initial

imagej_tiff.py

+#!/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)
+
+
+
+
+
+

test.py

+#!/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()
+