contributes to https://gitlab.pasteur.fr/nyx/TaggingBackends/-/issues/1

pyproject.toml

@@ -2,7 +2,7 @@
 name = "MaggotUBA-adapter"
 version = "0.1.0"
 description = "Interface between MaggotUBA and the Nyx tagging UI"
-authors = ["François Laurent <francois.laurent@posteo.net>"]
+authors = ["François Laurent"]
 license = "MIT"
 packages = [
 	{ include = "maggotuba", from = "src" },
@@ -10,7 +10,7 @@ packages = [
 
 [tool.poetry.dependencies]
 python = "^3.8,<3.11"
-taggingbackends = {git = "https://gitlab.pasteur.fr/nyx/TaggingBackends", rev = "main"}
+taggingbackends = {git = "https://gitlab.pasteur.fr/nyx/TaggingBackends", rev = "dev"}
 structured-temporal-convolution = {git = "git@gitlab.pasteur.fr:les-larves/structured-temporal-convolution.git", branch="dev-branch"}
 torch = "^1.11.0"
 numpy = "^1.19.3"

src/maggotuba/models/predict_model.py

@@ -60,7 +60,7 @@ def predict_model(backend):
         config_file = [file for file in model_files if file.name.endswith("config.json")]
         model = RandomForest(config_file[-1]).load()
         # assign labels
-        labels = Labels()
+        labels = Labels(tracking=input_files)
         if isinstance(data, dict):
             ref_length = np.mean(np.concatenate([
                 model.body_length(spines) for spines in data.values()
@@ -84,12 +84,14 @@ def predict_model(backend):
                     labels[run, larva] = dict(zip(t[mask], predictions))
         # save the predicted labels to file
         if metadata:
-            labels[run]['metadata'] = metadata
+            labels.metadata = metadata
         else:
-            labels[run]['metadata'] = {'filename': file.name}
-        labels.metadata['labels'] = ["run", "bend", "stop", "hunch", "back", "roll"]
-        labels.metadata['label_colors'] = ["#000000", "#ff0000", "#00ff00",
-                "#0000ff", "#00ffff", "#ffff00"]
+            labels.metadata = {'filename': file.name}
+        labels.labelspec = {
+                "names": ["run", "bend", "stop", "hunch", "back", "roll"],
+                "colors": ["#000000", "#ff0000", "#00ff00", "#0000ff",
+                    "#00ffff", "#ffff00"]
+                }
         labels.dump(backend.processed_data_dir() / "predicted.labels")