weight downloads moved to a common place

deepface/commons/file_utils.py

@@ -1,31 +0,0 @@
-# built-in dependencies
-import os
-
-# 3rd party dependencies
-import gdown
-
-# project dependencies
-from deepface.commons.logger import Logger
-
-logger = Logger()
-
-
-def download_external_file(file_name: str, exact_file_path: str, url: str) -> None:
-    """
-    Download an external file
-    Args:
-        file_name (str): file name with extension
-        exact_file_path (str): exact location of the file with file name
-        url (str): url to be downloaded
-    Returns:
-        None
-    """
-    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)
-        except Exception as err:
-            raise ValueError(
-                f"Exception while downloading {file_name} from {url} to {exact_file_path}."
-                "You may consider to download it and copy to the target destination."
-            ) from err

deepface/commons/weight_utils.py

@@ -0,0 +1,65 @@
+# built-in dependencies
+import os
+from typing import Optional
+import zipfile
+import bz2
+
+# 3rd party dependencies
+import gdown
+
+# project dependencies
+from deepface.commons import folder_utils
+from deepface.commons.logger import Logger
+
+logger = Logger()
+
+
+def download_weights_if_necessary(
+    file_name: str, source_url: str, compress_type: Optional[str] = None
+) -> str:
+    """
+    Download the weights of a pre-trained model from external source if not downloaded yet.
+    Args:
+        file_name (str): target file name with extension
+        source_url (url): source url to be downloaded
+        compress_type (optional str): compress type e.g. zip or bz2
+    Returns
+        target_file (str): exact path for the target file
+    """
+    home = folder_utils.get_deepface_home()
+
+    target_file = os.path.join(home, ".deepface/weights", file_name)
+
+    if os.path.isfile(target_file):
+        logger.debug(f"{file_name} is already available at {target_file}")
+        return target_file
+
+    try:
+        logger.info(f"🔗 {file_name} will be downloaded from {source_url} to {target_file}...")
+
+        if compress_type is None:
+            gdown.download(source_url, target_file, quiet=False)
+        elif compress_type is not None:
+            gdown.download(source_url, f"{target_file}.{compress_type}", quiet=False)
+
+    except Exception as err:
+        exception_msg = (
+            f"⛓️‍💥 Exception while downloading {file_name} from {source_url}. "
+            f"You may consider to download it manually to {target_file}."
+        )
+        logger.error(exception_msg)
+        raise ValueError(exception_msg) from err
+
+    # uncompress downloaded file
+    if compress_type == "zip":
+        with zipfile.ZipFile(f"{target_file}.zip", "r") as zip_ref:
+            zip_ref.extractall(os.path.join(home, ".deepface/weights"))
+            logger.info("{target_file}.zip unzipped")
+    elif compress_type == "bz2":
+        bz2file = bz2.BZ2File(f"{target_file}.bz2")
+        data = bz2file.read()
+        with open(target_file, "wb") as f:
+            f.write(data)
+        logger.info("{target_file}.bz2 unzipped")
+
+    return target_file

deepface/models/demography/Age.py

@@ -1,8 +1,9 @@
-import os
+# 3rd party dependencies
-import gdown
 import numpy as np
+
+# project dependencies
 from deepface.models.facial_recognition import VGGFace
-from deepface.commons import package_utils, folder_utils
+from deepface.commons import package_utils, weight_utils
 from deepface.models.Demography import Demography
 from deepface.commons.logger import Logger
 
@@ -65,21 +66,13 @@ def load_model(
     # --------------------------
 
     # load weights
-
+    weight_file = weight_utils.download_weights_if_necessary(
-    home = folder_utils.get_deepface_home()
+        file_name="age_model_weights.h5", source_url=url
-    output = os.path.join(home, ".deepface/weights/age_model_weights.h5")
+    )
-
+    age_model.load_weights(weight_file)
-    if not os.path.isfile(output):
-        logger.info(f"{os.path.basename(output)} will be downloaded...")
-        gdown.download(url, output, quiet=False)
-
-    age_model.load_weights(output)
 
     return age_model
 
-    # --------------------------
-
-
 def find_apparent_age(age_predictions: np.ndarray) -> np.float64:
     """
     Find apparent age prediction from a given probas of ages

deepface/models/demography/Emotion.py

@@ -1,13 +1,9 @@
-# built-in dependencies
-import os
-
 # 3rd party dependencies
-import gdown
 import numpy as np
 import cv2
 
 # project dependencies
-from deepface.commons import package_utils, folder_utils
+from deepface.commons import package_utils, weight_utils
 from deepface.models.Demography import Demography
 from deepface.commons.logger import Logger
 
@@ -96,13 +92,10 @@ def load_model(
 
     # ----------------------------
 
-    home = folder_utils.get_deepface_home()
+    weight_file = weight_utils.download_weights_if_necessary(
-    output = os.path.join(home, ".deepface/weights/facial_expression_model_weights.h5")
+        file_name="facial_expression_model_weights.h5", source_url=url
+    )
 
-    if not os.path.isfile(output):
+    model.load_weights(weight_file)
-        logger.info(f"{os.path.basename(output)} will be downloaded...")
-        gdown.download(url, output, quiet=False)
-
-    model.load_weights(output)
 
     return model

deepface/models/demography/Gender.py

@@ -1,13 +1,9 @@
-# built-in dependencies
-import os
-
 # 3rd party dependencies
-import gdown
 import numpy as np
 
 # project dependencies
 from deepface.models.facial_recognition import VGGFace
-from deepface.commons import package_utils, folder_utils
+from deepface.commons import package_utils, weight_utils
 from deepface.models.Demography import Demography
 from deepface.commons.logger import Logger
 
@@ -72,14 +68,10 @@ def load_model(
     # --------------------------
 
     # load weights
+    weight_file = weight_utils.download_weights_if_necessary(
+        file_name="gender_model_weights.h5", source_url=url
+    )
 
-    home = folder_utils.get_deepface_home()
+    gender_model.load_weights(weight_file)
-    output = os.path.join(home, ".deepface/weights/gender_model_weights.h5")
-
-    if not os.path.isfile(output):
-        logger.info(f"{os.path.basename(output)} will be downloaded...")
-        gdown.download(url, output, quiet=False)
-
-    gender_model.load_weights(output)
 
     return gender_model

deepface/models/demography/Race.py

@@ -1,13 +1,9 @@
-# built-in dependencies
-import os
-
 # 3rd party dependencies
-import gdown
 import numpy as np
 
 # project dependencies
 from deepface.models.facial_recognition import VGGFace
-from deepface.commons import package_utils, folder_utils
+from deepface.commons import package_utils, weight_utils
 from deepface.models.Demography import Demography
 from deepface.commons.logger import Logger
 
@@ -69,14 +65,10 @@ def load_model(
     # --------------------------
 
     # load weights
+    weight_file = weight_utils.download_weights_if_necessary(
+        file_name="race_model_single_batch.h5", source_url=url
+    )
 
-    home = folder_utils.get_deepface_home()
+    race_model.load_weights(weight_file)
-    output = os.path.join(home, ".deepface/weights/race_model_single_batch.h5")
-
-    if not os.path.isfile(output):
-        logger.info(f"{os.path.basename(output)} will be downloaded...")
-        gdown.download(url, output, quiet=False)
-
-    race_model.load_weights(output)
 
     return race_model

deepface/models/face_detection/CenterFace.py

@@ -5,10 +5,9 @@ from typing import List
 # 3rd party dependencies
 import numpy as np
 import cv2
-import gdown
 
 # project dependencies
-from deepface.commons import folder_utils
+from deepface.commons import weight_utils
 from deepface.models.Detector import Detector, FacialAreaRegion
 from deepface.commons.logger import Logger
 
@@ -29,19 +28,9 @@ class CenterFaceClient(Detector):
         """
         Download pre-trained weights of CenterFace model if necessary and load built model
         """
-        home = folder_utils.get_deepface_home()
+        weights_path = weight_utils.download_weights_if_necessary(
-        weights_path = os.path.join(home, ".deepface/weights/centerface.onnx")
+            file_name="centerface.onnx", source_url=WEIGHTS_URL
-
+        )
-        if not os.path.isfile(weights_path):
-            logger.info(f"Downloading CenterFace weights from {WEIGHTS_URL} to {weights_path}...")
-            try:
-                gdown.download(WEIGHTS_URL, weights_path, quiet=False)
-            except Exception as err:
-                raise ValueError(
-                    f"Exception while downloading CenterFace weights from {WEIGHTS_URL}."
-                    f"You may consider to download it to {weights_path} manually."
-                ) from err
-            logger.info(f"CenterFace model is just downloaded to {os.path.basename(weights_path)}")
 
         return CenterFace(weight_path=weights_path)
 

deepface/models/face_detection/Dlib.py

@@ -1,9 +1,11 @@
+# built-in dependencies
 from typing import List
-import os
+
-import bz2
+# 3rd party dependencies
-import gdown
 import numpy as np
-from deepface.commons import folder_utils
+
+# project dependencies
+from deepface.commons import weight_utils
 from deepface.models.Detector import Detector, FacialAreaRegion
 from deepface.commons.logger import Logger
 
@@ -30,25 +32,14 @@ class DlibClient(Detector):
             ) from e
 
         # check required file exists in the home/.deepface/weights folder
-        home = folder_utils.get_deepface_home()
+        weight_file = weight_utils.download_weights_if_necessary(
-        filename = "shape_predictor_5_face_landmarks.dat"
+            file_name="shape_predictor_5_face_landmarks.dat",
-        filepath = os.path.join(home, ".deepface/weights/", filename)
+            source_url="http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2",
-
+            compress_type="bz2",
-        if not os.path.isfile(filepath):
+        )
-            logger.info(f"{filename + '.bz2'} is going to be downloaded")
-
-            url = f"http://dlib.net/files/{filename + '.bz2'}"
-            output = filepath + ".bz2"
-
-            gdown.download(url, output, quiet=False)
-
-            zipfile = bz2.BZ2File(output)
-            data = zipfile.read()
-            with open(filepath, "wb") as f:
-                f.write(data)
 
         face_detector = dlib.get_frontal_face_detector()
-        sp = dlib.shape_predictor(filepath)
+        sp = dlib.shape_predictor(weight_file)
 
         detector = {}
         detector["face_detector"] = face_detector

deepface/models/face_detection/FastMtCnn.py

@@ -1,13 +1,19 @@
+# built-in dependencies
 from typing import Any, Union, List
+
+# 3rd party dependencies
 import cv2
 import numpy as np
-from deepface.models.Detector import Detector, FacialAreaRegion
 
-# Link -> https://github.com/timesler/facenet-pytorch
+# project dependencies
-# Examples https://www.kaggle.com/timesler/guide-to-mtcnn-in-facenet-pytorch
+from deepface.models.Detector import Detector, FacialAreaRegion
 
 
 class FastMtCnnClient(Detector):
+    """
+    Fast MtCnn Detector from github.com/timesler/facenet-pytorch
+    """
+
     def __init__(self):
         self.model = self.build_model()
 
@@ -69,7 +75,7 @@ class FastMtCnnClient(Detector):
                 "Please install using 'pip install facenet-pytorch'"
             ) from e
 
-        device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+        device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
         face_detector = fast_mtcnn(device=device)
 
         return face_detector

deepface/models/face_detection/MediaPipe.py

@@ -1,11 +1,18 @@
+# built-in dependencies
 from typing import Any, List
-import numpy as np
-from deepface.models.Detector import Detector, FacialAreaRegion
 
-# Link - https://google.github.io/mediapipe/solutions/face_detection
+# 3rd party dependencies
+import numpy as np
+
+# project dependencies
+from deepface.models.Detector import Detector, FacialAreaRegion
 
 
 class MediaPipeClient(Detector):
+    """
+    MediaPipe from google.github.io/mediapipe/solutions/face_detection
+    """
+
     def __init__(self):
         self.model = self.build_model()
 
@@ -69,7 +76,13 @@ class MediaPipeClient(Detector):
             # left_ear = (int(landmarks[5].x * img_width), int(landmarks[5].y * img_height))
 
             facial_area = FacialAreaRegion(
-                x=x, y=y, w=w, h=h, left_eye=left_eye, right_eye=right_eye, confidence=confidence
+                x=x,
+                y=y,
+                w=w,
+                h=h,
+                left_eye=left_eye,
+                right_eye=right_eye,
+                confidence=float(confidence),
             )
             resp.append(facial_area)
 

deepface/models/face_detection/MtCnn.py

@@ -1,6 +1,11 @@
+# built-in dependencies
 from typing import List
+
+# 3rd party dependencies
 import numpy as np
 from mtcnn import MTCNN
+
+# project dependencies
 from deepface.models.Detector import Detector, FacialAreaRegion
 
 # pylint: disable=too-few-public-methods

deepface/models/face_detection/OpenCv.py

@@ -1,7 +1,12 @@
+# built-in dependencies
 import os
 from typing import Any, List
+
+# 3rd party dependencies
 import cv2
 import numpy as np
+
+#project dependencies
 from deepface.models.Detector import Detector, FacialAreaRegion
 
 

deepface/models/face_detection/RetinaFace.py

@@ -1,6 +1,11 @@
+# built-in dependencies
 from typing import List
+
+# 3rd party dependencies
 import numpy as np
 from retinaface import RetinaFace as rf
+
+# project dependencies
 from deepface.models.Detector import Detector, FacialAreaRegion
 
 # pylint: disable=too-few-public-methods

deepface/models/face_detection/Ssd.py

@@ -1,11 +1,14 @@
+# built-in dependencies
 from typing import List
-import os
 from enum import IntEnum
-import gdown
+
+# 3rd party dependencies
 import cv2
 import numpy as np
+
+# project dependencies
 from deepface.models.face_detection import OpenCv
-from deepface.commons import folder_utils
+from deepface.commons import weight_utils
 from deepface.models.Detector import Detector, FacialAreaRegion
 from deepface.commons.logger import Logger
 
@@ -25,21 +28,17 @@ class SsdClient(Detector):
             model (dict)
         """
 
-        home = folder_utils.get_deepface_home()
-
         # model structure
-        output_model = os.path.join(home, ".deepface/weights/deploy.prototxt")
+        output_model = weight_utils.download_weights_if_necessary(
-        if not os.path.isfile(output_model):
+            file_name="deploy.prototxt",
-            logger.info(f"{os.path.basename(output_model)} will be downloaded...")
+            source_url="https://github.com/opencv/opencv/raw/3.4.0/samples/dnn/face_detector/deploy.prototxt",
-            url = "https://github.com/opencv/opencv/raw/3.4.0/samples/dnn/face_detector/deploy.prototxt"
+        )
-            gdown.download(url, output_model, quiet=False)
 
         # pre-trained weights
-        output_weights = os.path.join(home, ".deepface/weights/res10_300x300_ssd_iter_140000.caffemodel")
+        output_weights = weight_utils.download_weights_if_necessary(
-        if not os.path.isfile(output_weights):
+            file_name="res10_300x300_ssd_iter_140000.caffemodel",
-            logger.info(f"{os.path.basename(output_weights)} will be downloaded...")
+            source_url="https://github.com/opencv/opencv_3rdparty/raw/dnn_samples_face_detector_20170830/res10_300x300_ssd_iter_140000.caffemodel",
-            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)
 
         try:
             face_detector = cv2.dnn.readNetFromCaffe(output_model, output_weights)
@@ -50,10 +49,7 @@ class SsdClient(Detector):
                 + "You can install it as pip install opencv-contrib-python."
             ) from err
 
-        return {
+        return {"face_detector": face_detector, "opencv_module": OpenCv.OpenCvClient()}
-            "face_detector": face_detector,
-            "opencv_module": OpenCv.OpenCvClient()
-        }
 
     def detect_faces(self, img: np.ndarray) -> List[FacialAreaRegion]:
         """
@@ -97,11 +93,17 @@ class SsdClient(Detector):
             bottom = 6
 
         faces = detections[0][0]
-        faces = faces[(faces[:, ssd_labels.is_face] == 1) & (faces[:, ssd_labels.confidence] >= 0.90)]
+        faces = faces[
+            (faces[:, ssd_labels.is_face] == 1) & (faces[:, ssd_labels.confidence] >= 0.90)
+        ]
         margins = [ssd_labels.left, ssd_labels.top, ssd_labels.right, ssd_labels.bottom]
         faces[:, margins] = np.int32(faces[:, margins] * 300)
-        faces[:, margins] = np.int32(faces[:, margins] * [aspect_ratio_x, aspect_ratio_y, aspect_ratio_x, aspect_ratio_y])
+        faces[:, margins] = np.int32(
-        faces[:, [ssd_labels.right, ssd_labels.bottom]] -= faces[:, [ssd_labels.left, ssd_labels.top]]
+            faces[:, margins] * [aspect_ratio_x, aspect_ratio_y, aspect_ratio_x, aspect_ratio_y]
+        )
+        faces[:, [ssd_labels.right, ssd_labels.bottom]] -= faces[
+            :, [ssd_labels.left, ssd_labels.top]
+        ]
 
         resp = []
         for face in faces:

deepface/models/face_detection/Yolo.py

@@ -1,9 +1,12 @@
-import os
+# built-in dependencies
 from typing import Any, List
+
+# 3rd party dependencies
 import numpy as np
-import gdown
+
+# project dependencies
 from deepface.models.Detector import Detector, FacialAreaRegion
-from deepface.commons import folder_utils
+from deepface.commons import weight_utils
 from deepface.commons.logger import Logger
 
 logger = Logger()
@@ -26,7 +29,7 @@ class YoloClient(Detector):
             model (Any)
         """
 
-        # Import the Ultralytics YOLO model
+        # Import the optional Ultralytics YOLO model
         try:
             from ultralytics import YOLO
         except ModuleNotFoundError as e:
@@ -35,23 +38,12 @@ class YoloClient(Detector):
                 "Please install using 'pip install ultralytics'"
             ) from e
 
-        home = folder_utils.get_deepface_home()
+        weight_file = weight_utils.download_weights_if_necessary(
-        weight_path = os.path.join(home, PATH)
+            file_name="yolov8n-face.pt", source_url=WEIGHT_URL
-
+        )
-        # Download the model's weights if they don't exist
-        if not os.path.isfile(weight_path):
-            logger.info(f"Downloading Yolo weights from {WEIGHT_URL} to {weight_path}...")
-            try:
-                gdown.download(WEIGHT_URL, weight_path, quiet=False)
-            except Exception as err:
-                raise ValueError(
-                    f"Exception while downloading Yolo weights from {WEIGHT_URL}."
-                    f"You may consider to download it to {weight_path} manually."
-                ) from err
-            logger.info(f"Yolo model is just downloaded to {os.path.basename(weight_path)}")
 
         # Return face_detector
-        return YOLO(weight_path)
+        return YOLO(weight_file)
 
     def detect_faces(self, img: np.ndarray) -> List[FacialAreaRegion]:
         """

deepface/models/face_detection/YuNet.py

@@ -5,10 +5,9 @@ from typing import Any, List
 # 3rd party dependencies
 import cv2
 import numpy as np
-import gdown
 
 # project dependencies
-from deepface.commons import folder_utils
+from deepface.commons import weight_utils
 from deepface.models.Detector import Detector, FacialAreaRegion
 from deepface.commons.logger import Logger
 
@@ -40,16 +39,13 @@ class YuNetClient(Detector):
             raise ValueError(f"YuNet requires opencv-python >= 4.8 but you have {cv2.__version__}")
 
         # pylint: disable=C0301
-        url = "https://github.com/opencv/opencv_zoo/raw/main/models/face_detection_yunet/face_detection_yunet_2023mar.onnx"
+        weight_file = weight_utils.download_weights_if_necessary(
-        file_name = "face_detection_yunet_2023mar.onnx"
+            file_name="face_detection_yunet_2023mar.onnx",
-        home = folder_utils.get_deepface_home()
+            source_url="https://github.com/opencv/opencv_zoo/raw/main/models/face_detection_yunet/face_detection_yunet_2023mar.onnx",
-        output = os.path.join(home, ".deepface/weights", file_name)
+        )
-        if not os.path.isfile(output):
-            logger.info(f"{file_name} will be downloaded...")
-            gdown.download(url, output, quiet=False)
 
         try:
-            face_detector = cv2.FaceDetectorYN_create(output, "", (0, 0))
+            face_detector = cv2.FaceDetectorYN_create(weight_file, "", (0, 0))
         except Exception as err:
             raise ValueError(
                 "Exception while calling opencv.FaceDetectorYN_create module."

deepface/models/facial_recognition/ArcFace.py

@@ -1,6 +1,5 @@
-import os
+# project dependencies
-import gdown
+from deepface.commons import package_utils, weight_utils
-from deepface.commons import package_utils, folder_utils
 from deepface.models.FacialRecognition import FacialRecognition
 
 from deepface.commons.logger import Logger
@@ -79,20 +78,13 @@ def load_model(
     model = Model(inputs, embedding, name=base_model.name)
 
     # ---------------------------------------
-    # check the availability of pre-trained weights
+    weight_file = weight_utils.download_weights_if_necessary(
+        file_name="arcface_weights.h5", source_url=url
+    )
 
-    home = folder_utils.get_deepface_home()
+    model.load_weights(weight_file)
-
-    file_name = "arcface_weights.h5"
-    output = os.path.join(home, ".deepface/weights", file_name)
-
-    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)
-
     return model
 
 

deepface/models/facial_recognition/DeepID.py

@@ -1,6 +1,5 @@
-import os
+# project dependencies
-import gdown
+from deepface.commons import package_utils, weight_utils
-from deepface.commons import package_utils, folder_utils
 from deepface.models.FacialRecognition import FacialRecognition
 from deepface.commons.logger import Logger
 
@@ -86,13 +85,10 @@ def load_model(
 
     # ---------------------------------
 
-    home = folder_utils.get_deepface_home()
+    weight_file = weight_utils.download_weights_if_necessary(
-    output = os.path.join(home, ".deepface/weights/deepid_keras_weights.h5")
+        file_name="deepid_keras_weights.h5", source_url=url
+    )
 
-    if not os.path.isfile(output):
+    model.load_weights(weight_file)
-        logger.info(f"{os.path.basename(output)} will be downloaded...")
-        gdown.download(url, output, quiet=False)
-
-    model.load_weights(output)
 
     return model

deepface/models/facial_recognition/Dlib.py

@@ -1,9 +1,11 @@
+# built-in dependencies
 from typing import List
-import os
+
-import bz2
+# 3rd party dependencies
-import gdown
 import numpy as np
-from deepface.commons import folder_utils
+
+# project dependencies
+from deepface.commons import weight_utils
 from deepface.models.FacialRecognition import FacialRecognition
 from deepface.commons.logger import Logger
 
@@ -57,7 +59,7 @@ class DlibClient(FacialRecognition):
 class DlibResNet:
     def __init__(self):
 
-        ## this is not a must dependency. do not import it in the global level.
+        # This is not a must dependency. Don't import it in the global level.
         try:
             import dlib
         except ModuleNotFoundError as e:
@@ -66,21 +68,11 @@ class DlibResNet:
                 "Please install using 'pip install dlib' "
             ) from e
 
-        home = folder_utils.get_deepface_home()
+        weight_file = weight_utils.download_weights_if_necessary(
-        filename = "dlib_face_recognition_resnet_model_v1.dat"
+            file_name="dlib_face_recognition_resnet_model_v1.dat",
-        weight_file = os.path.join(home, ".deepface/weights", filename)
+            source_url="http://dlib.net/files/dlib_face_recognition_resnet_model_v1.dat.bz2",
-
+            compress_type="bz2",
-        # download pre-trained model if it does not exist
+        )
-        if not os.path.isfile(weight_file):
-            logger.info(f"{filename} is going to be downloaded")
-            url = f"http://dlib.net/files/{filename + '.bz2'}"
-            output = weight_file + ".bz2"
-            gdown.download(url, output, quiet=False)
-
-            zipfile = bz2.BZ2File(output)
-            data = zipfile.read()
-            with open(weight_file, "wb") as f:
-                f.write(data)
 
         self.model = dlib.face_recognition_model_v1(weight_file)
 

deepface/models/facial_recognition/Facenet.py

@@ -1,6 +1,5 @@
-import os
+# project dependencies
-import gdown
+from deepface.commons import package_utils, weight_utils
-from deepface.commons import package_utils, folder_utils
 from deepface.models.FacialRecognition import FacialRecognition
 from deepface.commons.logger import Logger
 
@@ -1666,20 +1665,10 @@ def load_facenet128d_model(
     """
     model = InceptionResNetV1()
 
-    # -----------------------------------
+    weight_file = weight_utils.download_weights_if_necessary(
-
+        file_name="facenet_weights.h5", source_url=url
-    home = folder_utils.get_deepface_home()
+    )
-    output = os.path.join(home, ".deepface/weights/facenet_weights.h5")
+    model.load_weights(weight_file)
-
-    if not os.path.isfile(output):
-        logger.info(f"{os.path.basename(output)} will be downloaded...")
-        gdown.download(url, output, quiet=False)
-
-    # -----------------------------------
-
-    model.load_weights(output)
-
-    # -----------------------------------
 
     return model
 
@@ -1695,19 +1684,9 @@ def load_facenet512d_model(
 
     model = InceptionResNetV1(dimension=512)
 
-    # -------------------------
+    weight_file = weight_utils.download_weights_if_necessary(
-
+        file_name="facenet512_weights.h5", source_url=url
-    home = folder_utils.get_deepface_home()
+    )
-    output = os.path.join(home, ".deepface/weights/facenet512_weights.h5")
+    model.load_weights(weight_file)
-
-    if not os.path.isfile(output):
-        logger.info(f"{os.path.basename(output)} will be downloaded...")
-        gdown.download(url, output, quiet=False)
-
-    # -------------------------
-
-    model.load_weights(output)
-
-    # -------------------------
 
     return model

deepface/models/facial_recognition/FbDeepFace.py

@@ -1,7 +1,5 @@
-import os
+# project dependencies
-import zipfile
+from deepface.commons import package_utils, weight_utils
-import gdown
-from deepface.commons import package_utils, folder_utils
 from deepface.models.FacialRecognition import FacialRecognition
 from deepface.commons.logger import Logger
 
@@ -84,20 +82,11 @@ def load_model(
 
     # ---------------------------------
 
-    home = folder_utils.get_deepface_home()
+    weight_file = weight_utils.download_weights_if_necessary(
-    filename = "VGGFace2_DeepFace_weights_val-0.9034.h5"
+        file_name="VGGFace2_DeepFace_weights_val-0.9034.h5", source_url=url, compress_type="zip"
-    output = os.path.join(home, ".deepface/weights", filename)
+    )
 
-    if not os.path.isfile(output):
+    base_model.load_weights(weight_file)
-        logger.info(f"{filename} will be downloaded...")
-        output_zipped = output + ".zip"
-        gdown.download(url, output_zipped, quiet=False)
-
-        # unzip VGGFace2_DeepFace_weights_val-0.9034.h5.zip
-        with zipfile.ZipFile(output_zipped, "r") as zip_ref:
-            zip_ref.extractall(os.path.join(home, ".deepface/weights"))
-
-    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

@@ -1,12 +1,8 @@
-# built-in dependencies
-import os
-
 # 3rd party dependencies
-import gdown
 import tensorflow as tf
 
 # project dependencies
-from deepface.commons import package_utils, folder_utils
+from deepface.commons import package_utils, weight_utils
 from deepface.models.FacialRecognition import FacialRecognition
 from deepface.commons.logger import Logger
 
@@ -74,15 +70,11 @@ class GhostFaceNetClient(FacialRecognition):
 def load_model():
     model = GhostFaceNetV1()
 
-    home = folder_utils.get_deepface_home()
+    weight_file = weight_utils.download_weights_if_necessary(
-    output = os.path.join(home, ".deepface/weights/ghostfacenet_v1.h5")
+        file_name="ghostfacenet_v1.h5", source_url=PRETRAINED_WEIGHTS
+    )
 
-    if not os.path.isfile(output):
+    model.load_weights(weight_file)
-        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}")
-
-    model.load_weights(output)
 
     return model
 

deepface/models/facial_recognition/OpenFace.py

@@ -1,7 +1,8 @@
-import os
+# 3rd party dependencies
-import gdown
 import tensorflow as tf
-from deepface.commons import package_utils, folder_utils
+
+# project dependencies
+from deepface.commons import package_utils, weight_utils
 from deepface.models.FacialRecognition import FacialRecognition
 from deepface.commons.logger import Logger
 
@@ -380,16 +381,11 @@ def load_model(
 
     # -----------------------------------
 
-    home = folder_utils.get_deepface_home()
+    weight_file = weight_utils.download_weights_if_necessary(
-    output = os.path.join(home, ".deepface/weights/openface_weights.h5")
+        file_name="openface_weights.h5", source_url=url
+    )
 
-    if not os.path.isfile(output):
+    model.load_weights(weight_file)
-        logger.info(f"{os.path.basename(output)} will be downloaded...")
-        gdown.download(url, output, quiet=False)
-
-    # -----------------------------------
-
-    model.load_weights(output)
 
     # -----------------------------------
 

deepface/models/facial_recognition/SFace.py

@@ -1,14 +1,12 @@
 # built-in dependencies
-import os
 from typing import Any, List
 
 # 3rd party dependencies
 import numpy as np
 import cv2 as cv
-import gdown
 
 # project dependencies
-from deepface.commons import folder_utils
+from deepface.commons import weight_utils
 from deepface.models.FacialRecognition import FacialRecognition
 from deepface.commons.logger import Logger
 
@@ -55,14 +53,11 @@ def load_model(
     Construct SFace model, download its weights and load
     """
 
-    home = folder_utils.get_deepface_home()
+    weight_file = weight_utils.download_weights_if_necessary(
-    output = os.path.join(home, ".deepface/weights/face_recognition_sface_2021dec.onnx")
+        file_name="face_recognition_sface_2021dec.onnx", source_url=url
+    )
 
-    if not os.path.isfile(output):
+    model = SFaceWrapper(model_path=weight_file)
-        logger.info(f"{os.path.basename(output)} weights will be downloaded...")
-        gdown.download(url, output, quiet=False)
-
-    model = SFaceWrapper(model_path=output)
 
     return model
 

deepface/models/facial_recognition/VGGFace.py

@@ -1,8 +1,11 @@
+# built-in dependencies
 from typing import List
-import os
+
-import gdown
+# 3rd party dependencies
 import numpy as np
-from deepface.commons import package_utils, folder_utils
+
+# project dependencies
+from deepface.commons import package_utils, weight_utils
 from deepface.modules import verification
 from deepface.models.FacialRecognition import FacialRecognition
 from deepface.commons.logger import Logger
@@ -133,14 +136,11 @@ def load_model(
 
     model = base_model()
 
-    home = folder_utils.get_deepface_home()
+    weight_file = weight_utils.download_weights_if_necessary(
-    output = os.path.join(home, ".deepface/weights/vgg_face_weights.h5")
+        file_name="vgg_face_weights.h5", source_url=url
+    )
 
-    if not os.path.isfile(output):
+    model.load_weights(weight_file)
-        logger.info(f"{os.path.basename(output)} will be downloaded...")
-        gdown.download(url, output, quiet=False)
-
-    model.load_weights(output)
 
     # 2622d dimensional model
     # vgg_face_descriptor = Model(inputs=model.layers[0].input, outputs=model.layers[-2].output)

deepface/models/spoofing/FasNet.py

@@ -1,6 +1,3 @@
-# Minivision's Silent-Face-Anti-Spoofing Repo licensed under Apache License 2.0
-# Ref: github.com/minivision-ai/Silent-Face-Anti-Spoofing/blob/master/src/model_lib/MiniFASNet.py
-
 # built-in dependencies
 from typing import Union
 
@@ -9,15 +6,18 @@ import cv2
 import numpy as np
 
 # project dependencies
-from deepface.commons import folder_utils, file_utils
+from deepface.commons import weight_utils
 from deepface.commons.logger import Logger
 
 logger = Logger()
 
-# pylint: disable=line-too-long, too-few-public-methods
+# pylint: disable=line-too-long, too-few-public-methods, nested-min-max
 class Fasnet:
     """
     Mini Face Anti Spoofing Net Library from repo: github.com/minivision-ai/Silent-Face-Anti-Spoofing
+
+    Minivision's Silent-Face-Anti-Spoofing Repo licensed under Apache License 2.0
+    Ref: github.com/minivision-ai/Silent-Face-Anti-Spoofing/blob/master/src/model_lib/MiniFASNet.py
     """
 
     def __init__(self):
@@ -29,21 +29,18 @@ class Fasnet:
                 "You must install torch with `pip install pytorch` command to use face anti spoofing module"
             ) from err
 
-        home = folder_utils.get_deepface_home()
         device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
         self.device = device
 
         # download pre-trained models if not installed yet
-        file_utils.download_external_file(
+        first_model_weight_file = weight_utils.download_weights_if_necessary(
             file_name="2.7_80x80_MiniFASNetV2.pth",
-            exact_file_path=f"{home}/.deepface/weights/2.7_80x80_MiniFASNetV2.pth",
+            source_url="https://github.com/minivision-ai/Silent-Face-Anti-Spoofing/raw/master/resources/anti_spoof_models/2.7_80x80_MiniFASNetV2.pth",
-            url="https://github.com/minivision-ai/Silent-Face-Anti-Spoofing/raw/master/resources/anti_spoof_models/2.7_80x80_MiniFASNetV2.pth",
         )
 
-        file_utils.download_external_file(
+        second_model_weight_file = weight_utils.download_weights_if_necessary(
             file_name="4_0_0_80x80_MiniFASNetV1SE.pth",
-            exact_file_path=f"{home}/.deepface/weights/4_0_0_80x80_MiniFASNetV1SE.pth",
+            source_url="https://github.com/minivision-ai/Silent-Face-Anti-Spoofing/raw/master/resources/anti_spoof_models/4_0_0_80x80_MiniFASNetV1SE.pth",
-            url="https://github.com/minivision-ai/Silent-Face-Anti-Spoofing/raw/master/resources/anti_spoof_models/4_0_0_80x80_MiniFASNetV1SE.pth",
         )
 
         # guarantees Fasnet imported and torch installed
@@ -56,9 +53,7 @@ class Fasnet:
         second_model = FasNetBackbone.MiniFASNetV1SE(conv6_kernel=(5, 5)).to(device)
 
         # load model weight for first model
-        state_dict = torch.load(
+        state_dict = torch.load(first_model_weight_file, map_location=device)
-            f"{home}/.deepface/weights/2.7_80x80_MiniFASNetV2.pth", map_location=device
-        )
         keys = iter(state_dict)
         first_layer_name = keys.__next__()
 
@@ -74,9 +69,7 @@ class Fasnet:
             first_model.load_state_dict(state_dict)
 
         # load model weight for second model
-        state_dict = torch.load(
+        state_dict = torch.load(second_model_weight_file, map_location=device)
-            f"{home}/.deepface/weights/4_0_0_80x80_MiniFASNetV1SE.pth", map_location=device
-        )
         keys = iter(state_dict)
         first_layer_name = keys.__next__()
 
@@ -191,7 +184,6 @@ def _get_new_box(src_w, src_h, bbox, scale):
     y = bbox[1]
     box_w = bbox[2]
     box_h = bbox[3]
-    # pylint: disable=nested-min-max
     scale = min((src_h - 1) / box_h, min((src_w - 1) / box_w, scale))
     new_width = box_w * scale
     new_height = box_h * scale