Created
November 23, 2019 18:12
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CNN กับ Marvel Cinematic Universe (test model)
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| from PIL import Image | |
| import numpy as np | |
| import tensorflow as tf | |
| import matplotlib.pyplot as plt | |
| import model | |
| import os | |
| classes = {'BlackPanther', 'DoctorStrange', 'IronMan', 'ScarletWitch', 'SpiderMan', 'Thor'} | |
| train_dir = 'raw' | |
| logs_train_dir = './train_logs' | |
| BATCH_SIZE = 1 | |
| CLASSES = 6 | |
| def get_random_dir(classes, train_dir): | |
| class_len = len(classes) | |
| random_class = np.random.randint(0, class_len) | |
| class_name = classes[random_class] | |
| path_random_dir = train_dir +'/' + class_name | |
| return path_random_dir | |
| def get_random_image(path_random_dir): | |
| random_dir = os.listdir(path_random_dir) | |
| len_random_dir = len(random_dir) | |
| random_image = np.random.randint(0, len_random_dir) | |
| image_name = random_dir[int(random_image)] | |
| path_random_image = path_random_dir + '/' + image_name | |
| return path_random_image | |
| def get_one_image(image): | |
| img = Image.open(image) | |
| plt.imshow(img) | |
| plt.show() | |
| image = np.array(img) | |
| return image | |
| def evaluate_image(image_array): | |
| with tf.Graph().as_default(): | |
| image = tf.cast(image_array, tf.float32) | |
| image = tf.image.per_image_standardization(image) | |
| image = tf.reshape(image, [1, 64, 64, 3]) | |
| logic = model.inference(image, BATCH_SIZE, CLASSES) | |
| logic = tf.nn.softmax(logic) | |
| x = tf.compabt.v1.placeholder(tf.float32, shape=[64, 64, 3]) | |
| saver = tf.compat.v1.train.Saver() | |
| with tf.compat.v1.Session() as sess: | |
| print("Reading checkpoints...") | |
| ckpt = tf.train.get_checkpoint_state(logs_train_dir) | |
| if ckpt and ckpt.model_checkpoint_path: | |
| global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1] | |
| saver.restore(sess, ckpt.model_checkpoint_path) | |
| print('Loading..., step is %s' % global_step) | |
| else: | |
| print('Error') | |
| prediction = sess.run(logic, feed_dict={x: image_array}) | |
| max_index = np.argmax(prediction) | |
| result = classes[max_index] | |
| result = str(result) + str(prediction[:, int(max_index)]) | |
| return result |
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