Forked from tutorial/linear_machine_projection/4
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 | system/array_2d_uint8/1 | Input |
| id | system/uint64/1 | Input |
| projections | system/array_2d_floats/1 | Output |
| Endpoint Name | Data Format | Nature |
|---|---|---|
| subspace | tutorial/linear_machine/1 | Input |
xxxxxxxxxximport numpy as npdef project(image, weights, biases, input_subtract, input_divide): image = (image - input_subtract) / input_divide image = np.dot(image, weights.T) + biases return imageclass Algorithm: def __init__(self): self.projections = [] def prepare(self, data_loaders): # Loads the model at the beginning loader = data_loaders.loaderOf("subspace") for i in range(loader.count()): view = loader.view("subspace", i) data, _, _ = view[0] data = data["subspace"] self.weights = data.weights self.biases = data.biases self.input_subtract = data.input_subtract self.input_divide = data.input_divide return True def process(self, inputs, data_loaders, outputs): # collect all the image projections for the current template image = inputs['image'].data.value.astype('float64').flatten() projection = project(image, self.weights, self.biases, self.input_subtract, self.input_divide) self.projections.append(projection) # generate the results (when all the images of the current template have been # projected) if inputs["id"].isDataUnitDone(): outputs['projections'].write({ 'value': np.array(self.projections, dtype=np.float64) }) self.projections = [] return TrueThe code for this algorithm in Python
The ruler at 80 columns indicate suggested POSIX line breaks (for readability).
The editor will automatically enlarge to accomodate the entirety of your input
Use keyboard shortcuts for search/replace and faster editing. For example, use Ctrl-F (PC) or Cmd-F (Mac) to search through this box
This algorithm linearizes and accumulates images into a buffer, before applying a linear transformation (e.g. using a projection matrix computed by principal component analysis). The linear transformation relies on the Bob library.
The inputs are:
The output projections is a two-dimensional array of floats (64 bits), the number of rows corresponding to the number of accumulated images (with the same identifier), and the number of columns to the output dimensionality after applying the linear transformation.
| Updated | Name | Databases/Protocols | Analyzers | |||
|---|---|---|---|---|---|---|
| amohammadi/tutorial/eigenface/1/atnt-eigenfaces-66-comp | atnt/5@idiap | tutorial/postperf_iso/1 | ||||
| amohammadi/tutorial/eigenface/1/atnt-eigenfaces-67-comp | atnt/5@idiap | tutorial/postperf_iso/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.