質問編集履歴
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追記
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長くなるとアレなので、なるべく短く説明しましたので、不足があったら申し訳ありません。
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### 追記
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念の為、全コードを追記します。
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```python
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import os
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import cv2
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import numpy as np
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import TensorFlow as tf
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from フォルダ名 import cifar10
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image_path = '画像フォルダ'
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classes = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
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#画像の読み込み
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images = []
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files = os.listdir(image_path)
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for file in files:
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img = cv2.imread(os.path.join(image_path, file))
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img = img[:, :, ::-1]
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height = img.shape[0]
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width = img.shape[1]
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cropped_size = min(width, height)
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sx = (width - cropped_size) // 2
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sy = (height - cropped_size) // 2
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cropped_img = img[sy:sy + cropped_size, sx:sx + cropped_size]
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resized_img = cv2.resize(cropped_img, (32, 32))
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images.append(resized_img)
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images = np.array(images)
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FLAGS = tf.app.flags.FLAGS
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FLAGS.batch_size = len(images)
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image, = tf.train.slice_imput_producer([images], shuffle = False)
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reshaped_image = tf.cast(image, tf.float32)
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resized_image = tf.image.resize_image_with_crop_or_pad(reshaped_image, 24, 24)
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float_image = tf.image.per_image_standardization(resized_image)
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#バッチ入力の設定
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images = tf.train.batch([float_image], batch_size = FLAGS.batch_size)
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#予測器の作成
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logits = cifar10.inference(images)
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softmax = tf.nn.softmax(logits)
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prediction = tf.argmax(softmax, 1)
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#移動平均版の学習データを復元するように設定
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variable_averages = tf.train.ExponentialMovingAverage(cifar10.MOVING_AVERAGE_DECAY)
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variables_to_restore = variable_averages.variables_to_restore()
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saver = tf.train.Saver(variables_to_restore)
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sess = tf.Session()
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checkpoint = tf.train.latest_checkpoint('cifar10_train')
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if checkpoint:
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saver.restore(session, checkpoint)
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#複数の画像がキューに詰められた状態なので、一つずつ取り出して処理するランナーの生成
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coord = tf.train.Coordinator()
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try:
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#処理を行うスレッドの生成
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threads = []
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for qr in tf.get_collection(tf.GraphKeys.QUEUE_RUNNERS):
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threads.extend(qr.create_threads(sess, coord = coord, daemon = True, start = True))
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softmaxs, predictions = session.run([softmax, prediction])
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for f, s, p in zip(files, softmaxs, predictions):
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print(f, classes[p])
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print(list(s))
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print()
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except Exception as e:
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coord.request_stop(e)
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#スレッドを止める
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coord.request_stop()
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coord.join(threads, stop_grace_period_secs = 10)
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```
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結果は,
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画像ファイル名 dog
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[0.02342324234, 0.00324532453, ..., ..., ..., ..., ..., ..., ..., ...]
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以下省略
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のような形です。
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