Update path handling

benchmarks/Perform-Experiments.ipynb

@@ -86,7 +86,7 @@
    "source": [
     "target_paths = [\"lfwe\", \"dataset\", \"outputs\", \"outputs/test\", \"results\"]\n",
     "for target_path in target_paths:\n",
-    "    if os.path.exists(target_path) != True:\n",
+    "    if not os.path.exists(target_path):\n",
     "        os.mkdir(target_path)\n",
     "        print(f\"{target_path} is just created\")"
    ]
@@ -132,7 +132,7 @@
     "    np.save(target_path, pairs)\n",
     "    np.save(labels_path, labels)\n",
     "else:\n",
-    "    if os.path.exists(pairs_touch) != True:\n",
+    "    if not os.path.exists(pairs_touch):\n",
     "        # loading pairs takes some time. but if we extract these pairs as image, no need to load it anymore\n",
     "        pairs = np.load(target_path)\n",
     "    labels = np.load(labels_path)    "
@@ -165,17 +165,17 @@
     "    img1_target = f\"lfwe/test/{i}_1.jpg\"\n",
     "    img2_target = f\"lfwe/test/{i}_2.jpg\"\n",
     "    \n",
-    "    if os.path.exists(img1_target) != True:\n",
+    "    if not os.path.exists(img1_target):\n",
     "        img1 = pairs[i][0]\n",
     "        # plt.imsave(img1_target, img1/255) #works for my mac\n",
     "        plt.imsave(img1_target, img1) #works for my debian\n",
     "    \n",
-    "    if os.path.exists(img2_target) != True:\n",
+    "    if not os.path.exists(img2_target):\n",
     "        img2 = pairs[i][1]\n",
     "        # plt.imsave(img2_target, img2/255) #works for my mac\n",
     "        plt.imsave(img2_target, img2) #works for my debian\n",
     "    \n",
-    "if os.path.exists(pairs_touch) != True:\n",
+    "if not os.path.exists(pairs_touch):\n",
     "    open(pairs_touch,'a').close()"
    ]
   },
@@ -208,7 +208,7 @@
     "                alignment_text = \"aligned\" if align is True else \"unaligned\"\n",
     "                task = f\"{model_name}_{detector_backend}_{distance_metric}_{alignment_text}\"\n",
     "                output_file = f\"outputs/test/{task}.csv\"\n",
-    "                if os.path.exists(output_file) is True:\n",
+    "                if os.path.exists(output_file):\n",
     "                     #print(f\"{output_file} is available already\")\n",
     "                     continue\n",
     "                \n",

deepface/commons/file_utils.py

@@ -20,7 +20,7 @@ def download_external_file(file_name: str, exact_file_path: str, url: str) -> No
     Returns:
         None
     """
-    if os.path.exists(exact_file_path) is False:
+    if not os.path.exists(exact_file_path):
         logger.info(f"Downloading MiniFASNetV2 weights to {exact_file_path}")
         try:
             gdown.download(url, exact_file_path, quiet=False)

deepface/commons/folder_utils.py

@@ -1,5 +1,4 @@
 import os
-from pathlib import Path
 from deepface.commons.logger import Logger
 
 logger = Logger()
@@ -13,16 +12,16 @@ def initialize_folder() -> None:
         OSError: if the folder cannot be created.
     """
     home = get_deepface_home()
-    deepface_home_path = home + "/.deepface"
+    deepface_home_path = os.path.join(home, ".deepface")
-    weights_path = deepface_home_path + "/weights"
+    weights_path = os.path.join(deepface_home_path, "weights")
 
     if not os.path.exists(deepface_home_path):
         os.makedirs(deepface_home_path, exist_ok=True)
-        logger.info(f"Directory {home}/.deepface created")
+        logger.info(f"Directory {deepface_home_path} has been created")
 
     if not os.path.exists(weights_path):
         os.makedirs(weights_path, exist_ok=True)
-        logger.info(f"Directory {home}/.deepface/weights created")
+        logger.info(f"Directory {weights_path} has been created")
 
 
 def get_deepface_home() -> str:
@@ -32,4 +31,4 @@ def get_deepface_home() -> str:
     Returns:
         str: the home directory.
     """
-    return str(os.getenv("DEEPFACE_HOME", default=str(Path.home())))
+    return str(os.getenv("DEEPFACE_HOME", default=os.path.expanduser("~")))

deepface/commons/image_utils.py

@@ -26,14 +26,13 @@ def list_images(path: str) -> List[str]:
         for file in f:
             exact_path = os.path.join(r, file)
 
-            _, ext = os.path.splitext(exact_path)
+            ext_lower = os.path.splitext(exact_path)[-1].lower()
-            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"]:
+                if img.format.lower() in {"jpeg", "png"}:
                     images.append(exact_path)
     return images
 
@@ -86,17 +85,17 @@ def load_image(img: Union[str, np.ndarray]) -> Tuple[np.ndarray, str]:
         return load_image_from_base64(img), "base64 encoded string"
 
     # The image is a url
-    if img.lower().startswith("http://") or img.lower().startswith("https://"):
+    if img.lower().startswith(("http://", "https://")):
         return load_image_from_web(url=img), img
 
     # The image is a path
-    if os.path.isfile(img) is not True:
+    if not os.path.isfile(img):
         raise ValueError(f"Confirm that {img} exists")
 
     # image must be a file on the system then
 
     # image name must have english characters
-    if img.isascii() is False:
+    if not img.isascii():
         raise ValueError(f"Input image must not have non-english characters - {img}")
 
     img_obj_bgr = cv2.imread(img)
@@ -125,8 +124,8 @@ def load_image_from_base64(uri: str) -> np.ndarray:
     # content type is safer option than file extension
     with Image.open(io.BytesIO(decoded_bytes)) as img:
         file_type = img.format.lower()
-        if file_type not in ["jpeg", "png"]:
+        if file_type not in {"jpeg", "png"}:
-            raise ValueError(f"input image can be jpg or png, but it is {file_type}")
+            raise ValueError(f"Input image can be jpg or png, but it is {file_type}")
 
     nparr = np.fromstring(decoded_bytes, np.uint8)
     img_bgr = cv2.imdecode(nparr, cv2.IMREAD_COLOR)

deepface/models/demography/Age.py

@@ -67,14 +67,13 @@ def load_model(
     # load weights
 
     home = folder_utils.get_deepface_home()
+    output = os.path.join(home, ".deepface/weights/age_model_weights.h5")
 
-    if os.path.isfile(home + "/.deepface/weights/age_model_weights.h5") != True:
+    if not os.path.isfile(output):
         logger.info("age_model_weights.h5 will be downloaded...")
-
-        output = home + "/.deepface/weights/age_model_weights.h5"
         gdown.download(url, output, quiet=False)
 
-    age_model.load_weights(home + "/.deepface/weights/age_model_weights.h5")
+    age_model.load_weights(output)
 
     return age_model
 
@@ -89,6 +88,6 @@ def find_apparent_age(age_predictions: np.ndarray) -> np.float64:
     Returns:
         apparent_age (float)
     """
-    output_indexes = np.array(list(range(0, 101)))
+    output_indexes = np.arange(0, 101)
     apparent_age = np.sum(age_predictions * output_indexes)
     return apparent_age

deepface/models/demography/Emotion.py

@@ -97,13 +97,12 @@ def load_model(
     # ----------------------------
 
     home = folder_utils.get_deepface_home()
+    output = os.path.join(home, ".deepface/weights/facial_expression_model_weights.h5")
 
-    if os.path.isfile(home + "/.deepface/weights/facial_expression_model_weights.h5") != True:
+    if not os.path.isfile(output):
-        logger.info("facial_expression_model_weights.h5 will be downloaded...")
+        logger.info("Facial_expression_model_weights.h5 will be downloaded...")
-
-        output = home + "/.deepface/weights/facial_expression_model_weights.h5"
         gdown.download(url, output, quiet=False)
 
-    model.load_weights(home + "/.deepface/weights/facial_expression_model_weights.h5")
+    model.load_weights(output)
 
     return model

deepface/models/demography/Gender.py

@@ -74,13 +74,12 @@ def load_model(
     # load weights
 
     home = folder_utils.get_deepface_home()
+    output = os.path.join(home, ".deepface/weights/gender_model_weights.h5")
 
-    if os.path.isfile(home + "/.deepface/weights/gender_model_weights.h5") != True:
+    if not os.path.isfile(output):
         logger.info("gender_model_weights.h5 will be downloaded...")
-
-        output = home + "/.deepface/weights/gender_model_weights.h5"
         gdown.download(url, output, quiet=False)
 
-    gender_model.load_weights(home + "/.deepface/weights/gender_model_weights.h5")
+    gender_model.load_weights(output)
 
     return gender_model

deepface/models/demography/Race.py

@@ -71,13 +71,12 @@ def load_model(
     # load weights
 
     home = folder_utils.get_deepface_home()
+    output = os.path.join(home, ".deepface/weights/race_model_single_batch.h5")
 
-    if os.path.isfile(home + "/.deepface/weights/race_model_single_batch.h5") != True:
+    if not os.path.isfile(output):
         logger.info("race_model_single_batch.h5 will be downloaded...")
-
-        output = home + "/.deepface/weights/race_model_single_batch.h5"
         gdown.download(url, output, quiet=False)
 
-    race_model.load_weights(home + "/.deepface/weights/race_model_single_batch.h5")
+    race_model.load_weights(output)
 
     return race_model

deepface/models/face_detection/CenterFace.py

@@ -29,7 +29,9 @@ class CenterFaceClient(Detector):
         """
         Download pre-trained weights of CenterFace model if necessary and load built model
         """
-        weights_path = f"{folder_utils.get_deepface_home()}/.deepface/weights/centerface.onnx"
+        home = folder_utils.get_deepface_home()
+        weights_path = os.path.join(home, ".deepface/weights/centerface.onnx")
+
         if not os.path.isfile(weights_path):
             logger.info(f"Downloading CenterFace weights from {WEIGHTS_URL} to {weights_path}...")
             try:

deepface/models/face_detection/Dlib.py

@@ -20,8 +20,6 @@ class DlibClient(Detector):
         Returns:
             model (Any)
         """
-        home = folder_utils.get_deepface_home()
-
         # this is not a must dependency. do not import it in the global level.
         try:
             import dlib
@@ -32,24 +30,25 @@ class DlibClient(Detector):
             ) from e
 
         # check required file exists in the home/.deepface/weights folder
-        if os.path.isfile(home + "/.deepface/weights/shape_predictor_5_face_landmarks.dat") != True:
+        home = folder_utils.get_deepface_home()
+        filename = "shape_predictor_5_face_landmarks.dat"
+        filepath = os.path.join(home, ".deepface/weights/", filename)
 
-            file_name = "shape_predictor_5_face_landmarks.dat.bz2"
+        if not os.path.isfile(filepath):
-            logger.info(f"{file_name} is going to be downloaded")
+            logger.info(f"{filename + '.bz2'} is going to be downloaded")
 
-            url = f"http://dlib.net/files/{file_name}"
+            url = f"http://dlib.net/files/{filename + '.bz2'}"
-            output = f"{home}/.deepface/weights/{file_name}"
+            output = filepath + ".bz2"
 
             gdown.download(url, output, quiet=False)
 
             zipfile = bz2.BZ2File(output)
             data = zipfile.read()
-            newfilepath = output[:-4]  # discard .bz2 extension
+            with open(filepath, "wb") as f:
-            with open(newfilepath, "wb") as f:
                 f.write(data)
 
         face_detector = dlib.get_frontal_face_detector()
-        sp = dlib.shape_predictor(home + "/.deepface/weights/shape_predictor_5_face_landmarks.dat")
+        sp = dlib.shape_predictor(filepath)
 
         detector = {}
         detector["face_detector"] = face_detector

deepface/models/face_detection/OpenCv.py

@@ -138,8 +138,8 @@ class OpenCvClient(Detector):
         """
         opencv_path = self.__get_opencv_path()
         if model_name == "haarcascade":
-            face_detector_path = opencv_path + "haarcascade_frontalface_default.xml"
+            face_detector_path = os.path.join(opencv_path, "haarcascade_frontalface_default.xml")
-            if os.path.isfile(face_detector_path) != True:
+            if not os.path.isfile(face_detector_path):
                 raise ValueError(
                     "Confirm that opencv is installed on your environment! Expected path ",
                     face_detector_path,
@@ -148,8 +148,8 @@ class OpenCvClient(Detector):
             detector = cv2.CascadeClassifier(face_detector_path)
 
         elif model_name == "haarcascade_eye":
-            eye_detector_path = opencv_path + "haarcascade_eye.xml"
+            eye_detector_path = os.path.join(opencv_path, "haarcascade_eye.xml")
-            if os.path.isfile(eye_detector_path) != True:
+            if not os.path.isfile(eye_detector_path):
                 raise ValueError(
                     "Confirm that opencv is installed on your environment! Expected path ",
                     eye_detector_path,
@@ -168,11 +168,4 @@ class OpenCvClient(Detector):
         Returns:
             installation_path (str)
         """
-        opencv_home = cv2.__file__
+        return os.path.join(os.path.dirname(cv2.__file__), "data")
-        folders = opencv_home.split(os.path.sep)[0:-1]
-
-        path = folders[0]
-        for folder in folders[1:]:
-            path = path + "/" + folder
-
-        return path + "/data/"

deepface/models/face_detection/Ssd.py

@@ -28,35 +28,21 @@ class SsdClient(Detector):
         home = folder_utils.get_deepface_home()
 
         # model structure
-        if os.path.isfile(home + "/.deepface/weights/deploy.prototxt") != True:
+        output_model = os.path.join(home, ".deepface/weights/deploy.prototxt")
-
+        if not os.path.isfile(output_model):
-            logger.info("deploy.prototxt will be downloaded...")
+            logger.info(f"{os.path.basename(output_model)} will be downloaded...")
-
             url = "https://github.com/opencv/opencv/raw/3.4.0/samples/dnn/face_detector/deploy.prototxt"
-
+            gdown.download(url, output_model, quiet=False)
-            output = home + "/.deepface/weights/deploy.prototxt"
-
-            gdown.download(url, output, quiet=False)
 
         # pre-trained weights
-        if (
+        output_weights = os.path.join(home, ".deepface/weights/res10_300x300_ssd_iter_140000.caffemodel")
-            os.path.isfile(home + "/.deepface/weights/res10_300x300_ssd_iter_140000.caffemodel")
+        if not os.path.isfile(output_weights):
-            != True
+            logger.info(f"{os.path.basename(output_weights)} will be downloaded...")
-        ):
-
-            logger.info("res10_300x300_ssd_iter_140000.caffemodel will be downloaded...")
-
             url = "https://github.com/opencv/opencv_3rdparty/raw/dnn_samples_face_detector_20170830/res10_300x300_ssd_iter_140000.caffemodel"
-
+            gdown.download(url, output_weights, quiet=False)
-            output = home + "/.deepface/weights/res10_300x300_ssd_iter_140000.caffemodel"
-
-            gdown.download(url, output, quiet=False)
 
         try:
-            face_detector = cv2.dnn.readNetFromCaffe(
+            face_detector = cv2.dnn.readNetFromCaffe(output_model, output_weights)
-                home + "/.deepface/weights/deploy.prototxt",
-                home + "/.deepface/weights/res10_300x300_ssd_iter_140000.caffemodel",
-            )
         except Exception as err:
             raise ValueError(
                 "Exception while calling opencv.dnn module."

deepface/models/face_detection/Yolo.py

@@ -9,7 +9,7 @@ from deepface.commons.logger import Logger
 logger = Logger()
 
 # Model's weights paths
-PATH = "/.deepface/weights/yolov8n-face.pt"
+PATH = ".deepface/weights/yolov8n-face.pt"
 
 # Google Drive URL from repo (https://github.com/derronqi/yolov8-face) ~6MB
 WEIGHT_URL = "https://drive.google.com/uc?id=1qcr9DbgsX3ryrz2uU8w4Xm3cOrRywXqb"
@@ -35,7 +35,8 @@ class YoloClient(Detector):
                 "Please install using 'pip install ultralytics'"
             ) from e
 
-        weight_path = f"{folder_utils.get_deepface_home()}{PATH}"
+        home = folder_utils.get_deepface_home()
+        weight_path = os.path.join(home, PATH)
 
         # Download the model's weights if they don't exist
         if not os.path.isfile(weight_path):

deepface/models/face_detection/YuNet.py

@@ -43,15 +43,13 @@ class YuNetClient(Detector):
         url = "https://github.com/opencv/opencv_zoo/raw/main/models/face_detection_yunet/face_detection_yunet_2023mar.onnx"
         file_name = "face_detection_yunet_2023mar.onnx"
         home = folder_utils.get_deepface_home()
-        if os.path.isfile(home + f"/.deepface/weights/{file_name}") is False:
+        output = os.path.join(home, ".deepface/weights", file_name)
+        if not os.path.isfile(output):
             logger.info(f"{file_name} will be downloaded...")
-            output = home + f"/.deepface/weights/{file_name}"
             gdown.download(url, output, quiet=False)
 
         try:
-            face_detector = cv2.FaceDetectorYN_create(
+            face_detector = cv2.FaceDetectorYN_create(output, "", (0, 0))
-                home + f"/.deepface/weights/{file_name}", "", (0, 0)
-            )
         except Exception as err:
             raise ValueError(
                 "Exception while calling opencv.FaceDetectorYN_create module."

deepface/models/facial_recognition/ArcFace.py

@@ -84,13 +84,11 @@ def load_model(
     home = folder_utils.get_deepface_home()
 
     file_name = "arcface_weights.h5"
-    output = home + "/.deepface/weights/" + file_name
+    output = os.path.join(home, ".deepface/weights", file_name)
-
-    if os.path.isfile(output) != True:
 
+    if not os.path.isfile(output):
         logger.info(f"{file_name} will be downloaded to {output}")
         gdown.download(url, output, quiet=False)
-
     # ---------------------------------------
 
     model.load_weights(output)

deepface/models/facial_recognition/DeepID.py

@@ -87,13 +87,12 @@ def load_model(
     # ---------------------------------
 
     home = folder_utils.get_deepface_home()
+    output = os.path.join(home, ".deepface/weights/deepid_keras_weights.h5")
 
-    if os.path.isfile(home + "/.deepface/weights/deepid_keras_weights.h5") != True:
+    if not os.path.isfile(output):
-        logger.info("deepid_keras_weights.h5 will be downloaded...")
+        logger.info(f"{os.path.basename(output)} will be downloaded...")
-
-        output = home + "/.deepface/weights/deepid_keras_weights.h5"
         gdown.download(url, output, quiet=False)
 
-    model.load_weights(home + "/.deepface/weights/deepid_keras_weights.h5")
+    model.load_weights(output)
 
     return model

deepface/models/facial_recognition/Dlib.py

@@ -67,21 +67,19 @@ class DlibResNet:
             ) from e
 
         home = folder_utils.get_deepface_home()
-        weight_file = home + "/.deepface/weights/dlib_face_recognition_resnet_model_v1.dat"
+        filename = "dlib_face_recognition_resnet_model_v1.dat"
+        weight_file = os.path.join(home, ".deepface/weights", filename)
 
         # download pre-trained model if it does not exist
-        if os.path.isfile(weight_file) != True:
+        if not os.path.isfile(weight_file):
-            logger.info("dlib_face_recognition_resnet_model_v1.dat is going to be downloaded")
+            logger.info(f"{filename} is going to be downloaded")
-
+            url = f"http://dlib.net/files/{filename + 'bz2'}"
-            file_name = "dlib_face_recognition_resnet_model_v1.dat.bz2"
+            output = weight_file + ".bz2"
-            url = f"http://dlib.net/files/{file_name}"
-            output = f"{home}/.deepface/weights/{file_name}"
             gdown.download(url, output, quiet=False)
 
             zipfile = bz2.BZ2File(output)
             data = zipfile.read()
-            newfilepath = output[:-4]  # discard .bz2 extension
+            with open(weight_file, "wb") as f:
-            with open(newfilepath, "wb") as f:
                 f.write(data)
 
         self.model = dlib.face_recognition_model_v1(weight_file)

deepface/models/facial_recognition/Facenet.py

@@ -1669,16 +1669,15 @@ def load_facenet128d_model(
     # -----------------------------------
 
     home = folder_utils.get_deepface_home()
+    output = os.path.join(home, ".deepface/weights/facenet_weights.h5")
 
-    if os.path.isfile(home + "/.deepface/weights/facenet_weights.h5") != True:
+    if not os.path.isfile(output):
-        logger.info("facenet_weights.h5 will be downloaded...")
+        logger.info(f"{os.path.basename(output)} will be downloaded...")
-
-        output = home + "/.deepface/weights/facenet_weights.h5"
         gdown.download(url, output, quiet=False)
 
     # -----------------------------------
 
-    model.load_weights(home + "/.deepface/weights/facenet_weights.h5")
+    model.load_weights(output)
 
     # -----------------------------------
 
@@ -1699,16 +1698,15 @@ def load_facenet512d_model(
     # -------------------------
 
     home = folder_utils.get_deepface_home()
+    output = os.path.join(home, ".deepface/weights/facenet512_weights.h5")
 
-    if os.path.isfile(home + "/.deepface/weights/facenet512_weights.h5") != True:
+    if not os.path.isfile(output):
-        logger.info("facenet512_weights.h5 will be downloaded...")
+        logger.info(f"{os.path.basename(output)} will be downloaded...")
-
-        output = home + "/.deepface/weights/facenet512_weights.h5"
         gdown.download(url, output, quiet=False)
 
     # -------------------------
 
-    model.load_weights(home + "/.deepface/weights/facenet512_weights.h5")
+    model.load_weights(output)
 
     # -------------------------
 

deepface/models/facial_recognition/FbDeepFace.py

@@ -85,19 +85,19 @@ def load_model(
     # ---------------------------------
 
     home = folder_utils.get_deepface_home()
+    filename = "VGGFace2_DeepFace_weights_val-0.9034.h5"
+    output = os.path.join(home, ".deepface/weights", filename)
 
-    if os.path.isfile(home + "/.deepface/weights/VGGFace2_DeepFace_weights_val-0.9034.h5") != True:
+    if not os.path.isfile(output):
-        logger.info("VGGFace2_DeepFace_weights_val-0.9034.h5 will be downloaded...")
+        logger.info(f"{filename} will be downloaded...")
-
+        output_zipped = output + ".zip"
-        output = home + "/.deepface/weights/VGGFace2_DeepFace_weights_val-0.9034.h5.zip"
+        gdown.download(url, output_zipped, quiet=False)
-
-        gdown.download(url, output, quiet=False)
 
         # unzip VGGFace2_DeepFace_weights_val-0.9034.h5.zip
-        with zipfile.ZipFile(output, "r") as zip_ref:
+        with zipfile.ZipFile(output_zipped, "r") as zip_ref:
-            zip_ref.extractall(home + "/.deepface/weights/")
+            zip_ref.extractall(os.path.join(home, ".deepface/weights"))
 
-    base_model.load_weights(home + "/.deepface/weights/VGGFace2_DeepFace_weights_val-0.9034.h5")
+    base_model.load_weights(output)
 
     # drop F8 and D0. F7 is the representation layer.
     deepface_model = Model(inputs=base_model.layers[0].input, outputs=base_model.layers[-3].output)

deepface/models/facial_recognition/GhostFaceNet.py

@@ -75,9 +75,9 @@ def load_model():
     model = GhostFaceNetV1()
 
     home = folder_utils.get_deepface_home()
-    output = home + "/.deepface/weights/ghostfacenet_v1.h5"
+    output = os.path.join(home, ".deepface/weights/ghostfacenet_v1.h5")
 
-    if os.path.isfile(output) is not True:
+    if not os.path.isfile(output):
         logger.info(f"Pre-trained weights is downloaded from {PRETRAINED_WEIGHTS} to {output}")
         gdown.download(PRETRAINED_WEIGHTS, output, quiet=False)
         logger.info(f"Pre-trained weights is just downloaded to {output}")

deepface/models/facial_recognition/OpenFace.py

@@ -381,16 +381,15 @@ def load_model(
     # -----------------------------------
 
     home = folder_utils.get_deepface_home()
+    output = os.path.join(home, ".deepface/weights/openface_weights.h5")
 
-    if os.path.isfile(home + "/.deepface/weights/openface_weights.h5") != True:
+    if not os.path.isfile(output):
-        logger.info("openface_weights.h5 will be downloaded...")
+        logger.info(f"{os.path.basename(output)} will be downloaded...")
-
-        output = home + "/.deepface/weights/openface_weights.h5"
         gdown.download(url, output, quiet=False)
 
     # -----------------------------------
 
-    model.load_weights(home + "/.deepface/weights/openface_weights.h5")
+    model.load_weights(output)
 
     # -----------------------------------
 

deepface/models/facial_recognition/SFace.py

@@ -56,16 +56,13 @@ def load_model(
     """
 
     home = folder_utils.get_deepface_home()
+    output = os.path.join(home, ".deepface/weights/face_recognition_sface_2021dec.onnx")
 
-    file_name = home + "/.deepface/weights/face_recognition_sface_2021dec.onnx"
+    if not os.path.isfile(output):
+        logger.info(f"{os.path.basename(output)} weights will be downloaded...")
+        gdown.download(url, output, quiet=False)
 
-    if not os.path.isfile(file_name):
+    model = SFaceWrapper(model_path=output)
-
-        logger.info("sface weights will be downloaded...")
-
-        gdown.download(url, file_name, quiet=False)
-
-    model = SFaceWrapper(model_path=file_name)
 
     return model
 

deepface/models/facial_recognition/VGGFace.py

@@ -134,10 +134,10 @@ def load_model(
     model = base_model()
 
     home = folder_utils.get_deepface_home()
-    output = home + "/.deepface/weights/vgg_face_weights.h5"
+    output = os.path.join(home, ".deepface/weights/vgg_face_weights.h5")
 
-    if os.path.isfile(output) != True:
+    if not os.path.isfile(output):
-        logger.info("vgg_face_weights.h5 will be downloaded...")
+        logger.info(f"{os.path.basename(output)} will be downloaded...")
         gdown.download(url, output, quiet=False)
 
     model.load_weights(output)

deepface/modules/recognition.py

@@ -97,8 +97,8 @@ def find(
 
     tic = time.time()
 
-    if os.path.isdir(db_path) is not True:
+    if not os.path.isdir(db_path):
-        raise ValueError("Passed db_path does not exist!")
+        raise ValueError(f"Passed path {db_path} does not exist!")
 
     file_parts = [
         "ds",

tests/test_extract_faces.py

@@ -83,7 +83,7 @@ def test_file_types_while_loading_base64():
     img1_path = "dataset/img47.jpg"
     img1_base64 = image_to_base64(image_path=img1_path)
 
-    with pytest.raises(ValueError, match="input image can be jpg or png, but it is"):
+    with pytest.raises(ValueError, match="Input image can be jpg or png, but it is"):
         _ = image_utils.load_image_from_base64(uri=img1_base64)
 
     img2_path = "dataset/img1.jpg"