Loading imagej_tiff.py 0 → 100755 +247 −0 Original line number Diff line number Diff line #!/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 0 → 100644 +14 −0 Original line number Diff line number Diff line #!/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() test.tiff 0 → 100644 +7.19 MiB Loading image diff... 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imagej_tiff.py 0 → 100755 +247 −0 Original line number Diff line number Diff line #!/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 0 → 100644 +14 −0 Original line number Diff line number Diff line #!/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()
