Skip to content

L
lwir-nn
Commit 2c44046d authored by Andrey Filippov's avatar Andrey Filippov
Browse files
Options

Updating for LWIR data

parent d5384e3f
Show whitespace changes
Inline Side-by-side
Showing with 2445 additions and 450 deletions
explore_data6.py 0 → 100644
View file @ 2c44046d
This source diff could not be displayed because it is too large. You can view the blob instead.
imagej_tiff.py
View file @ 2c44046d
......@@ -55,6 +55,50 @@ class bcolors:
BOLD = '\033[1m'
BOLDWHITE = '\033[1;37m'
UNDERLINE = '\033[4m'
class IJML:
# as devined in ImageDtt.java
ML_OTHER_TARGET = 0 # Offset to target disparity data in ML_OTHER_INDEX layer tile
ML_OTHER_GTRUTH = 2 # Offset to ground truth disparity data in ML_OTHER_INDEX layer tile
ML_OTHER_GTRUTH_STRENGTH = 4 # Offset to ground truth confidence data in ML_OTHER_INDEX layer tile
ML_OTHER_GTRUTH_RMS = 6 # Offset to ground truth RMS in ML_OTHER_INDEX layer tile
ML_OTHER_GTRUTH_RMS_SPLIT = 8 # Offset to ground truth combined FG/BG RMS in ML_OTHER_INDEX layer tile
ML_OTHER_GTRUTH_FG_DISP = 10 # Offset to ground truth FG disparity in ML_OTHER_INDEX layer tile
ML_OTHER_GTRUTH_FG_STR = 12 # Offset to ground truth FG strength in ML_OTHER_INDEX layer tile
ML_OTHER_GTRUTH_BG_DISP = 14 # Offset to ground truth BG disparity in ML_OTHER_INDEX layer tile
ML_OTHER_GTRUTH_BG_STR = 16 # Offset to ground truth BG strength in ML_OTHER_INDEX layer tile
ML_OTHER_AUX_DISP = 18 # Offset to AUX heuristic disparity in ML_OTHER_INDEX layer tile
ML_OTHER_AUX_STR = 20 # Offset to AUX heuristic strength in ML_OTHER_INDEX layer tile
# indices
TARGET = ML_OTHER_TARGET // 2
GTRUTH = ML_OTHER_GTRUTH // 2
STRENGTH = ML_OTHER_GTRUTH_STRENGTH // 2
RMS = ML_OTHER_GTRUTH_RMS // 2
RMS_SPLIT = ML_OTHER_GTRUTH_RMS_SPLIT // 2
FG_DISP = ML_OTHER_GTRUTH_FG_DISP // 2
FG_STR = ML_OTHER_GTRUTH_FG_STR // 2
BG_DISP = ML_OTHER_GTRUTH_BG_DISP // 2
BG_STR = ML_OTHER_GTRUTH_BG_STR // 2
AUX_DISP = ML_OTHER_AUX_DISP // 2
AUX_STR = ML_OTHER_AUX_STR // 2
SIGNED = (TARGET, GTRUTH, FG_DISP, BG_DISP)
UNSIGNED_RMS = (RMS, RMS_SPLIT)
NUM_VALUES = 11
class IJFGBG:
DSI_NAMES = ["disparity","strength","rms","rms-split","fg-disp","fg-str","bg-disp","bg-str","aux-disp","aux-str"]
DISPARITY = 0
STRENGTH = 1
RMS = 2
RMS_SPLIT = 3
FG_DISP = 4
FG_STR = 5
BG_DISP = 6
BG_STR = 7
AUX_DISP = 8
AUX_STR = 9
# reshape to tiles
def get_tile_images(image, width=8, height=8):
......@@ -95,7 +139,6 @@ Examples:
'''
class imagej_tiff:
# imagej stores labels lengths in this tag
__TIFF_TAG_LABELS_LENGTHS = 50838
# imagej stores labels conents in this tag
......@@ -133,9 +176,7 @@ class imagej_tiff:
tif.seek(i)
a = np.array(tif)
a = np.reshape(a,(a.shape[0],a.shape[1],1))
#a = a[:,:,np.newaxis]
# scale for 8-bits
# exclude layer named 'other'
if self.bpp==8:
......@@ -151,7 +192,7 @@ class imagej_tiff:
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:
......@@ -171,24 +212,24 @@ class imagej_tiff:
indx += 1
else:
other_label = "other"
# print(tile_list)
# 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(
# tiles_other=np.empty((num_tiles,3),dtype=float)
tiles_other=self.gettilesvalues( # returns nparray of 11 floats (was 3)
tif = tif,
tile_list=tile_list,
label=other_label)
for nl,label in enumerate(layers):
tif.seek(self.labels.index(label))
tif.seek(self.labels.index(label)) #'hor-pairs' is not in list
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)])
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)
......@@ -212,6 +253,7 @@ class imagej_tiff:
self.corr2d = tiles_corr
self.target_disparity = tiles_other[...,0]
self.gt_ds = tiles_other[...,1:3]
self.payload = tiles_other#[...,0:12]
pass
# init done, close the image
......@@ -220,26 +262,29 @@ class imagej_tiff:
# 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))
res = np.empty((l.shape[0],l.shape[1], IJML.NUM_VALUES)) # was just 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]
for k in range(res.shape[2]):
res[i,j,k] = m[k * 2]
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
for k in range(res.shape[2]):
if k in IJML.SIGNED:
res[i,j,k] = ((m[2 * k] - 128) * 256 + m[2 * k + 1]) / 128
elif k in IJML.UNSIGNED_RMS:
res[i,j,k] = (m[2 * k]*256+m[2 * k + 1])/4096.0
else:
res[i,j,k] = (m[2 * k]*256+m[2 * k + 1])/65536.0
else:
res[i,j,0] = np.nan
res[i,j,1] = np.nan
res[i,j,2] = np.nan
for k in range(res.shape[2]):
res[i,j,k] = np.nan
# NaNize
# NaNize - TODO: update !
if self.bpp==8:
a = res[:,:,0]
a[a==-256] = np.nan
b = res[:,:,1]
......@@ -249,11 +294,12 @@ class imagej_tiff:
return res
# 3 values per tile: target disparity, GT disparity, GT confidence
# With LWIR/aux there are more!
def gettilesvalues(self,
tif,
tile_list,
label=""):
res = np.empty((len(tile_list),3),dtype=float)
res = np.empty((len(tile_list), IJML.NUM_VALUES),dtype=float) # was only 3
tif.seek(self.labels.index(label))
layer = np.array(tif) # 8 or 32 bits
tilesX = layer.shape[1]//self.tileW
......@@ -262,30 +308,29 @@ class imagej_tiff:
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]
for k in range(res.shape[1]):
res[i,k] = m[k * 2]
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
for k in range(res.shape[1]):
if k in IJML.SIGNED:
res[i,k] = ((m[2 * k] - 128) * 256 + m[2 * k + 1]) / 128
elif k in IJML.UNSIGNED_RMS:
res[i,k] = (m[2 * k]*256+m[2 * k + 1])/4096.0
else:
res[i,k] = (m[2 * k]*256+m[2 * k + 1])/65536.0
else:
res[i,0] = np.nan
res[i,1] = np.nan
res[i,2] = np.nan
# NaNize
for k in range(res.shape[1]):
res[i,k] = np.nan
# NaNize update!
if self.bpp==8:
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):
......@@ -308,14 +353,13 @@ class imagej_tiff:
return b
# get np.array of a channel
# * do not handle out of bounds
# * does 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:
......@@ -332,9 +376,7 @@ class imagej_tiff:
# 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()
......@@ -355,7 +397,6 @@ class imagej_tiff:
# puts etrees in infoss
def __parse_info(self):
infos = []
for info in self.infos:
infos.append(ET.fromstring(info))
......@@ -376,12 +417,12 @@ class imagej_tiff:
# tiles are squares
self.tileW = int(self.props['tileWidth'])
self.tileH = int(self.props['tileWidth'])
if self.bpp==8:
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
......@@ -412,9 +453,10 @@ 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 = "1521849031_093189-ML_DATA-32B-O-OFFS1.0.tiff"
# fname = "1521849031_093189-ML_DATA-08B-O-OFFS1.0.tiff"
fname = "/data_ssd/lwir3d/models/002/1562390096_605721/v01/ml32/1562390096_605721-ML_DATA-32B-AOT-FZ0.03-AG.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"
......@@ -437,8 +479,11 @@ if __name__ == "__main__":
# 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 (" No min/max are provided in 32-bit mode)")
print(ijt.image.shape)
......@@ -448,7 +493,8 @@ if __name__ == "__main__":
#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)
# tiles = ijt.getstack(['diagm-pair','diago-pair','hor-pairs','vert-pairs'],shape_as_tiles=True)
tiles = ijt.getstack(['diagm-aux','diago-aux','hor-aux','vert-aux'],shape_as_tiles=True)
print("Stack of images shape: "+str(tiles.shape))
print(bcolors.BOLDWHITE+"time: "+str(time.time())+bcolors.ENDC)
......@@ -511,6 +557,7 @@ if __name__ == "__main__":
ijt.show_images()
plt.show()
input("All done. Press ENTER to close images and exit...")
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment