datanormalization

README.md

@@ -1,41 +1,14 @@
 ---
 license: cdla-permissive-2.0
-size_categories:
-- 1K<n<10K
 task_categories:
 - image-segmentation
 tags:
 - biology
-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-*
-language:
-- en
-pretty_name: plant organs
-annotations_creators:
-  - crowdsourced
+size_categories:
+- 1K<n<10K
 ---
 
-# PLANT ORGANS
+# PLANT ORGANS - WIP
 
 Photos of various plants with their major, above ground organs labeled.
 Includes labels for stem, leafs, fruits and flowers.
@@ -43,17 +16,4 @@ Includes labels for stem, leafs, fruits and flowers.
 Note, that categories listed above do not necessarily correspond to a correct
 botanical term for the given part of the plant photographed.
 
-Instead they correspond to the conventional understanding of them.
-
-# ID - Label Map
-
-Following table describes pixel values corresponding to labels in provided masks.
-The first label, "void", represents the background.
-
-| Index | Label |
-|-------|-------|
-|0 | void |
-|1 | Fruit |
-|2 | Leaf |
-|3 | Flower |
-|4 | Stem |
+Instead they correspond to the conventional understanding of them.

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plantorgans.py

@@ -0,0 +1,168 @@
+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

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