Datasets
Models
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datasets.py
=======================
Houses the DynamicImageDataset class, also functions to help with image color channel normalization, transformers, etc..
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class2one_hotc C s ddä S )zvTransformer for random 90 degree rotation image.
Returns
-------
function
Transformer function for operation.
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ť}|| S )a╣ Get data transformers for training test and validation sets.
Parameters
----------
patch_size:int
Original patch size being transformed.
mean : list of float
Mean RGB
std : list of float
Std RGB
resize : int
Which patch size to resize to.
transform_platform : str
Use pytorch or albumentation transforms.
elastic : bool
Whether to add elastic deformations from albumentations.
Returns
-------
dict
Transformers.
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patch_sizer r! ┌resize┌transform_platform┌elasticZdata_transformsr r r ┌get_data_transforms% sJ . . 0░<Fr/ c C s t d| |ddŹS )zĘCreate transformers.
Parameters
----------
mean : list
See get_data_transforms.
std : list
See get_data_transforms.
Returns
-------
dict
Transformers.
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| â }|jâ jâ jâ jâ }|jâ jâ jâ jâ }tjt||dŹ|d â tj|d â}|S )a Find mean and standard deviation of images in batches.
Parameters
----------
normalization_file : str
File to store normalization information.
dataset_opts : type
Dictionary storing information to create DynamicDataset class.
Returns
-------
dict
Stores RGB mean, stdev.
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rV c C s" |d| |dŹ}|d |d j â fS )a Run albumentations and return an image and its segmentation mask.
Parameters
----------
img : array
Image as array
mask : array
Categorical pixel by pixel.
transformer :
Transformation object.
Returns
-------
tuple arrays
Image and mask array.
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ddä Z dd ä Z dS ) rA aˇ Generate image dataset that accesses images and annotations via dask.
Parameters
----------
dataset_df : dataframe
Dataframe with WSI, which set it is in (train/test/val) and corresponding WSI labels if applicable.
set : str
Whether train, test, val or pass (normalization) set.
patch_info_file : str
SQL db with positional and annotation information on each slide.
transformers : dict
Contains transformers to apply on images.
input_dir : str
Directory where images comes from.
target_names : list/str
Names of initial targets, which may be modified.
pos_annotation_class : str
If selected and predicting on WSI, this class is labeled as a positive from the WSI, while the other classes are not.
other_annotations : list
Other annotations to consider from patch info db.
segmentation : bool
Conducting segmentation task?
patch_size : int
Patch size.
fix_names : bool
Whether to change the names of dataset_df.
target_segmentation_class : list
Now can be used for classification as well, matched with two below options, samples images only from this class. Can specify this and below two options multiple times.
target_threshold : list
Sampled only if above this threshold of occurence in the patches.
oversampling_factor : list
Over sample them at this amount.
n_segmentation_classes : int
Number classes to segment.
gdl : bool
Using generalized dice loss?
mt_bce : bool
For multi-target prediction tasks.
classify_annotations : bool
For classifying annotations.
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z DynamicImageDataset.__init__c C sF t j| j|jgddŹjddŹ| _| jjd | _| jrB| jj|jâ dS )zžConcatenate this dataset with others. Updates its own internal attributes.
Parameters
----------
other_dataset : DynamicImageDataset
Other image dataset.
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other_datasetr r r rÄ F s zDynamicImageDataset.concatc C s* | j j| j d |k | _ | j jd | _| S )zĽReduce the sample set to just images from one ID.
Parameters
----------
ID : str
Basename/ID to predict on.
Returns
-------
self
rd r )rő râ rĹ rĺ )r` rd r r r ┌ retain_IDU s
z DynamicImageDataset.retain_IDc c s6 x0| j d jâ D ] }tj| â}||j|âfV qW dS )zĺGenerator similar to groupby, but splits up by ID, generates (ID,data) using retain_ID.
Returns
-------
generator
ID, DynamicDataset
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j|â|dŹ}|S )zđWeight loss function with weights inversely proportional to the class appearence.
Parameters
----------
i : int
If multi-target, class used for weighting.
Returns
-------
self
Dataset.
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â d | _| jS ) ap Label binarize some annotations or threshold them if classifying slide annotations.
Parameters
----------
binarizer : LabelBinarizer
Binarizes the labels of a column(s)
num_targets : int
Number of desired targets to preidict on.
binary_threshold : float
Amount of annotation in patch before positive annotation.
Returns
-------
binarizer
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Parameters
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p : float
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