In [52]:

    

#import
import matplotlib.pyplot as plt
from glob import glob
import numpy as np
import scipy.ndimage as ndimg
import skimage.filter as filter
import skimage.transform as transform

def layer_mean(img2d):
    img1d = img2d[img2d<255]
    return img1d.mean()

def hist_max(img2d):
    img1d = img2d[img2d<255]
    [n, bins, patches]= plt.hist(img1d, np.arange(255), fig)
    return bins[n.argmax()]

def edge_length(img2d):
    "gray input"
    imgsize = float((img2d.shape[0]*img2d.shape[1]))
    med_filter = ndimg.median_filter(img2d, size = (5,5))
    edges = filter.canny(med_filter,3)
    return edges.sum()/imgsize

def edge_sobel_h(img2d):
    "gray input"
    imgsize = float((img2d.shape[0]*img2d.shape[1]))
    med_filter = ndimg.median_filter(img2d, size = (5,5))
    edges_h = filter.hsobel(med_filter/255.)
    return edges_h.sum()/imgsize

def edge_sobel_v(img2d):
    "gray input"
    imgsize = float((img2d.shape[0]*img2d.shape[1]))
    med_filter = ndimg.median_filter(img2d, size = (5,5))
    edges_v = filter.vsobel(med_filter/255.)
    return edges_v.sum()/imgsize

def edge_sobel(img2d):
    "gray input"
    imgsize = float((img2d.shape[0]*img2d.shape[1]))
    med_filter = ndimg.median_filter(img2d, size = (5,5))
    edges = filter.sobel(med_filter/255.)
    return edges.sum()/imgsize

def houghLine(img2d):
    "gray input"
    med_filter = ndimg.median_filter(img2d, size = (5,5))
    edges = filter.sobel(med_filter/255.)
    [H,theta,distances] = transform.hough_line(edges);
    imgsize = float(len(theta)*len(distances))
    return H.sum()/imgsize
def features(img2d, img2d_gray):
    #red mean
    red_mean = layer_mean(img2d[...,0])
    green_mean = layer_mean(img2d[...,1])
    blue_mean = layer_mean(img2d[...,2])
    gray_mean = layer_mean(img2d_gray)
    
    red_most = hist_max(img2d[...,0])
    green_most = hist_max(img2d[...,1])
    blue_most = hist_max(img2d[...,2])
    gray_most = hist_max(img2d_gray)
    
    length = edge_length(img2d_gray)
    sobel_h = edge_sobel_h(img2d_gray)
    sobel_v = edge_sobel_v(img2d_gray)
    sobel = edge_sobel(img2d_gray)
    hough = houghLine(img2d_gray)
    return np.array([red_mean, green_mean, blue_mean, gray_mean,\
                     red_most,green_most,blue_most, gray_most,\
                     length, sobel_h, sobel_v, sobel, hough])


    

In [79]:

    

categ = ['airplanes','bat','bear']
fts = np.array([])
for categ_idx, subfolder in enumerate(categ):
    fNames = glob('classifier/'+subfolder+'/*.jpg')
    print len(fNames)
    for i, fileName in enumerate(fNames):       
        img = ndimg.imread(fileName)
        img_gray = ndimg.imread(fileName, flatten=True)
        ft_temp = features(img, img_gray)
        fts = np.concatenate((fts,ft_temp), axis = 1)


    

    




5
6
6






    

In [81]:

    

fts.shape


    

    
Out[81]:





(221,)

In [69]:

    

fts = np.array([])
a = np.array([1,2])
print np.concatenate((fts,a), axis = 0)


    

    




[ 1.  2.]

In [30]:

    

result


    

    
Out[30]:





array([ 176.46634535,  167.72385981,  138.22395376,  168.95305912])

In [5]:

    

medfilted = ndimg.median_filter(img_gray,size=(5,5))
plt.figure()
plt.imshow(medfilted,cmap=plt.cm.gray);


    

In [6]:

    

edges = canny(medfilted/255.,3)


    

In [17]:

    

import skimage.filter as filter
sob = filter.sobel(medfilted/255.)
plt.figure()
plt.imshow(sob, cmap=plt.cm.gray)
plt.figure()
plt.hist(sob.flatten(),np.arange(100)/1000.);


    

In [18]:

    

[n,bins,patches] = plt.hist(img_gray.flatten(),np.arange(255))
plt.figure()
plt.plot(bins[:-1],n)


    

    
Out[18]:





[<matplotlib.lines.Line2D at 0x10a47a250>]






    













    

In [19]:

    

bins[n.argmax()]


    

    
Out[19]:





210

In [163]:

    

np.arange(3)


    

    
Out[163]:





array([0, 1, 2])

In [1]:

    

img_gray.shape


    

    




---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-1-6fee6844e4fb> in <module>()
----> 1 img_gray.shape

NameError: name 'img_gray' is not defined

In [26]:

    

hist_max(img[...,0])


    

    




0
254






    
Out[26]:





253






    

In [56]:

    

plt.hist?


    

In [ ]: