def blend_bg(self, bg, weight=0.5):
if weight > 1 or weight < 0:
raise GridError("weight must be between 0 and 1")
if bg.ndim == 3:
bg = rgb2uint(bg)
logger.warn('3-channel image convert to 8-bit uint')
if bg.shape != self.shape:
xf, yf = self.shape
try:
bg = crop(bg, 0, 0, xf, yf)
except Exception as E:
raise GridError('bg shape %s did not match grid shape %s and '
'cropping failed with: %s' %
(bg.shape, self.shape, E.message))
else:
logger.warn("bg image cropped to grid dimensions %s" %
self.shape)
# Normalize the values to balance weight sum
scale = bg.max() / self.zz.max()
zzout = self.zz * scale
alpha = 1 - weight
return (weight * zzout + (alpha * bg)) #/ scale
def zoom_bg(self, *coords, **kwds):
""" Zoom in on current image and set the zoomed background as the
background of a new canvas. Note that because indicies will always
resume at 0, particle positions will not maintain their relative
positions.
"""
# avoid set_bg() because uses size of coords, not coords itself
inplace = kwds.pop('inplace', False)
autogrid = kwds.pop('autogrid', True)
if inplace:
cout = self
else:
cout = Canvas.copy(self)
cout._background = crop(cout._background, coords)
cout.rez = cout._background.shape[0:2]
if autogrid:
xmag = self.rx / float(cout.rx)
ymag = self.ry / float(cout.ry)
cout.grid.xdiv = rint(self.grid.xdiv / xmag)
cout.grid.ydiv = rint(self.grid.ydiv / ymag)
if not inplace:
return cout
def load(f, rez=None):
"""Load an image file located in the data directory.
Parameters
----------
f : string
File name.
rez : (int, int) or None
Crop image to desired length by width.
Returns
-------
img : ndarray
Image loaded from skimage.data_dir.
"""
img = imread(op.join(data_dir, f))
if rez:
rx, ry = rez
img = crop(img, (0, 0, rx, ry))
return img
def load(f, rez=None):
"""Load an image file located in the data directory.
Parameters
----------
f : string
File name.
rez : (int, int) or None
Crop image to desired length by width.
Returns
-------
img : ndarray
Image loaded from skimage.data_dir.
"""
img = imread(op.join(data_dir, f))
if rez:
rx, ry = rez
img = crop(img, (0,0,rx,ry))
return img
def blend_bg(self, bg, weight=0.5):
if weight > 1 or weight < 0:
raise GridError("weight must be between 0 and 1")
if bg.ndim == 3:
bg = rgb2uint(bg)
logger.warn('3-channel image convert to 8-bit uint')
if bg.shape != self.shape:
xf, yf = self.shape
try:
bg = crop(bg, 0, 0, xf, yf)
except Exception as E:
raise GridError('bg shape %s did not match grid shape %s and '
'cropping failed with: %s' % (bg.shape, self.shape, E.message))
else:
logger.warn("bg image cropped to grid dimensions %s" % self.shape)
# Normalize the values to balance weight sum
scale = bg.max() / self.zz.max()
zzout = self.zz * scale
alpha = 1-weight
return (weight * zzout + ( alpha * bg) ) #/ scale
def patchshow(self, *args, **kwargs):
""" ...
args/kwargs include alpha, edgecolors, linestyles
Notes:
Matplotlib API is setup that args or kwargs can be entered. Order is
important for args, but the correspond to same kwargs.
"""
axes, kwargs = _parse_ax(*args, **kwargs)
title = kwargs.pop('title', None)
bgonly = kwargs.pop('bgonly', False)
annotate = kwargs.pop('annotate', False)
zoom = kwargs.pop('zoom', None)
grid = kwargs.pop('grid', False)
gcolor = kwargs.pop('gcolor', None)
gstyle = kwargs.pop('gstyle', None)
gunder = kwargs.pop('gunder',False)
pmap = kwargs.pop('pmap', None)
nolabel = kwargs.pop('nolabel', None)
alpha = kwargs.get('alpha', None)
edgecolor = kwargs.get('edgecolor', None)
linewidth = kwargs.get('linewidth', None)
linestyle = kwargs.get('linestyle', None)
# Some keywords to savefig; not all supported
save = kwargs.pop('save', None)
dpi = kwargs.pop('dpi', None)
bbox_inches = kwargs.pop('bbox_inches', None)
# GET NOT POP
cmap = kwargs.get('cmap', None)
# Implement later
if cmap in ['pbinary', 'pbinary_r']:
raise CanvasPlotError('"pbinary(_r)" color map only valid for .show()')
# grid defaults
if gcolor or gunder or gstyle and not grid:
grid = True
# If user enters gcolor/gstyle, assume default grid
if grid and not gcolor:
gcolor = GCOLOR
if grid and not gstyle:
gstyle = 'solid'
# Corner case, don't touch
if pmap and cmap and bgonly:
bgonly = False
if bgonly and not cmap:
raise CanvasPlotError('"bgonly" is only valid when a colormap is'
' passed.')
if cmap:
bg = self.graybackground
else:
bg = self.background
if zoom:
xi, yi, xf, yf = zoom
bg = crop(bg, zoom)
#Overwrite axes image
if not axes:
fig, axes = plt.subplots()
else:
axes.images=[]
# DONT PASS ALL KWARGS
axes.imshow(bg, cmap=cmap)
# FOR PATICLES IN IMAGE ONLY.
in_and_edges = self.pin + self.pedge
# PARTICLE FACECOLOR, ALPHA and other PATCH ARGS
# http://matplotlib.org/api/artist_api.html#matplotlib.patches.Patch
patches = [p.particle.as_patch(facecolor=p.color, alpha=alpha,
edgecolor=edgecolor, linestyle=linestyle, linewidth=linewidth)
for p in in_and_edges]
# If no particles or grid, just pass to avoid deep mpl exceptiosn
if patches or grid:
if patches:
if pmap:
kwargs['cmap'] = pmap
if 'cmap' in kwargs and not bgonly:
ppatch = PatchCollection(patches, **kwargs) #cmap and Patch Args
ppatch.set_array(np.arange(len(patches)))
# Use settings passed to "patches"
else:
ppatch = PatchCollection(patches, match_original=True, **kwargs) #
# Grid under particles
if gunder:
axes.add_collection(self.grid.as_patch(
edgecolors=gcolor, linestyles=gstyle))
if patches:
axes.add_collection(ppatch)
# Grid over particles
else:
if patches:
axes.add_collection(ppatch)
if grid:
axes.add_collection(self.grid.as_patch(
edgecolors=gcolor, linestyles=gstyle))
axes = self._annotate_plot(axes, annotate, title)
if zoom:
axes.set_xlim(xi, xf)
axes.set_ylim(yf, yi)
if nolabel:
axes.xaxis.set_visible(False)
axes.yaxis.set_visible(False)
if nolabel == 'x':
axes.yaxis.set_visible(True)
elif nolabel == 'y':
axes.xaxis.set_visible(True)
if save:
path = _parse_path(save)
plt.savefig(path, dpi=dpi, bbox_inches=bbox_inches)
return axes
if __name__ == '__main__':
import matplotlib.pyplot as plt
from skimage.io import imread
from pyparty.utils import crop
# img = imread('/home/glue/Desktop/imgproc_supplemental/images/Test_Data/Ilastik_Analysis_Final/class_10_labels/Noise_class_10labels_modified.png')
from pyparty.data import nanolabels
img = nanolabels()
# print ptools.unique(img)
# plt.imshow(img, cmap='spectral')
# plt.show()
img = crop(img, (0,0,512,512))
mc = MultiCanvas.from_labeled(img, 'gay', 'f*g',
storecolors=False,
ignore=0,
#mapper=[
#('singles', 1),
#('dimers', 2),
#('trimer', 3),
#('bigs', 4)
#],
)
mc.show(annotate=False, names=True, nolabel=True)
# plt.imshow(nanolabels())
plt.show()
