This document provides an overview of using various Python libraries and tools for image recognition, including OpenCV for image processing, Selenium and Hulu for web scraping, Keras and TensorFlow for building convolutional neural networks, and Bottle as a web framework. Code examples are given for preprocessing images, creating a CNN model in Keras to classify images into 10 classes using the CIFAR-10 dataset, and exporting the trained model.

1. Recognize Godzilla
by

2. Recoginize Godzilla
DEMO

4. :
: HTML CSS
: B
(´ ω )

5. Recognize Godzilla

10. 1954 29
https://ja.wikipedia.org/wiki/

19. :
http://www.asahi.com/articles/ASJ8X42B7J8XPUTB
001.html

22. INPUT
Not OUTPUT

27. T
LT
OpenCV

30. GE○ TUTAY○ Mac

31. Hulu

33. (´ )

34. 1,000 / ( )

35. Selenium
SeleniumHQ

36. Selenium Python
URL

37. 1

38. Ubuntu
Flash
(´ ω )

39. Google Chrome, Firefox
=> Mac Pro

41. Python => Selenium Firefox
=> (´ ω )
Firefox 48 selenium-webdriver
=> Firefox 47.x

43. => 5GB
=>
4 180GB^^;
=> 31
=> HDD

46. 1 => 4
(´ ω )
Python

47. Python web driver
API
Python Selenium
Plugin JavaScript
Selenium

49. Plugin
1. html canvas
2.
(^o^)

52. screencapture

53. Cmd+Shift+4“
“

55. Python OSS
pyscreencapture

56. python Selenium
screencapture

60. (´ )

62. 32px x 32px

63. 1.
2.
1x25, 2x4, 3x1, 4x1, 5x1
1 = 192px
x 192px
3. 32px x 32px

64. ( 6 shift orz)

66. trimming.py
def trimming(image_path):
#
x = 334
y = 276
#
width = 1892
height = 1066
src = cv2.imread(image_path, 1)
dst = src[y:y + height, x:x + width]
file_name = re.sub('(godzilla[0-9]+.jpg)',
'trimming_1',
image_path)
cv2.imwrite(file_name, dst)

67. cutout.py
def cutout_square(img, image_path,
full_path, pixel_size, loop_count,
x_start_point, y_start_point, image_prefix):
for x in range(loop_count):
for y in range(loop_count):
x_start = x_start_point + (pixel_size * x)
x_end = x_start + pixel_size
y_start = y_start_point + (pixel_size * y)
y_end = y_start + pixel_size
dist = img[y_start:y_end, x_start:x_end]
resize_image = resize.to_target_pixel(dist)
if type(resize_image).__module__ ==
np.__name__:
write_image(resize_image,
image_prefix,
full_path)
else:
print(image_path)

68. resize.py
def to_target_pixel(image, pixel_size=32):
height = image.shape[0]
width = image.shape[1]
try:
resize_size_list = (
int(height / (height / pixel_size)),
int(width / (width/ pixel_size)))
return cv2.resize(image, resize_size_list)
except:
return False
(※ github )

70. ls Finder
=> cp mv too many arguments
=>
=> 5
=> (1 * 25) * (shift ) 5 * ( ) 20,000 ≒
2,500,000
=> ↑
=> (´ ω )

71. find xargs
$ find ${DIRECTORY} -name ${FIND_PARAM} -maxdepth 1
-print0 | xargs -0 -J {} mv {} ${MV_DIRECTORY}
${DIRECTORY}
${FIND_PARAM}
${MV_DIRECTORY}

75. 1x25
2x4
1x3, 1x4 shift
1x5

79. 32px x 32px
(´ ω )
=>
=>
=>

82. 3500 2017/03/16
500

83. =>

88. (^o^)

89. 3

90. (´ ω )

91. ※

92. 0:00(20176/03/17)
GO

94. 1000
ω ⊃ !!

97. facebook
github clone

98. python
$ python increase_picture.py ${IMAGE_FILE}
find
$ find ${IMG_FILES} -maxdepth 1 -exec
increase_picture.py {} ;

99. >>> datasets.shape
(41688, 1, 3072)
>>> labels.shape
(41688, 1)
41,688

102. (is("INPUT")) ? " " : " "
keras
CIFAR-10

103. The CIFAR-10 dataset

109. [1, 3072] numpy
3072
32px * 32px * 3(RGB) = 3072
RGB R G B
[[0, 0]] <-
0
1
cifar-10 10 0 9
2 0 1

111. keras
# (?)
batch_size = 32
# ! 10 2
nb_classes = 2
# (?)
nb_epoch = 4
# (?)
nb_filter = 10
# input image dimensions. img_rows = height, img_cols = width
img_rows, img_cols = 32, 32
# the CIFAR10 images are RGB
img_channels = 3

113. model.evaluate(X_test, Y_test)
> [0.6990031684412652, 0.4959700633390145]
°ω°

114. °ω°

115. # json
# yaml
json_string = model.to_json()
open(os.path.join('./model/', 'model.json'), 'w').write(json_str
#
model.save_weights(os.path.join('./model/','model_weights.hdf5'

116. bottle
※
※

117. DEMO

120. (´ ω )

121. etc
Data Set
hulu seleniu
keras OpenCV