add: tests

test/detection/test_vlm.py

@@ -6,7 +6,10 @@ from contextlib import nullcontext as does_not_raise
 import numpy as np
 import pytest
 
+from supervision.config import CLASS_NAME_DATA_FIELD
+from supervision.detection.core import Detections
 from supervision.detection.vlm import (
+    VLM,
     from_florence_2,
     from_google_gemini_2_0,
     from_google_gemini_2_5,
@@ -1122,3 +1125,110 @@ def test_from_google_gemini_2_5(
             assert masks is not None
             assert masks.shape == expected_results[4].shape
             assert np.array_equal(masks, expected_results[4])
+
+
+@pytest.mark.parametrize(
+    "exception, result, resolution_wh, classes, expected_detections",
+    [
+        (
+            pytest.raises(ValueError),
+            "",
+            (100, 100),
+            None,
+            None,
+        ),  # empty text
+        (
+            pytest.raises(ValueError),
+            "random text",
+            (100, 100),
+            None,
+            None,
+        ),  # random text
+        (
+            does_not_raise(),
+            "<|ref|>cat<|/ref|><|det|>[[100, 200, 300, 400]]<|/det|>",
+            (1000, 1000),
+            None,
+            Detections(
+                xyxy=np.array([[100.1, 200.2, 300.3, 400.4]]),
+                class_id=np.array([0]),
+                data={CLASS_NAME_DATA_FIELD: np.array(["cat"])},
+            ),
+        ),  # single box, no classes
+        (
+            does_not_raise(),
+            "<|ref|>cat<|/ref|><|det|>[[100, 200, 300, 400]]<|/det|>",
+            (1000, 1000),
+            ["cat", "dog"],
+            Detections(
+                xyxy=np.array([[100.1, 200.2, 300.3, 400.4]]),
+                class_id=np.array([0]),
+                data={CLASS_NAME_DATA_FIELD: np.array(["cat"])},
+            ),
+        ),  # single box, with classes
+        (
+            does_not_raise(),
+            "<|ref|>person<|/ref|><|det|>[[100, 200, 300, 400]]<|/det|>",
+            (1000, 1000),
+            ["cat", "dog"],
+            Detections.empty(),
+        ),  # single box, wrong class
+        (
+            does_not_raise(),
+            (
+                "<|ref|>cat<|/ref|><|det|>[[100, 200, 300, 400]]<|/det|>"
+                "<|ref|>dog<|/ref|><|det|>[[500, 600, 700, 800]]<|/det|>"
+            ),
+            (1000, 1000),
+            ["cat"],
+            Detections(
+                xyxy=np.array([[100.1, 200.2, 300.3, 400.4]]),
+                class_id=np.array([0]),
+                data={CLASS_NAME_DATA_FIELD: np.array(["cat"])},
+            ),
+        ),  # multiple boxes, one class correct
+        (
+            pytest.raises(ValueError),
+            "<|ref|>cat<|/ref|>",
+            (100, 100),
+            None,
+            None,
+        ),  # only ref
+        (
+            pytest.raises(ValueError),
+            "<|det|>[[100, 200, 300, 400]]<|/det|>",
+            (100, 100),
+            None,
+            None,
+        ),  # only det
+    ],
+)
+def test_from_deepseek_vl_2(
+    exception,
+    result: str,
+    resolution_wh: tuple[int, int],
+    classes: list[str] | None,
+    expected_detections: Detections,
+):
+    with exception:
+        detections = Detections.from_vlm(
+            vlm=VLM.DEEPSEEK_VL_2,
+            result=result,
+            resolution_wh=resolution_wh,
+            classes=classes,
+        )
+
+        if expected_detections is None:
+            return
+
+        assert len(detections) == len(expected_detections)
+
+        if len(detections) == 0:
+            return
+
+        assert np.allclose(detections.xyxy, expected_detections.xyxy, atol=1e-1)
+        assert np.array_equal(detections.class_id, expected_detections.class_id)
+        assert np.array_equal(
+            detections.data[CLASS_NAME_DATA_FIELD],
+            expected_detections.data[CLASS_NAME_DATA_FIELD],
+        )