TanTriggs image preprocessing for face recognition
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 |
|---|---|---|
| image_gray | system/array_2d_uint8/1 | Input |
| tantriggs_image | 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 |
|---|---|---|---|---|
| sigma0 | float64 | 1.0 | ||
| sigma1 | float64 | 2.0 | ||
| gamma | float64 | 0.2 | ||
| kernel_size | uint32 | 5 | ||
| threshold | float64 | 10.0 | ||
| alpha | float64 | 0.1 |
The code for this algorithm in Python
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This algorithm is a multi-stage image preprocessor relying on the Bob library. It implements the multi-stage preprocessing method described in [Tan07], the steps being a gamma correction, a difference of Gaussian filtering and a contrast equalization
Both inputs and outputs are expected to be grayscale images as two-dimensional arrays of floats (64 bits).
| [Tan07] |
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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.