def stretch_hist_equalize(self, ch_nb):
"""Stretch the current image's colors by performing histogram
equalization on channel *ch_nb*.
"""
LOG.info("Perform a histogram equalized contrast stretch.")
if(self.channels[ch_nb].size ==
np.ma.count_masked(self.channels[ch_nb])):
LOG.warning("Nothing to stretch !")
return
arr = self.channels[ch_nb]
nwidth = 2048.0
carr = arr.compressed()
imhist, bins = np.histogram(carr, nwidth, normed=True)
cdf = imhist.cumsum() - imhist[0]
cdf = cdf / cdf[-1]
res = np.ma.empty_like(arr)
res.mask = np.ma.getmaskarray(arr)
res[~res.mask] = np.interp(carr, bins[:-1], cdf)
self.channels[ch_nb] = res
def stretch_linear(self, ch_nb, cutoffs=(0.005, 0.005)):
"""Stretch linearly the contrast of the current image on channel
*ch_nb*, using *cutoffs* for left and right trimming.
"""
LOG.debug("Perform a linear contrast stretch.")
if((self.channels[ch_nb].size ==
np.ma.count_masked(self.channels[ch_nb])) or
self.channels[ch_nb].min() == self.channels[ch_nb].max()):
LOG.warning("Nothing to stretch !")
return
nwidth = 2048.0
arr = self.channels[ch_nb]
carr = arr.compressed()
hist, bins = np.histogram(carr, nwidth)
ndim = carr.size
left = 0
hist_sum = 0.0
i = 0
while i < nwidth and hist_sum < cutoffs[0]*ndim:
hist_sum = hist_sum + hist[i]
i = i + 1
left = bins[i-1]
right = 0
hist_sum = 0.0
i = nwidth - 1
while i >= 0 and hist_sum < cutoffs[1]*ndim:
hist_sum = hist_sum + hist[i]
i = i - 1
right = bins[i+1]
delta_x = (right - left)
LOG.debug("Interval: left=%f,right=%f width=%f"
%(left,right,delta_x))
if delta_x > 0.0:
self.channels[ch_nb] = np.ma.array((arr - left) / delta_x,
mask = arr.mask)
else:
self.channels[ch_nb] = np.ma.zeros(arr.shape)
LOG.warning("Unable to make a contrast stretch!")
def crude_stretch(self, ch_nb, min_stretch = None, max_stretch = None):
"""Perform simple linear stretching (without any cutoff) on the channel
*ch_nb* of the current image and normalize to the [0,1] range."""
if(min_stretch is None):
min_stretch = self.channels[ch_nb].min()
if(max_stretch is None):
max_stretch = self.channels[ch_nb].max()
if((not self.channels[ch_nb].mask.all()) and
max_stretch - min_stretch > 0):
stretched = ((self.channels[ch_nb].data - min_stretch) * 1.0 /
(max_stretch - min_stretch))
self.channels[ch_nb] = np.ma.array(stretched,
mask=self.channels[ch_nb].mask)
else:
LOG.warning("Nothing to stretch !")
def stretch_logarithmic(self, ch_nb, factor=100.):
"""Move data into range [1:factor] and do a normalized logarithmic
enhancement.
"""
LOG.debug("Perform a logarithmic contrast stretch.")
if ((self.channels[ch_nb].size ==
np.ma.count_masked(self.channels[ch_nb])) or
(self.channels[ch_nb].min() == self.channels[ch_nb].max())):
LOG.warning("Nothing to stretch !")
return
crange=(0., 1.0)
arr = self.channels[ch_nb]
b = float(crange[1] - crange[0])/np.log(factor)
c = float(crange[0])
slope = (factor-1.)/float(arr.max() - arr.min())
arr = 1. + (arr - arr.min())*slope
arr = c + b*np.log(arr)
self.channels[ch_nb] = arr
