few improvements

deepface/DeepFace.py

@@ -270,6 +270,7 @@ def find(
     silent: bool = False,
     refresh_database: bool = True,
     anti_spoofing: bool = False,
+    recursive: bool = True,
 ) -> List[pd.DataFrame]:
     """
     Identify individuals in a database
@@ -281,6 +282,8 @@ def find(
         db_path (string): Path to the folder containing image files. All detected faces
             in the database will be considered in the decision-making process.
 
+        recursive (bool): Walk db_path recursively (default True)
+
         model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
             OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
 
@@ -347,6 +350,7 @@ def find(
         silent=silent,
         refresh_database=refresh_database,
         anti_spoofing=anti_spoofing,
+        recursive=recursive,
     )
 
 

deepface/commons/image_utils.py

@@ -13,28 +13,45 @@ import cv2
 from PIL import Image
 
 
-def list_images(path: str) -> List[str]:
+def is_image(file_path: str) -> bool:
+    """
+    Check if a file is an image
+    Args:
+        file_path (str): path to the file
+    Returns:
+        bool: True if the file is an image, False otherwise
+    """
+    _, ext = os.path.splitext(file_path)
+    ext_lower = ext.lower()
+
+    if ext_lower not in {".jpg", ".jpeg", ".png", ".webp"}:
+        return False
+
+    with Image.open(file_path) as img:  # lazy
+        return img.format.lower() in ["jpeg", "png"]
+
+
+def list_images(path: str, recursive: bool = True) -> List[str]:
     """
     List images in a given path
     Args:
         path (str): path's location
+        recursive (bool): default True
     Returns:
         images (list): list of exact image paths
     """
     images = []
-    for r, _, f in os.walk(path):
+    if recursive:
-        for file in f:
+        for r, _, f in os.walk(path):
-            exact_path = os.path.join(r, file)
+            for file in f:
-
+                exact_path = os.path.join(r, file)
-            _, ext = os.path.splitext(exact_path)
+                if is_image(exact_path):
-            ext_lower = ext.lower()
-
-            if ext_lower not in {".jpg", ".jpeg", ".png"}:
-                continue
-
-            with Image.open(exact_path) as img:  # lazy
-                if img.format.lower() in ["jpeg", "png"]:
                     images.append(exact_path)
+    else:
+        for file in os.listdir(path):
+            exact_path = os.path.join(path, file)
+            if is_image(exact_path):
+                images.append(exact_path)
     return images
 
 
@@ -95,10 +112,6 @@ def load_image(img: Union[str, np.ndarray]) -> Tuple[np.ndarray, str]:
 
     # image must be a file on the system then
 
-    # image name must have english characters
-    if img.isascii() is False:
-        raise ValueError(f"Input image must not have non-english characters - {img}")
-
     img_obj_bgr = cv2.imread(img)
     # img_obj_rgb = cv2.cvtColor(img_obj_bgr, cv2.COLOR_BGR2RGB)
     return img_obj_bgr, img

deepface/modules/recognition.py

@@ -31,6 +31,7 @@ def find(
     silent: bool = False,
     refresh_database: bool = True,
     anti_spoofing: bool = False,
+    recursive: bool = True,
 ) -> List[pd.DataFrame]:
     """
     Identify individuals in a database
@@ -43,6 +44,8 @@ def find(
         db_path (string): Path to the folder containing image files. All detected faces
             in the database will be considered in the decision-making process.
 
+        recursive (bool): Walk db_path recursively (default True)
+
         model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
             OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
 
@@ -152,7 +155,7 @@ def find(
     pickled_images = [representation["identity"] for representation in representations]
 
     # Get the list of images on storage
-    storage_images = image_utils.list_images(path=db_path)
+    storage_images = image_utils.list_images(path=db_path, recursive=recursive)
 
     if len(storage_images) == 0 and refresh_database is True:
         raise ValueError(f"No item found in {db_path}")
@@ -374,6 +377,13 @@ def __find_bulk_embeddings(
             logger.error(f"Exception while extracting faces from {employee}: {str(err)}")
             img_objs = []
 
+        except KeyboardInterrupt:
+            needInterrupt = os.getenv("DEEPFACE_KEYBOARD_INTERRUPT", '0').lower() in ('true', '1', 't')
+            if not needInterrupt:
+                raise
+            else:
+                break
+
         if len(img_objs) == 0:
             representations.append(
                 {