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#-*-coding:utf-8-*-
import os
import sys
import numpy as np
from PIL import Image
import tensorflow as tf
_MODEL_DIR = os.path.join(os.path.abspath(os.path.dirname(__file__)), 'data/models/1547856517')
_IMAGE_SIZE = 64
_BATCH_SIZE = 128
_LABEL_MAP = {0:'drawings', 1:'hentai', 2:'neutral', 3:'porn', 4:'sexy'}
def standardize(img):
mean = np.mean(img)
std = np.std(img)
img = (img - mean) / std
return img
def load_image( infilename ) :
img = Image.open( infilename )
img = img.resize((_IMAGE_SIZE, _IMAGE_SIZE))
img.load()
data = np.asarray( img, dtype=np.float32 )
data = standardize(data)
return data
def predict(image_path):
with tf.Session() as sess:
graph = tf.get_default_graph();
tf.saved_model.loader.load(sess, [tf.saved_model.tag_constants.SERVING], _MODEL_DIR)
inputs = graph.get_tensor_by_name("input_tensor:0")
probabilities_op = graph.get_tensor_by_name('softmax_tensor:0')
class_index_op = graph.get_tensor_by_name('ArgMax:0')
image_data = load_image(image_path)
probabilities, class_index = sess.run([probabilities_op, class_index_op],
feed_dict={inputs: [image_data] * _BATCH_SIZE})
probabilities_dict = {_LABEL_MAP.get(i): l for i, l in enumerate(probabilities[0])}
pre_label = _LABEL_MAP.get(class_index[0])
result = {"class": pre_label, "probability": probabilities_dict}
return result
if __name__ == '__main__':
argv = sys.argv
if(len(argv) < 2):
print("usage: python nsfw_predict <image_path>")
image_path = argv[1]
print()
res = predict(image_path)
print(res)
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