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python3-imagej-tiff
Commit b88b2585 authored by Andrey Filippov's avatar Andrey Filippov
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updated for lwir16

parent d786da7b
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Showing with 423 additions and 433 deletions
imagej_tiff.py
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......@@ -58,21 +58,21 @@ class bcolors:
# reshape to tiles
def get_tile_images(image, width=8, height=8):
_nrows, _ncols, depth = image.shape
_size = image.size
_strides = image.strides
nrows, _m = divmod(_nrows, height)
ncols, _n = divmod(_ncols, width)
if _m != 0 or _n != 0:
return None
return np.lib.stride_tricks.as_strided(
np.ravel(image),
shape=(nrows, ncols, height, width, depth),
strides=(height * _strides[0], width * _strides[1], *_strides),
writeable=False
)
_nrows, _ncols, depth = image.shape
_size = image.size
_strides = image.strides
nrows, _m = divmod(_nrows, height)
ncols, _n = divmod(_ncols, width)
if _m != 0 or _n != 0:
return None
return np.lib.stride_tricks.as_strided(
np.ravel(image),
shape=(nrows, ncols, height, width, depth),
strides=(height * _strides[0], width * _strides[1], *_strides),
writeable=False
)
# TiffFile has no len exception
#import imageio
......@@ -80,7 +80,7 @@ def get_tile_images(image, width=8, height=8):
#from libtiff import TIFF
'''
Description:
Reads a tiff files with multiple layers that were saved by imagej
Reads 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
......@@ -95,459 +95,449 @@ Examples:
'''
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, layers = None, tile_list = None):
# 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 = []
# dictionary from decoded infos[0] xml data
self.props = {}
# bits per sample, type int
self.bpp = tif.tag[258][0]
self.__split_labels(tif.n_frames,tif.tag)
self.__parse_info()
try:
self.nan_bug = self.props['VERSION']== '1.0' # data between min and max is mapped to 0..254 instead of 1.255
except:
self.nan_bug = False # other files, not ML ones
# image layers stacked along depth - (think RGB)
self.image = []
if layers is None:
# 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))
# 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, layers = None, tile_list = None):
# 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 = []
# dictionary from decoded infos[0] xml data
self.props = {}
#a = a[:,:,np.newaxis]
# bits per sample, type int
self.bpp = tif.tag[258][0]
# scale for 8-bits
# exclude layer named 'other'
if self.bpp==8:
_min = self.data_min
_max = self.data_max
_MIN = 1
_MAX = 255
if (self.nan_bug):
_MIN = 0
_MAX = 254
else:
if self.labels[i]!='other':
a[a==0]=np.nan
a = a.astype(float)
if self.labels[i]!='other':
# a[a==0]=np.nan
a = (_max-_min)*(a-_MIN)/(_MAX-_MIN)+_min
# init
if i==0:
self.image = a
# stack along depth (think of RGB channels)
else:
self.image = np.append(self.image,a,axis=2)
else:
if tile_list is None:
indx = 0
for layer in layers:
tif.seek(self.labels.index(layer))
self.__split_labels(tif.n_frames,tif.tag)
self.__parse_info()
try:
self.nan_bug = self.props['VERSION']== '1.0' # data between min and max is mapped to 0..254 instead of 1.255
except:
self.nan_bug = False # other files, not ML ones
# image layers stacked along depth - (think RGB)
self.image = []
if layers is None:
# fill self.image
for i in range(self.nimages):
tif.seek(i)
a = np.array(tif)
if not indx:
self.image = np.empty((a.shape[0],a.shape[1],len(layers)),a.dtype)
self.image[...,indx] = a
indx += 1
a = np.reshape(a,(a.shape[0],a.shape[1],1))
#a = a[:,:,np.newaxis]
# exclude layer named 'other'
if self.bpp==8:
_min = self.data_min
_max = self.data_max
_MIN = 1
_MAX = 255
if (self.nan_bug):
_MIN = 0
_MAX = 254
else:
if self.labels[i]!='other':
a[a==0]=np.nan
a = a.astype(float)
if self.labels[i]!='other':
a = (_max-_min)*(a-_MIN)/(_MAX-_MIN)+_min
if i==0:
self.image = a
# stack along depth (think of RGB channels)
else:
self.image = np.append(self.image,a,axis=2)
else:
other_label = "other"
# print(tile_list)
num_tiles = len(tile_list)
num_layers = len(layers)
tiles_corr = np.empty((num_tiles,num_layers,self.tileH*self.tileW),dtype=float)
# tiles_other=np.empty((num_tiles,3),dtype=float)
tiles_other=self.gettilesvalues(
tif = tif,
tile_list=tile_list,
label=other_label)
for nl,label in enumerate(layers):
tif.seek(self.labels.index(label))
layer = np.array(tif) # 8 or 32 bits
tilesX = layer.shape[1]//self.tileW
for nt,tl in enumerate(tile_list):
ty = tl // tilesX
tx = tl % tilesX
# tiles_corr[nt,nl] = np.ravel(layer[self.tileH*ty:self.tileH*(ty+1),self.tileW*tx:self.tileW*(tx+1)])
a = np.ravel(layer[self.tileH*ty:self.tileH*(ty+1),self.tileW*tx:self.tileW*(tx+1)])
#convert from int8
if self.bpp==8:
a = a.astype(float)
if np.isnan(tiles_other[nt][0]):
# print("Skipping NaN tile ",tl)
a[...] = np.nan
else:
_min = self.data_min
_max = self.data_max
_MIN = 1
_MAX = 255
if (self.nan_bug):
_MIN = 0
_MAX = 254
else:
a[a==0] = np.nan
a = (_max-_min)*(a-_MIN)/(_MAX-_MIN)+_min
tiles_corr[nt,nl] = a
if tile_list is None:
indx = 0
for layer in layers:
tif.seek(self.labels.index(layer))
a = np.array(tif)
if not indx:
self.image = np.empty((a.shape[0],a.shape[1],len(layers)),a.dtype)
self.image[...,indx] = a
indx += 1
else:
other_label = "other"
# print(tile_list)
num_tiles = len(tile_list)
num_layers = len(layers)
tiles_corr = np.empty((num_tiles,num_layers,self.tileH*self.tileW),dtype=float)
# tiles_other=np.empty((num_tiles,3),dtype=float)
tiles_other=self.gettilesvalues(
tif = tif,
tile_list=tile_list,
label=other_label)
for nl,label in enumerate(layers):
tif.seek(self.labels.index(label))
layer = np.array(tif) # 8 or 32 bits
tilesX = layer.shape[1]//self.tileW
for nt,tl in enumerate(tile_list):
ty = tl // tilesX
tx = tl % tilesX
# tiles_corr[nt,nl] = np.ravel(layer[self.tileH*ty:self.tileH*(ty+1),self.tileW*tx:self.tileW*(tx+1)])
a = np.ravel(layer[self.tileH*ty:self.tileH*(ty+1),self.tileW*tx:self.tileW*(tx+1)])
#convert from int8
if self.bpp==8:
a = a.astype(float)
if np.isnan(tiles_other[nt][0]):
# print("Skipping NaN tile ",tl)
a[...] = np.nan
else:
_min = self.data_min
_max = self.data_max
_MIN = 1
_MAX = 255
if (self.nan_bug):
_MIN = 0
_MAX = 254
else:
a[a==0] = np.nan
a = (_max-_min)*(a-_MIN)/(_MAX-_MIN)+_min
tiles_corr[nt,nl] = a
pass
pass
self.corr2d = tiles_corr
self.target_disparity = tiles_other[...,0]
self.gt_ds = tiles_other[...,1:3]
pass
self.corr2d = tiles_corr
self.target_disparity = tiles_other[...,0]
self.gt_ds = tiles_other[...,1:3]
# init done, close the image
if (self.props['VERSION']== 2.0):
# self.tileH = self.image.shape[0]//self.props['tileStepY']
# self.tileW = self.image.shape[1]//self.props['tileStepX']
self.tileH = self.props['tileStepY']
self.tileW = self.props['tileStepX']
pass
# init done, close the image
tif.close()
# label == tiff layer name
def getvalues(self,label=""):
l = self.getstack([label],shape_as_tiles=True)
res = np.empty((l.shape[0],l.shape[1],3))
for i in range(res.shape[0]):
for j in range(res.shape[1]):
# 9x9 -> 81x1
m = np.ravel(l[i,j])
if self.bpp==32:
res[i,j,0] = m[0]
res[i,j,1] = m[2]
res[i,j,2] = m[4]
elif self.bpp==8:
res[i,j,0] = ((m[0]-128)*256+m[1])/128
res[i,j,1] = ((m[2]-128)*256+m[3])/128
res[i,j,2] = (m[4]*256+m[5])/65536.0
else:
res[i,j,0] = np.nan
res[i,j,1] = np.nan
res[i,j,2] = np.nan
# NaNize
a = res[:,:,0]
a[a==-256] = np.nan
b = res[:,:,1]
b[b==-256] = np.nan
c = res[:,:,2]
c[c==0] = np.nan
return res
# 3 values per tile: target disparity, GT disparity, GT confidence
def gettilesvalues(self,
tif.close()
# label == tiff layer name
def getvalues(self,label=""):
l = self.getstack([label],shape_as_tiles=True)
res = np.empty((l.shape[0],l.shape[1],3))
for i in range(res.shape[0]):
for j in range(res.shape[1]):
# 9x9 -> 81x1
m = np.ravel(l[i,j])
if self.bpp==32:
res[i,j,0] = m[0]
res[i,j,1] = m[2]
res[i,j,2] = m[4]
elif self.bpp==8:
res[i,j,0] = ((m[0]-128)*256+m[1])/128
res[i,j,1] = ((m[2]-128)*256+m[3])/128
res[i,j,2] = (m[4]*256+m[5])/65536.0
else:
res[i,j,0] = np.nan
res[i,j,1] = np.nan
res[i,j,2] = np.nan
# NaNize
a = res[:,:,0]
a[a==-256] = np.nan
b = res[:,:,1]
b[b==-256] = np.nan
c = res[:,:,2]
c[c==0] = np.nan
return res
# 3 values per tile: target disparity, GT disparity, GT confidence
def gettilesvalues(self,
tif,
tile_list,
label=""):
res = np.empty((len(tile_list),3),dtype=float)
tif.seek(self.labels.index(label))
layer = np.array(tif) # 8 or 32 bits
tilesX = layer.shape[1]//self.tileW
for i,tl in enumerate(tile_list):
ty = tl // tilesX
tx = tl % tilesX
m = np.ravel(layer[self.tileH*ty:self.tileH*(ty+1),self.tileW*tx:self.tileW*(tx+1)])
if self.bpp==32:
res[i,0] = m[0]
res[i,1] = m[2]
res[i,2] = m[4]
elif self.bpp==8:
res[i,0] = ((m[0]-128)*256+m[1])/128
res[i,1] = ((m[2]-128)*256+m[3])/128
res[i,2] = (m[4]*256+m[5])/65536.0
res = np.empty((len(tile_list),3),dtype=float)
tif.seek(self.labels.index(label))
layer = np.array(tif) # 8 or 32 bits
tilesX = layer.shape[1]//self.tileW
for i,tl in enumerate(tile_list):
ty = tl // tilesX
tx = tl % tilesX
m = np.ravel(layer[self.tileH*ty:self.tileH*(ty+1),self.tileW*tx:self.tileW*(tx+1)])
if self.bpp==32:
res[i,0] = m[0]
res[i,1] = m[2]
res[i,2] = m[4]
elif self.bpp==8:
res[i,0] = ((m[0]-128)*256+m[1])/128
res[i,1] = ((m[2]-128)*256+m[3])/128
res[i,2] = (m[4]*256+m[5])/65536.0
else:
res[i,0] = np.nan
res[i,1] = np.nan
res[i,2] = np.nan
# NaNize
a = res[...,0]
a[a==-256] = np.nan
b = res[...,1]
b[b==-256] = np.nan
c = res[...,2]
c[c==0] = np.nan
return res
# get ordered stack of images by provided items
# by index or label name. Divides into [self.tileH][self.tileW] tiles
def getstack(self,items=[],shape_as_tiles=False):
a = ()
if len(items)==0:
b = self.image
else:
res[i,0] = np.nan
res[i,1] = np.nan
res[i,2] = np.nan
# NaNize
a = res[...,0]
a[a==-256] = np.nan
b = res[...,1]
b[b==-256] = np.nan
c = res[...,2]
c[c==0] = np.nan
return res
# get ordered stack of images by provided items
# by index or label name
def getstack(self,items=[],shape_as_tiles=False):
a = ()
if len(items)==0:
b = 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
b = np.stack(a,axis=2)
if shape_as_tiles:
b = get_tile_images(b,self.tileW,self.tileH)
return b
# 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 infoss
def __parse_info(self):
infos = []
for info in self.infos:
infos.append(ET.fromstring(info))
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
b = np.stack(a,axis=2)
if shape_as_tiles:
b = get_tile_images(b,self.tileW,self.tileH)
return b
def trimStack (self, stack, radius = 0):
if (radius == 0):
radius=self.props['corrRadius']
corr_side = 2*radius+1
return stack[:,:,:,:corr_side,:corr_side]
# get np.array of a channel
# * do not handle out of bounds
def channel(self,index):
return self.image[:,:,index]
def getCorrsMeta(self,items=[]):
stack0 = self.getstack(items,shape_as_tiles=True)
stack = np.moveaxis(stack0, 4, 0) # slices - first index
radius=self.props['corrRadius']
num_meta=self.props['numMeta']
corr_side = 2*radius+1
corr_tiles = stack[:,:,:,:corr_side,:corr_side]
meta = stack[:,:,:,-1,:num_meta]
return corr_tiles, meta/self.props['tileMetaScale']
# 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)
self.infos = infos
# specifics
# properties dictionary
pd = {}
# display single image
def show_image(self,index):
# display using matplotlib
if infos:
for child in infos[0]:
#print(child.tag+"::::::"+child.text)
pd[child.tag] = child.text
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()
self.props = pd
# display using Pillow - need to scale
# tiles are squares
self.tileW = int(self.props['tileWidth'])
self.tileH = int(self.props['tileWidth'])
self.data_min = float(self.props['data_min'])
self.data_max = float(self.props['data_max'])
# 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:]
# 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 infoss
def __parse_info(self):
infos = []
for info in self.infos:
infos.append(ET.fromstring(info))
self.infos = infos
# specifics
# properties dictionary
pd = {}
if infos:
for child in infos[0]:
#print(child.tag+"::::::"+child.text)
pd[child.tag] = child.text
self.props = pd
file_version = float(self.props['VERSION'])
if (file_version < 2.0):
# tiles are squares (older version
self.tileW = int(self.props['tileWidth'])
self.tileH = int(self.props['tileWidth'])
self.data_min = float(self.props['data_min'])
self.data_max = float(self.props['data_max'])
else:
floats=['dispOffsetLow','tileMetaScale','disparity_low','dispOffset',
'fatZero','disparity_pwr','VERSION','dispOffsetHigh',
'disparity_high']
ints = ['metaGTConfidence','tileMetaSlice','indexReference','metaLastDiff',
'metaGTDisparity','metaFracValid', 'numScenes','metaTargetDisparity',
'disparity_steps','tileStepX', 'tileStepY', "corrRadius","numMeta"]
bools=['randomize_offsets']
for key in pd:
val = pd[key]
if key in bools:
if (val == '1') or (val == 'true') or (val == 'True'):
pd[key] = 1
else:
pd[key] = 0
pass
elif key in ints:
pd[key] = int(pd[key])
elif key in floats:
pd[key] = float(pd[key])
try:
pd['corrRadius'] = pd['corrRadius'] # not yet exists
except:
pd['corrRadius'] = 7
try:
pd['numMeta'] = pd['numMeta'] # not yet exists
except:
pd['numMeta'] = 6
pass
# 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__":
try:
fname = sys.argv[1]
except IndexError:
#fname = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/train/1527182807_896892/v02/ml/1527182807_896892-ML_DATA-08B-O-FZ0.05-OFFS0.40000.tiff"
fname = "/home/elphel/lwir16-proc/proc1/models/1626032208_613623/v01/ml32/1626032208_613623-ML-AUX-RND-DOFFS-5.000.tiff"
print(bcolors.BOLDWHITE+"time: "+str(time.time())+bcolors.ENDC)
try:
fname = sys.argv[1]
except IndexError:
fname = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/train/1527182807_896892/v02/ml/1527182807_896892-ML_DATA-08B-O-FZ0.05-OFFS0.40000.tiff"
# fname = "1521849031_093189-ML_DATA-32B-O-OFFS1.0.tiff"
# fname = "1521849031_093189-ML_DATA-08B-O-OFFS1.0.tiff"
#fname = "1521849031_093189-DISP_MAP-D0.0-46.tif"
#fname = "1526905735_662795-ML_DATA-08B-AIOTD-OFFS2.0.tiff"
#fname = "test.tiff"
print(bcolors.BOLDWHITE+"time: "+str(time.time())+bcolors.ENDC)
ijt = imagej_tiff(fname)
print(bcolors.BOLDWHITE+"time: "+str(time.time())+bcolors.ENDC)
print("TIFF stack labels: "+str(ijt.labels))
#print(ijt.infos)
rough_string = ET.tostring(ijt.infos[0], "utf-8")
reparsed = minidom.parseString(rough_string)
print(reparsed.toprettyxml(indent="\t"))
#print(ijt.props)
# needed properties:
print("Tiles shape: "+str(ijt.tileW)+"x"+str(ijt.tileH))
print("Data min: "+str(ijt.data_min))
print("Data max: "+str(ijt.data_max))
print(ijt.image.shape)
# layer order: ['diagm-pair', 'diago-pair', 'hor-pairs', 'vert-pairs', 'other']
# now split this into tiles:
#tiles = get_tile_images(ijt.image,ijt.tileW,ijt.tileH)
#print(tiles.shape)
tiles = ijt.getstack(['diagm-pair','diago-pair','hor-pairs','vert-pairs'],shape_as_tiles=True)
print("Stack of images shape: "+str(tiles.shape))
print(bcolors.BOLDWHITE+"time: "+str(time.time())+bcolors.ENDC)
# provide layer name
values = ijt.getvalues(label='other')
print("Stack of values shape: "+str(values.shape))
# each tile's disparity:
fig = plt.figure()
fig.suptitle("Estimated Disparity")
plt.imshow(values[:,:,0])
plt.colorbar()
fig = plt.figure()
fig.suptitle("Esitmated+Residual disparity")
plt.imshow(values[:,:,1])
plt.colorbar()
fig = plt.figure()
fig.suptitle("Residual disparity confidence")
plt.imshow(values[:,:,2])
plt.colorbar()
print(bcolors.BOLDWHITE+"time: "+str(time.time())+bcolors.ENDC)
#print(values)
#print(value_tiles[131,162].flatten())
#print(np.ravel(value_tiles[131,162]))
#values = np.empty((vt.shape[0],vt.shape[1],3))
#for i in range(values.shape[0]):
# for j in range(values.shape[1]):
# values[i,j,0] = get_v1()
#print(tiles[121,160,:,:,0].shape)
#_nrows = int(ijt.image.shape[0] / ijt.tileH)
#_ncols = int(ijt.image.shape[1] / ijt.tileW)
#_nrows = 32
#_ncols = 32
#print(str(_nrows)+" "+str(_ncols))
#fig, ax = plt.subplots(nrows=_nrows, ncols=_ncols)
#for i in range(_nrows):
# for j in range(_ncols):
# ax[i,j].imshow(tiles[i+100,j,:,:,0])
# ax[i,j].set_axis_off()
#for i in range(5):
# fig = plt.figure()
# plt.imshow(tiles[121,160,:,:,i])
# plt.colorbar()
ijt = imagej_tiff(fname)
#ijt.show_images(['other'])
print(bcolors.BOLDWHITE+"time: "+str(time.time())+bcolors.ENDC)
#ijt.show_images([0,3])
#ijt.show_images(['X-corr','Y-corr'])
#ijt.show_images(['R-vign',3])
print("TIFF stack labels: "+str(ijt.labels))
#print(ijt.infos)
ijt.show_images()
plt.show()
rough_string = ET.tostring(ijt.infos[0], "utf-8")
reparsed = minidom.parseString(rough_string)
print(reparsed.toprettyxml(indent="\t"))
# needed properties:
print("Tiles shape: "+str(ijt.tileW)+"x"+str(ijt.tileH))
try:
print("Data min: "+str(ijt.data_min))
print("Data max: "+str(ijt.data_max))
except:
print("Data min/max are not provided")
print(ijt.image.shape)
# tiles,tiles_meta = ijt.getCorrsMeta(['0-1','1-2','2-3','3-4'])
tiles,tiles_meta = ijt.getCorrsMeta([])
print("Corr stack shape: "+str(tiles.shape))
print("Meta stack shape: "+str(tiles_meta.shape))
exit (0)
#
# each tile's disparity:
# provide layer name
values = ijt.getvalues(label='other')
print("Stack of values shape: "+str(values.shape))
fig = plt.figure()
fig.suptitle("Estimated Disparity")
plt.imshow(values[:,:,0])
plt.colorbar()
# Examples
fig = plt.figure()
fig.suptitle("Esitmated+Residual disparity")
plt.imshow(values[:,:,1])
plt.colorbar()
# 1: get default stack of images
#a = ijt.getstack()
#print(a.shape)
fig = plt.figure()
fig.suptitle("Residual disparity confidence")
plt.imshow(values[:,:,2])
plt.colorbar()
# 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)
print(bcolors.BOLDWHITE+"time: "+str(time.time())+bcolors.ENDC)
# 3: will throw an error if there's no such label
#a = ijt.getstack([1,2,'Unknown'])
#print(a.shape)
ijt.show_images()
plt.show()
# 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)
# 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)
......
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