'''
Tools for reading ImageJ files.
Based on code originally written by Luis Pedro Coelho <luis@luispedro.org>,
2012, available at https://gist.github.com/luispedro/3437255, distributed
under the MIT License.
Modified
- 2014 by Jeffrey Zaremba (@jzaremba), https://github.com/losonczylab/sima
- 2015 by Scott Lowe (@scottclowe) and Sander Keemink (@swkeemink).
'''
from __future__ import division
from __future__ import unicode_literals
from builtins import str
from builtins import range
import sys
from itertools import product
import numpy as np
from skimage.draw import ellipse
import zipfile
if sys.version_info >= (3, 0):
import read_roi
def _parse_roi_file_py2(roi_obj):
"""Parses an individual ImageJ ROI
This is based on the Java implementation:
http://rsbweb.nih.gov/ij/developer/source/ij/io/RoiDecoder.java.html
This code can is not guaranteed to work for all C compilers on Windows.
Parameters
----------
roi_obj : file object
File object containing a single ImageJ ROI
Returns
-------
dict
Returns a parsed ROI object, a dictionary with either a `'polygons'`
or a `'mask'` field.
Raises
------
IOError
If there is an error reading the roi file object
ValueError
If unable to parse ROI
"""
# Note:
# _getX() calls with no assignment are present to move our pointer
# within the imageJ roi file through bytes that we do not currently use.
# In line comments indicate what they are; these could be extracted if
# needed in the future.
sub_pixel_resolution = 128
# Other options that are not currently used
# SPLINE_FIT = 1
# DOUBLE_HEADED = 2
# OUTLINE = 4
# OVERLAY_LABELS = 8
# OVERLAY_NAMES = 16
# OVERLAY_BACKGROUNDS = 32
# OVERLAY_BOLD = 64
# DRAW_OFFSET = 256
pos = [4]
def _get8():
"""Read 1 byte from the roi file object"""
pos[0] += 1
s = roi_obj.read(1)
if not s:
raise IOError('read_imagej_roi: Unexpected EOF')
return ord(s)
def _get16():
"""Read 2 bytes from the roi file object"""
b0 = _get8()
b1 = _get8()
return (b0 << 8) | b1
def _get16signed():
"""Read a signed 16 bit integer from 2 bytes from roi file object"""
b0 = _get8()
b1 = _get8()
out = (b0 << 8) | b1
# This is a signed integer, so need to check if the value is
# positive or negative.
if b0 > 127:
out = out - 65536
return out
def _get32():
"""Read 4 bytes from the roi file object"""
s0 = _get16()
s1 = _get16()
return (s0 << 16) | s1
def _getfloat():
"""Read a float from the roi file object"""
v = np.int32(_get32())
return v.view(np.float32)
def _getcoords(z=0):
"""Get the next coordinate of an roi polygon"""
if options & sub_pixel_resolution:
getc = _getfloat
points = np.empty((n_coordinates, 3), dtype=np.float32)
else:
getc = _get16
points = np.empty((n_coordinates, 3), dtype=np.int16)
points[:, 0] = [getc() for _ in range(n_coordinates)]
points[:, 1] = [getc() for _ in range(n_coordinates)]
points[:, 0] += left
points[:, 1] += top
points[:, 2] = z
return points
magic = roi_obj.read(4)
if magic != b'Iout':
raise IOError('read_imagej_roi: Magic number not found')
_get16() # version
roi_type = _get8()
# Discard extra second Byte:
_get8()
if not (0 <= roi_type < 11):
raise ValueError('read_imagej_roi: \
ROI type {} not supported'.format(roi_type))
top = _get16signed()
left = _get16signed()
bottom = _get16signed()
right = _get16signed()
n_coordinates = _get16()
x1 = _getfloat() # x1
y1 = _getfloat() # y1
x2 = _getfloat() # x2
y2 = _getfloat() # y2
_get16() # stroke width
_get32() # shape roi size
_get32() # stroke color
_get32() # fill color
subtype = _get16()
if subtype != 0 and subtype != 3:
raise ValueError('read_imagej_roi: \
ROI subtype {} not supported (!= 0)'.format(subtype))
options = _get16()
if subtype == 3 and roi_type == 7:
# ellipse aspect ratio
aspect_ratio = _getfloat()
else:
_get8() # arrow style
_get8() # arrow head size
_get16() # rectangle arc size
z = _get32() # position
if z > 0:
z -= 1 # Multi-plane images start indexing at 1 instead of 0
_get32() # header 2 offset
if roi_type == 0:
# Polygon
coords = _getcoords(z)
coords = coords.astype('float')
return {'polygons': coords}
elif roi_type == 1:
# Rectangle
coords = [[left, top, z], [right, top, z], [right, bottom, z],
[left, bottom, z]]
coords = np.array(coords).astype('float')
return {'polygons': coords}
elif roi_type == 2:
# Oval
width = right - left
height = bottom - top
# 0.5 moves the mid point to the center of the pixel
x_mid = (right + left) / 2.0 - 0.5
y_mid = (top + bottom) / 2.0 - 0.5
mask = np.zeros((z + 1, right, bottom), dtype=bool)
for y, x in product(np.arange(top, bottom), np.arange(left, right)):
mask[z, x, y] = ((x - x_mid) ** 2 / (width / 2.0) ** 2 +
(y - y_mid) ** 2 / (height / 2.0) ** 2 <= 1)
return {'mask': mask}
elif roi_type == 7:
if subtype == 3:
# ellipse
mask = np.zeros((1, right+10, bottom+10), dtype=bool)
r_radius = np.sqrt((x2-x1)**2+(y2-y1)**2)/2.0
c_radius = r_radius*aspect_ratio
r = (x1+x2)/2-0.5
c = (y1+y2)/2-0.5
shpe = mask.shape
orientation = np.arctan2(y2-y1, x2-x1)
X, Y = ellipse(r, c, r_radius, c_radius, shpe[1:], orientation)
mask[0, X, Y] = True
return {'mask': mask}
else:
# Freehand
coords = _getcoords(z)
coords = coords.astype('float')
return {'polygons': coords}
else:
try:
coords = _getcoords(z)
coords = coords.astype('float')
return {'polygons': coords}
except BaseException:
raise ValueError(
'read_imagej_roi: ROI type {} not supported'.format(roi_type))
def _parse_roi_file_py3(roi_source):
"""Parses an individual ImageJ ROI
This implementation utilises the read_roi package, which is more robust
but does only supports Python 3+ and not Python 2.7.
Parameters
----------
roi_source : str or file object
Path to file, or file object containing a single ImageJ ROI
Returns
-------
dict
Returns a parsed ROI object, a dictionary with either a `'polygons'`
or a `'mask'` field.
Raises
------
IOError
If there is an error reading the roi file object.
ValueError
If unable to parse ROI.
"""
# Use read_roi package to load up the roi as a dictionary
roi = read_roi.read_roi_file(roi_source)
# This is a dictionary with a single entry, whose key is the label
# of the roi. We need to get out its contents, which is another dictionary.
keys = list(roi.keys())
if len(keys) == 1:
roi = roi[keys[0]]
# Convert the roi dictionary into either polygon or a mask
if roi['type'] in ('polygon', 'freehand'):
# Polygon
coords = np.empty((roi['n'], 3), dtype=np.float)
coords[:, 0] = roi['x']
coords[:, 1] = roi['y']
coords[:, 2] = 0
return {'polygons': coords}
width = roi['width']
height = roi['height']
left = roi['left']
top = roi['top']
right = left + width
bottom = top - height
z = 0
if roi['type'] == 'rectangle':
# Rectangle
coords = [[left, top, z], [right, top, z], [right, bottom, z],
[left, bottom, z]]
coords = np.array(coords).astype('float')
return {'polygons': coords}
elif roi['type'] == 'oval':
# Oval
# 0.5 moves the mid point to the center of the pixel
x_mid = (right + left) / 2.0 - 0.5
y_mid = (top + bottom) / 2.0 - 0.5
mask = np.zeros((z + 1, right, bottom), dtype=bool)
for y, x in product(np.arange(top, bottom), np.arange(left, right)):
mask[z, x, y] = ((x - x_mid) ** 2 / (width / 2.0) ** 2 +
(y - y_mid) ** 2 / (height / 2.0) ** 2 <= 1)
return {'mask': mask}
elif roi['type'] == 'ellipse':
# ellipse
mask = np.zeros((1, right+10, bottom+10), dtype=bool)
r_radius = np.sqrt((x2-x1)**2+(y2-y1)**2)/2.0
c_radius = r_radius*aspect_ratio
r = (x1+x2)/2-0.5
c = (y1+y2)/2-0.5
shpe = mask.shape
orientation = np.arctan2(y2-y1, x2-x1)
X, Y = ellipse(r, c, r_radius, c_radius, shpe[1:], orientation)
mask[0, X, Y] = True
return {'mask': mask}
else:
raise ValueError(
'ROI type {} not supported'.format(roi['type'])
)
# Handle different functions on Python 2/3
parse_roi_file = _parse_roi_file_py3 if sys.version_info >= (3, 0) else _parse_roi_file_py2
[docs]def read_imagej_roi_zip(filename):
"""Reads an ImageJ ROI zip set and parses each ROI individually
Parameters
----------
filename : str
Path to the ImageJ ROis zip file
Returns
-------
roi_list : list
List of the parsed ImageJ ROIs
"""
roi_list = []
with zipfile.ZipFile(filename) as zf:
for name in zf.namelist():
roi = parse_roi_file(zf.open(name))
if roi is None:
continue
roi['label'] = str(name).rstrip('.roi')
roi_list.append(roi)
return roi_list