Deep learning to classify butterflies in Keras

from flickrapi import FlickrAPI
import urllib
import os
import configCopy the code

API_KEY = 'XXXXXXXXXXXXXXXXX'  // replace with your key
API_SECRET = 'XXXXXXXXXXXXXXXXX'  // replace with your secret
IMG_FOLDER = 'XXXXXXXXXXXXXXXXX'  // replace with your folder to store
 the imagesCopy the code

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')Copy the code

if isinstance(keywords, str):
    keywords_list = []
    keywords_list.append(keywords)
else:
    keywords_list = keyword
sif 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'Copy the code

results_folder = config.IMG_FOLDER + keyword.replace(""."_") + "/"
if not os.path.exists(results_folder):
    os.makedirs(results_folder)Copy the code

photos = flickr.walk(
    text=keyword,
    extras='url_m',
    license='1,2,4,5',
    per_page=50)Copy the code

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')Copy the code

butterflies = ['meadow brown butterfly'.'gatekeeper butterfly']
for butterfly in butterflies:
    download_flickr_photos(butterfly)Copy the code

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)Copy the code

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:
        breakCopy the code

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 = 1for batch in imgGen.flow(x, batch_size=1,
 save_to_dir='example_transformations',
 save_format='jpeg', save_prefix='all'):
    i += 1
    if i > 3:
        breakCopy the code

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')Copy the code

input_shape=(3, None, None)  # Theano
input_shape=(None, None, 3)  # TensorflowCopy the code

CNN = Sequential() cnn.add(Conv2D(32,(3,3), input_shape = (3, 75,75))Copy the code

cnn.add(Activation('relu'))
cnn.add(Dense(1))
cnn.add(Activation( 'sigmoid'))Copy the code

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 imagesCopy the code

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'Strides =2) 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(Dense(64))'relu'CNN)). The add (Dropout (0.25)) CNN. The add (Dense (1)) CNN. The add (Activation ('sigmoid'))
cnn.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=
['accuracy'])Copy the code

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)Copy the code

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')Copy the code