fixed image generation

ark_example.py

@@ -24,6 +24,8 @@ import datasets
 import pathlib
 import glob
 import tifffile
+import xarray as xr
+import numpy as np
 
 # Find for instance the citation on arxiv or on the dataset repo/website
 _CITATION = """\
@@ -61,7 +63,7 @@ https://huggingface.co/docs/datasets/repository_structure
 
 # TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
 class ArkExample(datasets.GeneratorBasedBuilder):
-    """The Dataset consists of 12 FOVs"""
+    """The Dataset consists of 11 FOVs"""
 
     VERSION = datasets.Version("0.0.1")
 
@@ -90,16 +92,17 @@ class ArkExample(datasets.GeneratorBasedBuilder):
         ),
     ]
 
-    DEFAULT_CONFIG_NAME = "base_dataset"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+    DEFAULT_CONFIG_NAME = (
+        "base_dataset"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+    )
 
     def _info(self):
-        # TODO: This method specifies the datasets.DatasetInfo object which contains information and typings for the dataset
-        if (
-            self.config.name == "base_dataset"
-        ):  # This is the name of the configuration selected in BUILDER_CONFIGS above
+        # This is the name of the configuration selected in BUILDER_CONFIGS above
+        if self.config.name == "base_dataset":
             features = datasets.Features(
                 {
-                    "fov": datasets.Sequence({"channel": datasets.Image()}),
+                    "Channel Data": datasets.Sequence(datasets.Image()),
+                    "Channel Names": datasets.Sequence(datasets.Value("string")),
                 }
             )
         else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
@@ -140,40 +143,35 @@ class ArkExample(datasets.GeneratorBasedBuilder):
             datasets.SplitGenerator(
                 name="base_dataset",
                 # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": pathlib.Path(data_dir)
-                },
+                gen_kwargs={"filepath": pathlib.Path(data_dir)},
             ),
         ]
 
     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
     def _generate_examples(self, filepath: pathlib.Path):
-        file_paths = list(filepath.rglob("*"))
-        
+
+        # Get all TMA paths
+        file_paths = list(pathlib.Path(filepath / "fovs").glob("*"))
+
+        # Loop over all the TMAs
         for fp in file_paths:
-            if fp.suffix in [".tiff", ".tif"]:
-                image_data = tifffile.imread(fp, key=0)
-                if self.config.name == "base_dataset":
-                    yield fp, {
-                        "chan": image_data,
-                        "path": fp
-                    }
-
-        # with open(filepath, encoding="utf-8") as f:
-        #     for key, row in enumerate(f):
-        #         data = json.loads(row)
-        #         if self.config.name == "first_domain":
-        #             # Yields examples as (key, example) tuples
-        #             yield key, {
-        #                 "sentence": data["sentence"],
-        #                 "option1": data["option1"],
-        #                 "answer": "" if split == "test" else data["answer"],
-        #             }
-        #         else:
-        #             yield key, {
-        #                 "sentence": data["sentence"],
-        #                 "option2": data["option2"],
-        #                 "second_domain_answer": ""
-        #                 if split == "test"
-        #                 else data["second_domain_answer"],
-        #             }
+
+            # Get the TMA FOV Name
+            fov_name = fp.stem
+            
+
+            # Get all channels per TMA FOV
+            channel_paths = fp.glob("*.tiff")
+
+            chan_data = []
+            chan_names = []
+            for chan in channel_paths:
+                chan_name = chan.stem
+                chan_image: np.ndarray = tifffile.imread(chan)
+
+                chan_data.append(chan_image)
+                chan_names.append(chan_name)
+
+            
+            if self.config.name == "base_dataset":
+                yield fov_name, {"Channel Data": chan_data, "Channel Names": chan_names}