1, 1, writeAverages)
def getImage(self, particleNumber):
return self.ih.read((particleNumber, self.dendroImages))
def addChildNode(self, node, leftIndex, rightIndex, index, writeAverages, level):
child = self._buildDendrogram(leftIndex, rightIndex, index, writeAverages, level+1)
node.addChild(child)
node.length += child.length
node.imageList += child.imageList
if writeAverages:
if node.image is None:
node.image = child.image
else:
node.image += child.image
del child.image # Allow to free child image memory
def _buildDendrogram(self, leftIndex, rightIndex, index, writeAverages=False, level=0):
#.........这里部分代码省略.........