def to_rgb_uint8(image, autoscale=True, force_color=None):
ndim = image.ndim
shape = image.shape
try:
colors = image.metadata['colors']
if len(colors) != shape[0]:
colors = None
except (AttributeError, KeyError):
colors = None
# 2D, grayscale
if ndim == 2:
force_color = [force_color] if force_color is not None else None
image = to_rgb(image, force_color)
# 2D non-interleaved RGB
elif ndim == 3 and shape[0] in [3, 4]:
image = image.transpose([1, 2, 0])
autoscale = False
# 2D, has colors attribute
elif ndim == 3 and colors is not None:
image = to_rgb(image, colors, True)
# 2D, RGB
elif ndim == 3 and shape[2] in [3, 4]:
pass
# 2D, is multichannel
elif ndim == 3 and shape[0] < 5: # guessing; could be small z-stack
image = to_rgb(image, None, True)
# 3D, grayscale
elif ndim == 3:
grayscale = True
# 3D, has colors attribute
elif ndim == 4 and colors is not None:
image = to_rgb(image, colors, True)
# 3D, RGB
elif ndim == 4 and shape[3] in [3, 4]:
pass
# 3D, is multichannel
elif ndim == 4 and shape[0] < 5:
image = to_rgb(image, None, True)
else:
raise ValueError(
"No display possible for frames of shape {0}".format(shape))
if autoscale:
image = (normalize(image) * 255).astype(np.uint8)
elif not np.issubdtype(image.dtype, np.uint8):
if np.issubdtype(image.dtype, np.integer):
max_value = np.iinfo(image.dtype).max
# sometimes 12-bit images are stored as unsigned 16-bit
if max_value == 2**16 - 1 and image.max() < 2**12:
max_value = 2**12 - 1
image = (image / max_value * 255).astype(np.uint8)
else:
if image.max() > 1: # unnormalized floats! normalize anyway
image = normalize(image)
image = (image * 255).astype(np.uint8)
return image
def annotate3d(centroids, image, **kwargs):
"""
An extension of annotate that annotates a 3D image and returns a scrollable
stack for display in IPython. Parameters: see annotate.
"""
if plots_to_frame is None:
raise ImportError('annotate3d requires pims 0.3 or later, please '
'update pims')
import matplotlib as mpl
import matplotlib.pyplot as plt
if image.ndim != 3 and not (image.ndim == 4 and image.shape[-1] in (3, 4)):
raise ValueError("image has incorrect dimensions. Please input a 3D "
"grayscale or RGB(A) image. For 2D image annotation, "
"use annotate. Multichannel images can be "
"converted to RGB using pims.display.to_rgb.")
# We want to normalize on the full image and stop imshow from normalizing.
normalized = (normalize(image) * 255).astype(np.uint8)
imshow_style = dict(vmin=0, vmax=255)
if '_imshow_style' in kwargs:
kwargs['imshow_style'].update(imshow_style)
else:
kwargs['imshow_style'] = imshow_style
# Suppress warning when >20 figures are opened
max_open_warning = mpl.rcParams['figure.max_open_warning']
mpl.rc('figure', max_open_warning=0)
figures = []
for i, imageZ in enumerate(normalized):
fig = plt.figure()
kwargs['ax'] = fig.gca()
centroidsZ = centroids[(centroids['z'] > i - 0.5) &
(centroids['z'] < i + 0.5)]
annotate(centroidsZ, imageZ, **kwargs)
figures.append(fig)
result = plots_to_frame(figures, width=512, bbox_inches='tight')
for fig in figures:
plt.close(fig)
mpl.rc('figure', max_open_warning=max_open_warning)
return result
def annotate3d(centroids, image, **kwargs):
"""Annotates a 3D image and returns a scrollable stack for display in
IPython.
Parameters
----------
centroids : DataFrame including columns x and y
image : image array (or string path to image file)
circle_size : Deprecated.
This will be removed in a future version of trackpy.
Use `plot_style={'markersize': ...}` instead.
color : single matplotlib color or a list of multiple colors
default None
invert : If you give a filepath as the image, specify whether to invert
black and white. Default True.
ax : matplotlib axes object, defaults to current axes
split_category : string, parameter to use to split the data into sections
default None
split_thresh : single value or list of ints or floats to split
particles into sections for plotting in multiple colors.
List items should be ordered by increasing value.
default None
imshow_style : dictionary of keyword arguments passed through to
the `Axes.imshow(...)` command the displays the image
plot_style : dictionary of keyword arguments passed through to
the `Axes.plot(...)` command that marks the features
Returns
-------
pims.Frame object containing a three-dimensional RGBA image
See Also
--------
annotate : annotation of 2D images
"""
if plots_to_frame is None:
raise ImportError('annotate3d requires pims 0.3 or later. Please '
'install/update pims')
import matplotlib as mpl
import matplotlib.pyplot as plt
if image.ndim != 3 and not (image.ndim == 4 and image.shape[-1] in (3, 4)):
raise ValueError("image has incorrect dimensions. Please input a 3D "
"grayscale or RGB(A) image. For 2D image annotation, "
"use annotate. Multichannel images can be "
"converted to RGB using pims.display.to_rgb.")
# We want to normalize on the full image and stop imshow from normalizing.
normalized = (normalize(image) * 255).astype(np.uint8)
imshow_style = dict(vmin=0, vmax=255)
if '_imshow_style' in kwargs:
kwargs['imshow_style'].update(imshow_style)
else:
kwargs['imshow_style'] = imshow_style
max_open_warning = mpl.rcParams['figure.max_open_warning']
was_interactive = plt.isinteractive()
try:
# Suppress warning when many figures are opened
mpl.rc('figure', max_open_warning=0)
# Turn off interactive mode (else the closed plots leave emtpy space)
plt.ioff()
figures = [None] * len(normalized)
for i, imageZ in enumerate(normalized):
fig = plt.figure()
kwargs['ax'] = fig.gca()
centroidsZ = centroids[(centroids['z'] > i - 0.5)
& (centroids['z'] < i + 0.5)]
annotate(centroidsZ, imageZ, **kwargs)
figures[i] = fig
result = plots_to_frame(figures,
width=512,
close_fig=True,
bbox_inches='tight')
finally:
# put matplotlib back in original state
if was_interactive:
plt.ion()
mpl.rc('figure', max_open_warning=max_open_warning)
return result
def annotate3d(centroids, image, **kwargs):
"""Annotates a 3D image and returns a scrollable stack for display in
IPython.
Parameters
----------
centroids : DataFrame including columns x and y
image : image array (or string path to image file)
circle_size : Deprecated.
This will be removed in a future version of trackpy.
Use `plot_style={'markersize': ...}` instead.
color : single matplotlib color or a list of multiple colors
default None
invert : If you give a filepath as the image, specify whether to invert
black and white. Default True.
ax : matplotlib axes object, defaults to current axes
split_category : string, parameter to use to split the data into sections
default None
split_thresh : single value or list of ints or floats to split
particles into sections for plotting in multiple colors.
List items should be ordered by increasing value.
default None
imshow_style : dictionary of keyword arguments passed through to
the `Axes.imshow(...)` command the displays the image
plot_style : dictionary of keyword arguments passed through to
the `Axes.plot(...)` command that marks the features
Returns
-------
pims.Frame object containing a three-dimensional RGBA image
See Also
--------
annotate : annotation of 2D images
"""
if plots_to_frame is None:
raise ImportError('annotate3d requires pims 0.3 or later, please '
'update pims')
import matplotlib as mpl
import matplotlib.pyplot as plt
if image.ndim != 3 and not (image.ndim == 4 and image.shape[-1] in (3, 4)):
raise ValueError("image has incorrect dimensions. Please input a 3D "
"grayscale or RGB(A) image. For 2D image annotation, "
"use annotate. Multichannel images can be "
"converted to RGB using pims.display.to_rgb.")
# We want to normalize on the full image and stop imshow from normalizing.
normalized = (normalize(image) * 255).astype(np.uint8)
imshow_style = dict(vmin=0, vmax=255)
if '_imshow_style' in kwargs:
kwargs['imshow_style'].update(imshow_style)
else:
kwargs['imshow_style'] = imshow_style
max_open_warning = mpl.rcParams['figure.max_open_warning']
was_interactive = plt.isinteractive()
try:
# Suppress warning when many figures are opened
mpl.rc('figure', max_open_warning=0)
# Turn off interactive mode (else the closed plots leave emtpy space)
plt.ioff()
figures = [None] * len(normalized)
for i, imageZ in enumerate(normalized):
fig = plt.figure()
kwargs['ax'] = fig.gca()
centroidsZ = centroids[(centroids['z'] > i - 0.5) &
(centroids['z'] < i + 0.5)]
annotate(centroidsZ, imageZ, **kwargs)
figures[i] = fig
result = plots_to_frame(figures, width=512, close_fig=True,
bbox_inches='tight')
finally:
# put matplotlib back in original state
if was_interactive:
plt.ion()
mpl.rc('figure', max_open_warning=max_open_warning)
return result
