fix COCO category IDs

docs/source/user_guide/dataset_creation/datasets.rst

@@ -1499,9 +1499,8 @@ where `labels.json` is a JSON file in the following format:
             ...
         ],
         "categories": [
-            ...
             {
-                "id": 2,
+                "id": 1,
                 "name": "cat",
                 "supercategory": "animal",
                 "keypoints": ["nose", "head", ...],
@@ -1524,7 +1523,7 @@ where `labels.json` is a JSON file in the following format:
             {
                 "id": 1,
                 "image_id": 1,
-                "category_id": 2,
+                "category_id": 1,
                 "bbox": [260, 177, 231, 199],
                 "segmentation": [...],
                 "keypoints": [224, 226, 2, ...],

docs/source/user_guide/export_datasets.rst

@@ -1646,9 +1646,8 @@ where `labels.json` is a JSON file in the following format:
         },
         "licenses": [],
         "categories": [
-            ...
             {
-                "id": 2,
+                "id": 1,
                 "name": "cat",
                 "supercategory": "animal"
             },
@@ -1669,7 +1668,7 @@ where `labels.json` is a JSON file in the following format:
             {
                 "id": 1,
                 "image_id": 1,
-                "category_id": 2,
+                "category_id": 1,
                 "bbox": [260, 177, 231, 199],
                 "segmentation": [...],
                 "score": 0.95,

fiftyone/utils/coco.py

@@ -563,15 +563,11 @@ def setup(self):
                 self.labels_path, extra_attrs=self.extra_attrs
             )
 
-            classes = None
             if classes_map is not None:
-                classes = _to_classes(classes_map)
-
-            if classes is not None:
-                info["classes"] = classes
+                info["classes"] = _to_classes(classes_map)
 
             image_ids = _get_matching_image_ids(
-                classes,
+                classes_map,
                 images,
                 annotations,
                 image_ids=self.image_ids,
@@ -907,12 +903,11 @@ def export_sample(self, image_or_path, label, metadata=None):
 
     def close(self, *args):
         if self._dynamic_classes:
-            classes = sorted(self._classes)
-            labels_map_rev = _to_labels_map_rev(classes)
+            labels_map_rev = _to_labels_map_rev(sorted(self._classes))
             for anno in self._annotations:
                 anno["category_id"] = labels_map_rev[anno["category_id"]]
-        else:
-            classes = self.classes
+        elif self.categories is None:
+            labels_map_rev = _to_labels_map_rev(self.classes)
 
         _info = self.info or {}
         _date_created = datetime.now().replace(microsecond=0).isoformat()
@@ -933,10 +928,10 @@ def close(self, *args):
             categories = [
                 {
                     "id": i,
-                    "name": l,
+                    "name": c,
                     "supercategory": None,
                 }
-                for i, l in enumerate(classes)
+                for c, i in sorted(labels_map_rev.items(), key=lambda t: t[1])
             ]
 
         labels = {
@@ -1681,7 +1676,7 @@ def download_coco_dataset_split(
         if classes is not None:
             # Filter by specified classes
             all_ids, any_ids = _get_images_with_classes(
-                image_ids, annotations, classes, all_classes
+                image_ids, annotations, classes, all_classes_map
             )
         else:
             all_ids = image_ids
@@ -1846,7 +1841,7 @@ def _parse_include_license(include_license):
 
 
 def _get_matching_image_ids(
-    all_classes,
+    classes_map,
     images,
     annotations,
     image_ids=None,
@@ -1862,7 +1857,7 @@ def _get_matching_image_ids(
 
     if classes is not None:
         all_ids, any_ids = _get_images_with_classes(
-            image_ids, annotations, classes, all_classes
+            image_ids, annotations, classes, classes_map
         )
     else:
         all_ids = image_ids
@@ -1930,7 +1925,7 @@ def _do_download(args):
 
 
 def _get_images_with_classes(
-    image_ids, annotations, target_classes, all_classes
+    image_ids, annotations, target_classes, classes_map
 ):
     if annotations is None:
         logger.warning("Dataset is unlabeled; ignoring classes requirement")
@@ -1939,11 +1934,12 @@ def _get_images_with_classes(
     if etau.is_str(target_classes):
         target_classes = [target_classes]
 
-    bad_classes = [c for c in target_classes if c not in all_classes]
+    labels_map_rev = {c: i for i, c in classes_map.items()}
+
+    bad_classes = [c for c in target_classes if c not in labels_map_rev]
     if bad_classes:
         raise ValueError("Unsupported classes: %s" % bad_classes)
 
-    labels_map_rev = _to_labels_map_rev(all_classes)
     class_ids = {labels_map_rev[c] for c in target_classes}
 
     all_ids = []
@@ -2029,7 +2025,7 @@ def _load_image_ids_json(json_path):
 
 
 def _to_labels_map_rev(classes):
-    return {c: i for i, c in enumerate(classes)}
+    return {c: i for i, c in enumerate(classes, 1)}
 
 
 def _to_classes(classes_map):

tests/unittests/import_export_tests.py

@@ -1317,6 +1317,65 @@ def test_coco_detection_dataset(self):
             {c["id"] for c in categories2},
         )
 
+        # Alphabetized 1-based categories by default
+
+        export_dir = self._new_dir()
+
+        dataset.export(
+            export_dir=export_dir,
+            dataset_type=fo.types.COCODetectionDataset,
+        )
+
+        dataset2 = fo.Dataset.from_dir(
+            dataset_dir=export_dir,
+            dataset_type=fo.types.COCODetectionDataset,
+            label_types="detections",
+            label_field="predictions",
+        )
+        categories2 = dataset2.info["categories"]
+
+        self.assertListEqual([c["id"] for c in categories2], [1, 2])
+        self.assertListEqual([c["name"] for c in categories2], ["cat", "dog"])
+
+        # Only load matching classes
+
+        export_dir = self._new_dir()
+
+        dataset.export(
+            export_dir=export_dir,
+            dataset_type=fo.types.COCODetectionDataset,
+        )
+
+        dataset2 = fo.Dataset.from_dir(
+            dataset_dir=export_dir,
+            dataset_type=fo.types.COCODetectionDataset,
+            label_types="detections",
+            label_field="predictions",
+            classes="cat",
+            only_matching=False,
+        )
+
+        self.assertEqual(len(dataset2), 2)
+        self.assertListEqual(
+            dataset2.distinct("predictions.detections.label"),
+            ["cat", "dog"],
+        )
+
+        dataset3 = fo.Dataset.from_dir(
+            dataset_dir=export_dir,
+            dataset_type=fo.types.COCODetectionDataset,
+            label_types="detections",
+            label_field="predictions",
+            classes="cat",
+            only_matching=True,
+        )
+
+        self.assertEqual(len(dataset3), 2)
+        self.assertListEqual(
+            dataset3.distinct("predictions.detections.label"),
+            ["cat"],
+        )
+
     @drop_datasets
     def test_voc_detection_dataset(self):
         dataset = self._make_dataset()