Add test set for CTC dataset

scripts/datasets/check_ctc.py

@@ -1,23 +1,29 @@
-from torch_em.data.datasets.ctc import get_ctc_segmentation_loader, CTC_URLS
+from torch_em.data.datasets.ctc import get_ctc_segmentation_loader, CTC_CHECKSUMS
 from torch_em.util.debug import check_loader
 from torch_em.data.sampler import MinInstanceSampler
 
-ROOT = "/scratch/projects/nim00007/sam/data/ctc/"
+ROOT = "/home/anwai/data/ctc/"
 
 
 # Some of the datasets have partial sparse labels:
 # - Fluo-N2DH-GOWT1
 # - Fluo-N2DL-HeLa
 # Maybe depends on the split?!
-def check_ctc_segmentation():
-    for name in CTC_URLS.keys():
+def check_ctc_segmentation(split):
+    ctc_dataset_names = list(CTC_CHECKSUMS["train"].keys())
+    for name in ctc_dataset_names:
         print("Checking dataset", name)
         loader = get_ctc_segmentation_loader(
-            ROOT, name, (1, 512, 512), 1, download=True,
+            path=ROOT,
+            dataset_name=name,
+            patch_shape=(1, 512, 512),
+            batch_size=1,
+            download=True,
+            split=split,
             sampler=MinInstanceSampler()
         )
-        check_loader(loader, 8, plt=True, save_path="ctc.png")
+        check_loader(loader, 8, plt=True)
 
 
 if __name__ == "__main__":
-    check_ctc_segmentation()
+    check_ctc_segmentation("train")

torch_em/data/datasets/ctc.py

@@ -6,66 +6,78 @@
 from . import util
 
 
-CTC_URLS = {
-    "BF-C2DL-HSC": "http://data.celltrackingchallenge.net/training-datasets/BF-C2DL-HSC.zip",
-    "BF-C2DL-MuSC": "http://data.celltrackingchallenge.net/training-datasets/BF-C2DL-MuSC.zip",
-    "DIC-C2DH-HeLa": "http://data.celltrackingchallenge.net/training-datasets/DIC-C2DH-HeLa.zip",
-    "Fluo-C2DL-Huh7": "http://data.celltrackingchallenge.net/training-datasets/Fluo-C2DL-Huh7.zip",
-    "Fluo-C2DL-MSC": "http://data.celltrackingchallenge.net/training-datasets/Fluo-C2DL-MSC.zip",
-    "Fluo-N2DH-GOWT1": "http://data.celltrackingchallenge.net/training-datasets/Fluo-N2DH-GOWT1.zip",
-    "Fluo-N2DH-SIM+": "http://data.celltrackingchallenge.net/training-datasets/Fluo-N2DH-SIM+.zip",
-    "Fluo-N2DL-HeLa": "http://data.celltrackingchallenge.net/training-datasets/Fluo-N2DL-HeLa.zip",
-    "PhC-C2DH-U373": "http://data.celltrackingchallenge.net/training-datasets/PhC-C2DH-U373.zip",
-    "PhC-C2DL-PSC": "http://data.celltrackingchallenge.net/training-datasets/PhC-C2DL-PSC.zip",
-}
 CTC_CHECKSUMS = {
-    "BF-C2DL-HSC": "0aa68ec37a9b06e72a5dfa07d809f56e1775157fb674bb75ff904936149657b1",
-    "BF-C2DL-MuSC": "ca72b59042809120578a198ba236e5ed3504dd6a122ef969428b7c64f0a5e67d",
-    "DIC-C2DH-HeLa": "832fed2d05bb7488cf9c51a2994b75f8f3f53b3c3098856211f2d39023c34e1a",
-    "Fluo-C2DL-Huh7": "1912658c1b3d8b38b314eb658b559e7b39c256917150e9b3dd8bfdc77347617d",
-    "Fluo-C2DL-MSC": "a083521f0cb673ae02d4957c5e6580c2e021943ef88101f6a2f61b944d671af2",
-    "Fluo-N2DH-GOWT1": "1a7bd9a7d1d10c4122c7782427b437246fb69cc3322a975485c04e206f64fc2c",
-    "Fluo-N2DH-SIM+": "3e809148c87ace80c72f563b56c35e0d9448dcdeb461a09c83f61e93f5e40ec8",
-    "Fluo-N2DL-HeLa": "35dd99d58e071aba0b03880128d920bd1c063783cc280f9531fbdc5be614c82e",
-    "PhC-C2DH-U373": "b18185c18fce54e8eeb93e4bbb9b201d757add9409bbf2283b8114185a11bc9e",
-    "PhC-C2DL-PSC": "9d54bb8febc8798934a21bf92e05d92f5e8557c87e28834b2832591cdda78422",
-
+    "train": {
+        "BF-C2DL-HSC": "0aa68ec37a9b06e72a5dfa07d809f56e1775157fb674bb75ff904936149657b1",
+        "BF-C2DL-MuSC": "ca72b59042809120578a198ba236e5ed3504dd6a122ef969428b7c64f0a5e67d",
+        "DIC-C2DH-HeLa": "832fed2d05bb7488cf9c51a2994b75f8f3f53b3c3098856211f2d39023c34e1a",
+        "Fluo-C2DL-Huh7": "1912658c1b3d8b38b314eb658b559e7b39c256917150e9b3dd8bfdc77347617d",
+        "Fluo-C2DL-MSC": "a083521f0cb673ae02d4957c5e6580c2e021943ef88101f6a2f61b944d671af2",
+        "Fluo-N2DH-GOWT1": "1a7bd9a7d1d10c4122c7782427b437246fb69cc3322a975485c04e206f64fc2c",
+        "Fluo-N2DH-SIM+": "3e809148c87ace80c72f563b56c35e0d9448dcdeb461a09c83f61e93f5e40ec8",
+        "Fluo-N2DL-HeLa": "35dd99d58e071aba0b03880128d920bd1c063783cc280f9531fbdc5be614c82e",
+        "PhC-C2DH-U373": "b18185c18fce54e8eeb93e4bbb9b201d757add9409bbf2283b8114185a11bc9e",
+        "PhC-C2DL-PSC": "9d54bb8febc8798934a21bf92e05d92f5e8557c87e28834b2832591cdda78422",
+    },
+    "test": {
+        "BF-C2DL-HSC": "fd1c05ec625fd0526c8369d1139babe137e885457eee98c10d957da578d0d5bc",
+        "BF-C2DL-MuSC": "c5cae259e6090e82a2596967fb54c8a768717c1772398f8546ad1c8df0820450",
+        "DIC-C2DH-HeLa": "5e5d5f2aa90aef99d750cf03f5c12d799d50b892f98c86950e07a2c5955ac01f",
+        "Fluo-C2DL-Huh7": "cc7359f8fb6b0c43995365e83ce0116d32f477ac644b2ca02b98bc253e2bcbbe",
+        "Fluo-C2DL-MSC": "c90b13e603dde52f17801d4f0cadde04ed7f21cc05296b1f0957d92dbfc8ffa6",
+        "Fluo-N2DH-GOWT1": "c6893ec2d63459de49d4dc21009b04275573403c62cc02e6ee8d0cb1a5068add",
+        "Fluo-N2DH-SIM+": "c4f257add739b284d02176057814de345dee2ac1a7438e360ccd2df73618db68",
+        "Fluo-N2DL-HeLa": "45cf3daf05e8495aa2ce0febacca4cf0928fab808c0b14ed2eb7289a819e6bb8",
+        "PhC-C2DH-U373": "7aa3162e4363a416b259149adc13c9b09cb8aecfe8165eb1428dd534b66bec8a",
+        "PhC-C2DL-PSC": "8c98ac6203e7490157ceb6aa1131d60a3863001b61fb75e784bc49d47ee264d5",
+    }
 }
 
 
-def _require_ctc_dataset(path, dataset_name, download):
-    dataset_names = list(CTC_URLS.keys())
+def get_ctc_url_and_checksum(dataset_name, split):
+    if split == "train":
+        _link_to_split = "training-datasets"
+    else:
+        _link_to_split = "test-datasets"
+
+    url = f"http://data.celltrackingchallenge.net/{_link_to_split}/{dataset_name}.zip"
+    checksum = CTC_CHECKSUMS[split][dataset_name]
+    return url, checksum
+
+
+def _require_ctc_dataset(path, dataset_name, download, split):
+    dataset_names = list(CTC_CHECKSUMS["train"].keys())
     if dataset_name not in dataset_names:
         raise ValueError(f"Inalid dataset: {dataset_name}, choose one of {dataset_names}.")
 
-    data_path = os.path.join(path, dataset_name)
+    data_path = os.path.join(path, split, dataset_name)
 
     if os.path.exists(data_path):
         return data_path
 
     os.makedirs(data_path)
-    url, checksum = CTC_URLS[dataset_name], CTC_CHECKSUMS[dataset_name]
+    url, checksum = get_ctc_url_and_checksum(dataset_name, split)
     zip_path = os.path.join(path, f"{dataset_name}.zip")
     util.download_source(zip_path, url, download, checksum=checksum)
-    util.unzip(zip_path, path, remove=True)
+    util.unzip(zip_path, os.path.join(path, split), remove=True)
 
     return data_path
 
 
-def _require_gt_images(data_path, splits):
+def _require_gt_images(data_path, vol_ids):
     image_paths, label_paths = [], []
 
-    if isinstance(splits, str):
-        splits = [splits]
+    if isinstance(vol_ids, str):
+        vol_ids = [vol_ids]
 
-    for split in splits:
-        image_folder = os.path.join(data_path, split)
-        assert os.path.join(image_folder), f"Cannot find split, {split} in {data_path}."
+    for vol_id in vol_ids:
+        image_folder = os.path.join(data_path, vol_id)
+        assert os.path.join(image_folder), f"Cannot find volume id, {vol_id} in {data_path}."
 
-        label_folder = os.path.join(data_path, f"{split}_GT", "SEG")
+        label_folder = os.path.join(data_path, f"{vol_id}_GT", "SEG")
 
         # copy over the images corresponding to the labeled frames
-        label_image_folder = os.path.join(data_path, f"{split}_GT", "IM")
+        label_image_folder = os.path.join(data_path, f"{vol_id}_GT", "IM")
         os.makedirs(label_image_folder, exist_ok=True)
 
         this_label_paths = glob(os.path.join(label_folder, "*.tif"))
@@ -88,7 +100,8 @@ def get_ctc_segmentation_dataset(
     path,
     dataset_name,
     patch_shape,
-    split=None,
+    split,
+    vol_id=None,
     download=False,
     **kwargs,
 ):
@@ -98,16 +111,18 @@ def get_ctc_segmentation_dataset(
     cell tracking challenge. If you use this data in your research please cite
     https://doi.org/10.1038/nmeth.4473
     """
-    data_path = _require_ctc_dataset(path, dataset_name, download)
+    assert split in ["train"]
 
-    if split is None:
-        splits = glob(os.path.join(data_path, "*_GT"))
-        splits = [os.path.basename(split) for split in splits]
-        splits = [split.rstrip("_GT") for split in splits]
+    data_path = _require_ctc_dataset(path, dataset_name, download, split)
+
+    if vol_id is None:
+        vol_ids = glob(os.path.join(data_path, "*_GT"))
+        vol_ids = [os.path.basename(vol_id) for vol_id in vol_ids]
+        vol_ids = [vol_id.rstrip("_GT") for vol_id in vol_ids]
     else:
-        splits = split
+        vol_ids = vol_id
 
-    image_path, label_path = _require_gt_images(data_path, splits)
+    image_path, label_path = _require_gt_images(data_path, vol_ids)
 
     kwargs = util.update_kwargs(kwargs, "ndim", 2)
     return torch_em.default_segmentation_dataset(
@@ -120,7 +135,8 @@ def get_ctc_segmentation_loader(
     dataset_name,
     patch_shape,
     batch_size,
-    split=None,
+    split,
+    vol_id=None,
     download=False,
     **kwargs,
 ):
@@ -131,7 +147,8 @@ def get_ctc_segmentation_loader(
         torch_em.default_segmentation_dataset, **kwargs
     )
     dataset = get_ctc_segmentation_dataset(
-        path, dataset_name, patch_shape, split=split, download=download, **ds_kwargs,
+        path, dataset_name, patch_shape, split=split, vol_id=vol_id, download=download, **ds_kwargs,
     )
+
     loader = torch_em.get_data_loader(dataset, batch_size, **loader_kwargs)
     return loader