mirror of
https://github.com/serengil/deepface.git
synced 2025-06-06 11:35:21 +00:00
weight downloads moved to a common place
This commit is contained in:
Sefik Ilkin Serengil
parent
0d1bce3618
commit
9d8d2ddf4f
@ -1,31 +0,0 @@
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# built-in dependencies
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import os
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# 3rd party dependencies
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import gdown
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# project dependencies
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from deepface.commons.logger import Logger
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logger = Logger()
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def download_external_file(file_name: str, exact_file_path: str, url: str) -> None:
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"""
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Download an external file
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Args:
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file_name (str): file name with extension
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exact_file_path (str): exact location of the file with file name
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url (str): url to be downloaded
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Returns:
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None
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"""
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if not os.path.exists(exact_file_path):
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logger.info(f"Downloading MiniFASNetV2 weights to {exact_file_path}")
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try:
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gdown.download(url, exact_file_path, quiet=False)
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except Exception as err:
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raise ValueError(
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f"Exception while downloading {file_name} from {url} to {exact_file_path}."
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"You may consider to download it and copy to the target destination."
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) from err
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65
deepface/commons/weight_utils.py
Normal file
65
deepface/commons/weight_utils.py
Normal file
@ -0,0 +1,65 @@
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# built-in dependencies
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import os
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from typing import Optional
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import zipfile
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import bz2
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# 3rd party dependencies
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import gdown
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# project dependencies
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from deepface.commons import folder_utils
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from deepface.commons.logger import Logger
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logger = Logger()
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def download_weights_if_necessary(
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file_name: str, source_url: str, compress_type: Optional[str] = None
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) -> str:
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"""
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Download the weights of a pre-trained model from external source if not downloaded yet.
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Args:
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file_name (str): target file name with extension
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source_url (url): source url to be downloaded
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compress_type (optional str): compress type e.g. zip or bz2
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Returns
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target_file (str): exact path for the target file
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"""
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home = folder_utils.get_deepface_home()
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target_file = os.path.join(home, ".deepface/weights", file_name)
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if os.path.isfile(target_file):
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logger.debug(f"{file_name} is already available at {target_file}")
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return target_file
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try:
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logger.info(f"🔗 {file_name} will be downloaded from {source_url} to {target_file}...")
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if compress_type is None:
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gdown.download(source_url, target_file, quiet=False)
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elif compress_type is not None:
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gdown.download(source_url, f"{target_file}.{compress_type}", quiet=False)
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except Exception as err:
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exception_msg = (
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f"⛓️💥 Exception while downloading {file_name} from {source_url}. "
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f"You may consider to download it manually to {target_file}."
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)
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logger.error(exception_msg)
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raise ValueError(exception_msg) from err
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# uncompress downloaded file
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if compress_type == "zip":
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with zipfile.ZipFile(f"{target_file}.zip", "r") as zip_ref:
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zip_ref.extractall(os.path.join(home, ".deepface/weights"))
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logger.info("{target_file}.zip unzipped")
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elif compress_type == "bz2":
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bz2file = bz2.BZ2File(f"{target_file}.bz2")
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data = bz2file.read()
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with open(target_file, "wb") as f:
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f.write(data)
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logger.info("{target_file}.bz2 unzipped")
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return target_file
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@ -1,8 +1,9 @@
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import os
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import gdown
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# 3rd party dependencies
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import numpy as np
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# project dependencies
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from deepface.models.facial_recognition import VGGFace
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from deepface.commons import package_utils, folder_utils
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from deepface.commons import package_utils, weight_utils
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from deepface.models.Demography import Demography
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from deepface.commons.logger import Logger
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@ -65,21 +66,13 @@ def load_model(
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# --------------------------
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# load weights
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home = folder_utils.get_deepface_home()
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output = os.path.join(home, ".deepface/weights/age_model_weights.h5")
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if not os.path.isfile(output):
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logger.info(f"{os.path.basename(output)} will be downloaded...")
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gdown.download(url, output, quiet=False)
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age_model.load_weights(output)
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weight_file = weight_utils.download_weights_if_necessary(
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file_name="age_model_weights.h5", source_url=url
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)
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age_model.load_weights(weight_file)
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return age_model
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# --------------------------
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def find_apparent_age(age_predictions: np.ndarray) -> np.float64:
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"""
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Find apparent age prediction from a given probas of ages
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@ -1,13 +1,9 @@
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# built-in dependencies
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import os
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# 3rd party dependencies
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import gdown
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import numpy as np
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import cv2
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# project dependencies
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from deepface.commons import package_utils, folder_utils
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from deepface.commons import package_utils, weight_utils
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from deepface.models.Demography import Demography
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from deepface.commons.logger import Logger
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@ -96,13 +92,10 @@ def load_model(
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# ----------------------------
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home = folder_utils.get_deepface_home()
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output = os.path.join(home, ".deepface/weights/facial_expression_model_weights.h5")
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weight_file = weight_utils.download_weights_if_necessary(
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file_name="facial_expression_model_weights.h5", source_url=url
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)
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if not os.path.isfile(output):
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logger.info(f"{os.path.basename(output)} will be downloaded...")
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gdown.download(url, output, quiet=False)
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model.load_weights(output)
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model.load_weights(weight_file)
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return model
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@ -1,13 +1,9 @@
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# built-in dependencies
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import os
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# 3rd party dependencies
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import gdown
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import numpy as np
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# project dependencies
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from deepface.models.facial_recognition import VGGFace
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from deepface.commons import package_utils, folder_utils
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from deepface.commons import package_utils, weight_utils
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from deepface.models.Demography import Demography
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from deepface.commons.logger import Logger
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@ -72,14 +68,10 @@ def load_model(
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# --------------------------
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# load weights
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weight_file = weight_utils.download_weights_if_necessary(
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file_name="gender_model_weights.h5", source_url=url
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)
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home = folder_utils.get_deepface_home()
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output = os.path.join(home, ".deepface/weights/gender_model_weights.h5")
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if not os.path.isfile(output):
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logger.info(f"{os.path.basename(output)} will be downloaded...")
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gdown.download(url, output, quiet=False)
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gender_model.load_weights(output)
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gender_model.load_weights(weight_file)
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return gender_model
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@ -1,13 +1,9 @@
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# built-in dependencies
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import os
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# 3rd party dependencies
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import gdown
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import numpy as np
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# project dependencies
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from deepface.models.facial_recognition import VGGFace
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from deepface.commons import package_utils, folder_utils
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from deepface.commons import package_utils, weight_utils
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from deepface.models.Demography import Demography
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from deepface.commons.logger import Logger
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@ -69,14 +65,10 @@ def load_model(
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# --------------------------
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# load weights
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weight_file = weight_utils.download_weights_if_necessary(
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file_name="race_model_single_batch.h5", source_url=url
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)
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home = folder_utils.get_deepface_home()
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output = os.path.join(home, ".deepface/weights/race_model_single_batch.h5")
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if not os.path.isfile(output):
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logger.info(f"{os.path.basename(output)} will be downloaded...")
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gdown.download(url, output, quiet=False)
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race_model.load_weights(output)
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race_model.load_weights(weight_file)
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return race_model
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@ -5,10 +5,9 @@ from typing import List
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# 3rd party dependencies
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import numpy as np
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import cv2
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import gdown
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# project dependencies
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from deepface.commons import folder_utils
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from deepface.commons import weight_utils
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from deepface.models.Detector import Detector, FacialAreaRegion
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from deepface.commons.logger import Logger
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@ -29,19 +28,9 @@ class CenterFaceClient(Detector):
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"""
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Download pre-trained weights of CenterFace model if necessary and load built model
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"""
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home = folder_utils.get_deepface_home()
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weights_path = os.path.join(home, ".deepface/weights/centerface.onnx")
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if not os.path.isfile(weights_path):
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logger.info(f"Downloading CenterFace weights from {WEIGHTS_URL} to {weights_path}...")
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try:
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gdown.download(WEIGHTS_URL, weights_path, quiet=False)
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except Exception as err:
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raise ValueError(
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f"Exception while downloading CenterFace weights from {WEIGHTS_URL}."
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f"You may consider to download it to {weights_path} manually."
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) from err
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logger.info(f"CenterFace model is just downloaded to {os.path.basename(weights_path)}")
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weights_path = weight_utils.download_weights_if_necessary(
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file_name="centerface.onnx", source_url=WEIGHTS_URL
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)
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return CenterFace(weight_path=weights_path)
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@ -1,9 +1,11 @@
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# built-in dependencies
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from typing import List
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import os
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import bz2
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import gdown
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# 3rd party dependencies
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import numpy as np
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from deepface.commons import folder_utils
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# project dependencies
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from deepface.commons import weight_utils
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from deepface.models.Detector import Detector, FacialAreaRegion
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from deepface.commons.logger import Logger
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@ -30,25 +32,14 @@ class DlibClient(Detector):
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) from e
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# check required file exists in the home/.deepface/weights folder
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home = folder_utils.get_deepface_home()
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filename = "shape_predictor_5_face_landmarks.dat"
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filepath = os.path.join(home, ".deepface/weights/", filename)
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if not os.path.isfile(filepath):
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logger.info(f"{filename + '.bz2'} is going to be downloaded")
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url = f"http://dlib.net/files/{filename + '.bz2'}"
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output = filepath + ".bz2"
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gdown.download(url, output, quiet=False)
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zipfile = bz2.BZ2File(output)
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data = zipfile.read()
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with open(filepath, "wb") as f:
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f.write(data)
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weight_file = weight_utils.download_weights_if_necessary(
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file_name="shape_predictor_5_face_landmarks.dat",
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source_url="http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2",
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compress_type="bz2",
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)
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face_detector = dlib.get_frontal_face_detector()
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sp = dlib.shape_predictor(filepath)
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sp = dlib.shape_predictor(weight_file)
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detector = {}
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detector["face_detector"] = face_detector
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@ -1,13 +1,19 @@
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# built-in dependencies
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from typing import Any, Union, List
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# 3rd party dependencies
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import cv2
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import numpy as np
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from deepface.models.Detector import Detector, FacialAreaRegion
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# Link -> https://github.com/timesler/facenet-pytorch
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# Examples https://www.kaggle.com/timesler/guide-to-mtcnn-in-facenet-pytorch
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# project dependencies
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from deepface.models.Detector import Detector, FacialAreaRegion
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class FastMtCnnClient(Detector):
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"""
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Fast MtCnn Detector from github.com/timesler/facenet-pytorch
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"""
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def __init__(self):
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self.model = self.build_model()
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@ -69,7 +75,7 @@ class FastMtCnnClient(Detector):
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"Please install using 'pip install facenet-pytorch'"
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) from e
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device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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face_detector = fast_mtcnn(device=device)
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return face_detector
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@ -1,11 +1,18 @@
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# built-in dependencies
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from typing import Any, List
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import numpy as np
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from deepface.models.Detector import Detector, FacialAreaRegion
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# Link - https://google.github.io/mediapipe/solutions/face_detection
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# 3rd party dependencies
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import numpy as np
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# project dependencies
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from deepface.models.Detector import Detector, FacialAreaRegion
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class MediaPipeClient(Detector):
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"""
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MediaPipe from google.github.io/mediapipe/solutions/face_detection
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"""
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def __init__(self):
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self.model = self.build_model()
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@ -69,7 +76,13 @@ class MediaPipeClient(Detector):
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# left_ear = (int(landmarks[5].x * img_width), int(landmarks[5].y * img_height))
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facial_area = FacialAreaRegion(
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x=x, y=y, w=w, h=h, left_eye=left_eye, right_eye=right_eye, confidence=confidence
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x=x,
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y=y,
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w=w,
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h=h,
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left_eye=left_eye,
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right_eye=right_eye,
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confidence=float(confidence),
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)
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resp.append(facial_area)
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@ -1,6 +1,11 @@
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# built-in dependencies
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from typing import List
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# 3rd party dependencies
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import numpy as np
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from mtcnn import MTCNN
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# project dependencies
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from deepface.models.Detector import Detector, FacialAreaRegion
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# pylint: disable=too-few-public-methods
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@ -1,7 +1,12 @@
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# built-in dependencies
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import os
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from typing import Any, List
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# 3rd party dependencies
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import cv2
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import numpy as np
|
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|
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#project dependencies
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from deepface.models.Detector import Detector, FacialAreaRegion
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|
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|
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|
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@ -1,6 +1,11 @@
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# built-in dependencies
|
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from typing import List
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# 3rd party dependencies
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import numpy as np
|
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from retinaface import RetinaFace as rf
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|
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# project dependencies
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from deepface.models.Detector import Detector, FacialAreaRegion
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|
||||
# pylint: disable=too-few-public-methods
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|
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@ -1,11 +1,14 @@
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# built-in dependencies
|
||||
from typing import List
|
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import os
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from enum import IntEnum
|
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import gdown
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|
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# 3rd party dependencies
|
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import cv2
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import numpy as np
|
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|
||||
# project dependencies
|
||||
from deepface.models.face_detection import OpenCv
|
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from deepface.commons import folder_utils
|
||||
from deepface.commons import weight_utils
|
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from deepface.models.Detector import Detector, FacialAreaRegion
|
||||
from deepface.commons.logger import Logger
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@ -25,21 +28,17 @@ class SsdClient(Detector):
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model (dict)
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"""
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||||
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home = folder_utils.get_deepface_home()
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# model structure
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output_model = os.path.join(home, ".deepface/weights/deploy.prototxt")
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if not os.path.isfile(output_model):
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logger.info(f"{os.path.basename(output_model)} will be downloaded...")
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url = "https://github.com/opencv/opencv/raw/3.4.0/samples/dnn/face_detector/deploy.prototxt"
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gdown.download(url, output_model, quiet=False)
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output_model = weight_utils.download_weights_if_necessary(
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file_name="deploy.prototxt",
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source_url="https://github.com/opencv/opencv/raw/3.4.0/samples/dnn/face_detector/deploy.prototxt",
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||||
)
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||||
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||||
# pre-trained weights
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||||
output_weights = os.path.join(home, ".deepface/weights/res10_300x300_ssd_iter_140000.caffemodel")
|
||||
if not os.path.isfile(output_weights):
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logger.info(f"{os.path.basename(output_weights)} will be downloaded...")
|
||||
url = "https://github.com/opencv/opencv_3rdparty/raw/dnn_samples_face_detector_20170830/res10_300x300_ssd_iter_140000.caffemodel"
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gdown.download(url, output_weights, quiet=False)
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output_weights = weight_utils.download_weights_if_necessary(
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file_name="res10_300x300_ssd_iter_140000.caffemodel",
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source_url="https://github.com/opencv/opencv_3rdparty/raw/dnn_samples_face_detector_20170830/res10_300x300_ssd_iter_140000.caffemodel",
|
||||
)
|
||||
|
||||
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 {
|
||||
"face_detector": face_detector,
|
||||
"opencv_module": OpenCv.OpenCvClient()
|
||||
}
|
||||
return {"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[:, [ssd_labels.right, ssd_labels.bottom]] -= faces[:, [ssd_labels.left, ssd_labels.top]]
|
||||
faces[:, margins] = np.int32(
|
||||
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:
|
||||
|
||||
@ -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_path = os.path.join(home, PATH)
|
||||
|
||||
# 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)}")
|
||||
weight_file = weight_utils.download_weights_if_necessary(
|
||||
file_name="yolov8n-face.pt", source_url=WEIGHT_URL
|
||||
)
|
||||
|
||||
# Return face_detector
|
||||
return YOLO(weight_path)
|
||||
return YOLO(weight_file)
|
||||
|
||||
def detect_faces(self, img: np.ndarray) -> List[FacialAreaRegion]:
|
||||
"""
|
||||
|
||||
@ -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"
|
||||
file_name = "face_detection_yunet_2023mar.onnx"
|
||||
home = folder_utils.get_deepface_home()
|
||||
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)
|
||||
weight_file = weight_utils.download_weights_if_necessary(
|
||||
file_name="face_detection_yunet_2023mar.onnx",
|
||||
source_url="https://github.com/opencv/opencv_zoo/raw/main/models/face_detection_yunet/face_detection_yunet_2023mar.onnx",
|
||||
)
|
||||
|
||||
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."
|
||||
|
||||
@ -1,6 +1,5 @@
|
||||
import os
|
||||
import gdown
|
||||
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
|
||||
@ -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()
|
||||
|
||||
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(weight_file)
|
||||
# ---------------------------------------
|
||||
|
||||
model.load_weights(output)
|
||||
|
||||
return model
|
||||
|
||||
|
||||
|
||||
@ -1,6 +1,5 @@
|
||||
import os
|
||||
import gdown
|
||||
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
|
||||
|
||||
@ -86,13 +85,10 @@ def load_model(
|
||||
|
||||
# ---------------------------------
|
||||
|
||||
home = folder_utils.get_deepface_home()
|
||||
output = os.path.join(home, ".deepface/weights/deepid_keras_weights.h5")
|
||||
weight_file = weight_utils.download_weights_if_necessary(
|
||||
file_name="deepid_keras_weights.h5", source_url=url
|
||||
)
|
||||
|
||||
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)
|
||||
model.load_weights(weight_file)
|
||||
|
||||
return model
|
||||
|
||||
@ -1,9 +1,11 @@
|
||||
# built-in dependencies
|
||||
from typing import List
|
||||
import os
|
||||
import bz2
|
||||
import gdown
|
||||
|
||||
# 3rd party dependencies
|
||||
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()
|
||||
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 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)
|
||||
weight_file = weight_utils.download_weights_if_necessary(
|
||||
file_name="dlib_face_recognition_resnet_model_v1.dat",
|
||||
source_url="http://dlib.net/files/dlib_face_recognition_resnet_model_v1.dat.bz2",
|
||||
compress_type="bz2",
|
||||
)
|
||||
|
||||
self.model = dlib.face_recognition_model_v1(weight_file)
|
||||
|
||||
|
||||
@ -1,6 +1,5 @@
|
||||
import os
|
||||
import gdown
|
||||
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
|
||||
|
||||
@ -1666,20 +1665,10 @@ def load_facenet128d_model(
|
||||
"""
|
||||
model = InceptionResNetV1()
|
||||
|
||||
# -----------------------------------
|
||||
|
||||
home = folder_utils.get_deepface_home()
|
||||
output = os.path.join(home, ".deepface/weights/facenet_weights.h5")
|
||||
|
||||
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)
|
||||
|
||||
# -----------------------------------
|
||||
weight_file = weight_utils.download_weights_if_necessary(
|
||||
file_name="facenet_weights.h5", source_url=url
|
||||
)
|
||||
model.load_weights(weight_file)
|
||||
|
||||
return model
|
||||
|
||||
@ -1695,19 +1684,9 @@ def load_facenet512d_model(
|
||||
|
||||
model = InceptionResNetV1(dimension=512)
|
||||
|
||||
# -------------------------
|
||||
|
||||
home = folder_utils.get_deepface_home()
|
||||
output = os.path.join(home, ".deepface/weights/facenet512_weights.h5")
|
||||
|
||||
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)
|
||||
|
||||
# -------------------------
|
||||
weight_file = weight_utils.download_weights_if_necessary(
|
||||
file_name="facenet512_weights.h5", source_url=url
|
||||
)
|
||||
model.load_weights(weight_file)
|
||||
|
||||
return model
|
||||
|
||||
@ -1,7 +1,5 @@
|
||||
import os
|
||||
import zipfile
|
||||
import gdown
|
||||
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
|
||||
|
||||
@ -84,20 +82,11 @@ 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)
|
||||
weight_file = weight_utils.download_weights_if_necessary(
|
||||
file_name="VGGFace2_DeepFace_weights_val-0.9034.h5", source_url=url, compress_type="zip"
|
||||
)
|
||||
|
||||
if not os.path.isfile(output):
|
||||
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)
|
||||
base_model.load_weights(weight_file)
|
||||
|
||||
# drop F8 and D0. F7 is the representation layer.
|
||||
deepface_model = Model(inputs=base_model.layers[0].input, outputs=base_model.layers[-3].output)
|
||||
|
||||
@ -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()
|
||||
output = os.path.join(home, ".deepface/weights/ghostfacenet_v1.h5")
|
||||
weight_file = weight_utils.download_weights_if_necessary(
|
||||
file_name="ghostfacenet_v1.h5", source_url=PRETRAINED_WEIGHTS
|
||||
)
|
||||
|
||||
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}")
|
||||
|
||||
model.load_weights(output)
|
||||
model.load_weights(weight_file)
|
||||
|
||||
return model
|
||||
|
||||
|
||||
@ -1,7 +1,8 @@
|
||||
import os
|
||||
import gdown
|
||||
# 3rd party dependencies
|
||||
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()
|
||||
output = os.path.join(home, ".deepface/weights/openface_weights.h5")
|
||||
weight_file = weight_utils.download_weights_if_necessary(
|
||||
file_name="openface_weights.h5", source_url=url
|
||||
)
|
||||
|
||||
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)
|
||||
model.load_weights(weight_file)
|
||||
|
||||
# -----------------------------------
|
||||
|
||||
|
||||
@ -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()
|
||||
output = os.path.join(home, ".deepface/weights/face_recognition_sface_2021dec.onnx")
|
||||
weight_file = weight_utils.download_weights_if_necessary(
|
||||
file_name="face_recognition_sface_2021dec.onnx", source_url=url
|
||||
)
|
||||
|
||||
if not os.path.isfile(output):
|
||||
logger.info(f"{os.path.basename(output)} weights will be downloaded...")
|
||||
gdown.download(url, output, quiet=False)
|
||||
|
||||
model = SFaceWrapper(model_path=output)
|
||||
model = SFaceWrapper(model_path=weight_file)
|
||||
|
||||
return model
|
||||
|
||||
|
||||
@ -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()
|
||||
output = os.path.join(home, ".deepface/weights/vgg_face_weights.h5")
|
||||
weight_file = weight_utils.download_weights_if_necessary(
|
||||
file_name="vgg_face_weights.h5", source_url=url
|
||||
)
|
||||
|
||||
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)
|
||||
model.load_weights(weight_file)
|
||||
|
||||
# 2622d dimensional model
|
||||
# vgg_face_descriptor = Model(inputs=model.layers[0].input, outputs=model.layers[-2].output)
|
||||
|
||||
@ -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",
|
||||
url="https://github.com/minivision-ai/Silent-Face-Anti-Spoofing/raw/master/resources/anti_spoof_models/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",
|
||||
)
|
||||
|
||||
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",
|
||||
url="https://github.com/minivision-ai/Silent-Face-Anti-Spoofing/raw/master/resources/anti_spoof_models/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",
|
||||
)
|
||||
|
||||
# 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(
|
||||
f"{home}/.deepface/weights/2.7_80x80_MiniFASNetV2.pth", map_location=device
|
||||
)
|
||||
state_dict = torch.load(first_model_weight_file, 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(
|
||||
f"{home}/.deepface/weights/4_0_0_80x80_MiniFASNetV1SE.pth", map_location=device
|
||||
)
|
||||
state_dict = torch.load(second_model_weight_file, 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
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user