batched represent

tests/test_represent.py

@@ -2,6 +2,7 @@
 import io
 import cv2
 import pytest
+import numpy as np
 
 # project dependencies
 from deepface import DeepFace
@@ -81,3 +82,49 @@ def test_max_faces():
     max_faces = 1
     results = DeepFace.represent(img_path="dataset/couple.jpg", max_faces=max_faces)
     assert len(results) == max_faces
+
+
+def test_batched_represent():
+    img_paths = [
+        "dataset/img1.jpg",
+        "dataset/img2.jpg",
+        "dataset/img3.jpg",
+        "dataset/img4.jpg",
+        "dataset/img5.jpg",
+    ]
+
+    def _test_for_model(model_name: str):
+        embedding_objs = DeepFace.represent(img_path=img_paths, model_name=model_name)
+        assert len(embedding_objs) == len(img_paths)
+        if model_name == "VGG-Face":
+            for embedding_obj in embedding_objs:
+                embedding = embedding_obj["embedding"]
+                logger.debug(f"Function returned {len(embedding)} dimensional vector")
+                assert len(embedding) == 4096
+        embedding_objs_one_by_one = [
+            embedding_obj
+            for img_path in img_paths
+            for embedding_obj in DeepFace.represent(img_path=img_path, model_name=model_name) 
+        ]
+        for embedding_obj_one_by_one, embedding_obj in zip(embedding_objs_one_by_one, embedding_objs):
+            assert np.allclose(
+                embedding_obj_one_by_one["embedding"], 
+                embedding_obj["embedding"],
+                rtol=1e-2,
+                atol=1e-2
+            )
+
+    for model_name in [
+        "VGG-Face", 
+        "Facenet", 
+        "Facenet512", 
+        "OpenFace", 
+        # "DeepFace", 
+        "DeepID", 
+        # "Dlib", 
+        "ArcFace", 
+        "SFace", 
+        "GhostFaceNet"
+    ]:
+        _test_for_model(model_name)
+    logger.info("✅ test batch represent function done")