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Delete ccagt.py

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- import json
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- import os
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- from collections import OrderedDict, defaultdict
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- from math import ceil
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-
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- import numpy as np
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- import pandas as pd
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-
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- import datasets
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-
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-
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- logger = datasets.logging.get_logger(__name__)
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-
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- CCAGT_CLASSES = OrderedDict(
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- {
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- 1: "NUCLEUS",
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- 2: "CLUSTER",
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- 3: "SATELLITE",
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- 4: "NUCLEUS_OUT_OF_FOCUS",
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- 5: "OVERLAPPED_NUCLEI",
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- 6: "NON_VIABLE_NUCLEUS",
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- 7: "LEUKOCYTE_NUCLEUS",
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- }
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- )
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-
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- _LICENSE = "CC BY NC 3.0 License"
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-
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- _CITATION = """\
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- @misc{CCAgTDataset,
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- doi = {10.17632/WG4BPM33HJ.2},
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- url = {https://data.mendeley.com/datasets/wg4bpm33hj/2},
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- author = {Jo{\\~{a}}o Gustavo Atkinson Amorim and Andr{\'{e}} Vict{\'{o}}ria Matias and Tainee Bottamedi and Vinícius Sanches and Ane Francyne Costa and Fabiana Botelho De Miranda Onofre and Alexandre Sherlley Casimiro Onofre and Aldo von Wangenheim},
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- title = {CCAgT: Images of Cervical Cells with AgNOR Stain Technique},
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- publisher = {Mendeley},
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- year = {2022},
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- copyright = {Attribution-NonCommercial 3.0 Unported}
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- }
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- """
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-
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- _HOMEPAGE = "https://data.mendeley.com/datasets/wg4bpm33hj"
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-
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- _DESCRIPTION = """\
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- The CCAgT (Images of Cervical Cells with AgNOR Stain Technique) dataset contains 9339 images (1600x1200 resolution where each pixel is 0.111µmX0.111µm) from 15 different slides stained using the AgNOR technique.
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- Each image has at least one label. In total, this dataset has more than 63K instances of annotated object.
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- The images are from the patients of the Gynecology and Colonoscopy Outpatient Clinic of the Polydoro Ernani de São Thiago University Hospital of the Universidade Federal de Santa Catarina (HU-UFSC).
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- """
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-
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- _DATA_URL = "https://md-datasets-cache-zipfiles-prod.s3.eu-west-1.amazonaws.com/wg4bpm33hj-2.zip"
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-
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-
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- def tvt(ids, tvt_size, seed=1609):
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- """From a list of indexes/ids (int) will generate the train-validation-test data.
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-
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- Based on `github.com/scikit-learn/scikit-learn/blob/37ac6788c9504ee409b75e5e24ff7d86c90c2ffb/sklearn/model_selection/_split.py#L2321`
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- """
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- n_samples = len(ids)
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-
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- qtd = {
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- "valid": ceil(n_samples * tvt_size[1]),
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- "test": ceil(n_samples * tvt_size[2]),
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- }
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- qtd["train"] = int(n_samples - qtd["valid"] - qtd["test"])
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-
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- rng = np.random.RandomState(seed)
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- permutatation = rng.permutation(ids)
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-
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- out = {
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- "train": set(permutatation[: qtd["train"]]),
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- "valid": set(permutatation[qtd["train"] : qtd["train"] + qtd["valid"]]),
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- "test": set(permutatation[qtd["train"] + qtd["valid"] :]),
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- }
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-
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- return out["train"], out["valid"], out["test"]
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-
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-
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- def annotations_per_image(df):
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- """
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- based on: https://github.com/johnnv1/CCAgT-utils/blob/54ade78e4ddb2e2ed9507b8a1633940897767cac/CCAgT_utils/describe.py#L152
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- """
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- df_describe_images = df.groupby(["image_id", "category_id"]).size().reset_index().rename(columns={0: "count"})
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- df_describe_images = df_describe_images.pivot(columns=["category_id"], index="image_id")
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- df_describe_images = df_describe_images.rename(CCAGT_CLASSES, axis=1)
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- df_describe_images["qtd_annotations"] = df_describe_images.sum(axis=1)
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- df_describe_images = df_describe_images.fillna(0)
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- df_describe_images["NORs"] = (
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- df_describe_images[
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- "count",
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- CCAGT_CLASSES[2],
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- ]
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- + df_describe_images[
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- "count",
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- CCAGT_CLASSES[3],
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- ]
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- )
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-
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- return df_describe_images
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-
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-
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- def tvt_by_nors(df, tvt_size=(0.7, 0.15, 0.15), **kwargs):
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- """This will split the CCAgT annotations based on the number of NORs
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- into each image. With a silly separation, first will split
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- between each fold images with one or less NORs, after will split
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- images with the amount of NORs is between 2 and 7, and at least will
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- split images that have more than 7 NORs.
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-
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- based on `https://github.com/johnnv1/CCAgT-utils/blob/54ade78e4ddb2e2ed9507b8a1633940897767cac/CCAgT_utils/split.py#L64`
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- """
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- if sum(tvt_size) != 1:
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- raise ValueError("The sum of `tvt_size` need to be equal to 1!")
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-
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- df_describe_imgs = annotations_per_image(df)
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-
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- img_ids = {}
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- img_ids["low_nors"] = df_describe_imgs.loc[(df_describe_imgs["NORs"] < 2)].index
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- img_ids["medium_nors"] = df_describe_imgs[(df_describe_imgs["NORs"] >= 2) * (df_describe_imgs["NORs"] <= 7)].index
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- img_ids["high_nors"] = df_describe_imgs[(df_describe_imgs["NORs"] > 7)].index
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-
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- train_ids = set({})
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- valid_ids = set({})
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- test_ids = set({})
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-
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- for k, ids in img_ids.items():
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- logger.info(f"Splitting {len(ids)} images with {k} quantity...")
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- if len(ids) == 0:
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- continue
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- _train, _valid, _test = tvt(ids, tvt_size, **kwargs)
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- logger.info(f">T: {len(_train)} V: {len(_valid)} T: {len(_test)}")
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- train_ids = train_ids.union(_train)
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- valid_ids = valid_ids.union(_valid)
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- test_ids = test_ids.union(_test)
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-
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- return train_ids, valid_ids, test_ids
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-
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-
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- def get_basename(path):
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- return os.path.splitext(os.path.basename(path))[0]
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-
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-
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- def get_slide_id(path):
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- bn = get_basename(path)
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- slide_id = bn.split("_")[0]
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- return slide_id
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-
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-
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- class CCAgTConfig(datasets.BuilderConfig):
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- """BuilderConfig for CCAgT."""
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-
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- seed = 1609
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- tvt_size = (0.7, 0.15, 0.15)
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-
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-
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- class CCAgT(datasets.GeneratorBasedBuilder):
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- """Images of Cervical Cells with AgNOR Stain Technique (CCAgT) dataset"""
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-
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- test_dummy_data = False
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-
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- VERSION = datasets.Version("2.0.0")
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-
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- BUILDER_CONFIG_CLASS = CCAgTConfig
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- BUILDER_CONFIGS = [
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- CCAgTConfig(name="semantic_segmentation", version=VERSION, description="The semantic segmentation variant."),
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- CCAgTConfig(name="object_detection", version=VERSION, description="The object detection variant."),
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- CCAgTConfig(name="instance_segmentation", version=VERSION, description="The instance segmentation variant."),
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- ]
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-
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- DEFAULT_CONFIG_NAME = "semantic_segmentation"
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-
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- def _info(self):
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- assert len(CCAGT_CLASSES) == 7
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-
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- if self.config.name == "semantic_segmentation":
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- features = datasets.Features(
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- {
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- "image": datasets.Image(),
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- "annotation": datasets.Image(),
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- }
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- )
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- elif self.config.name == "object_detection":
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- features = datasets.Features(
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- {
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- "image": datasets.Image(),
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- "objects": datasets.Sequence(
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- {
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- "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
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- "label": datasets.ClassLabel(names=list(CCAGT_CLASSES.values())),
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- }
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- ),
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- }
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- )
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- elif self.config.name == "instance_segmentation":
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- features = datasets.Features(
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- {
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- "image": datasets.Image(),
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- "objects": datasets.Sequence(
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- {
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- "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
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- "segment": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))),
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- "label": datasets.ClassLabel(names=list(CCAGT_CLASSES.values())),
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- }
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- ),
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- }
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- )
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- else:
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- raise NotImplementedError
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-
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- return datasets.DatasetInfo(
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- description=_DESCRIPTION,
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- features=features,
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- homepage=_HOMEPAGE,
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- license=_LICENSE,
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- citation=_CITATION,
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- )
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-
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- def _download_and_extract_all(self, dl_manager):
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- def extracted_by_slide(paths):
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- return {get_slide_id(path): dl_manager.extract(path) for path in paths}
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-
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- data_dir = dl_manager.download_and_extract(_DATA_URL)
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- base_path = os.path.join(data_dir, "wg4bpm33hj-2")
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-
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- logger.info("Extracting images...")
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- self.images_base_dir = os.path.join(base_path, "images")
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- images_to_extract = [
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- os.path.join(self.images_base_dir, fn) for fn in os.listdir(self.images_base_dir) if fn.endswith(".zip")
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- ]
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- self.images_extracted = extracted_by_slide(images_to_extract)
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-
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- if self.config.name == "semantic_segmentation":
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- logger.info("Extracting masks...")
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- self.masks_base_dir = os.path.join(base_path, "masks")
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- masks_to_extract = [
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- os.path.join(self.masks_base_dir, fn) for fn in os.listdir(self.masks_base_dir) if fn.endswith(".zip")
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- ]
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- self.masks_extracted = extracted_by_slide(masks_to_extract)
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- elif self.config.name in {"object_detection", "instance_segmentation"}:
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- logger.info("Reading COCO OD file...")
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- ccagt_OD_COCO_path = os.path.join(base_path, "CCAgT_COCO_OD.json")
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- with open(ccagt_OD_COCO_path, "r", encoding="utf-8") as json_file:
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- coco_OD = json.load(json_file)
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-
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- self._imageid_to_coco_OD_annotations = defaultdict(list)
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- for labels in coco_OD["annotations"]:
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- self._imageid_to_coco_OD_annotations[labels["image_id"]].append(labels)
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-
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- logger.info("Loading dataset info...")
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- ccagt_raw_path = os.path.join(base_path, "CCAgT.parquet.gzip")
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- with open(ccagt_raw_path, "rb") as f:
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- self._ccagt_info = pd.read_parquet(f, columns=["image_name", "category_id", "image_id", "slide_id"])
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- self._bn_to_imageid = pd.Series(
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- self._ccagt_info["image_id"].values, index=self._ccagt_info["image_name"]
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- ).to_dict()
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-
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- def _split_generators(self, dl_manager):
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- """Returns SplitGenerators."""
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-
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- def build_path(basename, tp="images"):
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- slide = basename.split("_")[0]
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- if tp == "images":
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- dir_path = self.images_extracted[slide]
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- ext = ".jpg"
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- else:
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- dir_path = self.masks_extracted[slide]
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- ext = ".png"
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-
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- return os.path.join(dir_path, slide, basename + ext)
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-
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- def images_and_masks(basenames):
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- for bn in basenames:
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- yield build_path(bn), build_path(bn, "masks")
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-
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- def images_and_boxes(basenames):
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- for bn in basenames:
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- image_id = self._bn_to_imageid[bn]
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- labels = [
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- {"bbox": annotation["bbox"], "label": annotation["category_id"] - 1}
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- for annotation in self._imageid_to_coco_OD_annotations[image_id]
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- ]
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-
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- yield build_path(bn), labels
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-
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- def images_and_instances(basenames):
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- for bn in basenames:
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- image_id = self._bn_to_imageid[bn]
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- instances = [
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- {
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- "bbox": annotation["bbox"],
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- "label": annotation["category_id"] - 1,
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- "segment": annotation["segmentation"],
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- }
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- for annotation in self._imageid_to_coco_OD_annotations[image_id]
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- ]
292
-
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- yield build_path(bn), instances
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-
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- self._download_and_extract_all(dl_manager)
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-
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- logger.info("Splitting dataset based on the NORs quantity by image...")
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- train_ids, valid_ids, test_ids = tvt_by_nors(
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- self._ccagt_info, tvt_size=self.config.tvt_size, seed=self.config.seed
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- )
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- train_bn_images = self._ccagt_info.loc[self._ccagt_info["image_id"].isin(train_ids), "image_name"].unique()
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- valid_bn_images = self._ccagt_info.loc[self._ccagt_info["image_id"].isin(valid_ids), "image_name"].unique()
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- test_bn_images = self._ccagt_info.loc[self._ccagt_info["image_id"].isin(test_ids), "image_name"].unique()
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-
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- if self.config.name == "semantic_segmentation":
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- train_data = images_and_masks(train_bn_images)
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- valid_data = images_and_masks(valid_bn_images)
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- test_data = images_and_masks(test_bn_images)
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- elif self.config.name == "object_detection":
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- train_data = images_and_boxes(train_bn_images)
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- valid_data = images_and_boxes(valid_bn_images)
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- test_data = images_and_boxes(test_bn_images)
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- elif self.config.name == "instance_segmentation":
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- train_data = images_and_instances(train_bn_images)
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- valid_data = images_and_instances(valid_bn_images)
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- test_data = images_and_instances(test_bn_images)
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- else:
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- raise NotImplementedError
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-
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- return [
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- datasets.SplitGenerator(
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- name=datasets.Split.TRAIN,
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- gen_kwargs={"data": train_data},
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- ),
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- datasets.SplitGenerator(
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- name=datasets.Split.TEST,
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- gen_kwargs={"data": test_data},
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- ),
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- datasets.SplitGenerator(
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- name=datasets.Split.VALIDATION,
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- gen_kwargs={"data": valid_data},
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- ),
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- ]
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-
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- def _generate_examples(self, data):
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- if self.config.name == "semantic_segmentation":
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- for img_path, msk_path in data:
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- img_basename = get_basename(img_path)
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- image_id = self._bn_to_imageid[img_basename]
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- yield image_id, {
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- "image": img_path,
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- "annotation": msk_path,
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- }
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- elif self.config.name == "object_detection":
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- for img_path, labels in data:
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- img_basename = get_basename(img_path)
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- image_id = self._bn_to_imageid[img_basename]
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- yield image_id, {"image": img_path, "objects": labels}
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- elif self.config.name == "instance_segmentation":
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- for img_path, instances in data:
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- img_basename = get_basename(img_path)
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- image_id = self._bn_to_imageid[img_basename]
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- yield image_id, {"image": img_path, "objects": instances}
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- else:
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- raise NotImplementedError