Merge pull request #1427 from dakotah-jones/patch-1

Update representation.py

deepface/modules/recognition.py

@@ -398,6 +398,7 @@ def __find_bulk_embeddings(
                 enforce_detection=enforce_detection,
                 align=align,
                 expand_percentage=expand_percentage,
+                color_face='bgr'  # `represent` expects images in bgr format.
             )
 
         except ValueError as err:

deepface/modules/representation.py

@@ -87,6 +87,7 @@ def represent(
         # we have run pre-process in verification. so, skip if it is coming from verify.
         target_size = model.input_shape
         if detector_backend != "skip":
+            # Images are returned in RGB format.
             img_objs = detection.extract_faces(
                 img_path=single_img_path,
                 detector_backend=detector_backend,
@@ -104,6 +105,9 @@ def represent(
             if len(img.shape) != 3:
                 raise ValueError(f"Input img must be 3 dimensional but it is {img.shape}")
 
+            # Convert to RGB format to keep compatability with `extract_faces`.
+            img = img[:, :, ::-1]
+
             # make dummy region and confidence to keep compatibility with `extract_faces`
             img_objs = [
                 {
@@ -130,7 +134,7 @@ def represent(
 
             img = img_obj["face"]
 
-            # bgr to rgb
+            # rgb to bgr
             img = img[:, :, ::-1]
 
             region = img_obj["facial_area"]

tests/test_represent.py

@@ -111,6 +111,27 @@ def test_max_faces():
     assert len(results) == max_faces
 
 
+def test_represent_detector_backend():
+    # Results using a detection backend.
+    results_1 = DeepFace.represent(img_path="dataset/img1.jpg")
+    assert len(results_1) == 1
+
+    # Results performing face extraction first.
+    faces = DeepFace.extract_faces(img_path="dataset/img1.jpg", color_face='bgr')
+    assert len(faces) == 1
+
+    # Images sent into represent need to be in BGR format.
+    img = faces[0]['face']
+    results_2 = DeepFace.represent(img_path=img, detector_backend="skip")
+    assert len(results_2) == 1
+
+    # The embeddings should be the exact same for both cases.
+    embedding_1 = results_1[0]['embedding']
+    embedding_2 = results_2[0]['embedding']
+    assert embedding_1 == embedding_2
+    logger.info("✅ test represent function for consistent output.")
+
+
 @pytest.mark.parametrize(
     "model_name",
     [