Add batched version of the find function

tests/test_find_batched.py

@@ -0,0 +1,99 @@
+# built-in dependencies
+import os
+
+# 3rd party dependencies
+import cv2
+import pandas as pd
+
+# project dependencies
+from deepface import DeepFace
+from deepface.modules import verification
+from deepface.commons import image_utils
+from deepface.commons.logger import Logger
+
+logger = Logger()
+
+
+threshold = verification.find_threshold(model_name="VGG-Face", distance_metric="cosine")
+
+
+def test_find_with_exact_path():
+    img_path = os.path.join("dataset", "img1.jpg")
+    results = DeepFace.find(img_path=img_path, db_path="dataset", silent=True, batched=True)
+    assert len(results) > 0
+    for result in results:
+        assert isinstance(result, list)
+
+        found_image_itself = False
+        for face in result:
+            assert isinstance(face, dict)
+            if face["identity"] == img_path:
+                # validate reproducability
+                assert face["distance"] < threshold
+                # one is img1.jpg itself
+                found_image_itself = True
+        assert found_image_itself
+
+    assert len(results[0]) > 1
+
+    logger.info("✅ test find for exact path done")
+
+
+def test_find_with_array_input():
+    img_path = os.path.join("dataset", "img1.jpg")
+    img1 = cv2.imread(img_path)
+    results = DeepFace.find(img1, db_path="dataset", silent=True, batched=True)
+    assert len(results) > 0
+    for result in results:
+        assert isinstance(result, list)
+
+        found_image_itself = False
+        for face in result:
+            assert isinstance(face, dict)
+            if face["identity"] == img_path:
+                # validate reproducability
+                assert face["distance"] < threshold
+                # one is img1.jpg itself
+                found_image_itself = True
+        assert found_image_itself
+
+    assert len(results[0]) > 1
+
+    logger.info("✅ test find for array input done")
+
+
+def test_find_with_extracted_faces():
+    img_path = os.path.join("dataset", "img1.jpg")
+    face_objs = DeepFace.extract_faces(img_path)
+    img = face_objs[0]["face"]
+    results = DeepFace.find(img, db_path="dataset", detector_backend="skip", silent=True, batched=True)
+    assert len(results) > 0
+    for result in results:
+        assert isinstance(result, list)
+
+        found_image_itself = False
+        for face in result:
+            assert isinstance(face, dict)
+            if face["identity"] == img_path:
+                # validate reproducability
+                assert face["distance"] < threshold
+                # one is img1.jpg itself
+                found_image_itself = True
+        assert found_image_itself
+
+    assert len(results[0]) > 1
+    logger.info("✅ test find for extracted face input done")
+
+
+def test_filetype_for_find():
+    """
+    only images as jpg and png can be loaded into database
+    """
+    img_path = os.path.join("dataset", "img1.jpg")
+    results = DeepFace.find(img_path=img_path, db_path="dataset", silent=True, batched=True)
+
+    result = results[0]
+
+    assert not any(face["identity"] == "dataset/img47.jpg" for face in result)
+
+    logger.info("✅ test wrong filetype done")