Commit be2fa3ca authored by Andrey Filippov's avatar Andrey Filippov

Added pydev project setup, calculation of the source data stats for

batch equalization
parent eba223a4
__pycache__
/.project
/.pydevproject
attic
*.log
\ No newline at end of file
<?xml version="1.0" encoding="UTF-8"?>
<projectDescription>
<name>python3-imagej-tiff</name>
<comment></comment>
<projects>
</projects>
<buildSpec>
<buildCommand>
<name>org.python.pydev.PyDevBuilder</name>
<arguments>
</arguments>
</buildCommand>
</buildSpec>
<natures>
<nature>org.python.pydev.pythonNature</nature>
</natures>
</projectDescription>
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<?eclipse-pydev version="1.0"?><pydev_project>
<pydev_property name="org.python.pydev.PYTHON_PROJECT_INTERPRETER">Default</pydev_property>
<pydev_property name="org.python.pydev.PYTHON_PROJECT_VERSION">python interpreter</pydev_property>
</pydev_project>
#!/usr/bin/env python3
from numpy import float64
__copyright__ = "Copyright 2018, Elphel, Inc."
__license__ = "GPL-3.0+"
__email__ = "andrey@elphel.com"
import os
import sys
import glob
import imagej_tiff as ijt
import numpy as np
import resource
import timeit
import matplotlib.pyplot as plt
from scipy.ndimage.filters import gaussian_filter
#http://stackoverflow.com/questions/287871/print-in-terminal-with-colors-using-python
class bcolors:
HEADER = '\033[95m'
OKBLUE = '\033[94m'
OKGREEN = '\033[92m'
WARNING = '\033[38;5;214m'
FAIL = '\033[91m'
ENDC = '\033[0m'
BOLD = '\033[1m'
BOLDWHITE = '\033[1;37m'
UNDERLINE = '\033[4m'
def print_time():
print(bcolors.BOLDWHITE+"time: "+str(time.time())+bcolors.ENDC)
class ExploreData:
def getComboList(self, top_dir):
patt = "*-DSI_COMBO.tiff"
tlist = []
for i in range(5):
pp = top_dir#) ,'**', patt) # works
for j in range (i):
pp = os.path.join(pp,'*')
pp = os.path.join(pp,patt)
tlist += glob.glob(pp)
print (pp+" "+str(len(tlist)))
if (self.debug_level > 0):
print("Found "+str(len(tlist))+" combo DSI files in "+top_dir+" :")
if (self.debug_level > 1):
print("\n".join(tlist))
return tlist
def loadComboFiles(self, tlist):
indx = 0
images = []
if (self.debug_level>2):
print(str(resource.getrusage(resource.RUSAGE_SELF)))
for combo_file in tlist:
tiff = ijt.imagej_tiff(combo_file,['disparity_rig','strength_rig'])
if not indx:
images = np.empty((len(tlist), tiff.image.shape[0],tiff.image.shape[1],tiff.image.shape[2]), tiff.image.dtype)
images[indx] = tiff.image
if (self.debug_level>2):
print(str(indx)+": "+str(resource.getrusage(resource.RUSAGE_SELF)))
indx += 1
return images
def getHistogramDSI(
self,
combo_rds,
disparity_bins = 1000,
strength_bins = 100,
disparity_min_drop = -0.1,
disparity_min_clip = -0.1,
disparity_max_drop = 100.0,
disparity_max_clip = 100.0,
strength_min_drop = 0.1,
strength_min_clip = 0.1,
strength_max_drop = 1.0,
strength_max_clip = 0.9,
normalize = True,
no_histogram = False
):
good_tiles = np.empty((combo_rds.shape[0], combo_rds.shape[1],combo_rds.shape[2]), dtype=bool)
for ids in range (combo_rds.shape[0]): #iterate over all scenes ds[2][rows][cols]
ds = combo_rds[ids]
disparity = ds[...,0]
strength = ds[...,1]
good_tiles[ids] = disparity >= disparity_min_drop
good_tiles[ids] &= disparity <= disparity_max_drop
good_tiles[ids] &= strength >= strength_min_drop
good_tiles[ids] &= strength <= strength_max_drop
# if not self.good_tiles is None:
# good_tiles[ids] &= self.good_tiles
disparity = np.nan_to_num(disparity, copy = False) # to be able to multiply by 0.0 in mask | copy=False, then out=disparity all done in-place
strength = np.nan_to_num(strength, copy = False) # likely should never happen
np.clip(disparity, disparity_min_clip, disparity_max_clip, out = disparity)
np.clip(strength, strength_min_clip, strength_max_clip, out = strength)
if no_histogram:
strength *= good_tiles[ids]
return None # no histogram, just condition data
hist, xedges, yedges = np.histogram2d( # xedges, yedges - just for debugging
x = combo_rds[...,1].flatten(),
y = combo_rds[...,0].flatten(),
bins= (strength_bins, disparity_bins),
range= ((strength_min_clip,strength_max_clip),(disparity_min_clip,disparity_max_clip)),
normed= normalize,
weights= good_tiles.flatten())
return hist, xedges, yedges
def __init__(self,
topdir_all,
debug_level = 0,
disparity_bins = 1000,
strength_bins = 100,
disparity_min_drop = -0.1,
disparity_min_clip = -0.1,
disparity_max_drop = 100.0,
disparity_max_clip = 100.0,
strength_min_drop = 0.1,
strength_min_clip = 0.1,
strength_max_drop = 1.0,
strength_max_clip = 0.9,
hist_sigma = 2.0, # Blur log histogram
hist_cutoff= 0.001 # of maximal
):
# file name
self.debug_level = debug_level
self.disparity_bins = disparity_bins
self.strength_bins = strength_bins
self.disparity_min_drop = disparity_min_drop
self.disparity_min_clip = disparity_min_clip
self.disparity_max_drop = disparity_max_drop
self.disparity_max_clip = disparity_max_clip
self.strength_min_drop = strength_min_drop
self.strength_min_clip = strength_min_clip
self.strength_max_drop = strength_max_drop
self.strength_max_clip = strength_max_clip
self.hist_sigma = hist_sigma # Blur log histogram
self.hist_cutoff= hist_cutoff # of maximal
self.pre_log_offs = 0.001 # of histogram maximum
self.good_tiles = None
filelist = self.getComboList(topdir_all)
combo_rds = self.loadComboFiles(filelist)
self.hist, xedges, yedges = self.getHistogramDSI(
combo_rds = combo_rds,
disparity_bins = self.disparity_bins,
strength_bins = self.strength_bins,
disparity_min_drop = self.disparity_min_drop,
disparity_min_clip = self.disparity_min_clip,
disparity_max_drop = self.disparity_max_drop,
disparity_max_clip = self.disparity_max_clip,
strength_min_drop = self.strength_min_drop,
strength_min_clip = self.strength_min_clip,
strength_max_drop = self.strength_max_drop,
strength_max_clip = self.strength_max_clip,
normalize = True,
no_histogram = False
)
log_offset = self.pre_log_offs * self.hist.max()
h_cutoff = hist_cutoff * self.hist.max()
lhist = np.log(self.hist + log_offset)
blurred_lhist = gaussian_filter(lhist, sigma = self.hist_sigma)
self.blurred_hist = np.exp(blurred_lhist) - log_offset
self.good_tiles = self.blurred_hist >= h_cutoff
self.blurred_hist *= self.good_tiles # set bad ones to zero
def getTrainDS(self, topdir_train):
self.trainlist = self.getComboList(topdir_train)
self.train_ds = self.loadComboFiles(self.trainlist)
self.getHistogramDSI(
combo_rds = self.train_ds, # will condition strength
disparity_bins = self.disparity_bins,
strength_bins = self.strength_bins,
disparity_min_drop = self.disparity_min_drop,
disparity_min_clip = self.disparity_min_clip,
disparity_max_drop = self.disparity_max_drop,
disparity_max_clip = self.disparity_max_clip,
strength_min_drop = self.strength_min_drop,
strength_min_clip = self.strength_min_clip,
strength_max_drop = self.strength_max_drop,
strength_max_clip = self.strength_max_clip,
normalize = True,
no_histogram = True)
pass
def assignBatchBins(self, disp_bins, str_bins):
hist_to_batch = np.zeros((self.blurred_hist.shape[0],self.blurred_hist.shape[1]),dtype=int) #zeros_like?
hist_to_batch_multi = np.ones((self.blurred_hist.shape[0],self.blurred_hist.shape[1]),dtype=int) #zeros_like?
scale_hist= (disp_bins * str_bins)/self.blurred_hist.sum()
norm_b_hist = self.blurred_hist * scale_hist
disp_list = [] # last disparity hist
# disp_multi = [] # number of disp rows to fit
disp_run_tot = 0.0
disp_batch = 0
disp=0
disp_hist = np.linspace(0,disp_bins * str_bins,disp_bins+1)
num_batch_bins = disp_bins * str_bins
batch_index = 0
num_members = np.zeros((num_batch_bins,),int)
while disp_batch < disp_bins:
#disp_multi.append(1)
# while (disp < self.disparity_bins):
# disp_target_tot =disp_hist[disp_batch+1]
disp_run_tot_new = disp_run_tot
disp0 = disp # start disaprity matching disp_run_tot
while (disp_run_tot_new < disp_hist[disp_batch+1]) and (disp < self.disparity_bins):
disp_run_tot_new += norm_b_hist[:,disp].sum()
disp+=1;
disp_multi = 1
while (disp_batch < (disp_bins - 1)) and (disp_run_tot_new >= disp_hist[disp_batch+2]):
disp_batch += 1 # only if large disp_bins and very high hist value
disp_multi += 1
# now disp_run_tot - before this batch disparity col
str_bins_corr = str_bins * disp_multi # if too narrow disparity column - multiply number of strength columns
str_bins_corr_last = str_bins_corr -1
str_hist = np.linspace(disp_run_tot, disp_run_tot_new, str_bins_corr + 1)
str_run_tot_new = disp_run_tot
# str_batch = 0
str_index=0
# wide_col = norm_b_hist[:,disp0:disp] #disp0 - first column, disp - last+ 1
#iterate in linescan along the column
for si in range(self.strength_bins):
for di in range(disp0, disp,1):
if norm_b_hist[si,di] > 0.0 :
str_run_tot_new += norm_b_hist[si,di]
# do not increment after last to avoid precision issues
if (batch_index < num_batch_bins) and (num_members[batch_index] > 0) and (str_index < str_bins_corr_last) and (str_run_tot_new > str_hist[str_index+1]):
batch_index += 1
str_index += 1
if batch_index < num_batch_bins :
hist_to_batch[si,di] = batch_index
num_members[batch_index] += 1
else:
pass
else:
hist_to_batch[si,di] = -1
batch_index += 1 # it was not incremented afterthe last in the column to avoid rounding error
disp_batch += 1
disp_run_tot = disp_run_tot_new
pass
return hist_to_batch
# total number of layers in tiff
#MAIN
if __name__ == "__main__":
try:
topdir_all = sys.argv[1]
except IndexError:
topdir_all = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/all"#test" #all/"
try:
topdir_train = sys.argv[2]
except IndexError:
topdir_train = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/train"#test" #all/"
ex_data = ExploreData(
topdir_all = topdir_all,
debug_level = 3,
disparity_bins = 200, #1000,
strength_bins = 100,
disparity_min_drop = -0.1,
disparity_min_clip = -0.1,
disparity_max_drop = 20.0, #100.0,
disparity_max_clip = 20.0, #100.0,
strength_min_drop = 0.1,
strength_min_clip = 0.1,
strength_max_drop = 1.0,
strength_max_clip = 0.9,
hist_sigma = 2.0, # Blur log histogram
hist_cutoff= 0.001) # of maximal
"""
tlist = getComboList(top_dir)
pre_log_offs = 0.001
combo_rds= loadComboFiles(tlist)
hist, xedges, yedges = getHistogramDSI(combo_rds)
lhist = np.log(hist + pre_log_offs)
blurred = gaussian_filter(lhist, sigma=2.0)
"""
mytitle = "Disparity_Strength histogram"
fig = plt.figure()
fig.canvas.set_window_title(mytitle)
fig.suptitle(mytitle)
# plt.imshow(lhist,vmin=-6,vmax=-2) # , vmin=0, vmax=.01)
plt.imshow(ex_data.blurred_hist, vmin=0, vmax=.1 * ex_data.blurred_hist.max())#,vmin=-6,vmax=-2) # , vmin=0, vmax=.01)
plt.colorbar(orientation='horizontal') # location='bottom')
bb = ex_data.assignBatchBins(
disp_bins = 20,
str_bins=10)
bb_display = bb.copy()
"""
bb_display %= 20
bb_mask = (bb > 0)
bb_scale = 1.0 * (bb_mask)
bb_scale = (1.05 * bb_scale)
bb_display = bb_display.astype(float) * bb_scale
"""
# bb_display = ( ((2 * (bb_display % 2) -1) * (2 * ((bb_display % 20)//10) -1) + 2)/2)*(bb > 0) #).astype(float)
bb_display = ( 1+ (bb_display % 2) + 2 * ((bb_display % 20)//10)) * (bb > 0) #).astype(float)
fig2 = plt.figure()
fig2.canvas.set_window_title("Batch indices")
fig2.suptitle("Batch index for each disparity/strength cell")
# plt.imshow(lhist,vmin=-6,vmax=-2) # , vmin=0, vmax=.01)
plt.imshow(bb_display) #, vmin=0, vmax=.1 * ex_data.blurred_hist.max())#,vmin=-6,vmax=-2) # , vmin=0, vmax=.01)
plt.colorbar(orientation='horizontal') # location='bottom')
# Get image stats for train data only
plt.ioff()
plt.show()
ex_data.getTrainDS(topdir_train)
pass
......@@ -102,7 +102,7 @@ class imagej_tiff:
__TIFF_TAG_LABELS_STRINGS = 50839
# init
def __init__(self,filename):
def __init__(self,filename, layers = None):
# file name
self.fname = filename
tif = Image.open(filename)
......@@ -123,6 +123,7 @@ class imagej_tiff:
# 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)
......@@ -149,6 +150,16 @@ class imagej_tiff:
# stack along depth (think of RGB channels)
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
# init done, close the image
tif.close()
......@@ -266,6 +277,7 @@ class imagej_tiff:
# properties dictionary
pd = {}
if infos:
for child in infos[0]:
#print(child.tag+"::::::"+child.text)
pd[child.tag] = child.text
......
#!/usr/bin/env python3
from numpy import float64
__copyright__ = "Copyright 2018, Elphel, Inc."
__license__ = "GPL-3.0+"
__email__ = "andrey@elphel.com"
import os
import sys
import glob
import imagej_tiff as ijt
import numpy as np
import resource
import timeit
import matplotlib.pyplot as plt
from scipy.ndimage.filters import gaussian_filter
#http://stackoverflow.com/questions/287871/print-in-terminal-with-colors-using-python
class bcolors:
HEADER = '\033[95m'
OKBLUE = '\033[94m'
OKGREEN = '\033[92m'
WARNING = '\033[38;5;214m'
FAIL = '\033[91m'
ENDC = '\033[0m'
BOLD = '\033[1m'
BOLDWHITE = '\033[1;37m'
UNDERLINE = '\033[4m'
def print_time():
print(bcolors.BOLDWHITE+"time: "+str(time.time())+bcolors.ENDC)
def getComboList(top_dir):
patt = "*-DSI_COMBO.tiff"
tlist = []
for i in range(5):
pp = top_dir#) ,'**', patt) # works
for j in range (i):
pp = os.path.join(pp,'*')
pp = os.path.join(pp,patt)
tlist += glob.glob(pp)
print (pp+" "+str(len(tlist)))
print("Found "+str(len(tlist))+" preprocessed tiff files:")
print("\n".join(tlist))
return tlist
def loadComboFiles(tlist):
indx = 0
images = []
print(str(resource.getrusage(resource.RUSAGE_SELF)))
for combo_file in tlist:
tiff = ijt.imagej_tiff(combo_file,['disparity_rig','strength_rig'])
if not indx:
images = np.empty((len(tlist), tiff.image.shape[0],tiff.image.shape[1],tiff.image.shape[2]), tiff.image.dtype)
images[indx] = tiff.image
print(str(indx)+": "+str(resource.getrusage(resource.RUSAGE_SELF)))
indx += 1
return images
def getHistogramDSI(
combo_rds,
disparity_bins = 1000,
strength_bins = 100,
disparity_min_drop = -0.1,
disparity_min_clip = -0.1,
disparity_max_drop = 100.0,
disparity_max_clip = 100.0,
strength_min_drop = 0.1,
strength_min_clip = 0.1,
strength_max_drop = 1.0,
strength_max_clip = 0.9,
normalize = True
):
good_tiles = np.empty((combo_rds.shape[0], combo_rds.shape[1],combo_rds.shape[2]), dtype=bool)
for ids in range (combo_rds.shape[0]): #iterate over all scenes ds[2][rows][cols]
ds = combo_rds[ids]
disparity = ds[...,0]
strength = ds[...,1]
good_tiles[ids] = disparity >= disparity_min_drop
good_tiles[ids] &= disparity <= disparity_max_drop
good_tiles[ids] &= strength >= strength_min_drop
good_tiles[ids] &= strength <= strength_max_drop
disparity = np.nan_to_num(disparity, copy = False) # to be able to multiply by 0.0 in mask | copy=False, then out=disparity all done in-place
strength = np.nan_to_num(strength, copy = False) # likely should never happen
np.clip(disparity, disparity_min_clip, disparity_max_clip, out = disparity)
np.clip(strength, strength_min_clip, strength_max_clip, out = strength)
pass
hist, xedges, yedges = np.histogram2d( # xedges, yedges - just for debugging
x = combo_rds[...,0].flatten(),
y = combo_rds[...,1].flatten(),
bins=(disparity_bins, strength_bins),
range=((disparity_min_clip,disparity_max_clip),(strength_min_clip,strength_max_clip)),
normed=normalize,
weights=good_tiles.flatten())
return hist, xedges, yedges
#MAIN
if __name__ == "__main__":
try:
top_dir = sys.argv[1]
except IndexError:
top_dir = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/all"#test" #all/"
tlist = getComboList(top_dir)
pre_log_offs = 0.001
combo_rds= loadComboFiles(tlist)
hist, xedges, yedges = getHistogramDSI(combo_rds)
lhist = np.log(hist.transpose() + pre_log_offs)
blurred = gaussian_filter(lhist, sigma=2.0)
mytitle = "Disparity/Strength histogram"
fig = plt.figure()
fig.canvas.set_window_title("Window title: "+mytitle)
fig.suptitle("Window subtitle: "+mytitle)
# plt.imshow(lhist,vmin=-6,vmax=-2) # , vmin=0, vmax=.01)
plt.imshow(blurred)#,vmin=-6,vmax=-2) # , vmin=0, vmax=.01)
plt.colorbar()
plt.ioff()
plt.show()
pass
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