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Merge pull request #6 from NatLee/patch/adjustment-0103-1

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New Shared Prediction Method.
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127
.github/CODE_OF_CONDUCT.md vendored Normal file
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@ -0,0 +1,127 @@
# Contributor Covenant Code of Conduct
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community include:
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those enforcing the Code of Conduct, for a specified period of time. This
includes avoiding interactions in community spaces as well as external channels
like social media. Violating these terms may lead to a temporary or
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### 3. Temporary Ban
**Community Impact**: A serious violation of community standards, including
sustained inappropriate behavior.
**Consequence**: A temporary ban from any sort of interaction or public
communication with the community for a specified period of time. No public or
private interaction with the people involved, including unsolicited interaction
with those enforcing the Code of Conduct, is allowed during this period.
Violating these terms may lead to a permanent ban.
### 4. Permanent Ban
**Community Impact**: Demonstrating a pattern of violation of community
standards, including sustained inappropriate behavior, harassment of an
individual, or aggression toward or disparagement of classes of individuals.
**Consequence**: A permanent ban from any sort of public interaction within
the community.
## Attribution
This Code of Conduct is adapted from the [Contributor Covenant][homepage],
version 2.0, available at
https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
Community Impact Guidelines were inspired by [Mozilla's code of conduct
enforcement ladder](https://github.com/mozilla/diversity).
[homepage]: https://www.contributor-covenant.org
For answers to common questions about this code of conduct, see the FAQ at
https://www.contributor-covenant.org/faq. Translations are available at
https://www.contributor-covenant.org/translations.

21
README.md
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@ -16,6 +16,9 @@
[![GitHub Sponsors](https://img.shields.io/github/sponsors/serengil?logo=GitHub&color=lightgray)](https://github.com/sponsors/serengil) [![GitHub Sponsors](https://img.shields.io/github/sponsors/serengil?logo=GitHub&color=lightgray)](https://github.com/sponsors/serengil)
[![Buy Me a Coffee](https://img.shields.io/badge/-buy_me_a%C2%A0coffee-gray?logo=buy-me-a-coffee)](https://buymeacoffee.com/serengil) [![Buy Me a Coffee](https://img.shields.io/badge/-buy_me_a%C2%A0coffee-gray?logo=buy-me-a-coffee)](https://buymeacoffee.com/serengil)
[![Hacker News](https://img.shields.io/badge/dynamic/json?color=orange&label=Hacker%20News&query=score&url=https%3A%2F%2Fhacker-news.firebaseio.com%2Fv0%2Fitem%2F42584896.json&logo=y-combinator)](https://news.ycombinator.com/item?id=42584896)
[![Product Hunt](https://img.shields.io/badge/Product%20Hunt-%E2%96%B2-orange?logo=producthunt)](https://www.producthunt.com/posts/deepface?embed=true&utm_source=badge-featured&utm_medium=badge&utm_souce=badge-deepface)
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@ -392,7 +395,7 @@ Before creating a PR, you should run the unit tests and linting locally by runni
There are many ways to support a project - starring⭐️ the GitHub repo is just one 🙏 There are many ways to support a project - starring⭐️ the GitHub repo is just one 🙏
If you do like this work, then you can support it financially on [Patreon](https://www.patreon.com/serengil?repo=deepface), [GitHub Sponsors](https://github.com/sponsors/serengil) or [Buy Me a Coffee](https://buymeacoffee.com/serengil). If you do like this work, then you can support it financially on [Patreon](https://www.patreon.com/serengil?repo=deepface), [GitHub Sponsors](https://github.com/sponsors/serengil) or [Buy Me a Coffee](https://buymeacoffee.com/serengil). Also, your company's logo will be shown on README on GitHub and PyPI if you become a sponsor in gold, silver or bronze tiers.
<a href="https://www.patreon.com/serengil?repo=deepface"> <a href="https://www.patreon.com/serengil?repo=deepface">
<img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/patreon.png" width="30%" height="30%"> <img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/patreon.png" width="30%" height="30%">
@ -402,13 +405,25 @@ If you do like this work, then you can support it financially on [Patreon](https
<img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/bmc-button.png" width="25%" height="25%"> <img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/bmc-button.png" width="25%" height="25%">
</a> </a>
Also, your company's logo will be shown on README on GitHub and PyPI if you become a sponsor in gold, silver or bronze tiers. Additionally, you can help us reach a wider audience by upvoting our posts on Hacker News and Product Hunt.
<div style="display: flex; align-items: center; gap: 10px;">
<!-- Hacker News Badge -->
<a href="https://news.ycombinator.com/item?id=42584896">
<img src="https://hackerbadge.vercel.app/api?id=42584896&type=orange" style="width: 250px; height: 54px;" width="250" alt="Featured on Hacker News">
</a>
<!-- Product Hunt Badge -->
<a href="https://www.producthunt.com/posts/deepface?embed=true&utm_source=badge-featured&utm_medium=badge&utm_souce=badge-deepface" target="_blank">
<img src="https://api.producthunt.com/widgets/embed-image/v1/featured.svg?post_id=753599&theme=light" alt="DeepFace - A Lightweight Deep Face Recognition Library for Python | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" />
</a>
</div>
## Citation ## Citation
Please cite deepface in your publications if it helps your research - see [`CITATIONS`](https://github.com/serengil/deepface/blob/master/CITATION.md) for more details. Here are its BibTex entries: Please cite deepface in your publications if it helps your research - see [`CITATIONS`](https://github.com/serengil/deepface/blob/master/CITATION.md) for more details. Here are its BibTex entries:
If you use deepface in your research for facial recogntion or face detection purposes, please cite these publications: If you use deepface in your research for facial recognition or face detection purposes, please cite these publications:
```BibTeX ```BibTeX
@article{serengil2024lightface, @article{serengil2024lightface,

176
deepface/DeepFace.py
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@ -2,7 +2,7 @@
import os import os
import warnings import warnings
import logging import logging
from typing import Any, Dict, List, Union, Optional from typing import Any, Dict, IO, List, Union, Optional
# this has to be set before importing tensorflow # this has to be set before importing tensorflow
os.environ["TF_USE_LEGACY_KERAS"] = "1" os.environ["TF_USE_LEGACY_KERAS"] = "1"
@ -68,28 +68,30 @@ def build_model(model_name: str, task: str = "facial_recognition") -> Any:
def verify( def verify(
img1_path: Union[str, np.ndarray, List[float]], img1_path: Union[str, np.ndarray, IO[bytes], List[float]],
img2_path: Union[str, np.ndarray, List[float]], img2_path: Union[str, np.ndarray, IO[bytes], List[float]],
model_name: str = "VGG-Face", model_name: str = "VGG-Face",
detector_backend: str = "opencv", detector_backend: str = "opencv",
distance_metric: str = "cosine", distance_metric: str = "cosine",
enforce_detection: bool = True, enforce_detection: bool = True,
align: bool = True, align: bool = True,
expand_percentage: int = 0, expand_percentage: int = 0,
normalization: str = "base", normalization: str = "base",
silent: bool = False, silent: bool = False,
threshold: Optional[float] = None, threshold: Optional[float] = None,
anti_spoofing: bool = False, anti_spoofing: bool = False,
) -> Dict[str, Any]: ) -> Dict[str, Any]:
""" """
Verify if an image pair represents the same person or different persons. Verify if an image pair represents the same person or different persons.
Args: Args:
img1_path (str or np.ndarray or List[float]): Path to the first image. img1_path (str or np.ndarray or IO[bytes] or List[float]): Path to the first image.
Accepts exact image path as a string, numpy array (BGR), base64 encoded images Accepts exact image path as a string, numpy array (BGR), a file object that supports
at least `.read` and is opened in binary mode, base64 encoded images
or pre-calculated embeddings. or pre-calculated embeddings.
img2_path (str or np.ndarray or List[float]): Path to the second image. img2_path (str or np.ndarray or IO[bytes] or List[float]): Path to the second image.
Accepts exact image path as a string, numpy array (BGR), base64 encoded images Accepts exact image path as a string, numpy array (BGR), a file object that supports
at least `.read` and is opened in binary mode, base64 encoded images
or pre-calculated embeddings. or pre-calculated embeddings.
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512, model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
@ -164,21 +166,22 @@ def verify(
def analyze( def analyze(
img_path: Union[str, np.ndarray], img_path: Union[str, np.ndarray, IO[bytes]],
actions: Union[tuple, list] = ("emotion", "age", "gender", "race"), actions: Union[tuple, list] = ("emotion", "age", "gender", "race"),
enforce_detection: bool = True, enforce_detection: bool = True,
detector_backend: str = "opencv", detector_backend: str = "opencv",
align: bool = True, align: bool = True,
expand_percentage: int = 0, expand_percentage: int = 0,
silent: bool = False, silent: bool = False,
anti_spoofing: bool = False, anti_spoofing: bool = False,
) -> List[Dict[str, Any]]: ) -> List[Dict[str, Any]]:
""" """
Analyze facial attributes such as age, gender, emotion, and race in the provided image. Analyze facial attributes such as age, gender, emotion, and race in the provided image.
Args: Args:
img_path (str or np.ndarray): The exact path to the image, a numpy array in BGR format, img_path (str or np.ndarray or IO[bytes]): The exact path to the image, a numpy array
or a base64 encoded image. If the source image contains multiple faces, the result will in BGR format, a file object that supports at least `.read` and is opened in binary
include information for each detected face. mode, or a base64 encoded image. If the source image contains multiple faces,
the result will include information for each detected face.
actions (tuple): Attributes to analyze. The default is ('age', 'gender', 'emotion', 'race'). actions (tuple): Attributes to analyze. The default is ('age', 'gender', 'emotion', 'race').
You can exclude some of these attributes from the analysis if needed. You can exclude some of these attributes from the analysis if needed.
@ -263,27 +266,28 @@ def analyze(
def find( def find(
img_path: Union[str, np.ndarray], img_path: Union[str, np.ndarray, IO[bytes]],
db_path: str, db_path: str,
model_name: str = "VGG-Face", model_name: str = "VGG-Face",
distance_metric: str = "cosine", distance_metric: str = "cosine",
enforce_detection: bool = True, enforce_detection: bool = True,
detector_backend: str = "opencv", detector_backend: str = "opencv",
align: bool = True, align: bool = True,
expand_percentage: int = 0, expand_percentage: int = 0,
threshold: Optional[float] = None, threshold: Optional[float] = None,
normalization: str = "base", normalization: str = "base",
silent: bool = False, silent: bool = False,
refresh_database: bool = True, refresh_database: bool = True,
anti_spoofing: bool = False, anti_spoofing: bool = False,
batched: bool = False, batched: bool = False,
) -> Union[List[pd.DataFrame], List[List[Dict[str, Any]]]]: ) -> Union[List[pd.DataFrame], List[List[Dict[str, Any]]]]:
""" """
Identify individuals in a database Identify individuals in a database
Args: Args:
img_path (str or np.ndarray): The exact path to the image, a numpy array in BGR format, img_path (str or np.ndarray or IO[bytes]): The exact path to the image, a numpy array
or a base64 encoded image. If the source image contains multiple faces, the result will in BGR format, a file object that supports at least `.read` and is opened in binary
include information for each detected face. mode, or a base64 encoded image. If the source image contains multiple
faces, the result will include information for each detected face.
db_path (string): Path to the folder containing image files. All detected faces db_path (string): Path to the folder containing image files. All detected faces
in the database will be considered in the decision-making process. in the database will be considered in the decision-making process.
@ -369,23 +373,24 @@ def find(
def represent( def represent(
img_path: Union[str, np.ndarray], img_path: Union[str, np.ndarray, IO[bytes]],
model_name: str = "VGG-Face", model_name: str = "VGG-Face",
enforce_detection: bool = True, enforce_detection: bool = True,
detector_backend: str = "opencv", detector_backend: str = "opencv",
align: bool = True, align: bool = True,
expand_percentage: int = 0, expand_percentage: int = 0,
normalization: str = "base", normalization: str = "base",
anti_spoofing: bool = False, anti_spoofing: bool = False,
max_faces: Optional[int] = None, max_faces: Optional[int] = None,
) -> List[Dict[str, Any]]: ) -> List[Dict[str, Any]]:
""" """
Represent facial images as multi-dimensional vector embeddings. Represent facial images as multi-dimensional vector embeddings.
Args: Args:
img_path (str or np.ndarray): The exact path to the image, a numpy array in BGR format, img_path (str or np.ndarray or IO[bytes]): The exact path to the image, a numpy array
or a base64 encoded image. If the source image contains multiple faces, the result will in BGR format, a file object that supports at least `.read` and is opened in binary
include information for each detected face. mode, or a base64 encoded image. If the source image contains multiple faces,
the result will include information for each detected face.
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512, model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet
@ -441,15 +446,16 @@ def represent(
def stream( def stream(
db_path: str = "", db_path: str = "",
model_name: str = "VGG-Face", model_name: str = "VGG-Face",
detector_backend: str = "opencv", detector_backend: str = "opencv",
distance_metric: str = "cosine", distance_metric: str = "cosine",
enable_face_analysis: bool = True, enable_face_analysis: bool = True,
source: Any = 0, source: Any = 0,
time_threshold: int = 5, time_threshold: int = 5,
frame_threshold: int = 5, frame_threshold: int = 5,
anti_spoofing: bool = False, anti_spoofing: bool = False,
output_path: Optional[str] = None,
) -> None: ) -> None:
""" """
Run real time face recognition and facial attribute analysis Run real time face recognition and facial attribute analysis
@ -478,6 +484,10 @@ def stream(
frame_threshold (int): The frame threshold for face recognition (default is 5). frame_threshold (int): The frame threshold for face recognition (default is 5).
anti_spoofing (boolean): Flag to enable anti spoofing (default is False). anti_spoofing (boolean): Flag to enable anti spoofing (default is False).
output_path (str): Path to save the output video. (default is None
If None, no video is saved).
Returns: Returns:
None None
""" """
@ -495,26 +505,28 @@ def stream(
time_threshold=time_threshold, time_threshold=time_threshold,
frame_threshold=frame_threshold, frame_threshold=frame_threshold,
anti_spoofing=anti_spoofing, anti_spoofing=anti_spoofing,
output_path=output_path,
) )
def extract_faces( def extract_faces(
img_path: Union[str, np.ndarray], img_path: Union[str, np.ndarray, IO[bytes]],
detector_backend: str = "opencv", detector_backend: str = "opencv",
enforce_detection: bool = True, enforce_detection: bool = True,
align: bool = True, align: bool = True,
expand_percentage: int = 0, expand_percentage: int = 0,
grayscale: bool = False, grayscale: bool = False,
color_face: str = "rgb", color_face: str = "rgb",
normalize_face: bool = True, normalize_face: bool = True,
anti_spoofing: bool = False, anti_spoofing: bool = False,
) -> List[Dict[str, Any]]: ) -> List[Dict[str, Any]]:
""" """
Extract faces from a given image Extract faces from a given image
Args: Args:
img_path (str or np.ndarray): Path to the first image. Accepts exact image path img_path (str or np.ndarray or IO[bytes]): Path to the first image. Accepts exact image path
as a string, numpy array (BGR), or base64 encoded images. as a string, numpy array (BGR), a file object that supports at least `.read` and is
opened in binary mode, or base64 encoded images.
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface', detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m', 'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m',
@ -584,11 +596,11 @@ def cli() -> None:
def detectFace( def detectFace(
img_path: Union[str, np.ndarray], img_path: Union[str, np.ndarray],
target_size: tuple = (224, 224), target_size: tuple = (224, 224),
detector_backend: str = "opencv", detector_backend: str = "opencv",
enforce_detection: bool = True, enforce_detection: bool = True,
align: bool = True, align: bool = True,
) -> Union[np.ndarray, None]: ) -> Union[np.ndarray, None]:
""" """
Deprecated face detection function. Use extract_faces for same functionality. Deprecated face detection function. Use extract_faces for same functionality.

78
deepface/commons/image_utils.py
View File

@ -1,7 +1,7 @@
# built-in dependencies # built-in dependencies
import os import os
import io import io
from typing import List, Union, Tuple from typing import Generator, IO, List, Union, Tuple
import hashlib import hashlib
import base64 import base64
from pathlib import Path from pathlib import Path
@ -14,6 +14,10 @@ from PIL import Image
from werkzeug.datastructures import FileStorage from werkzeug.datastructures import FileStorage
IMAGE_EXTS = {".jpg", ".jpeg", ".png"}
PIL_EXTS = {"jpeg", "png"}
def list_images(path: str) -> List[str]: def list_images(path: str) -> List[str]:
""" """
List images in a given path List images in a given path
@ -25,19 +29,31 @@ def list_images(path: str) -> List[str]:
images = [] images = []
for r, _, f in os.walk(path): for r, _, f in os.walk(path):
for file in f: for file in f:
exact_path = os.path.join(r, file) if os.path.splitext(file)[1].lower() in IMAGE_EXTS:
exact_path = os.path.join(r, file)
ext_lower = os.path.splitext(exact_path)[-1].lower() with Image.open(exact_path) as img: # lazy
if img.format.lower() in PIL_EXTS:
if ext_lower not in {".jpg", ".jpeg", ".png"}: images.append(exact_path)
continue
with Image.open(exact_path) as img: # lazy
if img.format.lower() in {"jpeg", "png"}:
images.append(exact_path)
return images return images
def yield_images(path: str) -> Generator[str, None, None]:
"""
Yield images in a given path
Args:
path (str): path's location
Yields:
image (str): image path
"""
for r, _, f in os.walk(path):
for file in f:
if os.path.splitext(file)[1].lower() in IMAGE_EXTS:
exact_path = os.path.join(r, file)
with Image.open(exact_path) as img: # lazy
if img.format.lower() in PIL_EXTS:
yield exact_path
def find_image_hash(file_path: str) -> str: def find_image_hash(file_path: str) -> str:
""" """
Find the hash of given image file with its properties Find the hash of given image file with its properties
@ -61,11 +77,11 @@ def find_image_hash(file_path: str) -> str:
return hasher.hexdigest() return hasher.hexdigest()
def load_image(img: Union[str, np.ndarray]) -> Tuple[np.ndarray, str]: def load_image(img: Union[str, np.ndarray, IO[bytes]]) -> Tuple[np.ndarray, str]:
""" """
Load image from path, url, base64 or numpy array. Load image from path, url, file object, base64 or numpy array.
Args: Args:
img: a path, url, base64 or numpy array. img: a path, url, file object, base64 or numpy array.
Returns: Returns:
image (numpy array): the loaded image in BGR format image (numpy array): the loaded image in BGR format
image name (str): image name itself image name (str): image name itself
@ -75,6 +91,14 @@ def load_image(img: Union[str, np.ndarray]) -> Tuple[np.ndarray, str]:
if isinstance(img, np.ndarray): if isinstance(img, np.ndarray):
return img, "numpy array" return img, "numpy array"
# The image is an object that supports `.read`
if hasattr(img, 'read') and callable(img.read):
if isinstance(img, io.StringIO):
raise ValueError(
'img requires bytes and cannot be an io.StringIO object.'
)
return load_image_from_io_object(img), 'io object'
if isinstance(img, Path): if isinstance(img, Path):
img = str(img) img = str(img)
@ -104,6 +128,32 @@ def load_image(img: Union[str, np.ndarray]) -> Tuple[np.ndarray, str]:
return img_obj_bgr, img return img_obj_bgr, img
def load_image_from_io_object(obj: IO[bytes]) -> np.ndarray:
"""
Load image from an object that supports being read
Args:
obj: a file like object.
Returns:
img (np.ndarray): The decoded image as a numpy array (OpenCV format).
"""
try:
_ = obj.seek(0)
except (AttributeError, TypeError, io.UnsupportedOperation):
seekable = False
obj = io.BytesIO(obj.read())
else:
seekable = True
try:
nparr = np.frombuffer(obj.read(), np.uint8)
img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
if img is None:
raise ValueError("Failed to decode image")
return img
finally:
if not seekable:
obj.close()
def load_image_from_base64(uri: str) -> np.ndarray: def load_image_from_base64(uri: str) -> np.ndarray:
""" """
Load image from base64 string. Load image from base64 string.

46
deepface/models/Demography.py
View File

@ -21,28 +21,54 @@ class Demography(ABC):
def predict(self, img: Union[np.ndarray, List[np.ndarray]]) -> Union[np.ndarray, np.float64]: def predict(self, img: Union[np.ndarray, List[np.ndarray]]) -> Union[np.ndarray, np.float64]:
pass pass
def _preprocess_batch_or_single_input(self, img: Union[np.ndarray, List[np.ndarray]]) -> np.ndarray: def _predict_internal(self, img_batch: np.ndarray) -> np.ndarray:
"""
Predict for single image or batched images.
This method uses legacy method while receiving single image as input.
And switch to batch prediction if receives batched images.
Args:
img_batch:
Batch of images as np.ndarray (n, x, y, c)
with n >= 1, x = image width, y = image height, c = channel
Or Single image as np.ndarray (1, x, y, c)
with x = image width, y = image height and c = channel
The channel dimension may be omitted if the image is grayscale. (For emotion model)
"""
if not self.model_name: # Check if called from derived class
raise NotImplementedError("no model selected")
assert img_batch.ndim == 4, "expected 4-dimensional tensor input"
# Single image
if img_batch.shape[0] == 1:
# Check if grayscale by checking last dimension, if not 3, it is grayscale.
if img_batch.shape[-1] != 3:
# Remove batch dimension
img_batch = img_batch.squeeze(0)
# Predict with legacy method.
return self.model(img_batch, training=False).numpy()[0, :]
# Batch of images
# Predict with batch prediction
return self.model.predict_on_batch(img_batch)
def _preprocess_batch_or_single_input(
self,
img: Union[np.ndarray, List[np.ndarray]]
) -> np.ndarray:
""" """
Preprocess single or batch of images, return as 4-D numpy array. Preprocess single or batch of images, return as 4-D numpy array.
Args: Args:
img: Single image as np.ndarray (224, 224, 3) or img: Single image as np.ndarray (224, 224, 3) or
List of images as List[np.ndarray] or List of images as List[np.ndarray] or
Batch of images as np.ndarray (n, 224, 224, 3) Batch of images as np.ndarray (n, 224, 224, 3)
Returns: Returns:
Four-dimensional numpy array (n, 224, 224, 3) Four-dimensional numpy array (n, 224, 224, 3)
""" """
if isinstance(img, list): # Convert from list to image batch. image_batch = np.array(img)
image_batch = np.array(img)
else:
image_batch = img
# Remove batch dimension in advance if exists # Remove batch dimension in advance if exists
image_batch = image_batch.squeeze() image_batch = image_batch.squeeze()
# Check input dimension # Check input dimension
if len(image_batch.shape) == 3: if len(image_batch.shape) == 3:
# Single image - add batch dimension # Single image - add batch dimension
image_batch = np.expand_dims(image_batch, axis=0) image_batch = np.expand_dims(image_batch, axis=0)
return image_batch return image_batch

6
deepface/models/demography/Age.py
View File

@ -54,9 +54,9 @@ class ApparentAgeClient(Demography):
# Preprocessing input image or image list. # Preprocessing input image or image list.
imgs = self._preprocess_batch_or_single_input(img) imgs = self._preprocess_batch_or_single_input(img)
# Batch prediction # Prediction from 3 channels image
age_predictions = self.model.predict_on_batch(imgs) age_predictions = self._predict_internal(imgs)
# Calculate apparent ages # Calculate apparent ages
apparent_ages = np.array( apparent_ages = np.array(
[find_apparent_age(age_prediction) for age_prediction in age_predictions] [find_apparent_age(age_prediction) for age_prediction in age_predictions]

17
deepface/models/demography/Emotion.py
View File

@ -72,14 +72,17 @@ class EmotionClient(Demography):
# Preprocessing input image or image list. # Preprocessing input image or image list.
imgs = self._preprocess_batch_or_single_input(img) imgs = self._preprocess_batch_or_single_input(img)
# Preprocess each image if imgs.shape[0] == 1:
processed_imgs = np.array([self._preprocess_image(img) for img in imgs]) # Preprocess single image and add channel dimension for grayscale images
processed_imgs = np.expand_dims(np.array([self._preprocess_image(img) for img in imgs]), axis=0)
# Add channel dimension for grayscale images else:
processed_imgs = np.expand_dims(processed_imgs, axis=-1) # Preprocess batch of images and add channel dimension for grayscale images
processed_imgs = np.expand_dims(np.array([self._preprocess_image(img) for img in imgs]), axis=-1)
# Reshape input for model (expected shape=(n, 48, 48, 1)), where n is the batch size
processed_imgs = processed_imgs.reshape(processed_imgs.shape[0], 48, 48, 1)
# Batch prediction # Prediction
predictions = self.model.predict_on_batch(processed_imgs) predictions = self._predict_internal(processed_imgs)
return predictions return predictions

4
deepface/models/demography/Gender.py
View File

@ -54,8 +54,8 @@ class GenderClient(Demography):
# Preprocessing input image or image list. # Preprocessing input image or image list.
imgs = self._preprocess_batch_or_single_input(img) imgs = self._preprocess_batch_or_single_input(img)
# Batch prediction # Prediction
predictions = self.model.predict_on_batch(imgs) predictions = self._predict_internal(imgs)
return predictions return predictions

4
deepface/models/demography/Race.py
View File

@ -54,8 +54,8 @@ class RaceClient(Demography):
# Preprocessing input image or image list. # Preprocessing input image or image list.
imgs = self._preprocess_batch_or_single_input(img) imgs = self._preprocess_batch_or_single_input(img)
# Batch prediction # Prediction
predictions = self.model.predict_on_batch(imgs) predictions = self._predict_internal(imgs)
return predictions return predictions

168
deepface/modules/demography.py
View File

@ -1,5 +1,5 @@
# built-in dependencies # built-in dependencies
from typing import Any, Dict, List, Union from typing import Any, Dict, List, Union, Optional
# 3rd party dependencies # 3rd party dependencies
import numpy as np import numpy as np
@ -117,8 +117,6 @@ def analyze(
f"Invalid action passed ({repr(action)})). " f"Invalid action passed ({repr(action)})). "
"Valid actions are `emotion`, `age`, `gender`, `race`." "Valid actions are `emotion`, `age`, `gender`, `race`."
) )
# ---------------------------------
resp_objects = []
img_objs = detection.extract_faces( img_objs = detection.extract_faces(
img_path=img_path, img_path=img_path,
@ -130,109 +128,105 @@ def analyze(
anti_spoofing=anti_spoofing, anti_spoofing=anti_spoofing,
) )
# Anti-spoofing check
if anti_spoofing and any(img_obj.get("is_real", True) is False for img_obj in img_objs): if anti_spoofing and any(img_obj.get("is_real", True) is False for img_obj in img_objs):
raise ValueError("Spoof detected in the given image.") raise ValueError("Spoof detected in the given image.")
# Prepare the input for the model def preprocess_face(img_obj: Dict[str, Any]) -> Optional[np.ndarray]:
valid_faces = [] """
face_regions = [] Preprocess the face image for analysis.
face_confidences = [] """
for img_obj in img_objs:
# Extract the face content
img_content = img_obj["face"] img_content = img_obj["face"]
# Check if the face content is empty
if img_content.shape[0] == 0 or img_content.shape[1] == 0: if img_content.shape[0] == 0 or img_content.shape[1] == 0:
continue return None
img_content = img_content[:, :, ::-1] # BGR to RGB
return preprocessing.resize_image(img=img_content, target_size=(224, 224))
# Convert the image to RGB format from BGR # Filter out empty faces
img_content = img_content[:, :, ::-1] face_data = [(preprocess_face(img_obj), img_obj["facial_area"], img_obj["confidence"])
# Resize the image to the target size for the model for img_obj in img_objs if img_obj["face"].size > 0]
img_content = preprocessing.resize_image(img=img_content, target_size=(224, 224))
if not face_data:
valid_faces.append(img_content)
face_regions.append(img_obj["facial_area"])
face_confidences.append(img_obj["confidence"])
# If no valid faces are found, return an empty list
if not valid_faces:
return [] return []
# Convert the list of valid faces to a numpy array # Unpack the face data
valid_faces, face_regions, face_confidences = zip(*face_data)
faces_array = np.array(valid_faces) faces_array = np.array(valid_faces)
# Create placeholder response objects for each face # Initialize the results list with face regions and confidence scores
for _ in range(len(valid_faces)): results = [{"region": region, "face_confidence": conf}
resp_objects.append({}) for region, conf in zip(face_regions, face_confidences)]
# Iterate over the actions and perform analysis
# For each action, predict the corresponding attribute
pbar = tqdm( pbar = tqdm(
range(0, len(actions)), actions,
desc="Finding actions", desc="Finding actions",
disable=silent if len(actions) > 1 else True, disable=silent if len(actions) > 1 else True,
) )
for index in pbar: for action in pbar:
action = actions[index]
pbar.set_description(f"Action: {action}") pbar.set_description(f"Action: {action}")
model = modeling.build_model(task="facial_attribute", model_name=action.capitalize())
predictions = model.predict(faces_array)
# If the model returns a single prediction, reshape it to match the number of faces.
# Determine the correct shape of predictions by using number of faces and predictions shape.
# Example: For 1 face with Emotion model, predictions will be reshaped to (1, 7).
if faces_array.shape[0] == 1 and len(predictions.shape) == 1:
# For models like `Emotion`, which return a single prediction for a single face
predictions = predictions.reshape(1, -1)
# Update the results with the predictions
# ----------------------------------------
# For emotion, calculate the percentage of each emotion and find the dominant emotion
if action == "emotion": if action == "emotion":
# Build the emotion model emotion_results = [
model = modeling.build_model(task="facial_attribute", model_name="Emotion") {
emotion_predictions = model.predict(faces_array) "emotion": {
label: 100 * pred[i] / pred.sum()
for idx, predictions in enumerate(emotion_predictions): for i, label in enumerate(Emotion.labels)
sum_of_predictions = predictions.sum() },
resp_objects[idx]["emotion"] = {} "dominant_emotion": Emotion.labels[np.argmax(pred)]
}
for i, emotion_label in enumerate(Emotion.labels): for pred in predictions
emotion_prediction = 100 * predictions[i] / sum_of_predictions ]
resp_objects[idx]["emotion"][emotion_label] = emotion_prediction for result, emotion_result in zip(results, emotion_results):
result.update(emotion_result)
resp_objects[idx]["dominant_emotion"] = Emotion.labels[np.argmax(predictions)] # ----------------------------------------
# For age, find the dominant age category (0-100)
elif action == "age": elif action == "age":
# Build the age model age_results = [{"age": int(np.argmax(pred) if len(pred.shape) > 0 else pred)}
model = modeling.build_model(task="facial_attribute", model_name="Age") for pred in predictions]
age_predictions = model.predict(faces_array) for result, age_result in zip(results, age_results):
result.update(age_result)
for idx, age in enumerate(age_predictions): # ----------------------------------------
resp_objects[idx]["age"] = int(age) # For gender, calculate the percentage of each gender and find the dominant gender
elif action == "gender": elif action == "gender":
# Build the gender model gender_results = [
model = modeling.build_model(task="facial_attribute", model_name="Gender") {
gender_predictions = model.predict(faces_array) "gender": {
label: 100 * pred[i]
for idx, predictions in enumerate(gender_predictions): for i, label in enumerate(Gender.labels)
resp_objects[idx]["gender"] = {} },
"dominant_gender": Gender.labels[np.argmax(pred)]
for i, gender_label in enumerate(Gender.labels): }
gender_prediction = 100 * predictions[i] for pred in predictions
resp_objects[idx]["gender"][gender_label] = gender_prediction ]
for result, gender_result in zip(results, gender_results):
resp_objects[idx]["dominant_gender"] = Gender.labels[np.argmax(predictions)] result.update(gender_result)
# ----------------------------------------
# For race, calculate the percentage of each race and find the dominant race
elif action == "race": elif action == "race":
# Build the race model race_results = [
model = modeling.build_model(task="facial_attribute", model_name="Race") {
race_predictions = model.predict(faces_array) "race": {
label: 100 * pred[i] / pred.sum()
for idx, predictions in enumerate(race_predictions): for i, label in enumerate(Race.labels)
sum_of_predictions = predictions.sum() },
resp_objects[idx]["race"] = {} "dominant_race": Race.labels[np.argmax(pred)]
}
for i, race_label in enumerate(Race.labels): for pred in predictions
race_prediction = 100 * predictions[i] / sum_of_predictions ]
resp_objects[idx]["race"][race_label] = race_prediction for result, race_result in zip(results, race_results):
result.update(race_result)
resp_objects[idx]["dominant_race"] = Race.labels[np.argmax(predictions)]
# Add the face region and confidence to the response objects return results
for idx, resp_obj in enumerate(resp_objects):
resp_obj["region"] = face_regions[idx]
resp_obj["face_confidence"] = face_confidences[idx]
return resp_objects

9
deepface/modules/detection.py
View File

@ -1,5 +1,5 @@
# built-in dependencies # built-in dependencies
from typing import Any, Dict, List, Tuple, Union, Optional from typing import Any, Dict, IO, List, Tuple, Union, Optional
# 3rd part dependencies # 3rd part dependencies
from heapq import nlargest from heapq import nlargest
@ -19,7 +19,7 @@ logger = Logger()
def extract_faces( def extract_faces(
img_path: Union[str, np.ndarray], img_path: Union[str, np.ndarray, IO[bytes]],
detector_backend: str = "opencv", detector_backend: str = "opencv",
enforce_detection: bool = True, enforce_detection: bool = True,
align: bool = True, align: bool = True,
@ -34,8 +34,9 @@ def extract_faces(
Extract faces from a given image Extract faces from a given image
Args: Args:
img_path (str or np.ndarray): Path to the first image. Accepts exact image path img_path (str or np.ndarray or IO[bytes]): Path to the first image. Accepts exact image path
as a string, numpy array (BGR), or base64 encoded images. as a string, numpy array (BGR), a file object that supports at least `.read` and is
opened in binary mode, or base64 encoded images.
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface', detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m', 'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m',

24
deepface/modules/recognition.py
View File

@ -136,7 +136,7 @@ def find(
representations = [] representations = []
# required columns for representations # required columns for representations
df_cols = [ df_cols = {
"identity", "identity",
"hash", "hash",
"embedding", "embedding",
@ -144,12 +144,12 @@ def find(
"target_y", "target_y",
"target_w", "target_w",
"target_h", "target_h",
] }
# Ensure the proper pickle file exists # Ensure the proper pickle file exists
if not os.path.exists(datastore_path): if not os.path.exists(datastore_path):
with open(datastore_path, "wb") as f: with open(datastore_path, "wb") as f:
pickle.dump([], f) pickle.dump([], f, pickle.HIGHEST_PROTOCOL)
# Load the representations from the pickle file # Load the representations from the pickle file
with open(datastore_path, "rb") as f: with open(datastore_path, "rb") as f:
@ -157,18 +157,15 @@ def find(
# check each item of representations list has required keys # check each item of representations list has required keys
for i, current_representation in enumerate(representations): for i, current_representation in enumerate(representations):
missing_keys = set(df_cols) - set(current_representation.keys()) missing_keys = df_cols - set(current_representation.keys())
if len(missing_keys) > 0: if len(missing_keys) > 0:
raise ValueError( raise ValueError(
f"{i}-th item does not have some required keys - {missing_keys}." f"{i}-th item does not have some required keys - {missing_keys}."
f"Consider to delete {datastore_path}" f"Consider to delete {datastore_path}"
) )
# embedded images
pickled_images = [representation["identity"] for representation in representations]
# Get the list of images on storage # Get the list of images on storage
storage_images = image_utils.list_images(path=db_path) storage_images = set(image_utils.yield_images(path=db_path))
if len(storage_images) == 0 and refresh_database is True: if len(storage_images) == 0 and refresh_database is True:
raise ValueError(f"No item found in {db_path}") raise ValueError(f"No item found in {db_path}")
@ -186,8 +183,13 @@ def find(
# Enforce data consistency amongst on disk images and pickle file # Enforce data consistency amongst on disk images and pickle file
if refresh_database: if refresh_database:
new_images = set(storage_images) - set(pickled_images) # images added to storage # embedded images
old_images = set(pickled_images) - set(storage_images) # images removed from storage pickled_images = {
representation["identity"] for representation in representations
}
new_images = storage_images - pickled_images # images added to storage
old_images = pickled_images - storage_images # images removed from storage
# detect replaced images # detect replaced images
for current_representation in representations: for current_representation in representations:
@ -232,7 +234,7 @@ def find(
if must_save_pickle: if must_save_pickle:
with open(datastore_path, "wb") as f: with open(datastore_path, "wb") as f:
pickle.dump(representations, f) pickle.dump(representations, f, pickle.HIGHEST_PROTOCOL)
if not silent: if not silent:
logger.info(f"There are now {len(representations)} representations in {file_name}") logger.info(f"There are now {len(representations)} representations in {file_name}")

48
deepface/modules/streaming.py
View File

@ -22,6 +22,7 @@ os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
IDENTIFIED_IMG_SIZE = 112 IDENTIFIED_IMG_SIZE = 112
TEXT_COLOR = (255, 255, 255) TEXT_COLOR = (255, 255, 255)
# pylint: disable=unused-variable # pylint: disable=unused-variable
def analysis( def analysis(
db_path: str, db_path: str,
@ -33,6 +34,7 @@ def analysis(
time_threshold=5, time_threshold=5,
frame_threshold=5, frame_threshold=5,
anti_spoofing: bool = False, anti_spoofing: bool = False,
output_path: Optional[str] = None,
): ):
""" """
Run real time face recognition and facial attribute analysis Run real time face recognition and facial attribute analysis
@ -62,6 +64,8 @@ def analysis(
anti_spoofing (boolean): Flag to enable anti spoofing (default is False). anti_spoofing (boolean): Flag to enable anti spoofing (default is False).
output_path (str): Path to save the output video. (default is None
If None, no video is saved).
Returns: Returns:
None None
""" """
@ -77,12 +81,31 @@ def analysis(
model_name=model_name, model_name=model_name,
) )
cap = cv2.VideoCapture(source if isinstance(source, str) else int(source))
if not cap.isOpened():
logger.error(f"Cannot open video source: {source}")
return
# Get video properties
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = cap.get(cv2.CAP_PROP_FPS)
fourcc = cv2.VideoWriter_fourcc(*"mp4v") # Codec for output file
# Ensure the output directory exists if output_path is provided
if output_path:
os.makedirs(os.path.dirname(output_path), exist_ok=True)
# Initialize video writer if output_path is provided
video_writer = (
cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (width, height))
if output_path
else None
)
freezed_img = None freezed_img = None
freeze = False freeze = False
num_frames_with_faces = 0 num_frames_with_faces = 0
tic = time.time() tic = time.time()
cap = cv2.VideoCapture(source) # webcam
while True: while True:
has_frame, img = cap.read() has_frame, img = cap.read()
if not has_frame: if not has_frame:
@ -91,9 +114,9 @@ def analysis(
# we are adding some figures into img such as identified facial image, age, gender # we are adding some figures into img such as identified facial image, age, gender
# that is why, we need raw image itself to make analysis # that is why, we need raw image itself to make analysis
raw_img = img.copy() raw_img = img.copy()
faces_coordinates = [] faces_coordinates = []
if freeze is False:
if not freeze:
faces_coordinates = grab_facial_areas( faces_coordinates = grab_facial_areas(
img=img, detector_backend=detector_backend, anti_spoofing=anti_spoofing img=img, detector_backend=detector_backend, anti_spoofing=anti_spoofing
) )
@ -101,7 +124,6 @@ def analysis(
# we will pass img to analyze modules (identity, demography) and add some illustrations # we will pass img to analyze modules (identity, demography) and add some illustrations
# that is why, we will not be able to extract detected face from img clearly # that is why, we will not be able to extract detected face from img clearly
detected_faces = extract_facial_areas(img=img, faces_coordinates=faces_coordinates) detected_faces = extract_facial_areas(img=img, faces_coordinates=faces_coordinates)
img = highlight_facial_areas(img=img, faces_coordinates=faces_coordinates) img = highlight_facial_areas(img=img, faces_coordinates=faces_coordinates)
img = countdown_to_freeze( img = countdown_to_freeze(
img=img, img=img,
@ -111,8 +133,8 @@ def analysis(
) )
num_frames_with_faces = num_frames_with_faces + 1 if len(faces_coordinates) else 0 num_frames_with_faces = num_frames_with_faces + 1 if len(faces_coordinates) else 0
freeze = num_frames_with_faces > 0 and num_frames_with_faces % frame_threshold == 0 freeze = num_frames_with_faces > 0 and num_frames_with_faces % frame_threshold == 0
if freeze: if freeze:
# add analyze results into img - derive from raw_img # add analyze results into img - derive from raw_img
img = highlight_facial_areas( img = highlight_facial_areas(
@ -144,22 +166,28 @@ def analysis(
tic = time.time() tic = time.time()
logger.info("freezed") logger.info("freezed")
elif freeze is True and time.time() - tic > time_threshold: elif freeze and time.time() - tic > time_threshold:
freeze = False freeze = False
freezed_img = None freezed_img = None
# reset counter for freezing # reset counter for freezing
tic = time.time() tic = time.time()
logger.info("freeze released") logger.info("Freeze released")
freezed_img = countdown_to_release(img=freezed_img, tic=tic, time_threshold=time_threshold) freezed_img = countdown_to_release(img=freezed_img, tic=tic, time_threshold=time_threshold)
display_img = img if freezed_img is None else freezed_img
cv2.imshow("img", img if freezed_img is None else freezed_img) # Save the frame to output video if writer is initialized
if video_writer:
video_writer.write(display_img)
if cv2.waitKey(1) & 0xFF == ord("q"): # press q to quit cv2.imshow("img", display_img)
if cv2.waitKey(1) & 0xFF == ord("q"):
break break
# kill open cv things # Release resources
cap.release() cap.release()
if video_writer:
video_writer.release()
cv2.destroyAllWindows() cv2.destroyAllWindows()

17
tests/test_find.py
View File

@ -95,12 +95,23 @@ def test_filetype_for_find():
def test_filetype_for_find_bulk_embeddings(): def test_filetype_for_find_bulk_embeddings():
imgs = image_utils.list_images("dataset") # List
list_imgs = image_utils.list_images("dataset")
assert len(imgs) > 0 assert len(list_imgs) > 0
# img47 is webp even though its extension is jpg # img47 is webp even though its extension is jpg
assert "dataset/img47.jpg" not in imgs assert "dataset/img47.jpg" not in list_imgs
# Generator
gen_imgs = list(image_utils.yield_images("dataset"))
assert len(gen_imgs) > 0
# img47 is webp even though its extension is jpg
assert "dataset/img47.jpg" not in gen_imgs
assert gen_imgs == list_imgs
def test_find_without_refresh_database(): def test_find_without_refresh_database():

21
tests/test_represent.py
View File

@ -1,5 +1,7 @@
# built-in dependencies # built-in dependencies
import io
import cv2 import cv2
import pytest
# project dependencies # project dependencies
from deepface import DeepFace from deepface import DeepFace
@ -18,6 +20,25 @@ def test_standard_represent():
logger.info("✅ test standard represent function done") logger.info("✅ test standard represent function done")
def test_standard_represent_with_io_object():
img_path = "dataset/img1.jpg"
default_embedding_objs = DeepFace.represent(img_path)
io_embedding_objs = DeepFace.represent(open(img_path, 'rb'))
assert default_embedding_objs == io_embedding_objs
# Confirm non-seekable io objects are handled properly
io_obj = io.BytesIO(open(img_path, 'rb').read())
io_obj.seek = None
no_seek_io_embedding_objs = DeepFace.represent(io_obj)
assert default_embedding_objs == no_seek_io_embedding_objs
# Confirm non-image io objects raise exceptions
with pytest.raises(ValueError, match='Failed to decode image'):
DeepFace.represent(io.BytesIO(open(r'../requirements.txt', 'rb').read()))
logger.info("✅ test standard represent with io object function done")
def test_represent_for_skipped_detector_backend_with_image_path(): def test_represent_for_skipped_detector_backend_with_image_path():
face_img = "dataset/img5.jpg" face_img = "dataset/img5.jpg"
img_objs = DeepFace.represent(img_path=face_img, detector_backend="skip") img_objs = DeepFace.represent(img_path=face_img, detector_backend="skip")