def checkPrevRun():
""" Checks for previous identical runs to prevent running identical sweeps"""
path = "..\\Analysis\\"+sweep+"\\"+str(sys.argv[1])+"\\"+sys.argv[2]+"\\Analysis.csv"
if not os.path.exists(path):
csv.writer(open(path,'wb'))
flag = 0
if os.path.exists(path):
checkFile = csv.reader(open(path,'r'))
for line in checkFile:
param = [str(num) for num in scaffold._registeredParams["valueChange"].defaultValue]
if param == line[:len(param)]:
return True
return False
scaffold.registerParameter("default_", 'default')
""" Default => no params are being swept """
scaffold.registerParameter("valueChange", valueChange)
"""Current parameter values swept."""
for i in range(len(keyChange)):
print "Setting ", keyChange[i], " as ", params[keyChange[i]]
print "\n"
density = sys.argv[1]
filename = sys.argv[2]
outputs = ["renderRemovedBackgroundOutput.avi",
"renderSeedImagesOutput.avi",
"renderDiffsOutput.avi",
"renderMasksOutput.avi",
"renderMorphedImagesOutput.avi",
"renderWatershedOutput.avi",
"renderRegionsOutput.avi",
"renderEllipsesOutput.avi",
"renderTracksOutput.avi",
"plotParticleDistanceOutput.png",
"plotPIVFieldOutput.mp4",
"plotDensityFieldOutput.avi",
"plotVelocityFieldOutput.avi"]
params["configFile"] = "..\\..\\Data\\July292011\\" + str(density) + "\Series" + str(filename) + "_Properties.xml"
for key in outputs:
filepath = "..\\Analysis\\"+sweep+"\\"+str(density) + "\\" + str(filename) + "\\" + key[:-4] +"\\"
name = "_".join(valueChange).replace(".","") + key[-4:]
params[key[:-4]] = filepath + name
if not os.path.exists(filepath):
os.makedirs(filepath)
return params
from xml.dom import minidom
import re
import os
import numpy as np
import _regions
import tables as tb
__all__ = ['CONFIG_FILE', 'LOCAL_GROUP_PREFIX',
'ExtractUniqueId',
'ParseConfig',
'createImageArray',
'LoadImages',
'ComputeForegroundMasks'
'MASK_LOW_THRESH', 'MASK_HIGH_THRESH']
CONFIG_FILE = scaffold.registerParameter("configFile", "")
"""
The location of the configuration file for this image series.
It should point to a _Properties.xml file.
"""
LOCAL_GROUP_PREFIX = scaffold.registerParameter("localGroupPrefix", "/data")
"""The prefix used for paths inside of the h5 file for this image sequence."""
class ExtractUniqueId(scaffold.Task):
"""
Extracts a unique ID from the configuration file. The ID is currently a
combination of the start date and time.
"""
""" Tasks related to analyzing particle tracks for density calculations. :Edited: August 13, 2013 - contact([email protected]) """ import numpy as np from numpy.linalg import norm import math import tables as tb import images import scaffold import particles NUM_GRID_CELLS = scaffold.registerParameter("numGridCells_density", [20, 20]) """The number of rows and columns in the particle grid.""" def _griddedPath(task, path): return "{0}{1}_{2}".format(path, *task._param(NUM_GRID_CELLS)) class GridParticles(scaffold.Task): """ Creates an NxM grid and assigns a cell number to each particle at each frame, which is useful for local correlations and general optimizations. """ name = "Grid Particles" dependencies = [particles.FindParticlesViaMorph, images.ParseConfig] def isComplete(self): return self.context.hasNode(self._cellsPath)
return task._param(param).format(task.context.root._v_name)
class _RenderTask(scaffold.Task):
_outputParam = None
def _render(self, images, mapping=lambda image: image):
if self._outputParam is None: raise NotImplemented
output = _formatOutput(self, self._outputParam)
seq = im.ImageSeq(map(mapping, images))
seq.writeMovie(output)
self.context.log("Rendered to {0}.", output)
RENDER_MASKS_OUTPUT = scaffold.registerParameter("renderMasksOutput", "../videos/{0}-masks.avi")
"""The file path to render the output video of RenderForegroundMasks to."""
class RenderForegroundMasks(_RenderTask):
name = "Render Foreground Masks"
dependencies = [im.ComputeForegroundMasks]
_outputParam = RENDER_MASKS_OUTPUT
def run(self):
images = self._import(im.ComputeForegroundMasks, "masks")
self._render(images, im.binaryToGray)
RENDER_DIFFS_OUTPUT = scaffold.registerParameter("renderDiffsOutput", "../videos/{0}-diffs.avi")
"""The file path to render the output video of RenderDifferences to."""
"""
import cv
import cv2
import numpy as np
import math
import tables as tb
import images as im
import scaffold
import utils
from scipy import stats
from trackpy import tracking
from matplotlib import pyplot as plt
ELLIPSE_MIN_AREA = scaffold.registerParameter("ellipseMinArea", 4.0)
"""The minimum area required to identify an ellipse."""
ELLIPSE_MAX_AREA = scaffold.registerParameter("ellipseMaxArea", 200.0)
"""The maximum area required to identify an ellipse."""
EXPECTED_ELLIPSES_PER_FRAME = scaffold.registerParameter("expectedEllipsesPerFrame", 200)
"""The mean number of ellipses we expect to find. It's used to optimize memory
allocation."""
class ParticleFinder(scaffold.Task):
"""Base class for all of the different ways to find particles."""
def _findEllipsesViaContours(self, masks):
"""Dumps the ellipses found in ``masks`` into the ellipse table.
``masks`` should be an iterable of binary images.
"""
"""
Tasks related to analyzing particle tracks.
"""
import numpy as np
from numpy.linalg import norm
import math
import tables as tb
import images
import scaffold
import particles
NUM_GRID_CELLS = scaffold.registerParameter("numGridCells", [20, 20])
"""The number of rows and columns in the particle grid."""
def _griddedPath(task, path):
return "{0}{1}_{2}".format(path, *task._param(NUM_GRID_CELLS))
class GridParticles(scaffold.Task):
"""
Creates an NxM grid and assigns a cell number to each particle at each
frame, which is useful for local correlations and general optimizations.
"""
name = "Grid Particles"
dependencies = [particles.TrackParticles, images.ParseConfig]
def isComplete(self):
return self.context.hasNode(self._cellsPath)
def export(self):
Tasks related to processing an image sequence to idenfity and track particles. :Edited: August 13, 2013 - contact([email protected]) """ from subprocess import call import cv, cv2, math, scaffold, utils, sys import numpy as np import tables as tb import images as im from scipy import stats from trackpy import tracking from matplotlib import pyplot as plt ELLIPSE_TABLE_PATH = "ellipses" ELLIPSE_MIN_AREA = scaffold.registerParameter("ellipseMinArea", 4.0) """The minimum area required to identify an ellipse.""" ELLIPSE_MAX_AREA = scaffold.registerParameter("ellipseMaxArea", 200.0) """The maximum area required to identify an ellipse.""" EXPECTED_ELLIPSES_PER_FRAME = scaffold.registerParameter("expectedEllipsesPerFrame", 200) """The mean number of ellipses we expect to find. It's used to optimize memory allocation.""" class ParticleFinder(scaffold.Task): """Base class for all of the different ways to find particles.""" def _findEllipsesViaContours(self, masks): """Dumps the ellipses found in ``masks`` into the ellipse table. ``masks`` should be an iterable of binary images. """
