ida_lib.core package¶

Submodules¶

ida_lib.core.pipeline module¶

class ida_lib.core.pipeline.Pipeline(pipeline_operations: list, resize: tuple = None, interpolation: str = 'bilinear', padding_mode: str = 'zeros', output_format: str = 'dict', output_type: Optional[<sphinx.ext.autodoc.importer._MockObject object at 0x7fe3c7104f28>] = None)¶

Bases: object

The pipeline object represents the pipeline with data transformation operations (pictures, points). When executed, on a batch of images,it applies the necessary transformations (being different on each image based on the probabilities of each operation included).

Considerations:

1) The images must be of the same size, or the RESIZE operation must be included so that the transformations can be applied correctly 2) To run the pipeline, it accepts any type of input metadata named in the input dict. In particular it gives special treatment

to data named as:

Mask: it is affected by geometric transformations and its output is discrete to values of 0-1

Segmap: generalization of mask. Every value is discrete

Image: affected by geometric and color transformations

Keypoints: geometric transformations are applied to them as coordinates.

Others: any other metadata will not be transformed (example: ‘tag’, ‘target’…)

Example:

pip = pipeline(resize = (25, 25), pipeline_operations=(

translate_pipeline(probability=0.5, translation=(3, 0.05)), vflip_pipeline(probability=0.5), hflip_pipeline(probability=0.5), contrast_pipeline(probability=0.5, contrast_factor=1), random_brightness_pipeline(probability=0.2, brightness_range=(1.5, 1.6)), random_scale_pipeline(probability=1, scale_range=(0.5, 1.5), center_deviation=20), random_rotate_pipeline(probability=0.2, degrees_range=(-50, 50), center_deviation=20))

))

get_data_types() → tuple¶: Returns the tuple of data types identified on the input data

ida_lib.core.pipeline_functional module¶

ida_lib.core.pipeline_functional.get_compose_matrix(operations: list, data_info: Optional[dict] = None) → <sphinx.ext.autodoc.importer._MockObject object at 0x7fe3c7104828>¶

Returns the transformation matrix composed by the multiplication in order of the input operations (according to their probability) If data_info is not None, go through the operations by entering the necessary information about the images (image center, shape..)

Parameters:	operations – list of pipeline operations data_info – dict with data info to configure operations parameters
Returns:	torch tensor of the transform matrix

ida_lib.core.pipeline_functional.get_compose_function(operations: list) → <sphinx.ext.autodoc.importer._MockObject object at 0x7fe3c71048d0>¶

returns the LUT table with the correspondence of each possible value according to the color operations to be implemented (according to their probability)

Parameters:	operations – list of pipeline operations
Returns:	compose function

ida_lib.core.pipeline_functional.preprocess_data(data: Union[list, dict], batch_info: Union[list, dict] = None, interpolation: str = None, resize: Optional[tuple] = None) → list¶

Combines the 2d information in a tensor and the points in a homogeneous coordinate matrix that allows applying the geometric operations in a single joint operation on the data and another on the points.

Loads the data as tensor in GPU to prepare them as input to a neural network
Analyze the data info required for the transformations (shape, bpp…)
Resize the 2d data and keypoints to the new shape

Parameters:	resize – if it is wanted to resize the data, indicate the new size data – list of elements to be transformed through the pipe batch_info – dict with the required data info interpolation – desired interpolation mode to be applied
Returns:	preprocessed and resized data, and dict with batch info

ida_lib.core.pipeline_functional.split_operations_by_type(operations: list) → tuple¶

Split input operations into sub-lists of each transformation type the normalization operation is placed last to apply correctly the other operations

Parameters:	operations – list of pipeline operations
Returns:	tuple of lists of the operations separated into color, geometry and independent

ida_lib.core.pipeline_functional.postprocess_data(batch: list, batch_info: dict, data_original: Optional[list] = None, visualize: bool = False, original_type: <sphinx.ext.autodoc.importer._MockObject object at 0x7fe3c715c9e8> = <sphinx.ext.autodoc.importer._MockObject object>, output_format: str = 'dict') → list¶

Restores the data to the original form; separating the matrix into the different 2d input data and point coordinates.

Parameters:

batch – list of elements to be transformed through the pipe
batch_info – dict with necessary information about the batch data
data_original – original batch before transforms
visualize – whether to run the visualization tool or not
original_type – torch original type of the input data to do the conversion of the output data to this type
output_format – whether to format the output element as a dict or as a tuple

Returns:

processed data

ida_lib.core.pipeline_functional.switch_point_positions(point_matrix, input_list)¶

ida_lib.core.pipeline_geometric_ops module¶

class ida_lib.core.pipeline_geometric_ops.HflipPipeline(probability: float = 1, exchange_points: tuple = None)¶

Bases: ida_lib.core.pipeline_operations.PipelineOperation

Horizontally flip the input image-mask-keypoints and 2d data

config_parameters(data_info: dict)¶

get_op_matrix() → <sphinx.ext.autodoc.importer._MockObject object at 0x7fe3c6f78ef0>¶

need_data_info() → bool¶

switch_points()¶

class ida_lib.core.pipeline_geometric_ops.VflipPipeline(probability: float, exchange_points: tuple = None)¶

Bases: ida_lib.core.pipeline_operations.PipelineOperation

Vertically flip the input image-mask-keypoints and 2d data

config_parameters(data_info: dict)¶

get_op_matrix() → <sphinx.ext.autodoc.importer._MockObject object at 0x7fe3c6fc30f0>¶

need_data_info() → bool¶

switch_points()¶

class ida_lib.core.pipeline_geometric_ops.RotatePipeline(degrees: int, center: <sphinx.ext.autodoc.importer._MockObject object at 0x7fe3c6f78780> = None, probability: float = 1)¶

Bases: ida_lib.core.pipeline_operations.PipelineOperation

Rotate the input image-mask-keypoints and 2d data by the input degrees

config_parameters(data_info: dict)¶

get_op_matrix() → <sphinx.ext.autodoc.importer._MockObject object at 0x7fe3c6f787b8>¶

need_data_info() → bool¶

static switch_points()¶

class ida_lib.core.pipeline_geometric_ops.ShearPipeline(shear: tuple, probability: float = 1)¶

Bases: ida_lib.core.pipeline_operations.PipelineOperation

Shear the input image-mask-keypoints and 2d data by the input shear factor

get_op_matrix() → <sphinx.ext.autodoc.importer._MockObject object at 0x7fe3c6f78a90>¶

static need_data_info() → bool¶

static switch_points()¶

class ida_lib.core.pipeline_geometric_ops.ScalePipeline(scale_factor: Union[float, tuple], probability: float = 1, center: <sphinx.ext.autodoc.importer._MockObject object at 0x7fe3c6f6eef0> = None)¶

Bases: ida_lib.core.pipeline_operations.PipelineOperation

Scale the input image-mask-keypoints and 2d data by the input scaling value

config_parameters(data_info: dict)¶

get_op_matrix()¶

need_data_info()¶

static switch_points()¶

class ida_lib.core.pipeline_geometric_ops.TranslatePipeline(translation: tuple, probability: float = 1)¶

Bases: ida_lib.core.pipeline_operations.PipelineOperation

Translate the input image-mask-keypoints and 2d data by the input translation

get_op_matrix() → <sphinx.ext.autodoc.importer._MockObject object at 0x7fe3c6f54390>¶

static need_data_info() → bool¶

static switch_points()¶

class ida_lib.core.pipeline_geometric_ops.RandomScalePipeline(probability: float, scale_range: tuple, keep_aspect: bool = True, center_deviation: int = None, center: <sphinx.ext.autodoc.importer._MockObject object at 0x7fe3c6f78550> = None)¶

Bases: ida_lib.core.pipeline_operations.PipelineOperation

Scale the input image-mask-keypoints and 2d data by a random scaling value calculated within the input range

config_parameters(data_info: dict)¶

get_op_matrix() → <sphinx.ext.autodoc.importer._MockObject object at 0x7fe3c6f78588>¶

need_data_info() → bool¶

static switch_points()¶

class ida_lib.core.pipeline_geometric_ops.RandomRotatePipeline(degrees_range: tuple, probability: float = 1, center_deviation: int = None, center: <sphinx.ext.autodoc.importer._MockObject object at 0x7fe3c6f789b0> = None)¶

Bases: ida_lib.core.pipeline_operations.PipelineOperation

Rotate the input image-mask-keypoints and 2d data by a random scaling value calculated within the input range

config_parameters(data_info: dict)¶

get_op_matrix() → <sphinx.ext.autodoc.importer._MockObject object at 0x7fe3c6f789e8>¶

need_data_info() → bool¶

static switch_points()¶

class ida_lib.core.pipeline_geometric_ops.RandomShearPipeline(probability: float, shear_range: tuple)¶

Bases: ida_lib.core.pipeline_operations.PipelineOperation

Shear the input image-mask-keypoints and 2d data by a random shear value calculated within the input range

get_op_matrix() → <sphinx.ext.autodoc.importer._MockObject object at 0x7fe3c6f78cc0>¶

static need_data_info() → bool¶

static switch_points()¶

class ida_lib.core.pipeline_geometric_ops.RandomTranslatePipeline(probability: float, translation_range: tuple, same_translation_on_axis: bool = False)¶

Bases: ida_lib.core.pipeline_operations.PipelineOperation

Translate the input image-mask-keypoints and 2d data by a random translation value calculated within the input range

get_op_matrix()¶

static need_data_info() → bool¶

static switch_points()¶

ida_lib.core.pipeline_local_ops module¶

class ida_lib.core.pipeline_local_ops.BlurPipeline(probability: float = 1, blur_size: tuple = (5, 5))¶

Bases: ida_lib.core.pipeline_operations.PipelineOperation

Blur input image ( non-weighted blur)

apply_to_image_if_probability(img: <sphinx.ext.autodoc.importer._MockObject object at 0x7fe3c6ec7da0>) → <sphinx.ext.autodoc.importer._MockObject object at 0x7fe3c6ec7d68>¶

get_op_matrix()¶

class ida_lib.core.pipeline_local_ops.GaussianBlurPipeline(probability: float = 1, blur_size: tuple = (5, 5))¶

Bases: ida_lib.core.pipeline_operations.PipelineOperation

Blur input image by a Gaussian function

apply_to_image_if_probability(img: <sphinx.ext.autodoc.importer._MockObject object at 0x7fe3c6ec77f0>) → <sphinx.ext.autodoc.importer._MockObject object at 0x7fe3c6ec7828>¶

get_op_matrix()¶

class ida_lib.core.pipeline_local_ops.GaussianNoisePipeline(probability: float = 1, var: float = 0.5)¶

Bases: ida_lib.core.pipeline_operations.PipelineOperation

Add gaussian noise to the input image (gaussian noise is a statistical noise having a probability density function (PDF) equal to that of the normal distribution)

apply_to_image_if_probability(img: <sphinx.ext.autodoc.importer._MockObject object at 0x7fe3c6f12668>) → <sphinx.ext.autodoc.importer._MockObject object at 0x7fe3c6f126a0>¶

get_op_matrix()¶

class ida_lib.core.pipeline_local_ops.PoissonNoisePipeline(probability: float = 1)¶

Bases: ida_lib.core.pipeline_operations.PipelineOperation

Add poison noise to the input image ( It is applied by adding Poisson-distributed noise)

apply_to_image_if_probability(img: <sphinx.ext.autodoc.importer._MockObject object at 0x7fe3c6ec75f8>) → <sphinx.ext.autodoc.importer._MockObject object at 0x7fe3c6ec7630>¶

get_op_matrix()¶

class ida_lib.core.pipeline_local_ops.SaltAndPepperNoisePipeline(probability=1, amount: Optional[float] = 0.01, s_vs_p: Optional[float] = 0.5)¶

Bases: ida_lib.core.pipeline_operations.PipelineOperation

Add salt and pepper noise to the input image (salt-and-pepper noise is a statistical noise compose of white (salt) and black (pepper) pixels)

apply_to_image_if_probability(img: <sphinx.ext.autodoc.importer._MockObject object at 0x7fe3c6f9e5c0>) → <sphinx.ext.autodoc.importer._MockObject object at 0x7fe3c6f9e198>¶

get_op_matrix()¶

class ida_lib.core.pipeline_local_ops.SpekleNoisePipeline(probability: float = 1, mean: Optional[float] = 0, var: Optional[float] = 0.01)¶

Bases: ida_lib.core.pipeline_operations.PipelineOperation

Add spekle noise to the input image (Speckle is a granular interference that inherently exists in and degrades the quality of the active radar, synthetic aperture radar (SAR), medical ultrasound and optical coherence tomography images. It is applied by adding the image multiplied by the noise matrix -> img + img * uniform_noise)

apply_to_image_if_probability(img: <sphinx.ext.autodoc.importer._MockObject object at 0x7fe3c6f9e278>) → <sphinx.ext.autodoc.importer._MockObject object at 0x7fe3c6f9e358>¶

get_op_matrix()¶

ida_lib.core.pipeline_operations module¶

class ida_lib.core.pipeline_operations.PipelineOperation(op_type: str, probability: float = 1)¶

Bases: abc.ABC

Abstract class of pipeline operations

apply_according_to_probability() → bool¶

get_op_matrix()¶

get_op_type()¶

ida_lib.core.pipeline_pixel_ops module¶

class ida_lib.core.pipeline_pixel_ops.ContrastPipeline(contrast_factor: float, probability: float = 1)¶

Bases: ida_lib.core.pipeline_operations.PipelineOperation

Change the contrast of the input image.

get_op_matrix()¶

transform_function(x: int) → float¶

class ida_lib.core.pipeline_pixel_ops.RandomContrastPipeline(contrast_range: tuple, probability: float = 1)¶

Bases: ida_lib.core.pipeline_operations.PipelineOperation

Change the contrast of the input image with a random contrast factor calculated within the input range

get_op_matrix()¶

transform_function(x)¶

class ida_lib.core.pipeline_pixel_ops.BrightnessPipeline(brightness_factor: float, probability: float = 1)¶

Bases: ida_lib.core.pipeline_operations.PipelineOperation

Change brightness of the input image

get_op_matrix()¶

get_op_type()¶

transform_function(x: int) → float¶

class ida_lib.core.pipeline_pixel_ops.RandomBrightnessPipeline(probability: float, brightness_range: tuple)¶

Bases: ida_lib.core.pipeline_operations.PipelineOperation

Change brightness of the input image to random amount calculated within the input range

get_op_matrix()¶

get_op_type()¶

transform_function(x: int) → float¶

class ida_lib.core.pipeline_pixel_ops.GammaPipeline(gamma_factor: float, probability: float = 1)¶

Bases: ida_lib.core.pipeline_operations.PipelineOperation

Change the luminance of the input image

get_op_matrix()¶

transform_function(x: int) → float¶

class ida_lib.core.pipeline_pixel_ops.RandomGammaPipeline(gamma_range: tuple, probability: float = 1)¶

Bases: ida_lib.core.pipeline_operations.PipelineOperation

Change the luminance of the input image by a random gamma factor calculated within the input range

get_op_matrix()¶

transform_function(x: int) → float¶

class ida_lib.core.pipeline_pixel_ops.NormalizePipeline(probability: float = 1, old_range: tuple = (0, 255), new_range: tuple = (0, 1))¶

Bases: ida_lib.core.pipeline_operations.PipelineOperation

Change the pixels value to a normalize range

get_op_matrix()¶

static transform_function(x: int) → float¶

class ida_lib.core.pipeline_pixel_ops.DenormalizePipeline(probability: float = 1, old_range: tuple = (0, 1), new_range: tuple = (0, 255))¶

Bases: ida_lib.core.pipeline_operations.PipelineOperation

Denormalize pixel value

get_op_matrix()¶

transform_function(x: int) → float¶

ida_lib.core package¶

Submodules¶

ida_lib.core.pipeline module¶

ida_lib.core.pipeline_functional module¶

ida_lib.core.pipeline_geometric_ops module¶

ida_lib.core.pipeline_local_ops module¶

ida_lib.core.pipeline_operations module¶

ida_lib.core.pipeline_pixel_ops module¶

Module contents¶