CityscapesGTFine

tensorbay/opendataset/CityscapesGTFine/__init__.py

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+#!/usr/bin/env python3
+#
+# Copyright 2021 Graviti. Licensed under MIT License.
+#
+# pylint: disable=invalid-name
+
+"""Dataloader of CIHP."""
+
+from tensorbay.opendataset.CIHP.loader import CIHP
+
+__all__ = ["CIHP"]

tensorbay/opendataset/CityscapesGTFine/catalog.json

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+{
+    "SEMANTIC_MASK": {
+        "categories": [
+            { "name": "Background", "categoryId": 0 },
+            { "name": "Hat", "categoryId": 1 },
+            { "name": "Hair", "categoryId": 2 },
+            { "name": "Glove", "categoryId": 3 },
+            { "name": "Sunglasses", "categoryId": 4 },
+            { "name": "UpperClothes", "categoryId": 5 },
+            { "name": "Dress", "categoryId": 6 },
+            { "name": "Coat", "categoryId": 7 },
+            { "name": "Socks", "categoryId": 8 },
+            { "name": "Pants", "categoryId": 9 },
+            { "name": "Torso-skin", "categoryId": 10 },
+            { "name": "Scarf", "categoryId": 11 },
+            { "name": "Skirt", "categoryId": 12 },
+            { "name": "Face", "categoryId": 13 },
+            { "name": "Left-arm", "categoryId": 14 },
+            { "name": "Right-arm", "categoryId": 15 },
+            { "name": "Left-leg", "categoryId": 16 },
+            { "name": "Right-leg", "categoryId": 17 },
+            { "name": "Left-shoe", "categoryId": 18 },
+            { "name": "Right-shoe", "categoryId": 19 }
+        ]
+    },
+    "INSTANCE_MASK": {
+        "categories": [{ "name": "Background", "categoryId": 0 }]
+    }
+}

tensorbay/opendataset/CityscapesGTFine/loader.py

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+#!/usr/bin/env python3
+#
+# Copyright 2021 Graviti. Licensed under MIT License.
+#
+# pylint: disable=invalid-name
+
+"""Dataloader of CIHP dataset."""
+
+import os
+
+from tensorbay.dataset import Data, Dataset
+from tensorbay.label import InstanceMask, SemanticMask
+
+DATASET_NAME = "CIHP"
+_SEGMENTS_INFO = {"train": "Training", "val": "Validation", "test": "Testing"}
+
+
+def CIHP(path: str) -> Dataset:
+    """`CIHP <https://github.com/Engineering-Course/CIHP_PGN>`_ dataset.
+
+    The file structure should be like::
+
+        <path>
+            Testing/
+                Images/
+                    0000002.jpg
+                    ...
+                test_id.txt
+            Training/
+                Images/
+                    0000006.jpg
+                    ...
+                Category_ids/
+                    0000006.png
+                    ...
+                Instance_ids/
+                    0000006.png
+                    ...
+                train_id.txt
+            Validation/
+                Images/
+                    0000001.jpg
+                    ...
+                Category_ids/
+                    0000001.png
+                    ...
+                Instance_ids/
+                    0000001.png
+                    ...
+                val_id.txt
+
+    Arguments:
+        path: The root directory of the dataset.
+
+    Returns:
+        Loaded :class:`~tensorbay.dataset.dataset.Dataset` instance.
+
+    """
+    root_path = os.path.join(
+        os.path.abspath(os.path.expanduser(path)), "instance-level_human_parsing"
+    )
+
+    dataset = Dataset(DATASET_NAME)
+    dataset.load_catalog(os.path.join(os.path.dirname(__file__), "catalog.json"))
+
+    for segment_name, segment_path in _SEGMENTS_INFO.items():
+        segment = dataset.create_segment(segment_name)
+        segment_abspath = os.path.join(root_path, segment_path)
+        image_path = os.path.join(segment_abspath, "Images")
+        with open(
+            os.path.join(segment_abspath, f"{segment_name}_id.txt"), "r", encoding="utf-8"
+        ) as fp:
+            if segment_name == "test":
+                for stem in fp:
+                    segment.append(Data(os.path.join(image_path, f"{stem.rstrip()}.jpg")))
+            else:
+                category_path = os.path.join(segment_abspath, "Category_ids")
+                instance_path = os.path.join(segment_abspath, "Instance_ids")
+                for stem in fp:
+                    stem = stem.rstrip()
+                    data = Data(os.path.join(image_path, f"{stem}.jpg"))
+                    label = data.label
+                    png_filename = f"{stem}.png"
+                    label.semantic_mask = SemanticMask(os.path.join(category_path, png_filename))
+                    label.instance_mask = InstanceMask(os.path.join(instance_path, png_filename))
+                    segment.append(data)
+    return dataset