Last active
June 14, 2020 21:41
-
-
Save fredgido/81e65426ca2f948fb00dd352b15e3409 to your computer and use it in GitHub Desktop.
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| import pprint | |
| import tensorflow as tf | |
| import keras | |
| import tensorflow_hub as hub | |
| import numpy as np | |
| import PIL.Image as Image | |
| model_url = "https://tfhub.dev/google/imagenet/nasnet_mobile/classification/4" | |
| IMAGE_SHAPE = (224, 224) | |
| classifier = tf.keras.Sequential([hub.KerasLayer(model_url, input_shape=IMAGE_SHAPE + (3,))]) | |
| url = 'https://danbooru.donmai.us/data/sample/sample-a50d476225caabea727d571759d2918e.jpg' | |
| imagefile = keras.utils.get_file(url.split("/")[-1], url) | |
| imagefile = Image.open(imagefile).resize(IMAGE_SHAPE) | |
| imagefile = np.array(imagefile) / 255.0 | |
| print(imagefile.shape) | |
| book_url = 'https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt' | |
| labels_path = keras.utils.get_file(book_url.split("/")[-1], book_url) | |
| imagenet_labels = open(labels_path).read().splitlines() | |
| y = classifier.predict(imagefile[np.newaxis, ...]).tolist()[0] | |
| rez = list(zip(list(y), list(range(0, len(list(y)) - 1)))) | |
| t = sorted(rez, key=lambda x: x[0], reverse=True) | |
| cor = [(a, imagenet_labels[b]) for a, b in t] | |
| pprint.pprint(cor[:20]) |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment