def __call__(self, video, gtruths, cpfs, pool = None):
result = {}
pathdict = {}
for id, gtruth in gtruths.items():
gtruth.sort(key = lambda x: x.frame)
pathdict[id] = dict((x.frame, x) for x in gtruth)
requests = {}
for id, gtruth in gtruths.items():
frame, score, predicted, _ = marginals.pick([gtruth[0]], video,
last = gtruth[-1].frame,
pool = pool,
pairwisecost = self.pairwisecost,
upperthreshold = self.upperthreshold,
sigma = self.sigma,
erroroverlap = self.erroroverlap,
skip = self.skip,
rgbbin = self.rgbbin,
hogbin = self.hogbin)
requests[id] = (score, frame, predicted, [gtruth[0]])
result[id] = {}
usedclicks = len(gtruths)
logger.info("Used {0} clicks!".format(usedclicks))
numframes = sum(x[-1].frame - x[0].frame for x in gtruths.values())
reqclicks = [(int(numframes * x), x) for x in cpfs]
reqclicks.sort()
for clicks, cpf in reqclicks:
for _ in range(clicks - usedclicks):
id = max((y[0], x) for x, y in requests.items())[1]
givens = list(requests[id][3])
givens.append(pathdict[id][requests[id][1]])
givens.sort(key = lambda x: x.frame)
frame, score, predicted, _ = marginals.pick(givens, video,
last = max(pathdict[id]),
pool = pool,
pairwisecost = self.pairwisecost,
upperthreshold = self.upperthreshold,
sigma = self.sigma,
erroroverlap = self.erroroverlap,
skip = self.skip,
rgbbin = self.rgbbin,
hogbin = self.hogbin)
requests[id] = (score, frame, predicted, givens)
usedclicks += 1
logger.info("Used {0} clicks with {1} total in this cpf!"
.format(usedclicks, clicks))
for id, (_, _, path, _) in requests.iteritems():
result[id][cpf] = path
return result
logging.basicConfig(level = logging.INFO)
g = frameiterator("/scratch/vatic/syn-occlusion2")
b = [Box(592, 48, 592 + 103, 48 + 326, 20)]
stop = 22
g = frameiterator("/scratch/virat/frames/VIRAT_S_000302_04_000453_000484")
b = [Box(156, 96, 156 + 50, 96 + 24, 270),
Box(391, 83, 391 + 48, 83 + 22, 459)]
stop = 500
pool = multiprocessing.Pool(24)
frame, score, path, marginals = marginals.pick(b, g,
last = stop,
pool = pool,
pairwisecost = .01,
dim = (40, 40),
sigma = 1,
erroroverlap = 0.5,
hogbin = 8,
clickradius = 10,
c = 1)
pickle.dump(marginals, open("occlusion.pkl", "w"))
visualize.save(visualize.highlight_paths(g, [path]), lambda x: "tmp/path{0}.jpg".format(x))
print "frame {0} with score {1}".format(frame, score)
import pylab
import pickle
logging.basicConfig(level = logging.INFO)
g = Geppetto((720,480))
b = Rectangle((400, 100), color="white")
b.linear((400, 800), 20)
g.add(b)
#g = Geppetto()
#b = Rectangle((100, 100))
#b.linear((600, 100), 100)
#g.add(b)
#
#o = Rectangle((100, 350))
#o.linear((600, 350), 100)
#g.add(o)
pool = multiprocessing.Pool(24)
frame, score, path, m = marginals.pick([b[0], b[-1]], g, pool = pool,
pairwisecost = .001,
sigma = .1,
erroroverlap = 0.5)
#visualize.save(visualize.highlight_paths(g, [path, b], width = 3, colors = ["red", "green"]), lambda x: "tmp/path{0}.jpg".format(x))
print "frame {0} with score {1}".format(frame, score)
from vision import *
from vision.alearn import marginals
from vision import visualize, model
from vision.toymaker import *
import os
import multiprocessing
import logging
import random
import pylab
import pickle
logging.basicConfig(level=logging.INFO)
g = Geppetto((720, 480))
b = Rectangle((400, 100), color="white")
b.linear((400, 800), 20)
g.add(b)
pool = multiprocessing.Pool(24)
frame, score, path, m = marginals.pick([b[0], b[-1]],
g,
pool=pool,
pairwisecost=.001,
sigma=.1,
erroroverlap=0.5)
print "frame {0} with score {1}".format(frame, score)
def __call__(self, video, gtruths, cpfs, pool=None):
result = {}
pathdict = {}
for id, gtruth in gtruths.items():
gtruth.sort(key=lambda x: x.frame)
pathdict[id] = dict((x.frame, x) for x in gtruth)
requests = {}
for id, gtruth in gtruths.items():
frame, score, predicted, _ = marginals.pick(
[gtruth[0]],
video,
last=gtruth[-1].frame,
pool=pool,
pairwisecost=self.pairwisecost,
upperthreshold=self.upperthreshold,
sigma=self.sigma,
erroroverlap=self.erroroverlap,
skip=self.skip,
rgbbin=self.rgbbin,
hogbin=self.hogbin)
requests[id] = (score, frame, predicted, [gtruth[0]])
result[id] = {}
usedclicks = len(gtruths)
logger.info("Used {0} clicks!".format(usedclicks))
numframes = sum(x[-1].frame - x[0].frame for x in gtruths.values())
reqclicks = [(int(numframes * x), x) for x in cpfs]
reqclicks.sort()
for clicks, cpf in reqclicks:
for _ in range(clicks - usedclicks):
id = max((y[0], x) for x, y in requests.items())[1]
givens = list(requests[id][3])
givens.append(pathdict[id][requests[id][1]])
givens.sort(key=lambda x: x.frame)
frame, score, predicted, _ = marginals.pick(
givens,
video,
last=max(pathdict[id]),
pool=pool,
pairwisecost=self.pairwisecost,
upperthreshold=self.upperthreshold,
sigma=self.sigma,
erroroverlap=self.erroroverlap,
skip=self.skip,
rgbbin=self.rgbbin,
hogbin=self.hogbin)
requests[id] = (score, frame, predicted, givens)
usedclicks += 1
logger.info(
"Used {0} clicks with {1} total in this cpf!".format(
usedclicks, clicks))
for id, (_, _, path, _) in requests.iteritems():
result[id][cpf] = path
return result
#
#stop = 1768
g = frameiterator("/scratch/vatic/syn-occlusion2")
b = [Box(592, 48, 592 + 103, 48 + 326, 20)]
stop = 22
g = frameiterator("/scratch/virat/frames/VIRAT_S_000302_04_000453_000484")
b = [Box(156, 96, 156 + 50, 96 + 24, 270),
Box(391, 83, 391 + 48, 83 + 22, 459)]
stop = 500
pool = multiprocessing.Pool(24)
frame, score, path, marginals = marginals.pick(b, g,
last = stop,
pool = pool,
pairwisecost = .01,
dim = (40, 40),
sigma = 1,
erroroverlap = 0.5,
hogbin = 8,
clickradius = 10,
c = 1)
pickle.dump(marginals, open("occlusion.pkl", "w"))
visualize.save(visualize.highlight_paths(g, [path]), lambda x: "tmp/path{0}.jpg".format(x))
print "frame {0} with score {1}".format(frame, score)
