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Merge pull request #1423 from PyWoody/load_image_io

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load_image now accepts file objects that support being read
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Sefik Ilkin Serengil 2025-01-10 17:16:28 +00:00 committed by GitHub
commit 29200f4fd6
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4 changed files with 92 additions and 30 deletions
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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',

42
deepface/commons/image_utils.py
View File

@ -1,7 +1,7 @@
# built-in dependencies
import os
import io
from typing import Generator, List, Union, Tuple
from typing import Generator, IO, List, Union, Tuple
import hashlib
import base64
from pathlib import Path
@ -77,11 +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
@ -91,6 +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)
@ -120,6 +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',

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")