In [97]:

    

%matplotlib inline

from pycocotools.coco import COCO
import numpy as np
import skimage.io as io
import matplotlib.pyplot as plt
import pickle
import scipy.spatial as ss


    

In [16]:

    

# training set
dataType='train2014'
usingSet='10000coco'

# validation set
# dataType='val2014'
# usingSet='5kcoco'
#===================
dataDir='/media/haoran/DATA/Dataset/COCO/tools'
usingSetDir = '/media/haoran/Data/Dataset/VIPcoco/%s'%usingSet
InsFile='%s/annotations/instances_%s.json'%(dataDir,dataType)
CapFile='%s/annotations/captions_%s.json'%(dataDir,dataType)

Ins_coco=COCO(InsFile)
Cap_coco=COCO(CapFile)


    

    




loading annotations into memory...
0:00:11.707113
creating index...
index created!
loading annotations into memory...
0:00:01.220270
creating index...
index created!

In [17]:

    

SALICON = pickle.load(open('data/10000coco.p','rb'))
print SALICON['SALICON_id'][0]
print SALICON['SALICON_filename'][0]


    

    




9
COCO_train2014_000000000009.jpg

In [18]:

    

im_id_toshow = 0
I = io.imread('%s/images/%s/%s'%(dataDir,dataType,SALICON['SALICON_filename'][im_id_toshow]))
plt.imshow(I)


    

    
Out[18]:





<matplotlib.image.AxesImage at 0x7f3bca793d10>






    

In [19]:

    

im_id_toshow = 2
blankim = np.zeros((460,680,3),np.uint8)
plt.imshow(blankim)
annIds = Ins_coco.getAnnIds(SALICON['SALICON_id'][im_id_toshow])
anns = Ins_coco.loadAnns(annIds)
Ins_coco.showAnns(anns)
# plt.show()


    

In [11]:

    

Ins_coco.getAnnIds(134)


    

    
Out[11]:





[]

In [27]:

    

annIds = Ins_coco.getAnnIds(SALICON['SALICON_id'][1])
anns =Ins_coco.loadAnns(annIds)


    

In [29]:

    

len(anns)


    

    
Out[29]:





10

In [32]:

    

anns[1]


    

    
Out[32]:





{u'area': 2101.468949999999,
 u'bbox': [502.6, 105.47, 25.83, 132.38],
 u'category_id': 49,
 u'id': 694713,
 u'image_id': 89,
 u'iscrowd': 0,
 u'segmentation': [[510.13,
   236.77,
   502.6,
   233.54,
   506.91,
   185.11,
   505.83,
   145.29,
   511.21,
   116.23,
   525.2,
   105.47,
   528.43,
   187.26,
   518.74,
   190.49,
   515.52,
   227.09,
   516.59,
   234.62,
   510.13,
   237.85]]}

In [38]:

    

round(3/float(120),2)


    

    
Out[38]:





0.03

In [42]:

    

a.append([2,3])


    

In [63]:

    

a = [[1 ,2],[2 ,3],[3,4],[4,5]]
c = [[1,2]]


    

In [68]:

    

b = ss.distance.cdist(c,a)


    

In [72]:

    

b.mean()


    

    
Out[72]:





2.1213203435596428

In [73]:

    

d = {}
d[0] = 1


    

In [80]:

    

for i,[j,k] in enumerate(zip(a,a)):
    print i,j,k


    

    




0 [1, 2] [1, 2]
1 [2, 3] [2, 3]
2 [3, 4] [3, 4]
3 [4, 5] [4, 5]

In [92]:

    

ss ={}
ss[1][1]= 1
ss


    

    




---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-92-fc7fc539f184> in <module>()
      1 ss ={}
----> 2 ss[1][1]= 1
      3 ss

KeyError: 1

In [94]:

    

kk = 1
kk is not 2


    

    
Out[94]:





True

In [98]:

    

a = [1,2]
b = [2,3]
c = ss.distance.pdist([a,b])


    

In [106]:

    

c[0]


    

    
Out[106]:





1.4142135623730951

In [ ]: