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In [12]:

    
import pandas as pd
import numpy as np
import glob, os
from utils import *
from augment import *
import seaborn as sns
import matplotlib.pyplot as plt
import cPickle

sns.set_style("whitegrid")

%matplotlib inline

label_numbers =  {"Button":0, "Icon":1, "Image":2, "Input field":3,  "Text":4, "Navigation Menu":5}
number_label = {0:"Button", 1:"Icon", 2:"Image", 3:"Input field",  4:"Text", 5: "Navigation Menu"}

wireframes = ["One Page Website Wireframes.png", "sketch-psd-wireframe-templates-3.png", "img2.png", "img.png", "ui03.png", "34b5e120533561.562ece003eee5.png", "4c553642358074567722f94f3937f101.png", "WireframeKit-Retina.png"]



In [11]:

    
df = read_txts_and_combine() #uncomment if you want to reread all .txt logs into one .csv
#df = pd.read_csv("../img_labeled/logs/log_all.csv")
df.head()









    Out[11]:






  
    
      
      id
      x_abs
      y_abs
      x_rel
      y_rel
      width
      height
      label
      filename
    
  
  
    
      0
      0.0
      420.0
      119.0
      0.250000
      0.015635
      644.0
      63.0
      Navigation Menu
      10-best-memories-2014
    
    
      1
      1.0
      1070.0
      118.0
      0.636905
      0.015504
      216.0
      63.0
      Input field
      10-best-memories-2014
    
    
      2
      2.0
      606.0
      1408.0
      0.360714
      0.184995
      811.0
      192.0
      Text
      10-best-memories-2014
    
    
      3
      3.0
      488.0
      1717.0
      0.290476
      0.225595
      226.0
      226.0
      Image
      10-best-memories-2014
    
    
      4
      4.0
      718.0
      2122.0
      0.427381
      0.278807
      229.0
      228.0
      Image
      10-best-memories-2014



In [3]:

    
print "Number of labeled webpages: %i"%(len(df.groupby("filename")))
print "Number of labels: %i"%(len(df))









    



Number of labeled webpages: 103
Number of labels: 1809



In [4]:

    
print "Number of labels per category:"
print df.groupby("label").count().id









    



Number of labels per category:
label
Button             190
Form                70
Icon               273
Image              259
Input field        164
Line               111
List                95
Navigation Menu    119
Rating bar           1
Slider              12
Text               465
Video               18
label               32
Name: id, dtype: int64



In [5]:

    
df.groupby("label").count().id.plot(kind="bar")#, color=sns.color_palette()[0])









    Out[5]:





<matplotlib.axes._subplots.AxesSubplot at 0xc849a58>



In [6]:

    
#only take the labels for which we have at least 100 entries
df = filter_top_100(df)
print("%i labels left"%len(df))









    



1470 labels left



In [7]:

    
pickle_images("../img_labeled/logs/", df, 32)
pickle_images("../img_labeled/logs/", df, 64)
pickle_images("../img_labeled/logs/", df, 128)



In [8]:

    
path = "data_128.pkl"
x_train, y_train, x_test, y_test = load_pickle(path)



In [11]:

    
img=x_train[100]
plot_image(img[:, :, ::-1])









    Out[11]:





(<matplotlib.figure.Figure at 0xe4068d0>,
 <matplotlib.axes._subplots.AxesSubplot at 0xded9dd8>)



In [22]:

    
np.asarray(x_train)









    Out[22]:





array([[[[ 0.        ,  0.        ,  0.        , ...,  0.        ,
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         ..., 
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        [[ 0.        ,  0.        ,  0.        , ...,  0.        ,
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       [[[ 1.        ,  1.        ,  1.        , ...,  1.        ,
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        [[ 1.        ,  1.        ,  1.        , ...,  1.        ,
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         ..., 
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        [[ 1.        ,  1.        ,  1.        , ...,  1.        ,
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         ..., 
         [ 1.        ,  1.        ,  1.        , ...,  1.        ,
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       [[[ 0.86666667,  0.86666667,  0.87450981, ...,  0.87450981,
           0.85490197,  0.86666667],
         [ 0.86666667,  0.88627452,  0.88627452, ...,  0.86666667,
           0.85490197,  0.87450981],
         [ 0.88627452,  0.86666667,  0.88627452, ...,  0.86666667,
           0.85490197,  0.85490197],
         ..., 
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        [[ 0.80000001,  0.80000001,  0.81176472, ...,  0.81176472,
           0.78431374,  0.80000001],
         [ 0.80000001,  0.83137256,  0.83137256, ...,  0.80000001,
           0.78431374,  0.81176472],
         [ 0.83137256,  0.80000001,  0.83137256, ...,  0.80000001,
           0.78431374,  0.78431374],
         ..., 
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        [[ 0.63529414,  0.63529414,  0.65882355, ...,  0.65882355,
           0.61176473,  0.63529414],
         [ 0.63529414,  0.69411767,  0.69411767, ...,  0.63529414,
           0.61176473,  0.65882355],
         [ 0.69411767,  0.63529414,  0.69411767, ...,  0.63529414,
           0.61176473,  0.61176473],
         ..., 
         [ 1.        ,  1.        ,  1.        , ...,  1.        ,
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       ..., 
       [[[ 1.        ,  1.        ,  1.        , ...,  1.        ,
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         [ 1.        ,  1.        ,  1.        , ...,  1.        ,
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        [[ 1.        ,  1.        ,  1.        , ...,  1.        ,
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         ..., 
         [ 1.        ,  1.        ,  1.        , ...,  1.        ,
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        [[ 1.        ,  1.        ,  1.        , ...,  1.        ,
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         [ 1.        ,  1.        ,  1.        , ...,  1.        ,
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         [ 1.        ,  1.        ,  1.        , ...,  1.        ,
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         [ 1.        ,  1.        ,  1.        , ...,  1.        ,
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       [[[ 0.        ,  0.        ,  0.        , ...,  0.        ,
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        [[ 0.        ,  0.        ,  0.        , ...,  0.        ,
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        [[ 0.        ,  0.        ,  0.        , ...,  0.        ,
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       [[[ 1.        ,  1.        ,  1.        , ...,  1.        ,
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        [[ 1.        ,  1.        ,  1.        , ...,  1.        ,
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        [[ 1.        ,  1.        ,  1.        , ...,  1.        ,
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         [ 1.        ,  1.        ,  1.        , ...,  1.        ,
           1.        ,  1.        ]]]], dtype=float32)



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	id	x_abs	y_abs	x_rel	y_rel	width	height	label	filename
0	0.0	420.0	119.0	0.250000	0.015635	644.0	63.0	Navigation Menu	10-best-memories-2014
1	1.0	1070.0	118.0	0.636905	0.015504	216.0	63.0	Input field	10-best-memories-2014
2	2.0	606.0	1408.0	0.360714	0.184995	811.0	192.0	Text	10-best-memories-2014
3	3.0	488.0	1717.0	0.290476	0.225595	226.0	226.0	Image	10-best-memories-2014
4	4.0	718.0	2122.0	0.427381	0.278807	229.0	228.0	Image	10-best-memories-2014