Initial script for bollworm data

pest_management_opendata.py

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+import itertools as it
+import collections as cl
+from base64 import b64encode
+from pathlib import Path
+from dataclasses import dataclass, asdict
+from urllib.parse import urlparse, urlunparse
+
+import cv2
+import boto3
+import pandas as pd
+import awswrangler as wr
+from datasets import (
+    Split,
+    Image,
+    Value,
+    Features,
+    Sequence,
+    ClassLabel,
+    DatasetInfo,
+    SplitGenerator,
+    GeneratorBasedBuilder,
+)
+from shapely.wkt import loads
+
+__version__ = '20220912-2056'
+
+SplitInfo = cl.namedtuple('SplitInfo', 'dtype, basename, split')
+
+@dataclass
+class SplitPayload:
+    split: str
+    path: Path
+
+    def __post_init__(self):
+        self.path = Path(self.path)
+
+    def to_frame(self):
+        return (pd
+                .read_csv(self.path, compression='gzip')
+                .query(f'split == "{self.split}"'))
+
+#
+#
+#
+class SplitManager:
+    _splits = tuple(it.starmap(SplitInfo, (
+        (Split.TRAIN, 'dev', 'train'),
+        (Split.VALIDATION, 'dev', 'val'),
+        (Split.TEST, 'test', 'test'),
+    )))
+
+    @staticmethod
+    def custom_download(url, path):
+        remote = urlparse(url)
+        name = Path(remote.path)
+        if name.is_absolute():
+            name = name.relative_to(name.parents[-1])
+
+        s3 = boto3.client(remote.scheme, region_name='ap-south-1')
+        s3.download_file(remote.netloc, str(name), path)
+
+    @property
+    def labels(self):
+        df = wr.s3.read_csv(self.url('dev'), compression='gzip')
+        yield from df['label'].dropna().unique()
+
+    def __init__(self, bucket):
+        self.bucket = bucket
+        self.path = Path('metadata', __version__)
+
+    def __call__(self, dl_manager):
+        for i in self._splits:
+            url = self.url(i.basename)
+            path = dl_manager.download_custom(url, self.custom_download)
+            payload = SplitPayload(i.split, path)
+
+            yield SplitGenerator(name=i.dtype, gen_kwargs=asdict(payload))
+
+    def url(self, split):
+        path = self.path.joinpath(split).with_suffix('.csv.gz')
+        source = self.bucket._replace(path=path)
+
+        return urlunparse(source)
+
+#
+#
+#
+class ExampleManager:
+    _decode_flags = cv2.IMREAD_COLOR | cv.IMREAD_IGNORE_ORIENTATION
+    # _Pest = cl.namedtuple('_Pest', 'label, geometry')
+    # _Feature = cl.namedtuple('_Feature', 'image, pests')
+
+    @staticmethod
+    def features(labels):
+        return Features({
+            'image': Image(),
+            'pests': Sequence({
+                'label': ClassLabel(names=labels),
+                'geometry': Value('binary'),
+            }),
+        })
+
+    @staticmethod
+    def pests(df):
+        for i in df.dropna().itertuples(index=False):
+            geometry = loads(i.geometry)
+            yield {
+                'label': i.label,
+                'geometry': geometry.wkb,
+            }
+
+    @staticmethod
+    def load(url):
+        path = Path(url.path)
+        if path.is_absolute():
+            (*_, root) = path.parents
+            path = path.relative_to(root)
+
+        data = (boto3
+                .resource(url.scheme)
+                .Bucket(url.netloc)
+                .Object(str(path))
+                .get()
+                .get('Body')
+                .read())
+        image = np.asarray(bytearray(data))
+
+        return cv2.imdecode(image, self._decode_flags)
+
+    def __init__(self, payload):
+        self.payload = payload
+
+    def __iter__(self):
+        df = self.payload.to_frame()
+        for (i, g) in df.groupby('image', sort=False):
+            image = self.load(urlparse(i))
+            pests = self.pests(g)
+
+            yield {
+                'image': image,
+                'pests': list(pests),
+            }
+
+#
+#
+#
+class WadhwaniBollwormData(GeneratorBasedBuilder):
+    _bucket = urlparse('s3://wadhwaniai-agri-opendata')
+
+    def _info(self):
+        data = SplitManager(self._bucket)
+        labels = sorted(data.labels)
+        features = ExampleManager.features(labels)
+
+        return DatasetInfo(
+            homepage='https://github.com/WadhwaniAI/pest-management-opendata',
+            # citation=_CITATION,
+            # description=_DESCRIPTION,
+            license='CC-BY 4.0',
+            features=features,
+        )
+
+    def _split_generators(self, dl_manager):
+        splits = SplitManager(self._bucket)
+        return list(splits)
+
+    def _generate_examples(self, **kwargs):
+        payload = SplitPayload(**kwargs)
+        examples = ExampleManager(payload)
+        yield from examples