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# -*-coding:utf-8-*-
import sys
import json
import requests
_IMAGE_SIZE = 64
SERVER_URL = 'http://localhost:8601/v1/models/nsfw:predict'
_LABEL_MAP = {0:'drawings', 1:'hentai', 2:'neutral', 3:'porn', 4:'sexy'}
from PIL import Image
import numpy as np
def standardize(img):
mean = np.mean(img)
std = np.std(img)
img = (img - mean) / std
return img
def load_image(image_path):
img = Image.open(image_path)
img = img.resize((_IMAGE_SIZE, _IMAGE_SIZE))
img.load()
data = np.asarray(img, dtype="float32")
data = standardize(data)
data = data.astype(np.float16, copy=False)
return data
def nsfw_predict(image_data):
pay_load = json.dumps({"inputs": [image_data.tolist()]})
response = requests.post(SERVER_URL, data=pay_load)
data = response.json()
outputs = data['outputs']
predict_result = {"classes": _LABEL_MAP.get(outputs['classes'][0])}
predict_result['probabilities'] = {_LABEL_MAP.get(i): l for i, l in enumerate(outputs['probabilities'][0])}
return predict_result
if __name__ == '__main__':
image_path = ''
args = sys.argv
if len(args) < 2:
print("usage: python serving_client.py <image_path>")
image_data = load_image(image_path)
predict = nsfw_predict(image_data)
print(predict)
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