Commit da662515 authored by Andrey Filippov's avatar Andrey Filippov

Compared 3 methods of feeding data: 15x, then 20x more

parent be2fa3ca
This diff is collapsed.
......@@ -102,7 +102,7 @@ class imagej_tiff:
__TIFF_TAG_LABELS_STRINGS = 50839
# init
def __init__(self,filename, layers = None):
def __init__(self,filename, layers = None, tile_list = None):
# file name
self.fname = filename
tif = Image.open(filename)
......@@ -120,6 +120,10 @@ class imagej_tiff:
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 = []
......@@ -139,11 +143,16 @@ class imagej_tiff:
_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[a==0]=np.nan
a = (_max-_min)*(a-_MIN)/(_MAX-_MIN)+_min
# init
if i==0:
self.image = a
......@@ -151,15 +160,59 @@ class imagej_tiff:
else:
self.image = np.append(self.image,a,axis=2)
else:
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
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
# init done, close the image
tif.close()
......@@ -193,10 +246,46 @@ class imagej_tiff:
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
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):
......@@ -323,8 +412,9 @@ if __name__ == "__main__":
try:
fname = sys.argv[1]
except IndexError:
fname = "1521849031_093189-ML_DATA-32B-O-OFFS1.0.tiff"
fname = "1521849031_093189-ML_DATA-08B-O-OFFS1.0.tiff"
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"
......
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