skip or transform option, logs

src/convert_sly_to_yolov5.py

@@ -23,6 +23,8 @@
 TEAM_ID = int(os.environ["context.teamId"])
 WORKSPACE_ID = int(os.environ["context.workspaceId"])
 PROJECT_ID = int(os.environ["modal.state.slyProjectId"])
+PROCCESS_SHAPES = os.environ.get("modal.state.processShapes", "transform")
+PROCCESS_SHAPES_MSG = "skipped" if PROCCESS_SHAPES == "skip" else "transformed to rectangles"
 
 TRAIN_TAG_NAME = "train"
 VAL_TAG_NAME = "val"
@@ -78,19 +80,16 @@ def transform(api: sly.Api, task_id, context, state, app_logger):
         )
 
     error_classes = []
-    ok_classes = []
     for obj_class in meta.obj_classes:
         if obj_class.geometry_type != sly.Rectangle:
             error_classes.append(obj_class)
-        else:
-            ok_classes.append(obj_class)
     if len(error_classes) > 0:
         sly.logger.warn(
-            f"Project has unsupported classes. All unsupported classes will be transformed to rectangles: "
+            f"Project has unsupported classes. "
+            f"Objects with unsupported geometry types will be {PROCCESS_SHAPES_MSG}"
             f"{[obj_class.name for obj_class in error_classes]}"
         )
 
-
     def _write_new_ann(path, content):
         with open(path, "a") as f1:
             f1.write("\n".join(content))
@@ -109,6 +108,7 @@ def _add_to_split(image_id, img_name, split_ids, split_image_paths, labels_dir,
     for dataset in api.dataset.get_list(PROJECT_ID):
         images = api.image.get_list(dataset.id)
 
+        unsupported_shapes = 0
         train_ids = []
         train_image_paths = []
         val_ids = []
@@ -125,6 +125,10 @@ def _add_to_split(image_id, img_name, split_ids, split_image_paths, labels_dir,
 
                 yolov5_ann = []
                 for label in ann.labels:
+                    if label.obj_class.geometry_type != sly.Rectangle:
+                        unsupported_shapes += 1
+                        if PROCCESS_SHAPES == "skip":
+                            continue
                     yolov5_ann.append(transform_label(class_names, ann.img_size, label))
 
                 image_processed = False
@@ -167,6 +171,11 @@ def _add_to_split(image_id, img_name, split_ids, split_image_paths, labels_dir,
         api.image.download_paths(dataset.id, val_ids, val_image_paths)
 
         progress.iters_done_report(len(batch))
+        if unsupported_shapes > 0:
+            app_logger.warn(
+                f"Dataset {dataset.name} has {unsupported_shapes} objects with unsupported geometry types. "
+                f"These objects will be {PROCCESS_SHAPES_MSG}"
+            )
 
     data_yaml = {
         "train": "../{}/images/train".format(result_dir_name),