https://github.com/iola1999/nsfw-detect-onnx
https://github.com/iola1999/nsfw-detect-onnx
cnn keras nsfw nsfw-detection onnx-models
Last synced: 6 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/iola1999/nsfw-detect-onnx
- Owner: iola1999
- License: mit
- Created: 2023-06-26T16:35:13.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-06-27T07:14:46.000Z (over 2 years ago)
- Last Synced: 2025-04-09T23:12:54.294Z (6 months ago)
- Topics: cnn, keras, nsfw, nsfw-detection, onnx-models
- Homepage:
- Size: 9.77 KB
- Stars: 6
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# nsfw-detect-onnx
convert HDF5 model by [https://github.com/GantMan/nsfw_model](https://github.com/GantMan/nsfw_model) to onnx via [https://github.com/onnx/tensorflow-onnx](https://github.com/onnx/tensorflow-onnx).
## Model Download
see [releases](https://github.com/iola1999/nsfw-detect-onnx/releases).
## Demo
### Node.js
```js
const ort = require("onnxruntime-node");
const sharp = require("sharp");
async function loadImageAndResize(imagePath) {
const image = await sharp(imagePath).resize(299, 299).raw().toBuffer();
const normalizedImage = new Float32Array(1 * 299 * 299 * 3);
for (let i = 0; i < image.length; i++) {
normalizedImage[i] = image[i] / 255.0;
}
return normalizedImage;
}
async function runModel(imagePath, modelPath) {
const startTime = new Date().getTime();
const normalizedImage = await loadImageAndResize(imagePath);
const loadImgTime = new Date().getTime();
console.log(`Load image time: ${loadImgTime - startTime} ms`);
const session = await ort.InferenceSession.create(modelPath);
const modelLoadTime = new Date().getTime();
console.log(`Load model time: ${modelLoadTime - loadImgTime} ms`);
const inputName = session.inputNames[0];
const options = {
[inputName]: new ort.Tensor("float32", normalizedImage, [1, 299, 299, 3]),
};
const makeTensorTime = new Date().getTime();
console.log(`Make tensor time: ${makeTensorTime - modelLoadTime} ms`);
const feeds = {};
feeds[inputName] = options[inputName];
const results = await session.run(feeds);
const runModelTime = new Date().getTime();
console.log(`Run model time: ${runModelTime - makeTensorTime} ms`);
const outputName = session.outputNames[0];
const output = results[outputName];
const categories = ["drawings", "hentai", "neutral", "porn", "sexy"];
const sortedIndices = output.data
.map((value, index) => index)
.sort((a, b) => output.data[b] - output.data[a]);
const imagePreds = {};
imagePreds[imagePath] = {};
for (const index of sortedIndices) {
imagePreds[imagePath][categories[index]] = output.data[index].toString();
}
console.log(JSON.stringify(imagePreds, null, 2));
}
const imagePath = "./images/mnzl.jpg";
const modelPath = "./model.onnx";
runModel(imagePath, modelPath);
```
Output:
```plaintext
Load image time: 52 ms
Load model time: 369 ms
Make tensor time: 1 ms
Run model time: 94 ms
{
"../images/mnzl.jpg": {
"hentai": "0.6869526505470276",
"drawings": "0.2912766933441162",
"porn": "0.01450809370726347",
"sexy": "0.00663922680541873",
"neutral": "0.0006233601598069072"
}
}
```
### Python
```py
import numpy as np
import onnxruntime as ort
from PIL import Image
image_path = './images/95d4h.jpg'
image = Image.open(image_path)
image = image.resize((299, 299))
input_data = np.expand_dims(np.array(image), axis=0).astype(np.float32)
input_data /= 255.0
sess = ort.InferenceSession("model.onnx")
input_name = sess.get_inputs()[0].name
result = sess.run(None, {input_name: input_data})
categories = ['drawings', 'hentai', 'neutral', 'porn', 'sexy']
sorted_indices = np.argsort(result[0], axis=1).tolist()
probs = []
for i, single_indices in enumerate(sorted_indices):
single_probs = []
for j, index in enumerate(single_indices):
single_probs.append(result[0][i][index])
sorted_indices[i][j] = categories[index]
probs.append(single_probs)
image_preds = {}
image_preds[image_path] = {}
for _ in range(len(sorted_indices[0])):
image_preds[image_path][sorted_indices[0][_]] = str(probs[0][_])
import json
print(json.dumps(image_preds, sort_keys=True, indent=2))
# note that the sort is incorrect
```
## Acknowledgements
+ [https://github.com/GantMan/nsfw_model](https://github.com/GantMan/nsfw_model)
+ [https://github.com/onnx/tensorflow-onnx](https://github.com/onnx/tensorflow-onnx)
+ Generative Pre-trained Transformer 4