import urllib

import os

import config

API_KEY = 'XXXXXXXXXXXXXXXXX'  // replace with your key

API_SECRET = 'XXXXXXXXXXXXXXXXX'  // replace with your secret

IMG_FOLDER = 'XXXXXXXXXXXXXXXXX'  // replace with your folder to store the images

if not (isinstance(keywords, str) or isinstance(keywords, list)):

raise AttributeError('keywords must be a string or a list of strings')

if not (size in ['thumbnail', 'square', 'medium', 'original']):

raise AttributeError('size must be "thumbnail", "square", "medium" or "original"')

if not (max_nb_img == -1 or (max_nb_img > 0 and isinstance(max_nb_img, int))):

raise AttributeError('max_nb_img must be an integer greater than zero or equal to -1')

if isinstance(keywords, str):

keywords_list = []

keywords_list.append(keywords)

else:

keywords_list = keywords

if size == 'thumbnail':

size_url = 'url_t'

elif size == 'square':

size_url = 'url_q'

elif size == 'medium':

size_url = 'url_c'

elif size == 'original':

size_url = 'url_o'

results_folder = config.IMG_FOLDER + keyword.replace(" ", "_") + "/"

if not os.path.exists(results_folder):

os.makedirs(results_folder)

photos = flickr.walk(

text=keyword,

extras='url_m',

license='1,2,4,5',

per_page=50)

for photo in photos:

try:

url=photo.get('url_m')

print(url)

count += 1

urllib.request.urlretrieve(url,  results_folder + str(count) +".jpg")

except Exception as e:

print(e, 'Download failure')

butterflies = ['meadow brown butterfly', 'gatekeeper butterfly']

for butterfly in butterflies:

download_flickr_photos(butterfly)

from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img

i = load_img('data/train/maniola_jurtina/1.jpg' )

x = img_to_array(i)

x = x.reshape((1,) + x.shape)

imgGen = ImageDataGenerator(rotation_range = 90)

i = 1

for batch in imgGen.flow(x, batch_size=1, save_to_dir='example_transformations', save_format='jpeg', save_prefix='trsf'):

i += 1

if i > 3:

break

imgGen = ImageDataGenerator(

rotation_range = 40,

width_shift_range = 0.2,

height_shift_range = 0.2,

rescale = 1./255,

shear_range = 0.2,

zoom_range = 0.2,

horizontal_flip = True)

i = 1

for batch in imgGen.flow(x, batch_size=1, save_to_dir='example_transformations', save_format='jpeg', save_prefix='all'):

i += 1

if i > 3:

break

train_datagen = ImageDataGenerator(

rotation_range = 40,

width_shift_range = 0.2,

height_shift_range = 0.2,

rescale = 1./255,

shear_range = 0.2,

zoom_range = 0.2,

horizontal_flip = True)

validation_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(

'data/train',

batch_size=32,

class_mode='binary')

validation_generator = validation_datagen.flow_from_directory(

'data/validation',

batch_size=32,

class_mode='binary')

input_shape=(3, None, None)  # Theano

input_shape=(None, None, 3)  # Tensorflow

cnn = Sequential()

cnn.add(Conv2D(32,(3,3), input_shape = (3 ,75 ,75)))

cnn.add(Activation('relu'))

cnn.add(Dense(1))

cnn.add(Activation( 'sigmoid'))

from keras.models import Sequential

from keras.layers import Conv2D, MaxPooling2D

from keras.layers import Activation, Flatten, Dense, Dropout

from keras.preprocessing.image import ImageDataGenerator

import time

IMG_SIZE = # Replace with the size of your images

NB_CHANNELS = # 3 for RGB images or 1 for grayscale images

BATCH_SIZE = # Typical values are 8, 16 or 32

NB_TRAIN_IMG = # Replace with the total number training images

NB_VALID_IMG = # Replace with the total number validation images

cnn = Sequential()

cnn.add(Conv2D(filters=32,

kernel_size=(2,2),

strides=(1,1),

padding='same',

input_shape=(IMG_SIZE,IMG_SIZE,NB_CHANNELS),

data_format='channels_last'))

cnn.add(Activation('relu'))

cnn.add(MaxPooling2D(pool_size=(2,2),

strides=2))

cnn.add(Conv2D(filters=64,

kernel_size=(2,2),

strides=(1,1),

padding='valid'))

cnn.add(Activation('relu'))

cnn.add(MaxPooling2D(pool_size=(2,2),

strides=2))

cnn.add(Flatten())

cnn.add(Dense(64))

cnn.add(Activation('relu'))

cnn.add(Dropout(0.25))

cnn.add(Dense(1))

cnn.add(Activation('sigmoid'))

cnn.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])

train_datagen = ImageDataGenerator(

rotation_range = 40,

width_shift_range = 0.2,

height_shift_range = 0.2,

rescale = 1./255,

shear_range = 0.2,

zoom_range = 0.2,

horizontal_flip = True)

validation_datagen = ImageDataGenerator(rescale = 1./255)

train_generator = train_datagen.flow_from_directory(

'../flickr/img/train',

target_size=(IMG_SIZE,IMG_SIZE),

class_mode='binary',

batch_size = BATCH_SIZE)

validation_generator = validation_datagen.flow_from_directory(

'../flickr/img/validation',

target_size=(IMG_SIZE,IMG_SIZE),

class_mode='binary',

batch_size = BATCH_SIZE)

start = time.time()

cnn.fit_generator(

train_generator,

steps_per_epoch=NB_TRAIN_IMG//BATCH_SIZE,

epochs=50,

validation_data=validation_generator,

validation_steps=NB_VALID_IMG//BATCH_SIZE)

end = time.time()

print('Processing time:',(end - start)/60)

cnn.save_weights('cnn_baseline.h5')

编译组：孙梦琪、余书敏

相关链接：

https://www.freecodecamp.org/news/classify-butterfly-images-deep-learning-keras/

如需转载，请后台留言，遵守转载规范

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