In [39]:

    

import matplotlib.pyplot as plt
import numpy as np
import scipy as sc
import scipy.io as SIO
from scipy import misc
import skimage as ski
import os
from prettyprint import pp
import h5py
%matplotlib inline
import pylab
pylab.rcParams['figure.figsize'] = (10.0, 8.0)
from datetime import datetime


    

In [40]:

    

data_path = 'E:/University Central/IPLab/ELM Edge Detection/BSR/BSDS500/data'
images_path = os.path.join(data_path, 'images')
gt_path = os.path.join(data_path, 'groundTruth')
sub_folders = ['test', 'train', 'val']


    

In [8]:

    

# pp(os.listdir(gt_path + '/test'))
f_mat = os.path.join(gt_path + '/test', os.listdir(gt_path + '/test')[-1])
print f_mat


    

    




E:/University Central/IPLab/ELM Edge Detection/BSR/BSDS500/data\groundTruth/test\97010.mat

In [9]:

    

# mat_data_hdfs = h5py.File(f_mat, 'r')['/']
mat_data = sc.io.loadmat(f_mat)
print os.path.basename(f_mat)


    

    




97010.mat

In [12]:

    

gts = SIO.loadmat(f_mat)
gts = gts["groundTruth"].flatten()
bnds = [b["Boundaries"][0, 0] for b in gts]
segs = [gt["Segmentation"][0, 0] for gt in gts]
overall = np.zeros(bnds[0].shape)
print bnds[0].shape


    

    




(321L, 481L)

In [13]:

    

plt.imshow(bnds[0], cmap='gray')
plt.show()
plt.imshow(bnds[0].transpose(), cmap='gray')
plt.show()


    

In [14]:

    

for bnd in bnds:
    overall += bnd
plt.imshow(overall, cmap='gray')
plt.show()


    

In [15]:

    

for seg in segs:
    plt.imshow(seg)
    plt.show()


    

In [16]:

    

size_set = set()
path = os.path.join(images_path, sub_folders[0])
for im in os.listdir(path):
    img_addr  = os.path.join(path, im)
    img = misc.imread(img_addr)
    size_set.add(img.shape)


    

In [17]:

    

print size_set


    

    




set([(321L, 481L, 3L), (481L, 321L, 3L)])

In [18]:

    

path = os.path.join(images_path, sub_folders[0])
for im in (os.listdir(path))[9:10]:
    print im
    img_addr  = os.path.join(path, im)
    img = misc.imread(img_addr)
    plt.imshow(img)
    plt.show()
    print img.shape
    now = datetime.now()
    img = img.transpose((2,0,1))
    print 'transforming took ', (datetime.now() - now)
    for i in xrange(3):
        plt.imshow(img[i,:,:], cmap='gray')
        plt.show()


    

    




103078.jpg






    













    




(321L, 481L, 3L)
transforming took  0:00:00






    













    













    

In [43]:

    

from bsds500 import load_data
from datetime import datetime
now = datetime.now()
data = load_data(['train', 'val', 'test'])
print 'loading all data took ', (datetime.now() - now)
len(data)


    

    




loading all data took  0:00:15.883000






    
Out[43]:





3

In [36]:

    

boundaries = []
for mat in os.listdir(gt_path + '/val'):
    matdir = os.path.join(gt_path + '/val', mat)
    gts = SIO.loadmat(matdir)
    gts = gts["groundTruth"].flatten()
    bnds = [b["Boundaries"][0, 0] for b in gts]
    bnd_cnt.append(len(bnds))
    boundaries.append(bnds)
plt.hist(bnd_cnt)
plt.show()
print len([a for a in bnd_cnt if a == 6])


    

    













    




161

In [38]:

    

for n in range(10):
    print n, len([bnd for bnd in boundaries if len(bnd) == n])


    

    




0 0
1 0
2 0
3 0
4 0
5 69
6 18
7 11
8 2
9 0

In [32]:

    

bnd8 = [bnd for bnd in boundaries if len(bnd) == 8]


    

In [33]:

    

for bnd in bnd8[1]:
    plt.imshow(bnd, cmap='gray')
    plt.show()


    

In [ ]: