iris.nodes.iris_response package

Subpackages

• iris.nodes.iris_response.image_filters package
  • Submodules
  • iris.nodes.iris_response.image_filters.gabor_filters module
    • GaborFilter
      • GaborFilter.Parameters
      • GaborFilter.compute_kernel_values()
    • LogGaborFilter
      • LogGaborFilter.Parameters
      • LogGaborFilter.compute_kernel_values()
    • convert_to_fixpoint_kernelvalues()
    • get_radius()
    • get_xy_mesh()
    • normalize_kernel_values()
    • rotate()
    • upper_bound_Gabor_parameters()
    • upper_bound_LogGabor_parameters()
  • iris.nodes.iris_response.image_filters.image_filter_interface module
    • ImageFilter
      • ImageFilter.Parameters
      • ImageFilter.compute_kernel_values()
      • ImageFilter.kernel_values
  • Module contents
• iris.nodes.iris_response.probe_schemas package
  • Submodules
  • iris.nodes.iris_response.probe_schemas.probe_schema_interface module
    • ProbeSchema
      • ProbeSchema.ProbeSchemaParameters
      • ProbeSchema.generate_schema()
      • ProbeSchema.phis
      • ProbeSchema.rhos
  • iris.nodes.iris_response.probe_schemas.regular_probe_schema module
    • RegularProbeSchema
      • RegularProbeSchema.RegularProbeSchemaParameters
      • RegularProbeSchema.find_suitable_n_rows()
      • RegularProbeSchema.generate_schema()
  • Module contents

Submodules

iris.nodes.iris_response.conv_filter_bank module

class iris.nodes.iris_response.conv_filter_bank.ConvFilterBank(iris_code_version: str = 'v0.1', filters: ~typing.List[~iris.nodes.iris_response.image_filters.image_filter_interface.ImageFilter] = [<iris.nodes.iris_response.image_filters.gabor_filters.GaborFilter object>, <iris.nodes.iris_response.image_filters.gabor_filters.GaborFilter object>], probe_schemas: ~typing.List[~iris.nodes.iris_response.probe_schemas.probe_schema_interface.ProbeSchema] = [<iris.nodes.iris_response.probe_schemas.regular_probe_schema.RegularProbeSchema object>, <iris.nodes.iris_response.probe_schemas.regular_probe_schema.RegularProbeSchema object>])

Bases: Algorithm

Apply filter bank.

Algorithm steps:

1. Obtain filters and corresponding probe schemas.

2. Apply convolution to a given pair of normalized iris image using the filters and probe schemas.

3. Generate the iris response and corresponding mask response.

class Parameters(*, filters: List[ImageFilter], probe_schemas: List[ProbeSchema], iris_code_version: str)

Bases: Parameters

Default ConvFilterBank parameters.

filters: List[ImageFilter]

iris_code_version: str

probe_schemas: List[ProbeSchema]

run(normalization_output: NormalizedIris) → IrisFilterResponse

Apply filters to a normalized iris image.

Parameters:

normalization_output (NormalizedIris) – Output of the normalization process.

Returns:

filter responses.

Return type:

IrisFilterResponse

iris.nodes.iris_response.conv_filter_bank.polar_img_padding(img: ndarray, p_rows: int, p_cols: int) → ndarray

Apply zero-padding vertically and rotate-padding horizontally to a normalized image in polar coordinates.

Parameters:

• img (np.ndarray) – normalized image in polar coordinates.

• p_rows (int) – padding size on top and bottom.

• p_cols (int) – padding size on left and right.

Returns:

padded image.

Return type:

np.ndarray

Module contents