https://github.com/optidash-ai/optidash-node
The official Node integration for Optidash API
https://github.com/optidash-ai/optidash-node
image-analysis image-manipulation image-optimization image-processing image-resizing optidash
Last synced: about 1 month ago
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The official Node integration for Optidash API
- Host: GitHub
- URL: https://github.com/optidash-ai/optidash-node
- Owner: optidash-ai
- License: mit
- Created: 2020-05-13T21:10:13.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2020-07-01T19:58:46.000Z (almost 6 years ago)
- Last Synced: 2025-08-17T07:37:55.331Z (10 months ago)
- Topics: image-analysis, image-manipulation, image-optimization, image-processing, image-resizing, optidash
- Language: JavaScript
- Homepage: https://optidash.ai
- Size: 16.6 KB
- Stars: 5
- Watchers: 3
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
Optidash is a modern, AI-powered image optimization and processing API.
We will drastically speed-up your websites and save you money on bandwidth and storage.
---
The official Node integration for the Optidash API.
---
### Documentation
See the [Optidash API docs](https://docs.optidash.ai/).
### Installation
```bash
$ npm install optidash --save
```
### Quick examples
Optidash API enables you to provide your images for optimization in two ways - by uploading them directly to the API ([Image Upload](https://docs.optidash.ai/requests/image-upload)) or by providing a publicly available image URL ([Image Fetch](https://docs.optidash.ai/requests/image-fetch)).
You may also choose your preferred [response method](https://docs.optidash.ai/introduction#choosing-response-method-and-format) on a per-request basis. By default, the Optidash API will return a [JSON response](https://docs.optidash.ai/responses/json-response-format) with rich metadata pertaining to input and output images. Alternatively, you can use [binary responses](https://docs.optidash.ai/responses/binary-responses). When enabled, the API will respond with a full binary representation of the resulting (output) image. This Node module exposes two convenience methods for interacting with binary responses: `.toFile()` and `.toBuffer()`.
#### Image upload
Here is a quick example of uploading a local file for optimization and processing. It calls `.toJSON()` at a final step and instructs the API to return a JSON response.
```js
const Optidash = require("optidash");
// Pass your Optidash API Key to the constructor
const opti = new Optidash("your-api-key");
// Upload an image from disk, resize it to 100 x 75,
// automatically enhance, and adjust sharpness parameter
opti.upload("path/to/input.jpg")
.optimize({
compression: "medium"
})
.resize({
width: 100,
height: 75
})
.auto({
enhance: true
})
.adjust({
unsharp: 10
})
.toJSON((err, meta) => {
if (err) {
return console.log(err);
}
// You'll find the full JSON metadata within the `meta` object
if (meta.success) {
console.log(meta.output.url);
} else {
console.log(meta.message);
}
});
```
#### Image fetch
If you already have your source visuals publicly available online, we recommend using Image Fetch by default. That way you only have to send a JSON payload containing image URL and processing steps. This method is also much faster than uploading a full binary representation of the image.
```js
const Optidash = require("optidash");
// Pass your Optidash API Key to the constructor
const opti = new Optidash("your-api-key");
// Provide a publicly available image URL with `.fetch()` method,
// apply Gaussian blur using highly optimized PNG as the output format.
// We'll also use `.toFile()` method and stream the output image to disk
opti.fetch("https://www.website.com/image.jpg")
.optimize({
compression: "medium"
})
.filter({
blur: {
mode: "gaussian",
value: 10
}
})
.output({
format: "png"
})
.toFile("path/to/output.png", (err, meta) => {
if (err) {
return console.log(err);
}
// You'll find the full JSON metadata within the `meta` object
if (meta.success) {
console.log(meta.output.url);
} else {
console.log(meta.message);
}
});
```
### License
This software is distributed under the MIT License. See the [LICENSE](LICENSE) file for more information.