In [1]:
%load_ext autoreload
%autoreload 2
from matplotlib.pyplot import imshow
import matplotlib.pyplot as plt
%matplotlib inline
/home/d/nolearn/local/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')
In [2]:
import cPickle as pickle
import os; import sys; sys.path.append('..')
import gp
import gp.nets as nets
Using gpu device 0: GeForce GTX TITAN (CNMeM is disabled, CuDNN 4007)
/home/d/nolearn/local/lib/python2.7/site-packages/theano/tensor/signal/downsample.py:6: UserWarning: downsample module has been moved to the theano.tensor.signal.pool module.
"downsample module has been moved to the theano.tensor.signal.pool module.")
In [18]:
with open('../nets/IPMLB_FULL.p', 'rb') as f:
cnn = pickle.load(f)
cnn.uuid = 'IPMLB'
In [5]:
import numpy as np
import mahotas as mh
sys.path.append('../gp/')
import tifffile as tif
In [191]:
def cr(z_in):
images = []
probs = []
golds = []
rhoanas = []
for z in range(z_in,z_in+1): #70,72
image = tif.imread('/home/d/data/ac3/image/ac3_input_'+str(z).zfill(4)+'.tif')[100:800,100:800]
#prob = tif.imread('/home/d/data/ac3/prob/ac3_input_'+str(z).zfill(4)+'_syn.tif')[100:800,100:800]
prob = mh.imread('/home/d/data/ac3/probnew/000000092_000000092_'+str(z).zfill(9)+'_membrane-membrane.png')[100:800,100:800]
gold = tif.imread('/home/d/data/ac3/gold/ac3_labels_'+str(z).zfill(4)+'.tif')[100:800,100:800]
rhoana = tif.imread('/home/d/data/ac3/rhoana/z='+str(z).zfill(8)+'.tif')[100:800,100:800]
# rhoana = mh.imread('/home/d/data/ac3/rhoananew/000000092_000000092_'+str(z).zfill(9)+'_neuroproof.png')[100:800,100:800]
# rhoana_single = np.zeros((rhoana.shape[0], rhoana.shape[1]), dtype=np.uint64)
# rhoana_single[:, :] = rhoana[:,:,0]*256*256 + rhoana[:,:,1]*256 + rhoana[:,:,2]
# rhoana = rhoana_single
# print rhoana.shape, prob.shape
# image, prob, mask, gold, rhoana = gp.Util.read_section('/home/d/data/cylinderNEW/', z)
# images.append(image)
# probs.append(prob)
# golds.append(gold.astype(np.uint64))
# rhoanas.append(rhoana.astype(np.uint64))
bigM = gp.Legacy.create_bigM_without_mask(cnn, images, probs, rhoanas, verbose=True, max=1000000)
bigM_cylinder_after_95, out_cylinder_volume_after_auto_95, cylinder_auto_fixes_95, cylinder_auto_vi_s_95, vi_s_per_step2 = gp.Legacy.splits_global_from_M_automatic(
cnn, bigM, images, probs, rhoanas, golds, sureness_threshold=.8)
initial_median_vi = gp.Legacy.VI(golds, rhoanas)[0]
return cylinder_auto_fixes_95, vi_s_per_step2, initial_median_vi
In [180]:
imshow(rhoanas[0])
Out[180]:
<matplotlib.image.AxesImage at 0x7f2e2c071890>
In [181]:
groups = []
for z in range(50,55):
fixes, vis, initialvi = cr(z)
groups.append((fixes, vis, initialvi))
Loading z00000050.png
7104 generated in 379.008018017 seconds.
Grouped into 1744 patches in 0.0333898067474 seconds.
30 minutes done bigM_max= 0.994783639908
0.772613942623
Loading z00000051.png
6808 generated in 410.448157072 seconds.
Grouped into 1673 patches in 0.0311939716339 seconds.
30 minutes done bigM_max= 0.993903338909
0.791213452816
Loading z00000052.png
6908 generated in 345.054610014 seconds.
Grouped into 1693 patches in 0.0311348438263 seconds.
30 minutes done bigM_max= 0.995615720749
0.786356687546
Loading z00000053.png
6908 generated in 343.931591988 seconds.
Grouped into 1704 patches in 0.0306158065796 seconds.
30 minutes done bigM_max= 0.995332181454
0.792803943157
Loading z00000054.png
6908 generated in 346.02509594 seconds.
Grouped into 1697 patches in 0.0320188999176 seconds.
30 minutes done bigM_max= 0.994084119797
0.796086192131
In [189]:
v100 = [0]*len(groups)
v99 = [0]*len(groups)
v98 = [0]*len(groups)
v97 = [0]*len(groups)
v96 = [0]*len(groups)
v95 = [0]*len(groups)
v94 = [0]*len(groups)
v93 = [0]*len(groups)
v92 = [0]*len(groups)
v91 = [0]*len(groups)
i100 = [0]*len(groups)
i99 = [0]*len(groups)
i98 = [0]*len(groups)
i97 = [0]*len(groups)
i96 = [0]*len(groups)
i95 = [0]*len(groups)
i94 = [0]*len(groups)
i93 = [0]*len(groups)
i92 = [0]*len(groups)
i91 = [0]*len(groups)
for z,g in enumerate(groups):
for i,f in enumerate(g[0]):
# looping through fixes
if f[1] >= 0.99:
# add to 100 box
curvi = g[1][i][0] # we get the median
diffvi = abs(g[2] - curvi)
print g[2],curvi
v100[z] = diffvi
i100[z] = i
elif f[1] >= 0.98:
curvi = g[1][i][0] # we get the median
diffvi = abs(g[2] - curvi)
v99[z] = diffvi
i99[z] = i
elif f[1] >= 0.97:
curvi = g[1][i][0] # we get the median
diffvi = abs(g[2] - curvi)
v98[z] = diffvi
i98[z] = i
elif f[1] >= 0.96:
curvi = g[1][i][0] # we get the median
diffvi = abs(g[2] - curvi)
v97[z] = diffvi
i97[z] = i
elif f[1] >= 0.95:
curvi = g[1][i][0] # we get the median
diffvi = abs(g[2] - curvi)
v96[z] = diffvi
i96[z] = i
elif f[1] >= 0.94:
curvi = g[1][i][0] # we get the median
diffvi = abs(g[2] - curvi)
v95[z] = diffvi
i95[z] = i
elif f[1] >= 0.93:
curvi = g[1][i][0] # we get the median
diffvi = abs(g[2] - curvi)
v94[z] = diffvi
i94[z] = i
elif f[1] >= 0.92:
curvi = g[1][i][0] # we get the median
diffvi = abs(g[2] - curvi)
v93[z] = diffvi
i93[z] = i
elif f[1] >= 0.91:
curvi = g[1][i][0] # we get the median
diffvi = abs(g[2] - curvi)
v92[z] = diffvi
i92[z] = i
elif f[1] >= 0.90:
curvi = g[1][i][0] # we get the median
diffvi = abs(g[2] - curvi)
v91[z] = diffvi
i91[z] = i
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In [190]:
a= plt.boxplot([v100,v99,v98,v97,v96,v95,v94,v93,v92,v91])
b=plt.xticks(range(1,11),[i/100. for i in range(99,89,-1)])
# plt.ylim(-.25,1)
In [ ]:
In [ ]:
In [93]:
t_95 = []
for i,f in enumerate(t0_f):
if f[1] <= 0.95:
t_95.append(i)
break
for i,f in enumerate(t1_f):
if f[1] <= 0.95:
t_95.append(i)
break
for i,f in enumerate(t2_f):
if f[1] <= 0.95:
t_95.append(i)
break
for i,f in enumerate(t3_f):
if f[1] <= 0.95:
t_95.append(i)
break
for i,f in enumerate(t4_f):
if f[1] <= 0.95:
t_95.append(i)
break
In [94]:
t0_vis = []
for v in t0_vi[0:100]:
t0_vis.append(v[1])
t1_vis = []
for v in t1_vi[0:100]:
t1_vis.append(v[1])
t2_vis = []
for v in t2_vi[0:100]:
t2_vis.append(v[1])
t3_vis = []
for v in t3_vi[0:100]:
t3_vis.append(v[1])
t4_vis = []
for v in t4_vi[0:100]:
t4_vis.append(v[1])
In [95]:
fig = plt.figure()
plt.axvline(x=t_95[0], color='red')
# plt.axvline(x=t_95[1], color='red')
# plt.axvline(x=t_95[2], color='red')
plt.axvline(x=t_95[3], color='red')
plt.axvline(x=t_95[4], color='red')
plt.plot(t0_vis)
# plt.plot(t1_vis)
# plt.plot(t2_vis)
plt.plot(t3_vis)
plt.plot(t4_vis)
Out[95]:
[<matplotlib.lines.Line2D at 0x7f2e296a9490>]
In [71]:
t0_vis
Out[71]:
[]
In [221]:
fig = plt.figure(figsize=(10,7))
font = {'family' : 'sans-serif',
'weight' : 'normal',
'size' : 24}
plt.rc('font', **font)
plt.axvline(x=t_95[0], ymax=.77, color='blue', linewidth=3, linestyle=':')
# plt.axvline(x=t_95[1], color='red')
# plt.axvline(x=t_95[2], color='red')
plt.axvline(x=t_95[3], ymax=0.66, color='green', linewidth=3, linestyle=':')
plt.axvline(x=t_95[4], ymax=0.62, color='red', linewidth=3, linestyle=':')
plt.plot(t0_vis, color='blue', linewidth=3)
# plt.plot(t1_vis)
# plt.plot(t2_vis)
plt.plot(t3_vis, color='green', linewidth=3)
plt.plot(t4_vis, color='red', linewidth=3)
plt.ylabel('Variation of Information')
plt.xlabel('Correction')
plt.ylim(0.5,0.9)
plt.yticks(np.arange(0.5, 0.91, 0.1))
plt.savefig('/home/d/PAPERGFX/ptplot.pdf')
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Content source: VCG/gp
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