v1.1: add accelData

NYUv2.py

@@ -3,7 +3,7 @@
 import numpy as np
 import h5py
 import datasets
-from datasets import BuilderConfig, Features, Value, SplitGenerator, Split, Array2D, Image
+from datasets import BuilderConfig, Features, Value, SplitGenerator, Array2D, Image, Sequence
 import hashlib
 
 
@@ -39,7 +39,7 @@ _FILE_HASH = "520609c519fba3ba5ac58c8fefcc3530"
 class NYUv2(datasets.GeneratorBasedBuilder):
     """NYU Depth Dataset V2"""
 
-    VERSION = datasets.Version("1.0.0")
+    VERSION = datasets.Version("1.1.0")
 
     BUILDER_CONFIGS = [
         BuilderConfig(name="default", version=VERSION, description="Default configuration for NYUv2 dataset"),
@@ -54,6 +54,7 @@ class NYUv2(datasets.GeneratorBasedBuilder):
             "label": Array2D(dtype="int32", shape=(640, 480)),
             "scene": Value("string"),
             "scene_type": Value("string"),
+            'accelData': Sequence(Value("float32"), length=4),
         })
 
         return datasets.DatasetInfo(
@@ -88,12 +89,14 @@ class NYUv2(datasets.GeneratorBasedBuilder):
 
     def _generate_examples(self, filepath, split):
         with h5py.File(filepath, 'r') as f:
+            # available keys: ['accelData','depths','images','instances','labels','names','namesToIds','rawDepthFilenames','rawDepths','rawRgbFilenames','sceneTypes','scenes',]
             images = np.array(f['images'])
             depths = np.array(f['depths'])
             instances = np.array(f['instances'])
             labels = np.array(f['labels'])
             scenes = [''.join(chr(int(x)) for x in f[y]) for y in f.get('scenes')[0]]
             scene_types = [''.join(chr(int(x)) for x in f[y]) for y in f.get('sceneTypes')[0]]
+            accelData = np.array(f['accelData']).T
 
         for idx in range(images.shape[0]):
             yield idx, {
@@ -103,4 +106,5 @@ class NYUv2(datasets.GeneratorBasedBuilder):
                 "label": labels[idx],
                 "scene": scenes[idx],
                 "scene_type": scene_types[idx],
+                "accelData": accelData[idx],
             }

README.md

@@ -1,6 +1,9 @@
 ---
 size_categories:
 - 1K<n<10K
+task_categories:
+- depth-estimation
+- image-segmentation
 paperswithcode_id: nyuv2
 tags:
 - depth-estimation
@@ -27,15 +30,15 @@ dataset_info:
     dtype: string
   - name: scene_type
     dtype: string
+  - name: accelData
+    sequence: float32
+    length: 4
   splits:
   - name: train
-    num_bytes: 4096466619
+    num_bytes: 4096489803
     num_examples: 1449
   download_size: 2972037809
-  dataset_size: 4096466619
-task_categories:
-- depth-estimation
-- image-segmentation
+  dataset_size: 4096489803
 ---
 
 # NYU Depth Dataset V2
@@ -94,10 +97,11 @@ dataset = load_dataset("0jl/NYUv2", trust_remote_code=True, split="train")
 The dataset contains only one training split with the following features:
 
 - `image`: RGB image (PIL.Image.Image, shape: (640, 480, 3))
-- `depth`: Depth map (numpy.ndarray, shape: (640, 480), dtype: float32)
-- `label`: Semantic segmentation labels (numpy.ndarray, shape: (640, 480), dtype: int32)
+- `depth`: Depth map (2D array, shape: (640, 480), dtype: float32)
+- `label`: Semantic segmentation labels (2D array, shape: (640, 480), dtype: int32)
 - `scene`: Scene name (string)
 - `scene_type`: Scene type (string)
+- `accelData`: Acceleration data (list, shape: (4,), dtype: float32)
 
 
 ## Citation Information