def AnalyzeTrackings(simName,
simParams,
skillNames,
trackRuns,
path='.',
tag_filters=None):
dirName = os.path.join(path, simName)
trackFile = os.path.join(dirName, simParams['noisyTrackFile'])
tagFile = os.path.join(dirName, simParams['simTagFile'])
simTags = ParamUtils.ReadSimTagFile(tagFile)
keeperIDs = ParamUtils.process_tag_filters(simTags, tag_filters)
(true_tracks, true_falarms) = FilterMHTTracks(*ReadTracks(trackFile))
lastFrame = (max(trk['frameNums'][-1]
for trk in (true_tracks + true_falarms))
if len(true_tracks) + len(true_falarms) > 0 else 0)
true_AssocSegs, trackIndices = CreateSegments(true_tracks,
retindices=True,
lastFrame=lastFrame)
# TODO: need to filter trackIndices as well!
true_AssocSegs = FilterSegments(keeperIDs, true_AssocSegs)
true_FAlarmSegs, falarmIndices = CreateSegments(true_falarms,
retindices=True,
lastFrame=lastFrame)
true_FAlarmSegs = FilterSegments(keeperIDs, true_FAlarmSegs)
# Initializing the analysis data, which will hold a table of analysis
# results for this simulation
analysis = np.empty((len(skillNames), len(trackRuns)))
labels = [skillNames, trackRuns]
for trackerIndex, tracker in enumerate(trackRuns):
obvFilename = os.path.join(dirName,
simParams['result_file'] + "_" + tracker)
(obvTracks, obvFAlarms) = FilterMHTTracks(*ReadTracks(obvFilename))
trk_segs = CreateSegments(obvTracks + obvFAlarms, lastFrame=lastFrame)
trk_segs = FilterSegments(keeperIDs, trk_segs)
truthTable = MakeContingency(true_AssocSegs + true_FAlarmSegs,
trk_segs)
#print("Margin Sums: %d" % (len(truthTable['assocs_Correct']) +
# len(truthTable['assocs_Wrong']) +
# len(truthTable['falarms_Wrong']) +
# len(truthTable['falarms_Correct'])))
for skillIndex, skill in enumerate(skillNames):
analysis[skillIndex, trackerIndex] = Analyzers.skillcalcs[skill](
tracks=obvTracks,
falarms=obvFAlarms,
truthTable=truthTable,
true_tracks=true_tracks,
true_falarms=true_falarms,
track_indices=trackIndices,
falarm_indices=falarmIndices)
# (Skills x TrackRuns)
return larry(analysis, labels)
def main(args) :
dirName = os.path.join(args.directory, args.simName)
simParams = ParamUtils.ReadSimulationParams(os.path.join(dirName, 'simParams.conf'))
volData = ReadCorners(os.path.join(dirName, simParams['inputDataFile']),
path=dirName)['volume_data']
if args.doTracks :
origTrackData = FilterMHTTracks(*ReadTracks(os.path.join(dirName, simParams['simTrackFile'])))
noisyTrackData = FilterMHTTracks(*ReadTracks(os.path.join(dirName, simParams['noisyTrackFile'])))
DownsampleTracks(args.skipCnt, args.simName, args.newName, simParams, volData,
origTrackData, noisyTrackData, path=args.directory)
else :
DownsampleCorners(args.skipCnt, args.simName, args.newName, simParams,
volData, path=args.directory)
if __name__ == '__main__':
import os.path
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import AxesGrid
from ZigZag.TrackPlot import PlotPlainTracks, PlotSegments
from ZigZag.TrackUtils import CleanupTracks, FilterMHTTracks,\
CreateSegments, CompareSegments
from ZigZag.TrackFileUtils import ReadCorners, ReadTracks
dirPath = "/home/ben/Programs/Tracking/NewSim1"
#dirPath = "/home/bvr/TrackingStudy/SquallSim"
cornerVol = ReadCorners(os.path.join(dirPath, "InputDataFile"),
dirPath)['volume_data']
true_tracks, true_falarms = FilterMHTTracks(
*ReadTracks(os.path.join(dirPath, "noise_tracks")))
frameLims = (0, len(cornerVol))
print frameLims[1]
t = TITAN(distThresh=5)
for aVol in cornerVol:
t.TrackStep(aVol)
t.finalize()
tracks = t.tracks
falarms = []
CleanupTracks(tracks, falarms)
def main(args):
if args.bw_mode:
BW_mode()
if len(args.trackFiles) == 0: print "WARNING: No trackFiles given!"
if args.trackTitles is None:
args.trackTitles = args.trackFiles
else:
if len(args.trackTitles) != len(args.trackFiles):
raise ValueError("The number of TITLEs do not match the"
" number of TRACKFILEs.")
if args.statName is not None and args.statLonLat is None:
statData = ByName(args.statName)[0]
args.statLonLat = (statData['LON'], statData['LAT'])
if args.layout is None:
args.layout = (1, len(args.trackFiles))
if args.figsize is None:
args.figsize = plt.figaspect(float(args.layout[0]) / args.layout[1])
trackerData = [
FilterMHTTracks(*ReadTracks(trackFile))
for trackFile in args.trackFiles
]
polyData = [
_load_verts(f, tracks + falarms)
for f, (tracks, falarms) in zip(polyfiles, trackerData)
]
if args.statLonLat is not None:
for aTracker in trackerData:
CoordinateTransform(aTracker[0] + aTracker[1], args.statLonLat[0],
args.statLonLat[1])
for polys in polyData:
CoordinateTrans_lists(polys, args.statLonLat[0],
args.statLonLat[1])
if args.simTagFiles is None:
args.simTagFiles = [None]
multiTags = [
ReadSimTagFile(fname) if fname is not None else None
for fname in args.simTagFiles
]
if len(trackerData) > len(multiTags):
# Very rudimentary broadcasting of multiTags to match trackerData
tagMult = max(int(len(trackerData) // len(multiTags)), 1)
multiTags = multiTags * tagMult
theFig = plt.figure(figsize=args.figsize)
grid = AxesGrid(theFig,
111,
nrows_ncols=args.layout,
aspect=False,
share_all=True,
axes_pad=0.45)
showMap = (args.statLonLat is not None and args.displayMap)
# Can only do this if all other data being displayed will be in
# lon/lat coordinates
if args.radarFile is not None and args.statLonLat is not None:
if len(args.radarFile) > 1 and args.endFrame is not None:
args.radarFile = args.radarFile[args.endFrame]
else:
args.radarFile = args.radarFile[-1]
data = LoadRastRadar(args.radarFile)
for ax in grid:
MakeReflectPPI(data['vals'][0],
data['lats'],
data['lons'],
meth='pcmesh',
ax=ax,
colorbar=False,
axis_labels=False,
zorder=0,
alpha=0.6)
MakeTrackPlots(grid,
trackerData,
args.trackTitles,
showMap,
endFrame=args.endFrame,
tail=args.tail,
fade=args.fade,
multiTags=multiTags,
tag_filters=args.filters)
for ax, verts in zip(grid, polyData):
_to_polygons(verts[args.endFrame:args.endFrame + 1], ax)
if args.xlims is not None and np.prod(grid.get_geometry()) > 0:
grid[0].set_xlim(args.xlims)
if args.ylims is not None and np.prod(grid.get_geometry()) > 0:
grid[0].set_ylim(args.ylims)
if args.saveImgFile is not None:
theFig.savefig(args.saveImgFile)
if args.doShow:
plt.show()
def main(args):
for afile in args.trackfiles:
tracks, falarms = FilterMHTTracks(*ReadTracks(afile))
trackSegs, trkIndices = CreateSegments(tracks, retindices=True)
flatDists_x = np.array(
[np.diff(aSeg['xLocs']) for aSeg in trackSegs if len(aSeg) > 1])
flatDists_y = np.array(
[np.diff(aSeg['yLocs']) for aSeg in trackSegs if len(aSeg) > 1])
flatDists = np.hypot(flatDists_x, flatDists_y)
flatdeltaFrames = np.array([
np.diff(aSeg['frameNums']) for aSeg in trackSegs if len(aSeg) > 1
])
flatspeeds = flatDists / flatdeltaFrames
dists = defaultdict(float)
lens = defaultdict(int)
for segDist, segDelta, trkIndex in zip(flatDists, flatdeltaFrames,
trkIndices):
dists[trkIndex] += segDist
lens[trkIndex] += segDelta
trkDensity = defaultdict(int)
for seg in trackSegs:
trkDensity[seg['frameNums'][0]] += 1
falrmDensity = defaultdict(int)
for seg in falarms:
falrmDensity[seg['frameNums'][0]] += 1
# Convert to a list
dists = [dists[trkIndex] for trkIndex in xrange(len(tracks))]
lens = [lens[trkIndex] for trkIndex in xrange(len(tracks))]
startFrames = [np.min(aTrack['frameNums']) for aTrack in tracks]
startDensity = defaultdict(int)
for f in startFrames:
startDensity[f] += 1
endFrames = [np.max(aTrack['frameNums']) for aTrack in tracks]
endDensity = defaultdict(int)
for f in endFrames:
endDensity[f] += 1
startXs = [aTrack['xLocs'][0] for aTrack in tracks]
startYs = [aTrack['yLocs'][0] for aTrack in tracks]
endXs = [aTrack['xLocs'][-1] for aTrack in tracks]
endYs = [aTrack['yLocs'][-1] for aTrack in tracks]
devs = [
np.std(
np.hypot(
np.diff(trk['xLocs']) / np.diff(trk['frameNums']),
np.diff(trk['yLocs']) / np.diff(trk['frameNums'])))
for trk in tracks if len(trk) > 2
]
xLims, yLims, fLims = DomainFromTracks(tracks, falarms)
frames = np.arange(fLims[0], fLims[1] + 1)
trkDensity = [trkDensity[frame] for frame in frames]
falrmDensity = [falrmDensity[frame] for frame in frames]
startDensity = [startDensity[frame] for frame in frames]
endDensity = [endDensity[frame] for frame in frames]
plt.figure()
plt.plot(frames, trkDensity, label='tracks')
plt.plot(frames, falrmDensity, label="falarms")
plt.plot(frames, startDensity, '--', label="starts")
plt.plot(frames, endDensity, '--', label="ends")
plt.legend()
plt.xlabel('Frame Index')
plt.ylabel('# of detections')
print
print afile, ':'
print "Track Cnt: %4d" % len(tracks)
print "False Alarm Cnt: %4d" % len(falarms)
print "Track SegCnt:", len(trackSegs)
print "Limits: X", xLims, " Y", yLims, " Frames", fLims
print
print "Max Length: %4d Frames, %8.3f" % (np.max(lens), np.max(dists))
print "Mean Length: %4d Frames, %8.3f" % (np.mean(lens),
np.mean(dists))
print "Median Len : %4d Frames, %8.3f" % (np.median(lens),
np.median(dists))
print "Min Length: %4d Frames, %8.3f" % (np.min(lens), np.min(dists))
print
print "Max StdDev: %8.3f" % np.max(devs)
print "Mean StdDev: %8.3f" % np.mean(devs)
print "Median Dev: %8.3f" % np.median(devs)
print "Min StdDev: %8.3f" % np.min(devs)
print
for name, func in zip(["Max ", "Mean ", "Median", "Min "],
[np.max, np.mean, np.median, np.min]):
print name, ("Start: X %8.3f Y %8.3f Frame %4d" %
(func(startXs), func(startYs), func(startFrames)))
print(" End: X %8.3f Y %8.3f Frame %4d" %
(func(endXs), func(endYs), func(endFrames)))
print
print "Max Frame-to-Frame Dist: %4d Frames, %8.3f" % \
(np.max(flatdeltaFrames), np.max(flatDists))
print "Mean Frame-to-Frame Dist: %4d Frames, %8.3f" % \
(np.mean(flatdeltaFrames), np.mean(flatDists))
print "Med Frame-to-Frame Dist: %4d Frames, %8.3f" % \
(np.median(flatdeltaFrames),
np.median(flatDists))
print "Min Frame-to-Frame Dist: %4d Frames, %8.3f" % \
(np.min(flatdeltaFrames),
np.min(flatDists))
print "Speed:", flatspeeds.min(), flatspeeds.mean(), flatspeeds.max()
plt.show()
def main(args):
if args.bw_mode:
BW_mode()
if len(args.trackFiles) == 0:
print "WARNING: No trackFiles given!"
if len(args.truthTrackFile) == 0:
print "WARNING: No truth trackFiles given!"
if args.trackTitles is None:
args.trackTitles = args.trackFiles
else:
if len(args.trackTitles) != len(args.trackFiles):
raise ValueError("The number of TITLEs does not match the number"
" of TRACKFILEs")
if args.statName is not None and args.statLonLat is None:
statData = ByName(args.statName)[0]
args.statLonLat = (statData['LON'], statData['LAT'])
if args.layout is None:
args.layout = (1, max(len(args.trackFiles), len(args.truthTrackFile)))
if args.figsize is None:
args.figsize = plt.figaspect(float(args.layout[0]) / args.layout[1])
if args.simTagFiles is None:
args.simTagFiles = []
trackerData = [
FilterMHTTracks(*ReadTracks(trackFile))
for trackFile in args.trackFiles
]
truthData = [
FilterMHTTracks(*ReadTracks(trackFile))
for trackFile in args.truthTrackFile
]
multiTags = [ReadSimTagFile(fname) for fname in args.simTagFiles]
if len(multiTags) == 0:
multiTags = [None]
if args.statLonLat is not None:
for aTracker in trackerData + truthData:
CoordinateTransform(aTracker[0] + aTracker[1], args.statLonLat[0],
args.statLonLat[1])
if len(trackerData) != len(truthData):
# Basic broadcasting needed!
if len(truthData) > len(trackerData):
# Need to extend track data to match with the number of truth sets
if len(truthData) % len(trackerData) != 0:
raise ValueError("Can't extend TRACKFILE list to match with"
" the TRUTHFILE list!")
else:
# Need to extend truth sets to match with the number of track data
if len(trackerData) % len(truthData) != 0:
raise ValueError("Can't extend TRUTHFILE list to match with"
" the TRACKFILE list!")
trkMult = max(int(len(truthData) // len(trackerData)), 1)
trthMult = max(int(len(trackerData) // len(truthData)), 1)
trackerData = trackerData * trkMult
truthData = truthData * trthMult
tagMult = max(int(len(truthData) // len(multiTags)), 1)
multiTags = multiTags * tagMult
args.trackTitles = args.trackTitles * trkMult
theFig = plt.figure(figsize=args.figsize)
grid = AxesGrid(theFig,
111,
nrows_ncols=args.layout,
aspect=False,
share_all=True,
axes_pad=0.45)
showMap = (args.statLonLat is not None and args.displayMap)
if args.radarFile is not None and args.statLonLat is not None:
if len(args.radarFile) > 1 and args.endFrame is not None:
args.radarFile = args.radarFile[args.endFrame]
else:
args.radarFile = args.radarFile[-1]
data = LoadRastRadar(args.radarFile)
for ax in grid:
MakeReflectPPI(data['vals'][0],
data['lats'],
data['lons'],
meth='pcmesh',
ax=ax,
colorbar=False,
axis_labels=False,
zorder=0,
alpha=0.6)
MakeComparePlots(grid,
trackerData,
truthData,
args.trackTitles,
showMap,
endFrame=args.endFrame,
tail=args.tail,
fade=args.fade,
multiTags=multiTags,
tag_filters=args.filters)
if args.xlims is not None and np.prod(grid.get_geometry()) > 0:
grid[0].set_xlim(args.xlims)
if args.ylims is not None and np.prod(grid.get_geometry()) > 0:
grid[0].set_ylim(args.ylims)
if args.saveImgFile is not None:
theFig.savefig(args.saveImgFile)
if args.doShow:
plt.show()