def data(self, downsample = 1):
if self._type == "image":
Image = misc.imread(self._path)[:, :, 0:3]
if downsample != 1: Image = tb.downsampleImage(Image, downsample)
return Image[..., np.r_[2, 1, 0]].transpose((2, 0, 1))
elif self._type == "flow":
flow = tb.readFlow(self._path)
flow = flow[:, :, 0:2]
if downsample != 1: flow = OpticalFlow.downsampleMedian(flow, downsample)
flow = flow.transpose((2, 0, 1))
nanMask = np.isnan(flow)
data = (flow * 32.0).astype(np.int16)
data[nanMask] = np.iinfo(np.int16).max
return data
elif self._type == "leftdisparity":
disparity = tb.readDisparity(self._path)
nanMask = np.isnan(disparity)
disparity *= -1
if downsample != 1: raise Exception("no downsampling implemented for disparity")
data = (disparity * 32.0).astype(np.int16)
data[nanMask] = np.iinfo(np.int16).max
return data
elif self._type == "rightdisparity":
disparity = tb.readDisparity(self._path)
nanMask = np.isnan(disparity)
if downsample != 1: raise Exception("no downsampling implemented for disparity")
data = (disparity * 32.0).astype(np.int16)
data[nanMask] = np.iinfo(np.int16).max
return data
elif self._type == "leftdisparitychange":
disparityChange = tb.readDisparity(self._path)
nanMask = np.isnan(disparityChange)
disparityChange *= -1
if downsample != 1: raise Exception("no downsampling implemented for disparity")
data = (disparityChange * 32.0).astype(np.int16)
data[nanMask] = np.iinfo(np.int16).max
return data
elif self._type == "rightdisparitychange":
disparityChange = tb.readDisparity(self._path)
nanMask = np.isnan(disparityChange)
if downsample != 1: raise Exception("no downsampling implemented for disparity")
data = (disparityChange * 32.0).astype(np.int16)
data[nanMask] = np.iinfo(np.int16).max
return data
else:
raise Exception('unhandled data type')
def computeHistograms(resolution, subpath, collectionName, clips, skipIfExists=False, overwrite=True, numBins=5000, maxValue=1000):
dataPath = '/misc/lmbraid17/sceneflownet/common/data/4_bin-db'
savePath = '%s/hists/%s/%s' % (dataPath, resolution, subpath)
saveFile = '%s/%s.npz' % (savePath, collectionName)
completePath = os.path.dirname(saveFile)
os.system('mkdir -p %s' % completePath)
print 'savePath', savePath
print 'completePath', completePath
print 'saveFile', saveFile
histFlow = np.zeros(numBins)
histDisp = np.zeros(numBins)
histDispChange = np.zeros(numBins)
for clip in clips:
print 'processing', clip
for i in range(clip.startFrame(), clip.endFrame()+1):
frame = clip.frame(i)
if i%10 == 1:
print '%d/%d' % (i - clip.startFrame() + 1, clip.endFrame() - clip.startFrame() + 1)
# flow histogram
if frame.hasForwardFlowL():
values = tb.readFlow(frame.forwardFlowL())
mag = np.power(np.sum(np.power(values, 2), axis=2), 0.5)
curr_hist, _ = np.histogram(mag, bins=numBins, range=(0,maxValue))
histFlow += curr_hist
# disparity histogram
if frame.hasDispL():
values = tb.readDisparity(frame.dispL())
mag = np.abs(values)
curr_hist, _ = np.histogram(mag, bins=numBins, range=(0,maxValue))
histDisp += curr_hist
# disparity change histogram
if frame.hasBackwardDispChangeL():
values = tb.readDisparity(frame.forwardDispChangeL())
mag = np.abs(values)
curr_hist, _ = np.histogram(mag, bins=numBins, range=(0,maxValue))
histDispChange += curr_hist
np.savez(saveFile, histFlow=histFlow, histDisp=histDisp, histDispChange=histDispChange)
