def dumpmatlab(self, file, data, video, scale):
results = []
for id, track in enumerate(data):
for box in track.boxes:
if not box.lost:
data = {}
data['id'] = id
data['xtl'] = box.xtl
data['ytl'] = box.ytl
data['xbr'] = box.xbr
data['ybr'] = box.ybr
data['frame'] = box.frame
data['lost'] = box.lost
data['occluded'] = box.occluded
data['label'] = track.label
data['attributes'] = box.attributes
data['generated'] = box.generated
results.append(data)
from scipy.io import savemat as savematlab
savematlab(file,
{"annotations": results,
"num_frames": video.totalframes,
"slug": video.slug,
"skip": video.skip,
"width": int(video.width * scale),
"height": int(video.height * scale),
"scale": scale}, oned_as="row")
def dumpmatlab(self, file, data, video, scale):
results = []
for id, track in enumerate(data):
for box in track.boxes:
if not box.lost:
data = {}
data['id'] = id
data['xtl'] = box.xtl
data['ytl'] = box.ytl
data['xbr'] = box.xbr
data['ybr'] = box.ybr
data['frame'] = box.frame
data['lost'] = box.lost
data['occluded'] = box.occluded
data['label'] = track.label
data['attributes'] = box.attributes
data['generated'] = box.generated
results.append(data)
from scipy.io import savemat as savematlab
savematlab(file, {
"annotations": results,
"num_frames": video.totalframes,
"slug": video.slug,
"skip": video.skip,
"width": int(video.width * scale),
"height": int(video.height * scale),
"scale": scale
},
oned_as="row")
def dumpmatlab(file, data, video, scale, fields):
results = []
for id, track in enumerate(data):
for box in track.boxes:
if not box.lost:
data = {}
for f in fields:
d = trackletdataforfield(track, id, f)
if d is None:
d = boxdataforfield(box, track.velocities[box.frame],
f)
if data:
data[f] = d
results.append(data)
from scipy.io import savemat as savematlab
savematlab(file, {
"annotations": results,
"num_frames": video.totalframes,
"slug": video.slug,
"skip": video.skip,
"width": int(video.width * scale),
"height": int(video.height * scale),
"scale": scale
},
oned_as="row")
def dumpmatlab(self, file, data):
results = []
for id, track in enumerate(data):
for box in track.boxes:
data = {}
data['id'] = id
data['xtl'] = box.xtl
data['ytl'] = box.ytl
data['xbr'] = box.xbr
data['ybr'] = box.ybr
data['frame'] = box.frame
data['lost'] = box.lost
data['occluded'] = box.occluded
data['label'] = track.label
data['attributes'] = box.attributes
results.append(data)
from scipy.io import savemat as savematlab
savematlab(file, {"annotations": results}, oned_as="row")
def dumpmatlab(self, file, data):
results = []
for id, track in enumerate(data):
for box in track.boxes:
data = {}
data['id'] = id
data['xtl'] = box.xtl
data['ytl'] = box.ytl
data['xbr'] = box.xbr
data['ybr'] = box.ybr
data['frame'] = box.frame
data['lost'] = box.lost
data['occluded'] = box.occluded
data['label'] = track.label
data['attributes'] = box.attributes
results.append(data)
from scipy.io import savemat as savematlab
savematlab(file,
{"annotations": results}, oned_as="row")
def dumpmatlab(file, data, video, scale, fields):
results = []
for id, track in enumerate(data):
for box in track.boxes:
if not box.lost:
data = {}
for f in fields:
d = trackletdataforfield(track, id, f)
if d is None:
d = boxdataforfield(box, track.velocities[box.frame], f)
if data:
data[f] = d
results.append(data)
from scipy.io import savemat as savematlab
savematlab(file,
{"annotations": results,
"num_frames": video.totalframes,
"slug": video.slug,
"skip": video.skip,
"width": int(video.width * scale),
"height": int(video.height * scale),
"scale": scale}, oned_as="row")
model.dim,
model.hogweights(),
model.rgbweights(),
hogbin=model.hogbin)
x, y = numpy.unravel_index(numpy.argmin(costs), costs.shape)
x = int((x) / wr)
y = int((y) / hr)
result = Box(x, y, x + b.width, y + b.height, stop)
f = features.rgbmean(frames[b.frame].crop(b[0:4]))
print f, numpy.dot(f.transpose(), model.rgbweights())
f = features.rgbmean(frames[result.frame].crop(result[0:4]))
print f, numpy.dot(f.transpose(), model.rgbweights())
savematlab(open("weight.mat", "w"), {"w": model.hogweights()}, oned_as="row")
#numpy.set_printoptions(threshold='nan')
print model.hogweights().shape
print model.rgbweights()
print "Given", b
print "Predicted", result
pylab.figure(1)
pylab.subplot(221)
pylab.title("training")
pylab.imshow(numpy.asarray(highlight_box(frames[b.frame], b)))
pylab.subplot(222)
pylab.title("best")
from vision import features
from PIL import Image
from scipy.io import savemat as savematlab
im = Image.open("/scratch/vatic/syn-bounce-level/0/0/0.jpg")
f = features.hog(im)
savematlab(open("features.mat", "w"), {"py": f})
image = image.resize((int(wr * image.size[0]), int(hr * image.size[1])), 2)
costs = convolution.hogrgbmean(image, model.dim,
model.hogweights(), model.rgbweights(),
hogbin = model.hogbin)
x, y = numpy.unravel_index(numpy.argmin(costs), costs.shape)
x = int((x) / wr)
y = int((y) / hr)
result = Box(x, y, x + b.width, y + b.height, stop)
f = features.rgbmean(frames[b.frame].crop(b[0:4]))
print f, numpy.dot(f.transpose(), model.rgbweights())
f = features.rgbmean(frames[result.frame].crop(result[0:4]))
print f, numpy.dot(f.transpose(), model.rgbweights())
savematlab(open("weight.mat", "w"), {"w": model.hogweights()}, oned_as="row")
#numpy.set_printoptions(threshold='nan')
print model.hogweights().shape
print model.rgbweights()
print "Given", b
print "Predicted", result
pylab.figure(1)
pylab.subplot(221)
pylab.title("training")
pylab.imshow(numpy.asarray(highlight_box(frames[b.frame], b)))
pylab.subplot(222)
pylab.title("best")
from vision import features
import Image
from scipy.io import savemat as savematlab
im = Image.open("/scratch/vatic/syn-bounce-level/0/0/0.jpg")
f = features.hog(im)
savematlab(open("features.mat", "w"), {"py": f})
