feat(dataset): add dataloader for CIHP dataset

tensorbay/opendataset/CIHP/__init__.py

@@ -0,0 +1,11 @@
+#!/usr/bin/env python3
+#
+# Copyright 2021 Graviti. Licensed under MIT License.
+#
+# pylint: disable=invalid-name
+
+"""Dataloader of CIHP."""
+
+from .loader import CIHP
+
+__all__ = ["CIHP"]

tensorbay/opendataset/CIHP/catalog.json

@@ -0,0 +1,27 @@
+{
+    "SEMANTIC_MASK": {
+        "categories": [
+            { "name": "Background" },
+            { "name": "Hat" },
+            { "name": "Hair" },
+            { "name": "Glove" },
+            { "name": "Sunglasses" },
+            { "name": "UpperClothes" },
+            { "name": "Dress" },
+            { "name": "Coat" },
+            { "name": "Socks" },
+            { "name": "Pants" },
+            { "name": "Torso-skin" },
+            { "name": "Scarf" },
+            { "name": "Skirt" },
+            { "name": "Face" },
+            { "name": "Left-arm" },
+            { "name": "Right-arm" },
+            { "name": "Left-leg" },
+            { "name": "Right-leg" },
+            { "name": "Left-shoe" },
+            { "name": "Right-shoe" }
+        ]
+    },
+    "INSTANCE_MASK": {}
+}

tensorbay/opendataset/CIHP/loader.py

@@ -0,0 +1,96 @@
+#!/usr/bin/env python3
+#
+# Copyright 2021 Graviti. Licensed under MIT License.
+#
+# pylint: disable=invalid-name, missing-module-docstring
+
+import os
+
+from ...dataset import Data, Dataset
+from ...label import InstanceMask, SemanticMask
+
+DATASET_NAME = "CIHP"
+_SEGMENTS_INFO = (("train", "Training"), ("val", "Validation"), ("test", "Testing"))
+
+
+def CIHP(path: str) -> Dataset:
+    """Dataloader of the `CIHP`_ dataset.
+
+    .. _CIHP: https://github.com/Engineering-Course/CIHP_PGN
+
+    The file structure should be like::
+
+        <path>
+            instance-level_human_parsing/
+                Testing/
+                    Images/
+                        0000002.jpg
+                        ...
+                    test_id.txt
+                Training/
+                    Images/
+                        0000006.jpg
+                        ...
+                    Categories/
+                        0000006.png
+                        ...
+                    Human/
+                        0000006.png
+                        ...
+                    Instances/
+                        0000006.png
+                        ...
+                    train_id.txt
+                Validation/
+                    Images/
+                        0000001.jpg
+                        ...
+                    Categories/
+                        0000001.png
+                        ...
+                    Human/
+                        0000001.png
+                        ...
+                    Instances/
+                        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_pathname in _SEGMENTS_INFO:
+        segment = dataset.create_segment(segment_name)
+        work_path = os.path.join(root_path, segment_pathname)
+        image_path = os.path.join(work_path, "Images")
+        if segment_name == "test":
+            with open(os.path.join(work_path, "test_id.txt"), "r") as fp:
+                for filename in fp:
+                    segment.append(Data(os.path.join(image_path, f"{filename.strip()}.jpg")))
+        else:
+            category_path = os.path.join(work_path, "Category_ids")
+            instance_path = os.path.join(work_path, "Instance_ids")
+            with open(os.path.join(work_path, f"{segment_name}_id.txt"), "r") as fp:
+                for filename in fp:
+                    filename = filename.strip()
+                    data = Data(os.path.join(image_path, f"{filename}.jpg"))
+                    label = data.label
+                    label.semantic_mask = SemanticMask(
+                        os.path.join(category_path, f"{filename}.png")
+                    )
+                    label.instance_mask = InstanceMask(
+                        os.path.join(instance_path, f"{filename}.png")
+                    )
+                    segment.append(data)
+    return dataset

tensorbay/opendataset/__init__.py

@@ -64,6 +64,7 @@
     "CarConnection",
     "CCPD",
     "CCPDGreen",
+    "CIHP",
     "CoinImage",
     "COVIDChestXRay",
     "CompCars",