Ground Truth and Annotation Score tweeks

src/nrtk_explorer/app/core.py

@@ -5,6 +5,7 @@
 from trame_server.utils.namespace import Translator
 from nrtk_explorer.library import images_manager
 from nrtk_explorer.library.filtering import FilterProtocol
+from nrtk_explorer.library.dataset import get_dataset
 
 from nrtk_explorer.app.embeddings import EmbeddingsApp
 from nrtk_explorer.app.transforms import TransformsApp
@@ -130,8 +131,7 @@ def on_dataset_change(self, **kwargs):
         # Reset cache
         self.context.images_manager = images_manager.ImagesManager()
 
-        with open(self.state.current_dataset) as f:
-            dataset = json.load(f)
+        dataset = get_dataset(self.state.current_dataset, force_reload=True)
 
         self.state.num_images_max = len(dataset["images"])
         self.state.random_sampling_disabled = False

src/nrtk_explorer/app/image_ids.py

@@ -0,0 +1,6 @@
+def image_id_to_dataset_id(image_id: str) -> str:
+    return image_id.split("_")[-1]
+
+
+def image_id_to_result_id(image_id: str) -> str:
+    return f"result_{image_id}"

src/nrtk_explorer/app/image_meta.py

@@ -1,34 +1,38 @@
 from typing import TypedDict
+from nrtk_explorer.app.trame_utils import delete_state
 
-DatasetId = str
+ImageMetaId = str
 
 
-def image_id_to_meta(image_id: DatasetId):
-    return f"{image_id}_meta"
+def image_id_to_meta(image_id: str) -> ImageMetaId:
+    return f"meta_{image_id}"
 
 
 class DatasetImageMeta(TypedDict):
-    distance: int  # Transformed Embedding Distance
-    width: int
-    height: int
+    original_ground_to_original_detection_score: float
+    original_detection_to_transformed_detection_score: float
+    ground_truth_to_transformed_detection_score: float
 
 
 PartialDatasetImageMeta = TypedDict(
     "PartialDatasetImageMeta", {**DatasetImageMeta.__annotations__}, total=False
 )
 
-IMAGE_META_DEFAULTS: DatasetImageMeta = {"distance": 0, "width": 0, "height": 0}
+IMAGE_META_DEFAULTS: DatasetImageMeta = {
+    "original_ground_to_original_detection_score": 0,
+    "original_detection_to_transformed_detection_score": 0,
+    "ground_truth_to_transformed_detection_score": 0,
+}
 
 
-def update_image_meta(state, dataset_id: DatasetId, meta_patch: PartialDatasetImageMeta):
+def update_image_meta(state, dataset_id: str, meta_patch: PartialDatasetImageMeta):
     meta_key = image_id_to_meta(dataset_id)
     current_meta = {}
     if state.has(meta_key) and state[meta_key] is not None:
         current_meta = state[meta_key]
     state[meta_key] = {**IMAGE_META_DEFAULTS, **current_meta, **meta_patch}
 
 
-def delete_image_meta(state, dataset_id: DatasetId):
+def delete_image_meta(state, dataset_id: str):
     meta_key = image_id_to_meta(dataset_id)
-    if state.has(meta_key):
-        state[meta_key] = None
+    delete_state(state, meta_key)

src/nrtk_explorer/app/parameters.py

@@ -44,13 +44,11 @@ def on_server_ready(self, *args, **kwargs):
         self.state.change("current_transform")(self.on_current_transform_change)
 
     def on_current_transform_change(self, **kwargs):
-        print("current transform changed", self.state.current_transform)
         transform = self._transforms[self.state.current_transform]
         self.state.params_values = transform.get_parameters()
         self.state.params_descriptions = transform.get_parameters_description()
 
     def on_transform_parameters_changed(self, parameters, **kwargs):
-        print("on_transform_parameters_changed", parameters, kwargs)
         transform = self._transforms[self.state.current_transform]
         transform.set_parameters(parameters)
         self.state.params_values = transform.get_parameters()

src/nrtk_explorer/app/trame_utils.py

@@ -0,0 +1,7 @@
+from typing import Hashable
+from trame_server.state import State
+
+
+def delete_state(state: State, key: Hashable):
+    if state.has(key) and state[key] is not None:
+        state[key] = None