自建鉴黄服务

src/main/resources/python/yellow.py

+import sys
+import json
+import requests
+
+from PIL import Image
+import numpy as np
+
+_IMAGE_SIZE = 64
+# TensorFlow-serving 调用地址，这里要替换成自己的，后面会讲到如何安装
+SERVER_URL = 'http://172.17.0.4:8501/v1/models/image:predict'
+_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
+
+# 导入
+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
+
+# 分析
+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_data = load_image(sys.argv[1])
+    predict = nsfw_predict(image_data)
+    print(predict)

src/main/resources/python/机器学习.txt

+
+docker run -d --name serving_image tensorflow/serving
+
+# 2000 样本
+docker cp /home/nsfw/data/models/  serving_image:/models/image
+
+docker cp /home/nsfw/data/models/1547856517  serving_image:/models/image
+
+docker exec -it serving_image  /bin/bash
+
+tensorflow_model_server --port=8500 --rest_api_port=8501  --model_name=image --model_base_path=/models/image
+
+
+pip install pillow
+pip install numpy
+
+## 参考
+
+https://www.cnblogs.com/weiyinfu/p/9928363.html
+
+https://www.oschina.net/news/103861/nsfw-opensource
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