Recognize Godzillaby

Recoginize Godzilla

DEMO

:

: HTML CSS

: B

(´ ω )

Recognize Godzilla

1954 29

https://ja.wikipedia.org/wiki/

:http://www.asahi.com/articles/ASJ8X42B7J8XPUTB001.html

INPUTNot OUTPUT

TLT

OpenCV

GE○ TUTAY○ Mac

Hulu

(´ )

1,000 / ( )

Selenium

SeleniumHQ

Selenium PythonURL

1

UbuntuFlash

(´ ω )

Google Chrome, Firefox

=> Mac Pro

Python => Selenium Firefox

=> (´ ω )

Firefox 48 selenium-webdriver

=> Firefox 47.x

=> 5GB=>

4 180GB^^;=> 31=> HDD

1 => 4(´ ω )

Python

Python web driverAPI

Python Selenium

Plugin JavaScript

Selenium

Plugin

1. html canvas

2.

(^o^)

screencapture

Cmd+Shift+4“

“

Python OSS

pyscreencapture

python Seleniumscreencapture

(´ )

32px x 32px

1.

2.

1x25, 2x4, 3x1, 4x1, 5x1

1 = 192px

x 192px

3. 32px x 32px

( 6 shift orz)

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)

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)

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 )

ls Finder=> cp mv too many arguments

=> => 5

=> (1 * 25) * (shift ) 5 * ( ) 20,000 ≒2,500,000=> ↑=> (´ ω )

find xargs

$ find ${DIRECTORY} -name ${FIND_PARAM} -maxdepth 1 \

-print0 | xargs -0 -J {} mv {} ${MV_DIRECTORY}

${DIRECTORY}

${FIND_PARAM}

${MV_DIRECTORY}

1x25

2x4

1x3, 1x4 shift

1x5

32px x 32px(´ ω )

=> => =>

3500 2017/03/16

500

=>

(^o^)

3

(´ ω )

※

0:00(20176/03/17)

GO

1000

ω ⊃ !!

facebook

github clone

python

$ python increase_picture.py ${IMAGE_FILE}

find

$ find ${IMG_FILES} -maxdepth 1 -exec \

increase_picture.py {} \;

>>> datasets.shape

(41688, 1, 3072)

>>> labels.shape

(41688, 1)

41,688

(is("INPUT")) ? " " : " "

keras

CIFAR-10

The CIFAR-10 dataset

[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

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

model.evaluate(X_test, Y_test)

> [0.6990031684412652, 0.4959700633390145]

°ω°

°ω°

# json

# yaml

json_string = model.to_json()

open(os.path.join('./model/', 'model.json'), 'w').write(json_string)

#

model.save_weights(os.path.join('./model/','model_weights.hdf5'

bottle

※※

DEMO

(´ ω )

etc

Data Set

hulu seleniu

keras OpenCV