Merge branch 'main' into pulling_owlv2_config_into_env

inference/core/workflows/core_steps/visualizations/label/v1.py

@@ -206,7 +206,12 @@ def run(
         if text == "Class":
             labels = predictions["class_name"]
         elif text == "Tracker Id":
-            labels = [str(t) if t else "" for t in predictions.tracker_id]
+            if predictions.tracker_id is not None:
+                labels = [
+                    str(t) if t else "No Tracker ID" for t in predictions.tracker_id
+                ]
+            else:
+                labels = ["No Tracker ID"] * len(predictions)
         elif text == "Time In Zone":
             if "time_in_zone" in predictions.data:
                 labels = [
@@ -241,7 +246,6 @@ def run(
                 labels = [str(d) if d else "" for d in predictions[text]]
             except Exception:
                 raise ValueError(f"Invalid text type: {text}")
-
         annotated_image = annotator.annotate(
             scene=image.numpy_image.copy() if copy_image else image.numpy_image,
             detections=predictions,

requirements/_requirements.txt

@@ -31,3 +31,5 @@ tldextract~=5.1.2
 packaging~=24.0
 anthropic~=0.34.2
 pandas>=2.0.0,<2.3.0
+pytest>=8.0.0,<9.0.0  # this is not a joke, sam2 requires this as the fork we are using is dependent on that, yet
+# do not mark the dependency: https://github.com/SauravMaheshkar/samv2/blob/main/sam2/utils/download.py 

tests/workflows/integration_tests/execution/test_workflow_with_label_visualization.py

@@ -0,0 +1,73 @@
+import numpy as np
+
+from inference.core.env import WORKFLOWS_MAX_CONCURRENT_STEPS
+from inference.core.managers.base import ModelManager
+from inference.core.workflows.core_steps.common.entities import StepExecutionMode
+from inference.core.workflows.execution_engine.core import ExecutionEngine
+
+VISUALIZATION_WORKFLOW = {
+    "version": "1.0",
+    "inputs": [
+        {"type": "WorkflowImage", "name": "image"},
+        {
+            "type": "WorkflowParameter",
+            "name": "model_id",
+            "default_value": "yolov8n-640",
+        },
+        {"type": "WorkflowParameter", "name": "confidence", "default_value": 0.3},
+    ],
+    "steps": [
+        {
+            "type": "RoboflowObjectDetectionModel",
+            "name": "detection",
+            "image": "$inputs.image",
+            "model_id": "$inputs.model_id",
+            "confidence": "$inputs.confidence",
+        },
+        {
+            "type": "roboflow_core/label_visualization@v1",
+            "name": "label_visualization",
+            "predictions": "$steps.detection.predictions",
+            "image": "$inputs.image",
+            "text": "Tracker Id",
+        },
+    ],
+    "outputs": [
+        {"type": "JsonField", "name": "result", "selector": "$steps.detection.*"},
+        {
+            "type": "JsonField",
+            "name": "visualized",
+            "selector": "$steps.label_visualization.image",
+        },
+    ],
+}
+
+
+def test_workflow_when_detections_are_not_present(
+    model_manager: ModelManager,
+    crowd_image: np.ndarray,
+) -> None:
+    """This test covers bug in label annotator block."""
+    # given
+    workflow_init_parameters = {
+        "workflows_core.model_manager": model_manager,
+        "workflows_core.api_key": None,
+        "workflows_core.step_execution_mode": StepExecutionMode.LOCAL,
+    }
+    execution_engine = ExecutionEngine.init(
+        workflow_definition=VISUALIZATION_WORKFLOW,
+        init_parameters=workflow_init_parameters,
+        max_concurrent_steps=WORKFLOWS_MAX_CONCURRENT_STEPS,
+    )
+
+    # when
+    result = execution_engine.run(
+        runtime_parameters={"image": crowd_image, "confidence": 0.99999}
+    )
+
+    # then
+    assert isinstance(result, list), "Expected result to be list"
+    assert len(result) == 1, "Single image provided - single output expected"
+    assert (
+        len(result[0]["result"]["predictions"]) == 0
+    ), "Expected no predictions to be delivered"