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It is a binarization algorithm which can be used to select the optimal threshold between foreground and background quantities based on finding the minimum point of [cross variance](). This is particularly useful when performing segmentation in [image processing]().\n\n\n \"code\n\n\n
\n
\n\n

\n \n

\n\nYou can find an awesome description of the algorithm, which inspired this repository, [here](http://www.labbookpages.co.uk/software/imgProc/otsuThreshold.html).\n\n## 🚀 Getting Started\n\nUsing [`npm`]():\n\n```bash\nnpm install --save otsu\n```\n\nUsing [`yarn`]():\n\n```bash\nyarn add otsu\n```\n## ✍️ Usage\n\nIt's really simple to find the threshold of your image. All you have to do is supply the data!\n\nIn the example below, we have a one-dimensional array of intensity data. Most greyscale image formats, such as [MNIST](http://yann.lecun.com/exdb/mnist/), can be fed directly into otsu this way. However, images that consist of multiple attributes per pixel (such as `(r,g,b)`) will first require pre-processing.\n\n```javascript\nimport otsu from 'otsu';\n\nconst intensity = [255, 0, 128, 4, 95 ...];\nconst t = otsu(intensity);\n\nconsole.log(t); // i.e. 128\n\nconst thresholded = intensity.map(e => (e > t ? 1 : 0));\n```\n\n## ✌️ License\n[MIT](https://opensource.org/licenses/MIT)\n\n\n

\n \n \"Buy\n \n

\n","readmeFilename":"README.md"}