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TestNew.py

@@ -1,4 +1,4 @@
-import os
+# TestNew.py
 import json
 import random
 from datasets import (
@@ -15,8 +15,12 @@ from datasets import (
 )
 from huggingface_hub import hf_hub_url
 
+
+_REPO_ID = "iamirulofficial/TestNew"          # change if you ever fork the repo
+
+
 class ImageSubsetConfig(BuilderConfig):
-    """BuilderConfig for small vs. full dataset."""
+    """BuilderConfig for full dataset vs. small random sample."""
     def __init__(self, name, sample_size=None, **kwargs):
         super().__init__(
             name=name,
@@ -25,76 +29,83 @@ class ImageSubsetConfig(BuilderConfig):
         )
         self.sample_size = sample_size
 
+
 class MyImageDataset(GeneratorBasedBuilder):
-    """A dataset of images with actuated_angle from metadata.json."""
+    """Images + 2‑D actuated_angle labels stored in metadata.json."""
     BUILDER_CONFIGS = [
         ImageSubsetConfig(
             name="full",
-            sample_size=None,
-            description="All images (≈100 GB). Use with streaming=True."
+            sample_size=None,                     # all images
+            description="Entire dataset (≈100 GB)"
         ),
         ImageSubsetConfig(
             name="small",
-            sample_size=2,
-            description="Random subset of 10 000 images for quick iteration."
+            sample_size=2,                        # tiny sample for quick tests
+            description="Two random images"
         ),
     ]
     DEFAULT_CONFIG_NAME = "small"
 
-    def _info(self):
+    def _info(self) -> DatasetInfo:
         return DatasetInfo(
-            description="Images with a 2-D actuated_angle from metadata.json",
-            features=Features({
-                "image": Image(),                      # the image file
-                "actuated_angle": Sequence(Value("int32")),  # [angle0, angle1]
-            }),
+            description="Images with a 2‑D actuated_angle from metadata.json",
+            features=Features(
+                {
+                    "image": Image(),                    # PIL.Image will be returned
+                    "actuated_angle": Sequence(Value("int32")),  # [angle0, angle1]
+                }
+            ),
             supervised_keys=None,
         )
 
+    # --------------------------------------------------------------------- #
+    # Download phase                                                         #
+    # --------------------------------------------------------------------- #
     def _split_generators(self, dl_manager: DownloadManager):
-        data_dir = dl_manager.manual_dir or os.path.abspath(os.path.dirname(__file__))
-        images_dir = os.path.join(data_dir, "images")
-        repo_id = "iamirulofficial/TestNew"
+        # 1️⃣  Download metadata.json (tiny text file)
         meta_path = dl_manager.download(
-            hf_hub_url(repo_id, filename="metadata.json", repo_type="dataset")
+            hf_hub_url(_REPO_ID, "metadata.json", repo_type="dataset")
         )
 
+        # 2️⃣  Decide which filenames we need
+        with open(meta_path, encoding="utf-8") as f:
+            metadata = json.load(f)                      # {"frame_000.png": {"0":…, …}, …}
 
-        # load your filename → {"0":…, "1":…} mapping
-        with open(meta_path, "r", encoding="utf-8") as f:
-            metadata = json.load(f)
-
-        # sample if in "small" config
-        if self.config.sample_size:
-            all_fnames = list(metadata.keys())
+        all_fnames = list(metadata)
+        if self.config.sample_size:                      # small‑config branch
             random.seed(42)
-            selected = set(random.sample(all_fnames, self.config.sample_size))
+            selected = sorted(random.sample(all_fnames, self.config.sample_size))
         else:
-            selected = set(metadata.keys())
+            selected = sorted(all_fnames)                # full dataset
+
+        # 3️⃣  Build URLs → dl_manager.download() → local paths
+        url_dict = {
+            fname: hf_hub_url(
+                _REPO_ID, f"images/{fname}", repo_type="dataset"
+            )
+            for fname in selected
+        }
+        img_paths = dl_manager.download(url_dict)        # same keys, but local files
 
         return [
             SplitGenerator(
                 name=Split.TRAIN,
                 gen_kwargs={
-                    "images_dir": images_dir,
+                    "img_paths": img_paths,
                     "metadata": metadata,
-                    "selected": selected,
                 },
-            ),
+            )
         ]
 
-    def _generate_examples(self, images_dir, metadata, selected):
-        """Yields examples of the form { "image": path, "actuated_angle": [int, int] }"""
-        for idx, fname in enumerate(selected):
-            if fname not in metadata:
-                continue
-            image_path = os.path.join(images_dir, fname)
-            if not os.path.exists(image_path):
-                continue
-            meta = metadata[fname]
-            # extract the two angle values (keys "0" and "1")
-            angles = [ int(meta.get("0", 0)), int(meta.get("1", 0)) ]
-            yield idx, {
-                "image": image_path,
-                "actuated_angle": angles,
-            }
+    # --------------------------------------------------------------------- #
+    # Generate examples                                                     #
+    # --------------------------------------------------------------------- #
+    def _generate_examples(self, img_paths: dict, metadata: dict):
+        """
+        Yields (key, example) where example =
+        { "image": <local‑file‑path>, "actuated_angle": [int, int] }
+        """
+        for idx, (fname, local_path) in enumerate(img_paths.items()):
+            meta = metadata.get(fname, {})
+            angles = [int(meta.get("0", 0)), int(meta.get("1", 0))]
+            yield idx, {"image": local_path, "actuated_angle": angles}