StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics
https://doi.org/10.1109/TVCG.2020.3030352
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51 lines
1.4 KiB
51 lines
1.4 KiB
ndarray-warp
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============
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Inverse [image warping](http://www.cs.princeton.edu/courses/archive/fall00/cs426/lectures/warp/warp.pdf) operations for [ndarrays](https://github.com/mikolalysenko/ndarray).
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Example
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=======
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```javascript
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//Load input image
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var lena = require("luminance")(require("lena"))
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//Allocate storage for result
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var result = require("zeros")([512, 512])
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//Apply warp
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require("ndarray-warp")(result, lena, function(out, inp) {
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var dx = inp[0] - 256
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var dy = inp[1] - 256
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var r = Math.sqrt(dx * dx + dy * dy)
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var theta = Math.atan2(dy, dx)
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out[0] = 0.9 * r * Math.cos(theta + 0.01 * r) + 256
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out[1] = 0.7 * r * Math.sin(theta + 0.01 * r) + 256
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})
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//Save the result to stdout
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require("save-pixels")(result, "png").pipe(process.stdout)
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```
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Which produces the following image:
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<img src="https://raw.github.com/mikolalysenko/ndarray-warp/master/example/warp.png">
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Install
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=======
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npm install ndarray-warp
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### `require("ndarray-warp")(output, input, map(out_coord, in_coord))`
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Applies an inverse warp to an image
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* `output` is an ndarray image that gets the result of applying the warp
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* `input` is an ndarray iamge that is warped
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* `map(result, coord)` is a mapping from the coordinates of out image to input image.
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+ `result` gets the resulting coordinate in `input`
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+ `coord` is the coordinate in `output`
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# Credits
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(c) 2013 Mikola Lysenko. MIT License
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