本文整理汇总了Python中ij.WindowManager.getNonImageTitles方法的典型用法代码示例。如果您正苦于以下问题：Python WindowManager.getNonImageTitles方法的具体用法？Python WindowManager.getNonImageTitles怎么用？Python WindowManager.getNonImageTitles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ij.WindowManager的用法示例。
在下文中一共展示了WindowManager.getNonImageTitles方法的1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: run
# 需要导入模块: from ij import WindowManager [as 别名]
# 或者: from ij.WindowManager import getNonImageTitles [as 别名]
#.........这里部分代码省略.........
skel.setup("", skeleton)
status.showStatus("Analyzing skeleton...")
skel_result = skel.run()
status.showStatus("Computing graph based parameters...")
branch_lengths = []
summed_lengths = []
graphs = skel_result.getGraph()
for graph in graphs:
summed_length = 0.0
edges = graph.getEdges()
for edge in edges:
length = edge.getLength()
branch_lengths.append(length)
summed_length += length
summed_lengths.append(summed_length)
output_parameters["branch length mean"] = eztables.statistical.average(branch_lengths)
output_parameters["branch length median"] = eztables.statistical.median(branch_lengths)
output_parameters["branch length stdevp"] = eztables.statistical.stdevp(branch_lengths)
output_parameters["summed branch lengths mean"] = eztables.statistical.average(summed_lengths)
output_parameters["summed branch lengths median"] = eztables.statistical.median(summed_lengths)
output_parameters["summed branch lengths stdevp"] = eztables.statistical.stdevp(summed_lengths)
branches = list(skel_result.getBranches())
output_parameters["network branches mean"] = eztables.statistical.average(branches)
output_parameters["network branches median"] = eztables.statistical.median(branches)
output_parameters["network branches stdevp"] = eztables.statistical.stdevp(branches)
# Create/append results to a ResultsTable...
status.showStatus("Display results...")
if "Mito Morphology" in list(WindowManager.getNonImageTitles()):
rt = WindowManager.getWindow("Mito Morphology").getTextPanel().getOrCreateResultsTable()
else:
rt = ResultsTable()
rt.incrementCounter()
for key in output_order:
rt.addValue(key, str(output_parameters[key]))
# Add user comments intelligently
if user_comment != None and user_comment != "":
if "=" in user_comment:
comments = user_comment.split(",")
for comment in comments:
rt.addValue(comment.split("=")[0], comment.split("=")[1])
else:
rt.addValue("Comment", user_comment)
rt.show("Mito Morphology")
# Create overlays on the original ImagePlus and display them if 2D...
if imp.getNSlices() == 1:
status.showStatus("Generate overlays...")
IJ.run(skeleton, "Green", "")
IJ.run(binary, "Magenta", "")
skeleton_ROI = ImageRoi(0,0,skeleton.getProcessor())
skeleton_ROI.setZeroTransparent(True)
skeleton_ROI.setOpacity(1.0)
binary_ROI = ImageRoi(0,0,binary.getProcessor())
binary_ROI.setZeroTransparent(True)
binary_ROI.setOpacity(0.25)