feat: add DB columns

- predictor for product_insight and prediction tables
- source_image and barcode for logo_annotation table

robotoff/cli/logos.py

@@ -67,6 +67,8 @@ def insert_batch(data_path: pathlib.Path, model_name: str, model_version: str) -
                     index=i,
                     score=item["score"],
                     bounding_box=item["bounding_box"],
+                    barcode=image_instance.barcode,
+                    source_image=image_instance.source_image,
                 )
         seen_set.add(key)
 

robotoff/models.py

@@ -160,6 +160,8 @@ class ProductInsight(BaseModel):
     # Hunger Games) on a subset of products. Each product have 0+ campaign
     # tags
     campaign = BinaryJSONField(null=True, index=True, default=list)
+    # Confidence score of the insight, may be null
+    confidence = peewee.FloatField(null=True, index=True)
 
 
 class Prediction(BaseModel):
@@ -175,6 +177,7 @@ class Prediction(BaseModel):
         help_text="server domain linked to the insight", index=True
     )
     predictor = peewee.CharField(max_length=100, null=True)
+    confidence = peewee.FloatField(null=True, index=False)
 
 
 class AnnotationVote(BaseModel):
@@ -267,6 +270,8 @@ class LogoAnnotation(BaseModel):
     username = peewee.TextField(null=True, index=True)
     completed_at = peewee.DateTimeField(null=True, index=True)
     nearest_neighbors = BinaryJSONField(null=True)
+    barcode = peewee.CharField(max_length=100, null=True, index=True)
+    source_image = peewee.TextField(null=True, index=True)
 
     class Meta:
         constraints = [peewee.SQL("UNIQUE(image_prediction_id, index)")]

robotoff/prediction/types.py

@@ -18,6 +18,7 @@ class Prediction:
     source_image: Optional[str] = None
     server_domain: Optional[str] = None
     id: Optional[int] = None
+    confidence: Optional[float] = None
 
     def to_dict(self) -> dict[str, Any]:
         return dataclasses.asdict(self, dict_factory=dict_factory)

robotoff/workers/tasks/import_image.py

@@ -368,6 +368,8 @@ def run_logo_object_detection(
                         index=i,
                         score=item["score"],
                         bounding_box=item["bounding_box"],
+                        barcode=image_model.barcode,
+                        source_image=image_model.source_image,
                     )
                 )
             logger.info("%s logos found for image %s", len(logos), source_image)

scripts/insert_image_predictions.py

@@ -71,6 +71,8 @@ def insert_batch(data_path: pathlib.Path, model_name: str, model_version: str) -
                     index=i,
                     score=item["score"],
                     bounding_box=item["bounding_box"],
+                    barcode=image_instance.barcode,
+                    source_image=image_instance.source_image,
                 )
         seen_set.add(key)
 

tests/integration/models_utils.py

@@ -67,6 +67,7 @@ class Meta:
     server_domain = factory.LazyFunction(lambda: settings.OFF_SERVER_DOMAIN)
     automatic_processing = None
     predictor = None
+    confidence = None
 
 
 class AnnotationVoteFactory(UuidSequencer, PeeweeModelFactory):