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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
## Our Pledge
We as members, contributors, and leaders pledge to make participation in our
community a harassment-free experience for everyone, regardless of age, body
size, visible or invisible disability, ethnicity, sex characteristics, gender
identity and expression, level of experience, education, socio-economic status,
nationality, personal appearance, race, religion, or sexual identity
and orientation.
We pledge to act and interact in ways that contribute to an open, welcoming,
diverse, inclusive, and healthy community.
## Our Standards
Examples of behavior that contributes to a positive environment for our
community include:
* Demonstrating empathy and kindness toward other people
* Being respectful of differing opinions, viewpoints, and experiences
* Giving and gracefully accepting constructive feedback
* Accepting responsibility and apologizing to those affected by our mistakes,
and learning from the experience
* Focusing on what is best not just for us as individuals, but for the
overall community
Examples of unacceptable behavior include:
* The use of sexualized language or imagery, and sexual attention or
advances of any kind
* Trolling, insulting or derogatory comments, and personal or political attacks
* Public or private harassment
* Publishing others' private information, such as a physical or email
address, without their explicit permission
* Other conduct which could reasonably be considered inappropriate in a
professional setting
## Enforcement Responsibilities
Community leaders are responsible for clarifying and enforcing our standards of
acceptable behavior and will take appropriate and fair corrective action in
response to any behavior that they deem inappropriate, threatening, offensive,
or harmful.
Community leaders have the right and responsibility to remove, edit, or reject
comments, commits, code, wiki edits, issues, and other contributions that are
not aligned to this Code of Conduct, and will communicate reasons for moderation
decisions when appropriate.
## Scope
This Code of Conduct applies within all community spaces, and also applies when
an individual is officially representing the community in public spaces.
Examples of representing our community include using an official e-mail address,
posting via an official social media account, or acting as an appointed
representative at an online or offline event.
## Enforcement
Instances of abusive, harassing, or otherwise unacceptable behavior may be
reported to the community leaders responsible for enforcement at serengil@gmail.com.
All complaints will be reviewed and investigated promptly and fairly.
All community leaders are obligated to respect the privacy and security of the
reporter of any incident.
## Enforcement Guidelines
Community leaders will follow these Community Impact Guidelines in determining
the consequences for any action they deem in violation of this Code of Conduct:
### 1. Correction
**Community Impact**: Use of inappropriate language or other behavior deemed
unprofessional or unwelcome in the community.
**Consequence**: A private, written warning from community leaders, providing
clarity around the nature of the violation and an explanation of why the
behavior was inappropriate. A public apology may be requested.
### 2. Warning
**Community Impact**: A violation through a single incident or series
of actions.
**Consequence**: A warning with consequences for continued behavior. No
interaction with the people involved, including unsolicited interaction with
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
permanent ban.
### 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.

2
README.md
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@ -423,7 +423,7 @@ Additionally, you can help us reach a wider audience by upvoting our posts on Ha
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
@article{serengil2024lightface,

50
deepface/DeepFace.py
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@ -2,7 +2,7 @@
import os
import warnings
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
os.environ["TF_USE_LEGACY_KERAS"] = "1"
@ -68,8 +68,8 @@ def build_model(model_name: str, task: str = "facial_recognition") -> Any:
def verify(
img1_path: Union[str, np.ndarray, List[float]],
img2_path: Union[str, np.ndarray, List[float]],
img1_path: Union[str, np.ndarray, IO[bytes], List[float]],
img2_path: Union[str, np.ndarray, IO[bytes], List[float]],
model_name: str = "VGG-Face",
detector_backend: str = "opencv",
distance_metric: str = "cosine",
@ -84,12 +84,14 @@ def verify(
"""
Verify if an image pair represents the same person or different persons.
Args:
img1_path (str or np.ndarray or List[float]): Path to the first image.
Accepts exact image path as a string, numpy array (BGR), base64 encoded images
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), a file object that supports
at least `.read` and is opened in binary mode, base64 encoded images
or pre-calculated embeddings.
img2_path (str or np.ndarray or List[float]): Path to the second image.
Accepts exact image path as a string, numpy array (BGR), base64 encoded images
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), a file object that supports
at least `.read` and is opened in binary mode, base64 encoded images
or pre-calculated embeddings.
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
@ -164,7 +166,7 @@ def verify(
def analyze(
img_path: Union[str, np.ndarray],
img_path: Union[str, np.ndarray, IO[bytes]],
actions: Union[tuple, list] = ("emotion", "age", "gender", "race"),
enforce_detection: bool = True,
detector_backend: str = "opencv",
@ -176,9 +178,10 @@ def analyze(
"""
Analyze facial attributes such as age, gender, emotion, and race in the provided image.
Args:
img_path (str or np.ndarray): The exact path to the image, a numpy array in BGR format,
or a base64 encoded image. If the source image contains multiple faces, the result will
include information for each detected face.
img_path (str or np.ndarray or IO[bytes]): The exact path to the image, a numpy array
in BGR format, a file object that supports at least `.read` and is opened in binary
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').
You can exclude some of these attributes from the analysis if needed.
@ -263,7 +266,7 @@ def analyze(
def find(
img_path: Union[str, np.ndarray],
img_path: Union[str, np.ndarray, IO[bytes]],
db_path: str,
model_name: str = "VGG-Face",
distance_metric: str = "cosine",
@ -281,9 +284,10 @@ def find(
"""
Identify individuals in a database
Args:
img_path (str or np.ndarray): The exact path to the image, a numpy array in BGR format,
or a base64 encoded image. If the source image contains multiple faces, the result will
include information for each detected face.
img_path (str or np.ndarray or IO[bytes]): The exact path to the image, a numpy array
in BGR format, a file object that supports at least `.read` and is opened in binary
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
in the database will be considered in the decision-making process.
@ -369,7 +373,7 @@ def find(
def represent(
img_path: Union[str, np.ndarray],
img_path: Union[str, np.ndarray, IO[bytes]],
model_name: str = "VGG-Face",
enforce_detection: bool = True,
detector_backend: str = "opencv",
@ -383,9 +387,10 @@ def represent(
Represent facial images as multi-dimensional vector embeddings.
Args:
img_path (str or np.ndarray): The exact path to the image, a numpy array in BGR format,
or a base64 encoded image. If the source image contains multiple faces, the result will
include information for each detected face.
img_path (str or np.ndarray or IO[bytes]): The exact path to the image, a numpy array
in BGR format, a file object that supports at least `.read` and is opened in binary
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,
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet
@ -505,7 +510,7 @@ def stream(
def extract_faces(
img_path: Union[str, np.ndarray],
img_path: Union[str, np.ndarray, IO[bytes]],
detector_backend: str = "opencv",
enforce_detection: bool = True,
align: bool = True,
@ -519,8 +524,9 @@ def extract_faces(
Extract faces from a given image
Args:
img_path (str or np.ndarray): Path to the first image. Accepts exact image path
as a string, numpy array (BGR), or base64 encoded images.
img_path (str or np.ndarray or IO[bytes]): Path to the first image. Accepts exact image path
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',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m',

78
deepface/commons/image_utils.py
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@ -1,7 +1,7 @@
# built-in dependencies
import os
import io
from typing import List, Union, Tuple
from typing import Generator, IO, List, Union, Tuple
import hashlib
import base64
from pathlib import Path
@ -14,6 +14,10 @@ from PIL import Image
from werkzeug.datastructures import FileStorage
IMAGE_EXTS = {".jpg", ".jpeg", ".png"}
PIL_EXTS = {"jpeg", "png"}
def list_images(path: str) -> List[str]:
"""
List images in a given path
@ -25,19 +29,31 @@ def list_images(path: str) -> List[str]:
images = []
for r, _, f in os.walk(path):
for file in f:
exact_path = os.path.join(r, file)
ext_lower = os.path.splitext(exact_path)[-1].lower()
if ext_lower not in {".jpg", ".jpeg", ".png"}:
continue
with Image.open(exact_path) as img: # lazy
if img.format.lower() in {"jpeg", "png"}:
images.append(exact_path)
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:
images.append(exact_path)
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:
"""
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()
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:
img: a path, url, base64 or numpy array.
img: a path, url, file object, base64 or numpy array.
Returns:
image (numpy array): the loaded image in BGR format
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):
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):
img = str(img)
@ -104,6 +128,32 @@ def load_image(img: Union[str, np.ndarray]) -> Tuple[np.ndarray, str]:
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:
"""
Load image from base64 string.

9
deepface/modules/detection.py
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@ -1,5 +1,5 @@
# 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
from heapq import nlargest
@ -19,7 +19,7 @@ logger = Logger()
def extract_faces(
img_path: Union[str, np.ndarray],
img_path: Union[str, np.ndarray, IO[bytes]],
detector_backend: str = "opencv",
enforce_detection: bool = True,
align: bool = True,
@ -34,8 +34,9 @@ def extract_faces(
Extract faces from a given image
Args:
img_path (str or np.ndarray): Path to the first image. Accepts exact image path
as a string, numpy array (BGR), or base64 encoded images.
img_path (str or np.ndarray or IO[bytes]): Path to the first image. Accepts exact image path
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',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m',

20
deepface/modules/recognition.py
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@ -136,7 +136,7 @@ def find(
representations = []
# required columns for representations
df_cols = [
df_cols = {
"identity",
"hash",
"embedding",
@ -144,7 +144,7 @@ def find(
"target_y",
"target_w",
"target_h",
]
}
# Ensure the proper pickle file exists
if not os.path.exists(datastore_path):
@ -157,18 +157,15 @@ def find(
# check each item of representations list has required keys
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:
raise ValueError(
f"{i}-th item does not have some required keys - {missing_keys}."
f"Consider to delete {datastore_path}"
)
# embedded images
pickled_images = [representation["identity"] for representation in representations]
# Get the list of images on storage
storage_images = image_utils.list_images(path=db_path)
storage_images = set(image_utils.yield_images(path=db_path))
if len(storage_images) == 0 and refresh_database is True:
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
if refresh_database:
new_images = set(storage_images) - set(pickled_images) # images added to storage
old_images = set(pickled_images) - set(storage_images) # images removed from storage
# embedded images
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
for current_representation in representations:

17
tests/test_find.py
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@ -95,12 +95,23 @@ def test_filetype_for_find():
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
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():

21
tests/test_represent.py
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@ -1,5 +1,7 @@
# built-in dependencies
import io
import cv2
import pytest
# project dependencies
from deepface import DeepFace
@ -18,6 +20,25 @@ def test_standard_represent():
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():
face_img = "dataset/img5.jpg"
img_objs = DeepFace.represent(img_path=face_img, detector_backend="skip")