fix: allow to run object detection models on URL list file

robotoff/cli/main.py

@@ -6,6 +6,7 @@
 import typer
 
 from robotoff.elasticsearch.client import get_es_client
+from robotoff.off import get_barcode_from_url
 from robotoff.types import ObjectDetectionModel, PredictionType, WorkerQueue
 
 app = typer.Typer()
@@ -334,18 +335,29 @@ def run_object_detection_model(
     model_name: ObjectDetectionModel = typer.Argument(
         ..., help="Name of the object detection model"
     ),
+    input_path: Optional[Path] = typer.Option(
+        None,
+        exists=True,
+        file_okay=True,
+        dir_okay=False,
+        help="text file with image URLs to run object detection on. "
+        "If null, a query is performed in DB to fetch images without image predictions "
+        "for the specified model.",
+    ),
     limit: Optional[int] = typer.Option(None, help="Maximum numbers of job to launch"),
 ):
     """Launch object detection model jobs on all missing images (images
     without an ImagePrediction item for this model) in DB."""
     from typing import Callable
+    from urllib.parse import urlparse
 
     import tqdm
     from peewee import JOIN
 
     from robotoff import settings
     from robotoff.models import ImageModel, ImagePrediction, db
     from robotoff.off import generate_image_url
+    from robotoff.utils import text_file_iter
     from robotoff.workers.queues import enqueue_job, low_queue
     from robotoff.workers.tasks.import_image import (
         run_logo_object_detection,
@@ -360,27 +372,40 @@ def run_object_detection_model(
     else:
         func = run_nutriscore_object_detection
 
-    with db:
-        query = (
-            ImageModel.select(ImageModel.barcode, ImageModel.id)
-            .join(
-                ImagePrediction,
-                JOIN.LEFT_OUTER,
-                on=(
-                    (ImagePrediction.image_id == ImageModel.id)
-                    & (ImagePrediction.model_name == model_name.value)
-                ),
+    if input_path:
+        image_urls = list(text_file_iter(input_path))
+
+        for image_url in image_urls:
+            parsed_url = urlparse(image_url)
+            if not parsed_url.netloc or not parsed_url.scheme:
+                raise ValueError(f"invalid image URL: {image_url}")
+
+    else:
+        with db:
+            query = (
+                ImageModel.select(ImageModel.barcode, ImageModel.id)
+                .join(
+                    ImagePrediction,
+                    JOIN.LEFT_OUTER,
+                    on=(
+                        (ImagePrediction.image_id == ImageModel.id)
+                        & (ImagePrediction.model_name == model_name.value)
+                    ),
+                )
+                .where(ImagePrediction.model_name.is_null())
+                .tuples()
             )
-            .where(ImagePrediction.model_name.is_null())
-            .tuples()
-        )
-        if limit:
-            query = query.limit(limit)
-        missing_items = list(query)
+            if limit:
+                query = query.limit(limit)
+            image_urls = [
+                generate_image_url(barcode, image_id)
+                for barcode, image_id in query
+                if barcode.isdigit()
+            ]
 
-    if typer.confirm(f"{len(missing_items)} jobs are going to be launched, confirm?"):
-        for barcode, image_id in tqdm.tqdm(missing_items, desc="image"):
-            image_url = generate_image_url(barcode, image_id)
+    if typer.confirm(f"{len(image_urls)} jobs are going to be launched, confirm?"):
+        for image_url in tqdm.tqdm(image_urls, desc="image"):
+            barcode = get_barcode_from_url(image_url)
             enqueue_job(
                 func,
                 low_queue,