normalizatio (#7)

- Upload dataset (b1e50bc1db5b30a8f0fed20d7826bee8116a5c9e)
- Removing download script (7fed69a63c09fc6eac14bf461e7989999da148c2)
- Removing archives unrolling masks (e283da9250fce181ba069f9b60d4ddd560392493)

README.md

@@ -1,11 +1,33 @@
 ---
 license: cdla-permissive-2.0
+size_categories:
+- 1K<n<10K
 task_categories:
 - image-segmentation
 tags:
 - biology
-size_categories:
-- 1K<n<10K
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: label
+    dtype: image
+  splits:
+  - name: train
+    num_bytes: 9121146572.05
+    num_examples: 5745
+  - name: validation
+    num_bytes: 2367801100.383
+    num_examples: 1437
+  download_size: 11607836195
+  dataset_size: 11488947672.432999
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: validation
+    path: data/validation-*
 ---
 
 # PLANT ORGANS - WIP

sourcedata_labeled.tar.11 → data/train-00000-of-00019.parquet

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+size 484524734

semantic_masks.tar.gz → data/train-00001-of-00019.parquet

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+size 499906160

sourcedata_labeled.tar.01 → data/train-00002-of-00019.parquet

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+size 469730003

data/train-00003-of-00019.parquet

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data/train-00004-of-00019.parquet

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data/validation-00000-of-00005.parquet

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plantorgans.py

@@ -1,168 +0,0 @@
-import datasets
-import pandas as pd
-import glob
-from pathlib import Path
-from PIL import Image, ImageOps
-
-_DESCRIPTION = """Photos of various plants with their major, above ground organs labeled. Includes labels for stem, leafs, fruits and flowers."""
-
-_HOMEPAGE = "https://huggingface.co/datasets/jpodivin/plantorgans"
-
-_CITATION = """"""
-
-_LICENSE = "MIT"
-
-_BASE_URL = "https://huggingface.co/datasets/jpodivin/plantorgans/resolve/main/"
-_TRAIN_URLS = [_BASE_URL + f"sourcedata_labeled.tar.{i:02}" for i in range(0, 8)]
-_TEST_URLS = [_BASE_URL + f"sourcedata_labeled.tar.{i:02}" for i in range(8, 12)]
-_MASKS_URLS = [_BASE_URL + f"masks.tar.0{i}" for i in range(0, 2)]
-_SEMANTIC_MASKS_URLS = "semantic_masks.tar.gz"
-
-_SEMANTIC_METADATA_URLS = {
-    'train': 'https://huggingface.co/datasets/jpodivin/plantorgans/resolve/main/metadata_semantic_train.csv',
-    'test': 'https://huggingface.co/datasets/jpodivin/plantorgans/resolve/main/metadata_semantic_test.csv'
-}
-
-_PANOPTIC_METADATA_URLS = {
-    'train': 'https://huggingface.co/datasets/jpodivin/plantorgans/resolve/main/metadata_train.csv',
-    'test': 'https://huggingface.co/datasets/jpodivin/plantorgans/resolve/main/metadata_test.csv'
-}
-
-
-class PlantOrgansConfig(datasets.BuilderConfig):
-    """Builder Config for PlantOrgans"""
- 
-    def __init__(self, data_urls, metadata_urls, splits, **kwargs):
-        """BuilderConfig for PlantOrgans.
-        Args:
-          data_urls: list of `string`s, urls to download the zip files from.
-          metadata_urls: dictionary with keys 'train' and 'validation' containing the archive metadata URLs
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super().__init__(version=datasets.Version("1.0.0"), **kwargs)
-        self.data_urls = data_urls
-        self.metadata_urls = metadata_urls
-        self.splits = splits
-
-
-class PlantOrgans(datasets.GeneratorBasedBuilder):
-    """Plantorgans dataset
-    """
-    BUILDER_CONFIGS = [
-        PlantOrgansConfig(
-            name="semantic_segmentation_full",
-            description="This configuration contains segmentation masks.",
-            data_urls=_BASE_URL,
-            metadata_urls=_SEMANTIC_METADATA_URLS,
-            splits=['train', 'test'],
-        ),
-        PlantOrgansConfig(
-            name="instance_segmentation_full",
-            description="This configuration contains segmentation masks.",
-            data_urls=_BASE_URL,
-            metadata_urls=_PANOPTIC_METADATA_URLS,
-            splits=['train', 'test'],
-        ),
-    ]
-
-    def _info(self):
-        features=datasets.Features(
-                {
-                    "image": datasets.Image(),
-                    "mask": datasets.Image(),
-                    "image_name": datasets.Value(dtype="string"),
-                })
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=("image", "mask"),
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-            license=_LICENSE,
-        )
-
-
-    def _split_generators(self, dl_manager):
-        
-        train_archives_paths = dl_manager.download_and_extract(_TRAIN_URLS)
-        test_archives_paths = dl_manager.download_and_extract(_TEST_URLS)
-      
-        train_paths = []
-        test_paths = []
-
-        for p in train_archives_paths:
-            train_paths.extend(glob.glob(str(p)+'/sourcedata/labeled/**.jpg'))
-        for p in test_archives_paths:
-            test_paths.extend(glob.glob(str(p)+'/sourcedata/labeled/**.jpg'))
-        
-        if self.config.name == 'instance_segmentation_full':
-            metadata_urls = _PANOPTIC_METADATA_URLS
-            mask_urls = _MASKS_URLS
-            mask_glob = '/masks/**.png'
-        else:
-            metadata_urls = _SEMANTIC_METADATA_URLS
-            mask_urls = _SEMANTIC_MASKS_URLS
-            mask_glob = '/semantic_masks/**.png'
-
-        split_metadata_paths = dl_manager.download(metadata_urls)
-
-        mask_archives_paths = dl_manager.download_and_extract(mask_urls)
-            
-        mask_paths = []
-        for p in mask_archives_paths:
-            mask_paths.extend(glob.glob(str(p)+mask_glob))
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "images": train_paths,
-                    "metadata_path": split_metadata_paths["train"],
-                    "masks_path": mask_paths,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "images": test_paths,
-                    "metadata_path": split_metadata_paths["test"],
-                    "masks_path": mask_paths,
-                },
-            ),
-        ]
-
-
-    def _generate_examples(self, images, metadata_path, masks_path):
-        """
-        images: path to image directory
-        metadata_path: path to metadata csv
-        masks_path: path to masks
-        """
-
-        # Get local image paths
-        image_paths = pd.DataFrame(
-            [(str(Path(*Path(e).parts[-3:])), e) for e in images], columns=['image', 'image_path'])
-
-        # Get local mask paths
-        masks_paths = pd.DataFrame(
-            [(str(Path(*Path(e).parts[-2:])), e) for e in masks_path], columns=['mask', 'mask_path'])
-        
-        # Get all common about images and masks from csv
-        metadata = pd.read_csv(metadata_path)
-        metadata['image'] = metadata['image_path'].apply(lambda x: str(Path(x).parts[-1]))
-        metadata['mask'] = metadata['mask_path'].apply(lambda x: str(Path(x).parts[-1]))
-
-        # Merge dataframes
-        metadata = metadata.merge(masks_paths, on='mask', how='inner')
-        metadata = metadata.merge(image_paths, on='image', how='inner')
-
-        # Make examples and yield
-        for i, r in metadata.iterrows():
-            # Example contains paths to mask, source image, certainty of label,
-            # and name of source image.
-            example = {
-                'mask': r['mask_path'],
-                'image': r['image_path'],
-                'image_name': Path(r['image_path']).parts[-1],
-            }
-            yield i, example