画像特徴空間の可視化 [TensorFlowでDeep Learning 19] ref: https://qiita.com/kumonkumon/items/ab56708eebc01077d0ee

feture_extraction.py

import csv
import tensorflow as tf
import tensorflow_hub as hub

module = hub.Module("https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/feature_vector/2")
height, width = hub.get_expected_image_size(module)
ch = 3
batch_size = 10

image_list = tf.convert_to_tensor(['images/img_{:04d}.jpg'.format(i+1) for i in range(2500)])
input_queue = tf.train.slice_input_producer([image_list], num_epochs=1, shuffle=False)

image_bytes = tf.read_file(input_queue[0])
image = tf.image.decode_image(image_bytes, channels=ch)
image = tf.image.resize_bilinear([image], [height, width])
image = tf.reshape(image, tf.stack([height, width, ch]))
images = tf.train.batch([image], batch_size=batch_size, allow_smaller_final_batch=True)
features = module(images)

with open('feature_vecs.tsv', 'w') as fw:
    csv_writer = csv.writer(fw, delimiter='\t')
    with tf.Session() as sess:
        sess.run([tf.local_variables_initializer(),
                  tf.global_variables_initializer()])
        coord = tf.train.Coordinator()
        threads = tf.train.start_queue_runners(sess=sess, coord=coord)

        try:
            while not coord.should_stop():
                fvecs = sess.run(features)
                csv_writer.writerows(fvecs)
        except:
            pass
        finally:
            coord.request_stop()
            coord.join(threads)

file0.sh

$ montage images/img_*.jpg -tile 50x50 -geometry 50x50! sprite.jpg

file1.txt

import csv
import tensorflow as tf
import tensorflow_hub as hub

module = hub.Module("https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/feature_vector/2")
height, width = hub.get_expected_image_size(module)
ch = 3
batch_size = 10

image_list = tf.convert_to_tensor(['images/img_{:04d}.jpg'.format(i+1) for i in range(2500)])
input_queue = tf.train.slice_input_producer([image_list], num_epochs=1, shuffle=False)

image_bytes = tf.read_file(input_queue[0])
image = tf.image.decode_image(image_bytes, channels=ch)
image = tf.image.resize_bilinear([image], [height, width])
image = tf.reshape(image, tf.stack([height, width, ch]))
images = tf.train.batch([image], batch_size=batch_size, allow_smaller_final_batch=True)
features = module(images)

with open('feature_vecs.tsv', 'w') as fw:
    csv_writer = csv.writer(fw, delimiter='\t')
    with tf.Session() as sess:
        sess.run([tf.local_variables_initializer(),
                  tf.global_variables_initializer()])
        coord = tf.train.Coordinator()
        threads = tf.train.start_queue_runners(sess=sess, coord=coord)

        try:
            while not coord.should_stop():
                fvecs = sess.run(features)
                csv_writer.writerows(fvecs)
        except:
            pass
        finally:
            coord.request_stop()
            coord.join(threads)

file2.txt

import csv
import tensorflow as tf
import tensorflow_hub as hub

module = hub.Module("https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/feature_vector/2")
height, width = hub.get_expected_image_size(module)
ch = 3

filename = tf.placeholder(tf.string)
image_bytes = tf.read_file(filename)
image = tf.image.decode_image(image_bytes, channels=ch)
image = tf.image.resize_bilinear([image], [height, width])
features = module(image)

with open('feature_vecs.tsv', 'w') as fw:
    csv_writer = csv.writer(fw, delimiter='\t')
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())

        for image_path in ['images/img_{:04d}.jpg'.format(i+1) for i in range(2500)]:
            fvecs = sess.run(features, feed_dict={filename: image_path})
            csv_writer.writerows(fvecs)

file3.sh

$ cat projector_config.pbtxt
embeddings {
  tensor_path: "feature_vecs.tsv"
  sprite {
    image_path: "sprite.jpg"
    single_image_dim: 50
    single_image_dim: 50
  }
}

file4.sh

$ tensorboard --logdir .

projector_config.pbtxt

embeddings {
  tensor_path: "feature_vecs.tsv"
  sprite {
    image_path: "sprite.jpg"
    single_image_dim: 50
    single_image_dim: 50
  }
}

simple_feature_extraction.py

import csv
import tensorflow as tf
import tensorflow_hub as hub

module = hub.Module("https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/feature_vector/2")
height, width = hub.get_expected_image_size(module)
ch = 3

filename = tf.placeholder(tf.string)
image_bytes = tf.read_file(filename)
image = tf.image.decode_image(image_bytes, channels=ch)
image = tf.image.resize_bilinear([image], [height, width])
features = module(image)

with open('feature_vecs.tsv', 'w') as fw:
    csv_writer = csv.writer(fw, delimiter='\t')
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())

        for image_path in ['images/img_{:04d}.jpg'.format(i+1) for i in range(2500)]:
            fvecs = sess.run(features, feed_dict={filename: image_path})
            csv_writer.writerows(fvecs)