################################################################################################################################
This file is used to extract features from dataset and save it on disc
inputs:
outputs:
################################################################################################################################
import os
import random
import numpy as np
import tensorflow as tf
Just disables the warning, doesn't enable AVX/FMA
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import pickle
os._warn_preinit_stderr = 0
BOTTLENECK_TENSOR_NAME = 'pool_3/_reshape:0'
BOTTLENECK_TENSOR_SIZE = 2048
MODEL_INPUT_WIDTH = 299
MODEL_INPUT_HEIGHT = 299
MODEL_INPUT_DEPTH = 3
JPEG_DATA_TENSOR_NAME = 'DecodeJpeg/contents:0'
RESIZED_INPUT_TENSOR_NAME = 'ResizeBilinear:0'
MAX_NUM_IMAGES_PER_CLASS = 2 ** 27 - 1 # ~134M
def create_inception_graph():
""""Creates a graph from saved GraphDef file and returns a Graph object.
Returns:
Graph holding the trained Inception network, and various tensors we'll be
manipulating.
"""
with tf.compat.v1.Session() as sess:
model_filename = os.path.join(
'imagenet', 'classify_image_graph_def.pb')
with tf.gfile.FastGFile("imagenet/classify_image_graph_def.pb", 'rb') as f:
graph_def = tf.compat.v1.GraphDef()
graph_def.ParseFromString(f.read())
bottleneck_tensor, jpeg_data_tensor, resized_input_tensor = (
tf.import_graph_def(graph_def, name='', return_elements=[
BOTTLENECK_TENSOR_NAME, JPEG_DATA_TENSOR_NAME,
RESIZED_INPUT_TENSOR_NAME]))
return sess.graph, bottleneck_tensor, jpeg_data_tensor, resized_input_tensor
def run_bottleneck_on_image(sess, image_data, image_data_tensor,
bottleneck_tensor):
bottleneck_values = sess.run(
bottleneck_tensor,
{image_data_tensor: image_data})
bottleneck_values = np.squeeze(bottleneck_values)
return bottleneck_values
Get outputs from second-to-last layer in pre-built model
boots_files = [
'uploads/dogs_and_cats/Boots/' + f
for
f
in
os.listdir('uploads/dogs_and_cats/Boots')
]
sandals_files = [
'uploads/dogs_and_cats/Sandals/' + f
for
f
in
os.listdir('uploads/dogs_and_cats/Sandals')
]
shoes_files = [
'uploads/dogs_and_cats/Shoes/' + f
for
f
in
os.listdir('uploads/dogs_and_cats/Shoes')
]
slippers_files = [
'uploads/dogs_and_cats/Slippers/' + f
for
f
in
os.listdir('uploads/dogs_and_cats/Slippers')
]
apparel_files = [
'uploads/dogs_and_cats/apparel/' + f
for
f
in
os.listdir('uploads/dogs_and_cats/apparel')
]
all_files = boots_files + shoes_files + slippers_files + sandals_files + apparel_files
random.shuffle(all_files)
num_images = 10000
neighbor_list = all_files[:num_images]
with open('neighbor_list_recom.pickle', 'wb') as f:
pickle.dump(neighbor_list, f)
print("saved neighbour list")
extracted_features = np.ndarray((num_images, 2048))
sess = tf.compat.v1.Session()
graph, bottleneck_tensor, jpeg_data_tensor, resized_image_tensor = (create_inception_graph())
for i, filename in enumerate(neighbor_list):
image_data = tf.io.gfile.GFile(filename, 'rb').read()
features = run_bottleneck_on_image(sess, image_data, jpeg_data_tensor, bottleneck_tensor)
extracted_features[i:i + 1] = features
if i % 250 == 0:
print(i)
np.savetxt("saved_features_recom.txt", extracted_features)
print("saved exttracted features")
==========
Output Error
"C:\Users\Muhammad Khalid\Anaconda3\python.exe" "C:/Users/Muhammad Khalid/Desktop/Recommendation systems using image similarity powered by deep learning/Deeplearning_Image_Similarity-master/server/image_vectorizer.py"
WARNING: Logging before flag parsing goes to stderr.
W0823 21:18:45.101209 20020 init.py:308] Limited tf.compat.v2.summary API due to missing TensorBoard installation.
saved neighbour list
W0823 21:18:45.223723 20020 deprecation.py:323] From C:/Users/Muhammad Khalid/Desktop/Recommendation systems using image similarity powered by deep learning/Deeplearning_Image_Similarity-master/server/image_vectorizer.py:39: FastGFile.init (from tensorflow.python.platform.gfile) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.gfile.GFile.
Traceback (most recent call last):
File "C:/Users/Muhammad Khalid/Desktop/Recommendation systems using image similarity powered by deep learning/Deeplearning_Image_Similarity-master/server/image_vectorizer.py", line 113, in
image_data = tf.io.gfile.GFile(filename, 'rb').read()
File "C:\Users\Muhammad Khalid\Anaconda3\lib\site-packages\tensorflow\python\lib\io\file_io.py", line 122, in read
self._preread_check()
File "C:\Users\Muhammad Khalid\Anaconda3\lib\site-packages\tensorflow\python\lib\io\file_io.py", line 84, in _preread_check
compat.as_bytes(self.__name), 1024 * 512)
tensorflow.python.framework.errors_impl.UnknownError: NewRandomAccessFile failed to Create/Open: uploads/dogs_and_cats/Sandals/Athletic : Access is denied.
; Input/output error
Process finished with exit code 1
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent