test numpy array batched input

tests/test_extract_faces.py

@@ -157,6 +157,56 @@ def test_batch_extract_faces(detector_backend):
             ) <= rtol * img_obj_individual["confidence"]
     
 
+@pytest.mark.parametrize("detector_backend", [
+    "opencv",
+    "ssd",
+    "mtcnn",
+    "retinaface",
+    "yunet",
+    "centerface",
+    # optional
+    # "yolov11s",
+    # "mediapipe",
+    # "dlib",
+])
+def test_batch_extract_faces_with_nparray(detector_backend):
+    img_paths = [
+        "dataset/img2.jpg",
+        "dataset/img3.jpg",
+        "dataset/img11.jpg",
+        "dataset/couple.jpg"
+    ]
+    imgs = [
+        cv2.resize(image_utils.load_image(img_path)[0], (1920, 1080))
+        for img_path in img_paths
+    ]
+
+    # load images as numpy arrays
+    imgs_batch = np.stack(imgs, axis=0)
+
+    # extract faces in batch of numpy arrays
+    imgs_objs_batch = DeepFace.extract_faces(
+        img_path=imgs_batch,
+        detector_backend=detector_backend,
+        align=True,
+        enforce_detection=False,
+    )
+
+    # extract faces in batch of paths
+    imgs_objs_batch_paths = DeepFace.extract_faces(
+        img_path=imgs,
+        detector_backend=detector_backend,
+        align=True,
+        enforce_detection=False,
+    )
+
+    # compare results
+    for img_objs_batch, img_objs_batch_paths in zip(imgs_objs_batch, imgs_objs_batch_paths):
+        assert len(img_objs_batch) == len(img_objs_batch_paths), (
+            "Batch and individual extraction results should have the same number of detected faces"
+        )
+
+
 def test_backends_for_enforced_detection_with_non_facial_inputs():
     black_img = np.zeros([224, 224, 3])
     for detector in detectors: