generate features

.py

#export PATH=~/anaconda3/bin:$PATH

from tqdm import tqdm 
import tensorflow as tf 
from keras.applications.resnet50 import ResNet50
from keras.layers import Flatten, Input
from keras.models import Model
from keras.preprocessing import image
from keras.applications.imagenet_utils import preprocess_input
import numpy as np
from google.cloud import storage 
from io import BytesIO

model = ResNet50(weights='imagenet', pooling=max, include_top = False) 


client = storage.Client() 
bucket = client.get_bucket('nonane')

bucket_list = list(bucket.list_blobs())

f = BytesIO(file.download_as_string()) 

X_test = []

#file creation
train_filenames = open('train_filenames.txt', 'w+')
test_filenames = open('test_filenames.txt', 'w+')


train_featues = open('train_featues.txt', 'w+')
test_features = open('test_features.txt', 'w+')
 
i=0
#for file in tqdm(bucket_list[0:5000]):
for file in (bucket_list[0:5000]):
    try:
       # reading files
        f = BytesIO(file.download_as_string()) 
        img = image.load_img(f, target_size=(224, 224)) 
        x = image.img_to_array(img) 
        x = np.expand_dims(x, axis=0) 
        x = preprocess_input(x) 
        features = model.predict(x) 
        features_reduce = features.squeeze() 
        X_test.append(features_reduce)
        
        #depending on file path write info to proper files
        file_name = file.path.split('/o/')[1].split('%2F')     
        if file_name[0] == 'train':
            #print(file.path.split('/o/')[1].split('%2F')[1])
            train_filenames.write(file_name[1] + '\n')
            train_featues.write(' '.join(str(x) for x in features.squeeze()) + '\n')
        else:
            test_filenames.write(file_name[1] + '\n')
            test_features.write(' '.join(str(x) for x in features.squeeze()) + '\n')
        i = i + 1
	print()
    except:
         pass

print(i)

train_filenames.close()
test_filenames.close()
train_featues.close()
test_features.close()

#my_file = open('test_filenames.txt', 'r')
#print(my_file.read())
#my_file.close()