Resizes images to a given bounding box
Forked from anjos/rgb2gray/1
Algorithms have at least one input and one output. All algorithm endpoints are organized in groups. Groups are used by the platform to indicate which inputs and outputs are synchronized together. The first group is automatically synchronized with the channel defined by the block in which the algorithm is deployed.
| Endpoint Name | Data Format | Nature |
|---|---|---|
| input | system/array_2d_uint8/1 | Input |
| output | system/array_2d_uint8/1 | Output |
Parameters allow users to change the configuration of an algorithm when scheduling an experiment
| Name | Description | Type | Default | Range/Choices |
|---|---|---|---|---|
| width | The width of the output image | uint32 | 30 | |
| height | The height of the output image | uint32 | 40 | |
| interpolation | The interpolation method to use for the resizing operation | string | bilinear | nearest, bilinear, bicubic, cubic |
The code for this algorithm in Python
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This converter will normalize input (gray-scaled) images so they all output with the same, parameterized, specifications.
| Updated | Name | Databases/Protocols | Analyzers | |||
|---|---|---|---|---|---|---|
| anjos/anjos/livdet-lda/1/livdet-2013-biometrika-test | livdet2013/1@Biometrika | anjos/livdet_analysis/1 |
This table shows the number of times this algorithm has been successfully run using the given environment. Note this does not provide sufficient information to evaluate if the algorithm will run when submitted to different conditions.