refactor: all data

Class_Segmentation/1.png → data/class_segmentation.tar.gz

@@ -1,3 +1,3 @@
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-oid sha256:2a3d786608dbb3e70e737cc251bfdd54ca68c39785641b8c6d54b75b97fff715
-size 19984
+oid sha256:b6b859b5af1153513b186bfd3b533a4600d1c5c463a712c1b9c089b7cf0b2205
+size 455833

Class_Segmentation/11.png → data/images.tar.gz

@@ -1,3 +1,3 @@
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-oid sha256:d080d67722542640345b656bb2ebb13e94a1c301a3e9ce51036f3d1aaec7b288
-size 78870
+oid sha256:9b8d7facc832a219a6ed0e21f3f192bcac28af8e875eefbb4734e390f751a0dd
+size 46809599

Class_Segmentation/10.png → data/object_segmentation.tar.gz

@@ -1,3 +1,3 @@
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-oid sha256:0f47ac1f11fb5979f71b1f66dbe633aaeb091728e5067b4bfed19257bfcb1935
-size 28608
+oid sha256:bc52606bb631f2e48eab104ebb87dd14a95ebdf2ecd1340d61cc43de9be0580e
+size 510121

plantations.csv

@@ -1,14 +0,0 @@
-image_id;image_name
-1;Plantations_Segmentation/img/1.png
-2;Plantations_Segmentation/img/2.png
-3;Plantations_Segmentation/img/3.png
-4;Plantations_Segmentation/img/4.png
-5;Plantations_Segmentation/img/5.png
-6;Plantations_Segmentation/img/6.png
-7;Plantations_Segmentation/img/7.png
-8;Plantations_Segmentation/img/8.png
-9;Plantations_Segmentation/img/9.png
-10;Plantations_Segmentation/img/10.png
-11;Plantations_Segmentation/img/11.png
-12;Plantations_Segmentation/img/12.png
-13;Plantations_Segmentation/img/13.png

plantations_segmentation.py

@@ -0,0 +1,100 @@
+import datasets
+import pandas as pd
+
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {plantations_segmentation},
+author = {TrainingDataPro},
+year = {2023}
+}
+"""
+
+_DESCRIPTION = """\
+The dataset consist of screenshots from videos of basketball games with
+the ball labeled with a bounging box. 
+The dataset can be used to train a neural network in ball control recognition.
+The dataset is useful for automating the camera operator's work during a match,
+allowing the ball to be efficiently kept in frame.
+"""
+_NAME = 'plantations_segmentation'
+
+_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"
+
+_LICENSE = ""
+
+_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"
+
+
+class PlantationsSegmentation(datasets.GeneratorBasedBuilder):
+    """Small sample of image-text pairs"""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({
+                'image_id': datasets.Value('int32'),
+                'image': datasets.Image(),
+                'class_segmentation': datasets.Image(),
+                'object_segmentation': datasets.Image(),
+                'shapes': datasets.Value('string')
+            }),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        images = dl_manager.download(f"{_DATA}images.tar.gz")
+        class_segmentation_masks = dl_manager.download(
+            f"{_DATA}class_segmentation.tar.gz")
+        object_segmentation_masks = dl_manager.download(
+            f"{_DATA}object_segmentation.tar.gz")
+        annotations = dl_manager.download(f"{_DATA}{_NAME}.csv")
+        images = dl_manager.iter_archive(images)
+        class_segmentation_masks = dl_manager.iter_archive(
+            class_segmentation_masks)
+        object_segmentation_masks = dl_manager.iter_archive(
+            object_segmentation_masks)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "images": images,
+                    'class_segmentation_masks': class_segmentation_masks,
+                    'object_segmentation_masks': object_segmentation_masks,
+                    'annotations': annotations
+                }),
+        ]
+
+    def _generate_examples(self, images, class_segmentation_masks,
+                           object_segmentation_masks, annotations):
+        annotations_df = pd.read_csv(annotations)
+
+        for idx, ((image_path, image), (class_segmentation_path,
+                                        class_segmentation),
+                  (object_segmentation_path,
+                   object_segmentation)) in enumerate(
+                       zip(images, class_segmentation_masks,
+                           object_segmentation_masks)):
+            yield idx, {
+                'image_id':
+                    annotations_df.loc[
+                        annotations_df['image_name'] == image_path]
+                    ['image_id'].values[0],
+                "image": {
+                    "path": image_path,
+                    "bytes": image.read()
+                },
+                "class_segmentation": {
+                    "path": class_segmentation_path,
+                    "bytes": class_segmentation.read()
+                },
+                "object_segmentation": {
+                    "path": object_segmentation_path,
+                    "bytes": object_segmentation.read()
+                },
+                'shapes':
+                    annotations_df.loc[
+                        annotations_df['image_name'] == image_path]
+                    ['shapes'].values[0]
+            }