notebook.community

Edit and run 





In [1]:



    


import matplotlib
matplotlib.use("nbagg")
import math
import numpy as np
import matplotlib.pyplot as plt
import cv2
import re, os, glob, pickle, shutil,sys
from random import randint
import time
from shutil import *

os.environ["THEANO_FLAGS"] = "mode=FAST_RUN,device=cpu,floatX=float32"
import theano
import theano.tensor as T
from theano import *
theano.__version__
from theano.sandbox.cuda import dnn

import pandas as pd
from theano import tensor as T
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams


from theano.compile.nanguardmode import NanGuardMode

#from pom_funcs import *
from pom_room import POM_room
from pom_evaluator import POM_evaluator
import POMLayers1
from EM_funcs import *
import ZtoY
config.allow_gc =False

%load_ext autoreload
%autoreload 2

Run POM





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room = POM_room()
POMLayers1.room = room #TODO : modify POMLayers1 so that we don't need room, just config

a,p,alpha_black = 1.5,200,0.5

POMLauncher = POMLayers1.pomLayer()
POMLauncher.set_POM_params(a = a, alpha_black = alpha_black, prior_factor = p)


Q_out,Z_out,Shift = POMLauncher.run_POM(1,n_iter_pom = 300,step_0 = 0.002,use_unaries = True,T_0 = 10.0)
#room.save_dat(Q_out,fid,folder_out,verbose = True)



    











In [13]:



    


%matplotlib inline
room.plot_output(Q_out,0,1,0,thresh = 0.95,iteration=-1)
room.plot_output(Q_out,0,1,1,thresh = 0.95,iteration=-1)
room.plot_output(Q_out,0,1,2,thresh = 0.95,iteration=-1)
room.plot_output(Q_out,0,1,3,thresh = 0.95,iteration=-1)
room.plot_output(Q_out,0,1,8,thresh = 0.95,iteration=-1)
#room.plot_output(Q_out,0,1,1,thresh = 0.95,iteration=-10)



    


















    

















    

















    

















    

















    
























In [6]:



    


# test = np.load('./Potentials/Parts/EM5/c0/900.npy')
# plt.imshow(test[:,:,0])
# plt.show()

test1 = np.load('./Potentials/Parts/Run_transfer/c0/1.npy')
plt.imshow(test1[:,:,2])
plt.show()



    


















    




















    























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##### Run POM bulk



    











In [7]:



    


print room.parts_root_folder



    


















    






./Potentials/Parts/Run/

















In [5]:



    


import Config
room = POM_room()
POMLayers1.room = room 
room.img_index_list = range(2000,4000,5)
#TODO : modify POMLayers1 so that we don't need room, just config

a,p,alpha_black = 2.5,230,1.5

POMLauncher = POMLayers1.pomLayer()
POMLauncher.set_POM_params(a = a, alpha_black = alpha_black, prior_factor = p)

#optimised values
# POMLauncher.alphas.set_value(np.asarray([1.80283296,  1.80929637 , 1.88381839 , 1.6279335 ,  2.09338522 , 1.96566319,
#   1.78461742 , 2.05577421 , 0.        ]).astype(np.float32))
#Create folder
folder_out = './results/res_nounaries_a%f_p%f_ablack%f/'%(a,p,alpha_black)
if not os.path.exists(folder_out):
    os.makedirs(folder_out)

def runsave(fid,n_iter_pom = 300,step_0 = 0.002):
    last = len(room.img_index_list)
    if fid < last:
        Q_out,Z_out,Shift = POMLauncher.run_POM(fid,n_iter_pom = 300,step_0 = 0.002,use_unaries = False)
        room.save_dat(Q_out,fid,folder_out,verbose = True)
        #room.plot_output(Q_out,fid,1,8,thresh = 0.95,iteration=-1)



    











In [6]:



    


from joblib import Parallel, delayed
import multiprocessing

n_threads =40

for block in range(0,len(room.img_index_list)/n_threads+1):
    fids = range(n_threads*block,n_threads*(block+1))
    Parallel(n_jobs=n_threads)(delayed(runsave)(fid) for fid in fids)



    


















    






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Saved file : ./results/res_nounaries_a2.500000_p230.000000_ablack1.500000/00003860.dat
Saved file : ./results/res_nounaries_a2.500000_p230.000000_ablack1.500000/00003885.dat
Saved file : ./results/res_nounaries_a2.500000_p230.000000_ablack1.500000/00003930.dat
Saved file : ./results/res_nounaries_a2.500000_p230.000000_ablack1.500000/00003920.dat
Saved file : ./results/res_nounaries_a2.500000_p230.000000_ablack1.500000/00003875.dat
Saved file : ./results/res_nounaries_a2.500000_p230.000000_ablack1.500000/00003830.dat
Saved file : ./results/res_nounaries_a2.500000_p230.000000_ablack1.500000/00003865.dat
Saved file : ./results/res_nounaries_a2.500000_p230.000000_ablack1.500000/00003940.dat
Saved file : ./results/res_nounaries_a2.500000_p230.000000_ablack1.500000/00003810.dat
Saved file : ./results/res_nounaries_a2.500000_p230.000000_ablack1.500000/00003895.dat
Saved file : ./results/res_nounaries_a2.500000_p230.000000_ablack1.500000/00003820.dat

Run Baseline





In [3]:



    


from joblib import Parallel, delayed
import multiprocessing

room = POM_room()
POMLayers1.room = room #TODO : modify POMLayers1 so that we don't need room, just config
a,p,alpha_black = 1.8,270,0.0

POMLauncher = POMLayers1.pomLayer()
POMLauncher.set_POM_params(a = a, alpha_black = alpha_black, prior_factor = p)



thresh = 0.05
radius = 4

#print 'Setting a = %f, p = %f'%(a,p)

folder_out = './results/res_NMS_t%f_rad%f/'%(thresh,radius)
if not os.path.exists(folder_out):
    os.makedirs(folder_out)
   

def runsave(fid):
    Q_out = POMLauncher.run_NMS(fid,room,rad = radius,thresh_p  = thresh)
    room.save_dat(Q_out,fid,folder_out)
    

n_threads =40

for block in range(0,len(room.img_index_list)/n_threads+1):
    fids = range(n_threads*block,n_threads*(block+1))
    Parallel(n_jobs=n_threads)(delayed(runsave)(fid) for fid in fids)



    


















    






---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-3-71259ee01c35> in <module>()
      2 import multiprocessing
      3 
----> 4 room = POM_room()
      5 POMLayers1.room = room #TODO : modify POMLayers1 so that we don't need room, just config
      6 a,p,alpha_black = 1.8,270,0.0

/cvlabdata1/home/baque/python/deep_learning/POMv2/pom_room.pyc in __init__(self, parts_root_folder, with_templates)
     34             # Config about POM images
     35             self.H,self.W = self.get_HW_from_img()
---> 36             self.extract_templates()
     37 
     38 

/cvlabdata1/home/baque/python/deep_learning/POMv2/pom_room.pyc in extract_templates(self)
    125                     if (x1 - x0) > H/150.0 and (y1 - y0) > W/150.0: #Arbitrary criterium tu prevent too small BBs
    126                     #if (x1 - x0) > 3 and (y1 - y0) > 3:
--> 127                         templates_array[n_parts*cam + part,i,:] = np.asarray([x0,y0,x1,y1])
    128                     else:
    129                         rand_H,rand_W = random.randint(0,H-1),random.randint(0,W-1)

/home/baque/.local/lib/python2.7/site-packages/numpy/core/numeric.pyc in asarray(a, dtype, order)
    529 
    530     """
--> 531     return array(a, dtype, copy=False, order=order)
    532 
    533 

KeyboardInterrupt:

Test





In [20]:



    


parts = np.load('./Potentials/Parts/Run/c0/850.npy')
plt.imshow(parts[:,:,0])
plt.show()

plt.imshow(parts[:,:,1])
plt.show()

plt.imshow(parts[:,:,2])
plt.show()

plt.imshow(parts[:,:,3])

plt.imshow(parts[:,:,4])
plt.show()

plt.imshow(parts[:,:,5])
plt.show()

plt.imshow(parts[:,:,6])
plt.show()
plt.imshow(parts[:,:,7])
plt.show()

plt.imshow(parts[:,:,8])
plt.show()

from PIL import Image
im = Image.fromarray(np.uint8((parts[:,:,0]>0.2)*250.))
im.save('./test.png')



    


















    




















    
















    




















    
















    




















    
















    




















    
















    




















    
















    




















    
















    




















    
















    




















    























In [34]:



    


test = np.asarray([[0,1],[2,3]])
print test[0]
test_re = test.reshape((4))
print test_re



    


















    






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In [ ]:

Content source: pierrebaque/DeepOcclusion

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