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In [24]:

    
# Import used libraries
import tensorflow as tf
import keras

from keras.models import Sequential, Model
from keras.layers import Dense, Flatten, Conv2D, Dropout, Lambda, Cropping2D, Input, Activation, MaxPool2D
from keras.layers import add
from keras.layers.convolutional import Convolution2D
from keras.layers.normalization import BatchNormalization

import cv2
import datetime
import pickle
import json
import os
from tqdm import tqdm
import numpy as np
import pandas as pd
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt



In [18]:

    
# Load and display driving log
with open('config.json') as config_file:    
    config_data = json.load(config_file)
print("Data path: {}".format(config_data['data_path']))


path = config_data['data_path']
file = config_data['driving_log_file']
driving_log = pd.read_csv(path + file)
driving_log









    



Data path: /home/timo/Documents/mldata/car_sim_video_images/






    Out[18]:






  
    
      
      center
      left
      right
      steering
      throttle
      brake
      speed
    
  
  
    
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8036 rows × 7 columns



In [19]:

    
# Store all images as a numpy array
nb_images = 10 # len(driving_log)
images = []
labels = []
camera_names     = ('center', 'left', 'right')
# Initalize offsets of the steering angle for center, left and right images
steering_offsets = dict({key:val for key,val in zip(camera_names, [0, 0.2, -0.2])})

if not os.path.exists(path + config_data['pickle_file']):
    for camera in camera_names:
        print("Load '{}' images".format(camera))
        labels.append((driving_log['steering'][0:nb_images].values.reshape(-1,1) + steering_offsets[camera]))
        for row in tqdm(range(0, nb_images), unit=' images'):
            img = cv2.imread(path + str.replace(driving_log[camera][row], ' ', ''))
            images.append(np.array(img))

    for image in images.copy():
        images.append(np.fliplr(image))
    for label in labels.copy():
        labels.append(-1*label)
        
    images  = np.stack(images)#.reshape(-1, 160, 320, 3)
    labels  = np.concatenate(labels)
    image_shape = images.shape[1::]
    images, labels = shuffle(images, labels, random_state=42)
    
    # Split dataset to train, test
    X_train, X_test, y_train, y_test = train_test_split(images, labels, test_size=0.3, random_state=42)
    # Save as pickle file
    print("Save data to file: '{}'.".format(config_data['pickle_file']))
    with open(path + config_data['pickle_file'], 'wb') as f: 
        pickle.dump([X_train, X_test, y_train, y_test], f)
    print("Done.")
else:
    print("Load data from file: '{}'.".format(config_data['pickle_file']))
    with open(path + config_data['pickle_file'], 'rb') as f: 
        X_train, X_test, y_train, y_test = pickle.load(f)
    print("Done.")









    



  0%|          | 0/10 [00:00<?, ? images/s]





    



Load 'center' images






    



100%|██████████| 10/10 [00:00<00:00, 27.01 images/s]
100%|██████████| 10/10 [00:00<00:00, 205.53 images/s]
100%|██████████| 10/10 [00:00<00:00, 483.25 images/s]






    



Load 'left' images
Load 'right' images
Save data to file: 'images.p'.
Done.



In [20]:

    
# Visualize labels
plt.plot(y_train)
plt.show()



In [21]:

    
size_of = lambda x: x.shape[0] * x.shape[1] 
# Visualize random image
print("Number of images: 3x{} (center, left, right)".format(len(driving_log)))

img = X_train[0,::]
plt.imshow(img)
plt.show()
plt.imshow(np.fliplr(img))
plt.show()


r   = 100.0 / img.shape[1]
dim = (100, int(img.shape[0] * r))
dim = (64, 32)
# perform the actual resizing of the image and show it
resized = cv2.resize(img, dim, interpolation=cv2.INTER_CUBIC)
print(resized.shape)
plt.imshow(resized)
plt.show()
print(size_of(img))
print(size_of(resized))
print("Ratio: {}".format((size_of(img)/size_of(resized))))









    



Number of images: 3x8036 (center, left, right)






    












    












    



(32, 64, 3)






    












    



51200
2048
Ratio: 25.0

Implementation of Inception Network ??? Inception Network ??? Paper in Keras.



In [22]:

    
from keras.layers import concatenate

def conv_block(x, nb_filters, kernel_size=(3,3), strides=1):

    x1 = Convolution2D(nb_filters, kernel_size, strides=strides, padding='same')(x)
    #x1 = BatchNormalization()(x1)
    x1 = Activation('relu')(x1)
    
    return x1

def res_conv_block(x, kernel_size=(3,3)):
    nb_filters = keras.backend.int_shape(x)[3]
    x1 = Convolution2D(nb_filters, kernel_size=(1,1), padding='same')(x)
    x1 = Convolution2D(nb_filters, kernel_size, padding='same')(x1)
    #x1 = BatchNormalization()(x1)
    out = add([x, x1])
    
    return Activation('relu')(out)

def inception_A(x):
    nb_filters1 = 32
    nb_filters2 = keras.backend.int_shape(x)[3]
    
    x1 = Convolution2D(nb_filters1, kernel_size=(1,1), padding='same')(x)
    
    x2 = Convolution2D(nb_filters1, kernel_size=(1,1), padding='same')(x)
    x2 = Convolution2D(nb_filters1, kernel_size=(3,3), padding='same')(x2)
    
    x3 = Convolution2D(nb_filters1, kernel_size=(1,1), padding='same')(x)
    x3 = Convolution2D(nb_filters1, kernel_size=(3,3), padding='same')(x3)
    x3 = Convolution2D(nb_filters1, kernel_size=(3,3), padding='same')(x3)
    
    x123 = concatenate([x1, x2, x3])
    x123 = Convolution2D(nb_filters2, kernel_size=(1,1), padding='same')(x123)
    
    out = add([x, x123])
    return Activation('relu')(out)

def reduction_A(x, filter_bank={'k':192,'l':224,'m':256,'n':384}):
    x1 = Convolution2D(filter_bank['k'], kernel_size=(1,1), padding='same')(x)
    x1 = Convolution2D(filter_bank['l'], kernel_size=(3,3), padding='same')(x1)
    x1 = Convolution2D(filter_bank['m'], kernel_size=(3,3), padding='same', strides=2)(x1)
    
    x2 = Convolution2D(filter_bank['n'], kernel_size=(3,3), padding='same', strides=2)(x)
    
    x3 = MaxPool2D(strides=2)(x)
    
    return concatenate([x1, x2, x3])



In [23]:

    
init = Input((160, 320, 3))
x      = conv_block(init, 32)
x      = res_conv_block(x)
x      = conv_block(x, 64)
x      = inception_A(x)
x      = reduction_A(x, filter_bank={'k':24,'l':32,'m':48,'n':64})
x      = conv_block(x, 32, strides=2)
x      = conv_block(x, 32, strides=2)
x      = conv_block(x, 32, strides=2)
x      = conv_block(x, 32, strides=2)
x      = Flatten()(x)
x      = Dense(activation='relu', units=10)(x)
out    = Dense(units=1, use_bias=True)(x)
model1 = Model(init, out, name='Inception-v4')
model1.summary()









    



____________________________________________________________________________________________________
Layer (type)                     Output Shape          Param #     Connected to                     
====================================================================================================
input_1 (InputLayer)             (None, 160, 320, 3)   0                                            
____________________________________________________________________________________________________
conv2d_1 (Conv2D)                (None, 160, 320, 32)  896                                          
____________________________________________________________________________________________________
activation_1 (Activation)        (None, 160, 320, 32)  0                                            
____________________________________________________________________________________________________
conv2d_2 (Conv2D)                (None, 160, 320, 32)  1056                                         
____________________________________________________________________________________________________
conv2d_3 (Conv2D)                (None, 160, 320, 32)  9248                                         
____________________________________________________________________________________________________
add_1 (Add)                      (None, 160, 320, 32)  0                                            
____________________________________________________________________________________________________
activation_2 (Activation)        (None, 160, 320, 32)  0                                            
____________________________________________________________________________________________________
conv2d_4 (Conv2D)                (None, 160, 320, 64)  18496                                        
____________________________________________________________________________________________________
activation_3 (Activation)        (None, 160, 320, 64)  0                                            
____________________________________________________________________________________________________
conv2d_8 (Conv2D)                (None, 160, 320, 32)  2080                                         
____________________________________________________________________________________________________
conv2d_6 (Conv2D)                (None, 160, 320, 32)  2080                                         
____________________________________________________________________________________________________
conv2d_9 (Conv2D)                (None, 160, 320, 32)  9248                                         
____________________________________________________________________________________________________
conv2d_5 (Conv2D)                (None, 160, 320, 32)  2080                                         
____________________________________________________________________________________________________
conv2d_7 (Conv2D)                (None, 160, 320, 32)  9248                                         
____________________________________________________________________________________________________
conv2d_10 (Conv2D)               (None, 160, 320, 32)  9248                                         
____________________________________________________________________________________________________
concatenate_1 (Concatenate)      (None, 160, 320, 96)  0                                            
____________________________________________________________________________________________________
conv2d_11 (Conv2D)               (None, 160, 320, 64)  6208                                         
____________________________________________________________________________________________________
add_2 (Add)                      (None, 160, 320, 64)  0                                            
____________________________________________________________________________________________________
activation_4 (Activation)        (None, 160, 320, 64)  0                                            
____________________________________________________________________________________________________
conv2d_12 (Conv2D)               (None, 160, 320, 24)  1560                                         
____________________________________________________________________________________________________
conv2d_13 (Conv2D)               (None, 160, 320, 32)  6944                                         
____________________________________________________________________________________________________
conv2d_14 (Conv2D)               (None, 80, 160, 48)   13872                                        
____________________________________________________________________________________________________
conv2d_15 (Conv2D)               (None, 80, 160, 64)   36928                                        
____________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D)   (None, 80, 160, 64)   0                                            
____________________________________________________________________________________________________
concatenate_2 (Concatenate)      (None, 80, 160, 176)  0                                            
____________________________________________________________________________________________________
conv2d_16 (Conv2D)               (None, 40, 80, 32)    50720                                        
____________________________________________________________________________________________________
activation_5 (Activation)        (None, 40, 80, 32)    0                                            
____________________________________________________________________________________________________
conv2d_17 (Conv2D)               (None, 20, 40, 32)    9248                                         
____________________________________________________________________________________________________
activation_6 (Activation)        (None, 20, 40, 32)    0                                            
____________________________________________________________________________________________________
conv2d_18 (Conv2D)               (None, 10, 20, 32)    9248                                         
____________________________________________________________________________________________________
activation_7 (Activation)        (None, 10, 20, 32)    0                                            
____________________________________________________________________________________________________
conv2d_19 (Conv2D)               (None, 5, 10, 32)     9248                                         
____________________________________________________________________________________________________
activation_8 (Activation)        (None, 5, 10, 32)     0                                            
____________________________________________________________________________________________________
flatten_1 (Flatten)              (None, 1600)          0                                            
____________________________________________________________________________________________________
dense_1 (Dense)                  (None, 10)            16010                                        
____________________________________________________________________________________________________
dense_2 (Dense)                  (None, 1)             11                                           
====================================================================================================
Total params: 223,677.0
Trainable params: 223,677.0
Non-trainable params: 0.0
____________________________________________________________________________________________________



In [ ]:

    
now = datetime.datetime.now
batch_size = 3
epochs = 1

model1.compile(loss='mse',
              optimizer='adadelta',
              metrics=['accuracy'])

t = now()
model1.fit(X_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          validation_data=(X_test, y_test))
print('Training time: %s' % (now() - t))
score = model1.evaluate(X_test, y_test, verbose=0)
print('Test score:', score[0])
print('Test accuracy:', score[1])



In [ ]:

	center	left	right	steering	throttle	brake	speed
0	IMG/center_2016_12_01_13_30_48_287.jpg	IMG/left_2016_12_01_13_30_48_287.jpg	IMG/right_2016_12_01_13_30_48_287.jpg	0.0	0.0	0.0	22.148290
1	IMG/center_2016_12_01_13_30_48_404.jpg	IMG/left_2016_12_01_13_30_48_404.jpg	IMG/right_2016_12_01_13_30_48_404.jpg	0.0	0.0	0.0	21.879630
2	IMG/center_2016_12_01_13_31_12_937.jpg	IMG/left_2016_12_01_13_31_12_937.jpg	IMG/right_2016_12_01_13_31_12_937.jpg	0.0	0.0	0.0	1.453011
3	IMG/center_2016_12_01_13_31_13_037.jpg	IMG/left_2016_12_01_13_31_13_037.jpg	IMG/right_2016_12_01_13_31_13_037.jpg	0.0	0.0	0.0	1.438419
4	IMG/center_2016_12_01_13_31_13_177.jpg	IMG/left_2016_12_01_13_31_13_177.jpg	IMG/right_2016_12_01_13_31_13_177.jpg	0.0	0.0	0.0	1.418236
5	IMG/center_2016_12_01_13_31_13_279.jpg	IMG/left_2016_12_01_13_31_13_279.jpg	IMG/right_2016_12_01_13_31_13_279.jpg	0.0	0.0	0.0	1.403993
6	IMG/center_2016_12_01_13_31_13_381.jpg	IMG/left_2016_12_01_13_31_13_381.jpg	IMG/right_2016_12_01_13_31_13_381.jpg	0.0	0.0	0.0	1.389892
7	IMG/center_2016_12_01_13_31_13_482.jpg	IMG/left_2016_12_01_13_31_13_482.jpg	IMG/right_2016_12_01_13_31_13_482.jpg	0.0	0.0	0.0	1.375934
8	IMG/center_2016_12_01_13_31_13_584.jpg	IMG/left_2016_12_01_13_31_13_584.jpg	IMG/right_2016_12_01_13_31_13_584.jpg	0.0	0.0	0.0	1.362115
9	IMG/center_2016_12_01_13_31_13_686.jpg	IMG/left_2016_12_01_13_31_13_686.jpg	IMG/right_2016_12_01_13_31_13_686.jpg	0.0	0.0	0.0	1.348435
10	IMG/center_2016_12_01_13_31_13_786.jpg	IMG/left_2016_12_01_13_31_13_786.jpg	IMG/right_2016_12_01_13_31_13_786.jpg	0.0	0.0	0.0	1.334892
11	IMG/center_2016_12_01_13_31_13_890.jpg	IMG/left_2016_12_01_13_31_13_890.jpg	IMG/right_2016_12_01_13_31_13_890.jpg	0.0	0.0	0.0	1.318820
12	IMG/center_2016_12_01_13_31_13_991.jpg	IMG/left_2016_12_01_13_31_13_991.jpg	IMG/right_2016_12_01_13_31_13_991.jpg	0.0	0.0	0.0	1.305575
13	IMG/center_2016_12_01_13_31_14_092.jpg	IMG/left_2016_12_01_13_31_14_092.jpg	IMG/right_2016_12_01_13_31_14_092.jpg	0.0	0.0	0.0	1.292579
14	IMG/center_2016_12_01_13_31_14_194.jpg	IMG/left_2016_12_01_13_31_14_194.jpg	IMG/right_2016_12_01_13_31_14_194.jpg	0.0	0.0	0.0	1.279884
15	IMG/center_2016_12_01_13_31_14_295.jpg	IMG/left_2016_12_01_13_31_14_295.jpg	IMG/right_2016_12_01_13_31_14_295.jpg	0.0	0.0	0.0	1.267316
16	IMG/center_2016_12_01_13_31_14_398.jpg	IMG/left_2016_12_01_13_31_14_398.jpg	IMG/right_2016_12_01_13_31_14_398.jpg	0.0	0.0	0.0	1.254873
17	IMG/center_2016_12_01_13_31_14_500.jpg	IMG/left_2016_12_01_13_31_14_500.jpg	IMG/right_2016_12_01_13_31_14_500.jpg	0.0	0.0	0.0	1.242555
18	IMG/center_2016_12_01_13_31_14_602.jpg	IMG/left_2016_12_01_13_31_14_602.jpg	IMG/right_2016_12_01_13_31_14_602.jpg	0.0	0.0	0.0	1.230361
19	IMG/center_2016_12_01_13_31_14_702.jpg	IMG/left_2016_12_01_13_31_14_702.jpg	IMG/right_2016_12_01_13_31_14_702.jpg	0.0	0.0	0.0	1.218291
20	IMG/center_2016_12_01_13_31_14_803.jpg	IMG/left_2016_12_01_13_31_14_803.jpg	IMG/right_2016_12_01_13_31_14_803.jpg	0.0	0.0	0.0	1.206346
21	IMG/center_2016_12_01_13_31_14_904.jpg	IMG/left_2016_12_01_13_31_14_904.jpg	IMG/right_2016_12_01_13_31_14_904.jpg	0.0	0.0	0.0	1.194345
22	IMG/center_2016_12_01_13_31_15_005.jpg	IMG/left_2016_12_01_13_31_15_005.jpg	IMG/right_2016_12_01_13_31_15_005.jpg	0.0	0.0	0.0	1.182352
23	IMG/center_2016_12_01_13_31_15_106.jpg	IMG/left_2016_12_01_13_31_15_106.jpg	IMG/right_2016_12_01_13_31_15_106.jpg	0.0	0.0	0.0	1.170474
24	IMG/center_2016_12_01_13_31_15_208.jpg	IMG/left_2016_12_01_13_31_15_208.jpg	IMG/right_2016_12_01_13_31_15_208.jpg	0.0	0.0	0.0	1.156378
25	IMG/center_2016_12_01_13_31_15_308.jpg	IMG/left_2016_12_01_13_31_15_308.jpg	IMG/right_2016_12_01_13_31_15_308.jpg	0.0	0.0	0.0	1.144761
26	IMG/center_2016_12_01_13_31_15_411.jpg	IMG/left_2016_12_01_13_31_15_411.jpg	IMG/right_2016_12_01_13_31_15_411.jpg	0.0	0.0	0.0	1.133260
27	IMG/center_2016_12_01_13_31_15_513.jpg	IMG/left_2016_12_01_13_31_15_513.jpg	IMG/right_2016_12_01_13_31_15_513.jpg	0.0	0.0	0.0	1.121875
28	IMG/center_2016_12_01_13_32_35_588.jpg	IMG/left_2016_12_01_13_32_35_588.jpg	IMG/right_2016_12_01_13_32_35_588.jpg	0.0	0.0	0.0	1.087376
29	IMG/center_2016_12_01_13_32_39_212.jpg	IMG/left_2016_12_01_13_32_39_212.jpg	IMG/right_2016_12_01_13_32_39_212.jpg	0.0	0.0	0.0	0.755065
...	...	...	...	...	...	...	...
8006	IMG/center_2016_12_01_13_46_37_480.jpg	IMG/left_2016_12_01_13_46_37_480.jpg	IMG/right_2016_12_01_13_46_37_480.jpg	0.0	0.0	0.0	1.592460
8007	IMG/center_2016_12_01_13_46_37_511.jpg	IMG/left_2016_12_01_13_46_37_511.jpg	IMG/right_2016_12_01_13_46_37_511.jpg	0.0	0.0	0.0	1.586049
8008	IMG/center_2016_12_01_13_46_37_581.jpg	IMG/left_2016_12_01_13_46_37_581.jpg	IMG/right_2016_12_01_13_46_37_581.jpg	0.0	0.0	0.0	1.576481
8009	IMG/center_2016_12_01_13_46_37_613.jpg	IMG/left_2016_12_01_13_46_37_613.jpg	IMG/right_2016_12_01_13_46_37_613.jpg	0.0	0.0	0.0	1.570135
8010	IMG/center_2016_12_01_13_46_37_682.jpg	IMG/left_2016_12_01_13_46_37_682.jpg	IMG/right_2016_12_01_13_46_37_682.jpg	0.0	0.0	0.0	1.560663
8011	IMG/center_2016_12_01_13_46_37_714.jpg	IMG/left_2016_12_01_13_46_37_714.jpg	IMG/right_2016_12_01_13_46_37_714.jpg	0.0	0.0	0.0	1.554380
8012	IMG/center_2016_12_01_13_46_37_784.jpg	IMG/left_2016_12_01_13_46_37_784.jpg	IMG/right_2016_12_01_13_46_37_784.jpg	0.0	0.0	0.0	1.545003
8013	IMG/center_2016_12_01_13_46_37_815.jpg	IMG/left_2016_12_01_13_46_37_815.jpg	IMG/right_2016_12_01_13_46_37_815.jpg	0.0	0.0	0.0	1.538783
8014	IMG/center_2016_12_01_13_46_37_887.jpg	IMG/left_2016_12_01_13_46_37_887.jpg	IMG/right_2016_12_01_13_46_37_887.jpg	0.0	0.0	0.0	1.529500
8015	IMG/center_2016_12_01_13_46_37_916.jpg	IMG/left_2016_12_01_13_46_37_916.jpg	IMG/right_2016_12_01_13_46_37_916.jpg	0.0	0.0	0.0	1.523343
8016	IMG/center_2016_12_01_13_46_37_989.jpg	IMG/left_2016_12_01_13_46_37_989.jpg	IMG/right_2016_12_01_13_46_37_989.jpg	0.0	0.0	0.0	1.511102
8017	IMG/center_2016_12_01_13_46_38_033.jpg	IMG/left_2016_12_01_13_46_38_033.jpg	IMG/right_2016_12_01_13_46_38_033.jpg	0.0	0.0	0.0	1.505018
8018	IMG/center_2016_12_01_13_46_38_090.jpg	IMG/left_2016_12_01_13_46_38_090.jpg	IMG/right_2016_12_01_13_46_38_090.jpg	0.0	0.0	0.0	1.495939
8019	IMG/center_2016_12_01_13_46_38_135.jpg	IMG/left_2016_12_01_13_46_38_135.jpg	IMG/right_2016_12_01_13_46_38_135.jpg	0.0	0.0	0.0	1.489917
8020	IMG/center_2016_12_01_13_46_38_191.jpg	IMG/left_2016_12_01_13_46_38_191.jpg	IMG/right_2016_12_01_13_46_38_191.jpg	0.0	0.0	0.0	1.480929
8021	IMG/center_2016_12_01_13_46_38_237.jpg	IMG/left_2016_12_01_13_46_38_237.jpg	IMG/right_2016_12_01_13_46_38_237.jpg	0.0	0.0	0.0	1.474967
8022	IMG/center_2016_12_01_13_46_38_294.jpg	IMG/left_2016_12_01_13_46_38_294.jpg	IMG/right_2016_12_01_13_46_38_294.jpg	0.0	0.0	0.0	1.466069
8023	IMG/center_2016_12_01_13_46_38_339.jpg	IMG/left_2016_12_01_13_46_38_339.jpg	IMG/right_2016_12_01_13_46_38_339.jpg	0.0	0.0	0.0	1.460167
8024	IMG/center_2016_12_01_13_46_38_395.jpg	IMG/left_2016_12_01_13_46_38_395.jpg	IMG/right_2016_12_01_13_46_38_395.jpg	0.0	0.0	0.0	1.451358
8025	IMG/center_2016_12_01_13_46_38_440.jpg	IMG/left_2016_12_01_13_46_38_440.jpg	IMG/right_2016_12_01_13_46_38_440.jpg	0.0	0.0	0.0	1.445515
8026	IMG/center_2016_12_01_13_46_38_497.jpg	IMG/left_2016_12_01_13_46_38_497.jpg	IMG/right_2016_12_01_13_46_38_497.jpg	0.0	0.0	0.0	1.436795
8027	IMG/center_2016_12_01_13_46_38_543.jpg	IMG/left_2016_12_01_13_46_38_543.jpg	IMG/right_2016_12_01_13_46_38_543.jpg	0.0	0.0	0.0	1.431010
8028	IMG/center_2016_12_01_13_46_38_599.jpg	IMG/left_2016_12_01_13_46_38_599.jpg	IMG/right_2016_12_01_13_46_38_599.jpg	0.0	0.0	0.0	1.422377
8029	IMG/center_2016_12_01_13_46_38_644.jpg	IMG/left_2016_12_01_13_46_38_644.jpg	IMG/right_2016_12_01_13_46_38_644.jpg	0.0	0.0	0.0	1.416651
8030	IMG/center_2016_12_01_13_46_38_700.jpg	IMG/left_2016_12_01_13_46_38_700.jpg	IMG/right_2016_12_01_13_46_38_700.jpg	0.0	0.0	0.0	1.408105
8031	IMG/center_2016_12_01_13_46_38_745.jpg	IMG/left_2016_12_01_13_46_38_745.jpg	IMG/right_2016_12_01_13_46_38_745.jpg	0.0	0.0	0.0	1.402436
8032	IMG/center_2016_12_01_13_46_38_802.jpg	IMG/left_2016_12_01_13_46_38_802.jpg	IMG/right_2016_12_01_13_46_38_802.jpg	0.0	0.0	0.0	1.393976
8033	IMG/center_2016_12_01_13_46_38_846.jpg	IMG/left_2016_12_01_13_46_38_846.jpg	IMG/right_2016_12_01_13_46_38_846.jpg	0.0	0.0	0.0	1.388364
8034	IMG/center_2016_12_01_13_46_38_922.jpg	IMG/left_2016_12_01_13_46_38_922.jpg	IMG/right_2016_12_01_13_46_38_922.jpg	0.0	0.0	0.0	1.377208
8035	IMG/center_2016_12_01_13_46_38_947.jpg	IMG/left_2016_12_01_13_46_38_947.jpg	IMG/right_2016_12_01_13_46_38_947.jpg	0.0	0.0	0.0	1.374433