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%load_ext autoreload
%autoreload 2


    

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from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
import caffe
import cv2
from pandas import read_csv
import os

%matplotlib inline


    

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from utils import parse_folder, bbox, image_load


    

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from joblib import Parallel, delayed


    

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root_folder = '/home/ubuntu/dataset/'
run_mode = 'train_512'


    

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mode = run_mode
in_folder = os.path.join(root_folder, mode)


    

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names = parse_folder(in_folder, "jpeg")


    

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pd = read_csv(os.path.join(in_folder, "labels.txt"), names=['image', 'label'], index_col='image', header=0)


    

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def extract_filename_in_path(path):
    return path.strip('/').split('/')[-1].split('.')[0]


    

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for n in names:
    img = image_load(n)
    name_key = extract_filename_in_path(n)
    out_name = os.path.join(in_folder, "%d" % (pd.ix[name_key]['label']), name_key)
    break


    

In [11]:

    

pd.ix[pd.label==2].head()


    

    
Out[11]:






  

    

      

      
label
    
    

      
image
      

    
  
  

    

      
15_right
      
2
    
    

      
30_right
      
2
    
    

      
40_left
      
2
    
    

      
51_left
      
2
    
    

      
54_left
      
2
    
  

In [12]:

    

img = image_load(os.path.join(in_folder, "30_right.jpeg"))


    

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cimg = bbox(img)
plt.imshow(cimg)


    

    




Mon, 04 Jan 2016 13:36:49 [30818] LOG: bbox: Shape (512, 683, 3)






    
Out[13]:





<matplotlib.image.AxesImage at 0x7fba0abc7fd0>






    

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gimg = cv2.cvtColor(cimg, cv2.COLOR_RGB2GRAY)
plt.imshow(gimg, cmap='gray')


    

    
Out[14]:





<matplotlib.image.AxesImage at 0x7fba092db050>






    

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dimg = cv2.morphologyEx(gimg, cv2.MORPH_BLACKHAT, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (11, 11)))
timg = cv2.morphologyEx(dimg, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)))
timg[:, :5] = 0
timg[:, -5:] = 0
timg[:5, :] = 0
timg[-5:, :] = 0
plt.imshow(timg, cmap='gray')


    

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from skimage import exposure, img_as_float


    

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nimg = img[:,:,1]
nimg = img_as_float(nimg)
p2, p98 = np.percentile(nimg, (2, 98))
aimg = exposure.rescale_intensity(nimg, in_range=(p2, p98))
plt.imshow(aimg, cmap='gray')


    

    
Out[52]:





<matplotlib.image.AxesImage at 0x7fb95a13ffd0>






    

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