Commit f5c37d99 authored by Oleg Dzhimiev's avatar Oleg Dzhimiev

initial

parent 059f60e9
#!/usr/bin/env python3
'''
/**
* @file imagej_tiff.py
* @brief open multi layer tiff files, display layers and parse meta data
* @par <b>License</b>:
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
'''
__copyright__ = "Copyright 2018, Elphel, Inc."
__license__ = "GPL-3.0+"
__email__ = "oleg@elphel.com"
from PIL import Image
import xml.etree.ElementTree as ET
import numpy as np
import matplotlib.pyplot as plt
# TiffFile has no len exception
#import imageio
#from libtiff import TIFF
'''
Description:
Reads a tiff files with multiple layers that were saved by imagej
Methods:
.getstack(items=[])
returns np.array, layers are stacked along depth - think of RGB channels
@items - if empty = all, if not - items[i] - can be layer index or layer's label name
.channel(index)
returns np.array of a single layer
.show_images(items=[])
@items - if empty = all, if not - items[i] - can be layer index or layer's label name
.show_image(index)
Examples:
#1
'''
class imagej_tiff:
# imagej stores labels lengths in this tag
__TIFF_TAG_LABELS_LENGTHS = 50838
# imagej stores labels conents in this tag
__TIFF_TAG_LABELS_STRINGS = 50839
# init
def __init__(self,filename):
# file name
self.fname = filename
tif = Image.open(filename)
# total number of layers in tiff
self.nimages = tif.n_frames
# labels array
self.labels = []
# infos will contain xml data Elphel stores in some of tiff files
self.infos = []
self.__split_labels(tif.n_frames,tif.tag)
self.__parse_info()
# image layers stacked along depth - (think RGB)
self.image = []
# fill self.image
for i in range(self.nimages):
tif.seek(i)
a = np.array(tif)
a = np.reshape(a,(a.shape[0],a.shape[1],1))
#a = a[:,:,np.newaxis]
# init
if i==0:
self.image = a
# stack along depth (think of RGB channels)
else:
self.image = np.append(self.image,a,axis=2)
# init done, close the image
tif.close()
# get ordered stack of images by provided items
# by index or label name
def getstack(self,items=[]):
a = ()
if len(items)==0:
return self.image
else:
for i in items:
if type(i)==int:
a += (self.image[:,:,i],)
elif type(i)==str:
j = self.labels.index(i)
a += (self.image[:,:,j],)
# stack along depth
return np.stack(a,axis=2)
# get np.array of a channel
# * do not handle out of bounds
def channel(self,index):
return self.image[:,:,index]
# display images by index or label
def show_images(self,items=[]):
# show listed only
if len(items)>0:
for i in items:
if type(i)==int:
self.show_image(i)
elif type(i)==str:
j = self.labels.index(i)
self.show_image(j)
# show all
else:
for i in range(self.nimages):
self.show_image(i)
# display single image
def show_image(self,index):
# display using matplotlib
t = self.image[:,:,index]
mytitle = "("+str(index+1)+" of "+str(self.nimages)+") "+self.labels[index]
fig = plt.figure()
fig.canvas.set_window_title(self.fname+": "+mytitle)
fig.suptitle(mytitle)
#plt.imshow(t,cmap=plt.get_cmap('gray'))
plt.imshow(t)
plt.colorbar()
# display using Pillow - need to scale
# remove NaNs - no need
#t[np.isnan(t)]=np.nanmin(t)
# scale to [min/max*255:255] range
#t = (1-(t-np.nanmax(t))/(t-np.nanmin(t)))*255
#tmp_im = Image.fromarray(t)
#tmp_im.show()
# puts etrees in infos
def __parse_info(self):
infos = []
for info in self.infos:
infos.append(ET.fromstring(info))
self.infos = infos
# makes arrays of labels (strings) and unparsed xml infos
def __split_labels(self,n,tag):
# list
tag_lens = tag[self.__TIFF_TAG_LABELS_LENGTHS]
# string
tag_labels = tag[self.__TIFF_TAG_LABELS_STRINGS].decode()
# remove 1st element: it's something like IJIJlabl..
tag_labels = tag_labels[tag_lens[0]:]
tag_lens = tag_lens[1:]
# the last ones are images labels
# normally the difference is expected to be 0 or 1
skip = len(tag_lens) - n
self.labels = []
self.infos = []
for l in tag_lens:
string = tag_labels[0:l].replace('\x00','')
if skip==0:
self.labels.append(string)
else:
self.infos.append(string)
skip -= 1
tag_labels = tag_labels[l:]
#MAIN
if __name__ == "__main__":
#fname = "1521849031_093189-DISP_MAP-D0.0-46.tif"
fname = "test.tiff"
ijt = imagej_tiff(fname)
print(ijt.labels)
print(ijt.infos)
print(ijt.image.shape)
#ijt.show_images()
#ijt.show_images([0,3])
ijt.show_images(['X-corr','Y-corr'])
#ijt.show_images(['R-vign',3])
plt.show()
# Examples
# 1: get default stack of images
#a = ijt.getstack()
#print(a.shape)
# 2: get defined ordered stack of images by tiff image index or by label name
#a = ijt.getstack([1,2,'X-corr'])
#print(a.shape)
# 3: will throw an error if there's no such label
#a = ijt.getstack([1,2,'Unknown'])
#print(a.shape)
# 4: will throw an error if index is out of bounds
#a = ijt.getstack([1,2,'X-corr'])
#print(a.shape)
# 5: dev excercise
#a = np.array([[1,2],[3,4]])
#b = np.array([[5,6],[7,8]])
#c = np.array([[10,11],[12,13]])
#print("test1:")
#ka = (a,b,c)
#d = np.stack(ka,axis=2)
#print(d)
#print("test2:")
#e = np.stack((d[:,:,1],d[:,:,0]),axis=2)
#print(e)
#!/usr/bin/env python3
from PIL import Image
import xml.etree.ElementTree as ET
import numpy as np
import matplotlib.pyplot as plt
import imagej_tiff as ijt
tiff = ijt.imagej_tiff('test.tiff')
print(tiff.nimages)
print(tiff.labels)
print(tiff.infos)
tiff.show_images(['X-corr','Y-corr',0,2])
plt.show()
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