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january 26 improvements

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Sefik Ilkin Serengil 2024-01-26 17:52:55 +00:00
parent 88814e6d2b
commit 36665a9e96
7 changed files with 109 additions and 21 deletions
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83
deepface/DeepFace.py
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@ -2,7 +2,7 @@
import os
import warnings
import logging
from typing import Any, Dict, List, Tuple, Union
from typing import Any, Dict, List, Tuple, Union, Optional
# 3rd party dependencies
import numpy as np
@ -65,34 +65,49 @@ def verify(
Args:
img1_path (str or np.ndarray): Path to the first image. Accepts exact image path
as a string, numpy array (BGR), or base64 encoded images.
img2_path (str or np.ndarray): Path to the second image. Accepts exact image path
as a string, numpy array (BGR), or base64 encoded images.
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
OpenFace, DeepFace, DeepID, Dlib, ArcFace and SFace (default is VGG-Face).
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' (default is opencv)
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' (default is opencv).
distance_metric (string): Metric for measuring similarity. Options: 'cosine',
'euclidean', 'euclidean_l2' (default is cosine).
enforce_detection (boolean): If no face is detected in an image, raise an exception.
Set to False to avoid the exception for low-resolution images (default is True).
align (bool): Flag to enable face alignment (default is True).
normalization (string): Normalize the input image before feeding it to the model.
Options: base, raw, Facenet, Facenet2018, VGGFace, VGGFace2, ArcFace (default is base)
Returns:
result (dict): A dictionary containing verification results with following keys.
- 'verified' (bool): Indicates whether the images represent the same person (True)
or different persons (False).
- 'distance' (float): The distance measure between the face vectors.
A lower distance indicates higher similarity.
- 'max_threshold_to_verify' (float): The maximum threshold used for verification.
If the distance is below this threshold, the images are considered a match.
- 'model' (str): The chosen face recognition model.
- 'similarity_metric' (str): The chosen similarity metric for measuring distances.
- 'facial_areas' (dict): Rectangular regions of interest for faces in both images.
- 'img1': {'x': int, 'y': int, 'w': int, 'h': int}
Region of interest for the first image.
- 'img2': {'x': int, 'y': int, 'w': int, 'h': int}
Region of interest for the second image.
- 'time' (float): Time taken for the verification process in seconds.
"""
@ -122,37 +137,51 @@ def analyze(
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.
actions (tuple): Attributes to analyze. The default is ('age', 'gender', 'emotion', 'race').
You can exclude some of these attributes from the analysis if needed.
enforce_detection (boolean): If no face is detected in an image, raise an exception.
Set to False to avoid the exception for low-resolution images (default is True).
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' (default is opencv).
distance_metric (string): Metric for measuring similarity. Options: 'cosine',
'euclidean', 'euclidean_l2' (default is cosine).
align (boolean): Perform alignment based on the eye positions (default is True).
silent (boolean): Suppress or allow some log messages for a quieter analysis process
(default is False).
Returns:
results (List[Dict[str, Any]]): A list of dictionaries, where each dictionary represents
the analysis results for a detected face. Each dictionary in the list contains the
following keys:
- 'region' (dict): Represents the rectangular region of the detected face in the image.
- 'x': x-coordinate of the top-left corner of the face.
- 'y': y-coordinate of the top-left corner of the face.
- 'w': Width of the detected face region.
- 'h': Height of the detected face region.
- 'age' (float): Estimated age of the detected face.
- 'face_confidence' (float): Confidence score for the detected face.
Indicates the reliability of the face detection.
- 'dominant_gender' (str): The dominant gender in the detected face.
Either "Man" or "Woman."
Either "Man" or "Woman".
- 'gender' (dict): Confidence scores for each gender category.
- 'Man': Confidence score for the male gender.
- 'Woman': Confidence score for the female gender.
- 'dominant_emotion' (str): The dominant emotion in the detected face.
Possible values include "sad," "angry," "surprise," "fear," "happy,"
"disgust," and "neutral."
"disgust," and "neutral"
- 'emotion' (dict): Confidence scores for each emotion category.
- 'sad': Confidence score for sadness.
- 'angry': Confidence score for anger.
@ -161,9 +190,11 @@ def analyze(
- 'happy': Confidence score for happiness.
- 'disgust': Confidence score for disgust.
- 'neutral': Confidence score for neutrality.
- 'dominant_race' (str): The dominant race in the detected face.
Possible values include "indian," "asian," "latino hispanic,"
"black," "middle eastern," and "white."
- 'race' (dict): Confidence scores for each race category.
- 'indian': Confidence score for Indian ethnicity.
- 'asian': Confidence score for Asian ethnicity.
@ -190,6 +221,7 @@ def find(
enforce_detection: bool = True,
detector_backend: str = "opencv",
align: bool = True,
threshold: Optional[float] = None,
normalization: str = "base",
silent: bool = False,
) -> List[pd.DataFrame]:
@ -199,31 +231,51 @@ def find(
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.
db_path (string): Path to the folder containing image files. All detected faces
in the database will be considered in the decision-making process.
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
OpenFace, DeepFace, DeepID, Dlib, ArcFace and SFace (default is VGG-Face).
distance_metric (string): Metric for measuring similarity. Options: 'cosine',
'euclidean', 'euclidean_l2' (default is cosine).
enforce_detection (boolean): If no face is detected in an image, raise an exception.
Set to False to avoid the exception for low-resolution images (default is True).
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' (default is opencv).
align (boolean): Perform alignment based on the eye positions (default is True).
threshold (float): Specify a threshold to determine whether a pair represents the same
person or different individuals. This threshold is used for comparing distances.
If left unset, default pre-tuned threshold values will be applied based on the specified
model name and distance metric (default is None).
normalization (string): Normalize the input image before feeding it to the model.
Options: base, raw, Facenet, Facenet2018, VGGFace, VGGFace2, ArcFace (default is base).
silent (boolean): Suppress or allow some log messages for a quieter analysis process
(default is False).
Returns:
results (List[pd.DataFrame]): A list of pandas dataframes. Each dataframe corresponds
to the identity information for an individual detected in the source image.
The DataFrame columns include:
- 'identity': Identity label of the detected individual.
- 'target_x', 'target_y', 'target_w', 'target_h': Bounding box coordinates of the
target face in the database.
- 'source_x', 'source_y', 'source_w', 'source_h': Bounding box coordinates of the
detected face in the source image.
- '{model_name}_{distance_metric}': Similarity score between the faces based on the
- 'threshold': threshold to determine a pair whether same person or different persons
- 'distance': Similarity score between the faces based on the
specified model and distance metric
"""
return recognition.find(
@ -234,6 +286,7 @@ def find(
enforce_detection=enforce_detection,
detector_backend=detector_backend,
align=align,
threshold=threshold,
normalization=normalization,
silent=silent,
)
@ -254,27 +307,36 @@ def represent(
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.
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
OpenFace, DeepFace, DeepID, Dlib, ArcFace and SFace (default is VGG-Face.).
enforce_detection (boolean): If no face is detected in an image, raise an exception.
Default is True. Set to False to avoid the exception for low-resolution images
(default is True).
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' (default is opencv).
align (boolean): Perform alignment based on the eye positions (default is True).
normalization (string): Normalize the input image before feeding it to the model.
Default is base. Options: base, raw, Facenet, Facenet2018, VGGFace, VGGFace2, ArcFace
(default is base).
Returns:
results (List[Dict[str, Any]]): A list of dictionaries, each containing the
following fields:
- embedding (np.array): Multidimensional vector representing facial features.
The number of dimensions varies based on the reference model
(e.g., FaceNet returns 128 dimensions, VGG-Face returns 4096 dimensions).
- facial_area (dict): Detected facial area by face detection in dictionary format.
Contains 'x' and 'y' as the left-corner point, and 'w' and 'h'
as the width and height. If `detector_backend` is set to 'skip', it represents
the full image area and is nonsensical.
- face_confidence (float): Confidence score of face detection. If `detector_backend` is set
to 'skip', the confidence will be 0 and is nonsensical.
"""
@ -355,19 +417,28 @@ def extract_faces(
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.
target_size (tuple): final shape of facial image. black pixels will be
added to resize the image (default is (224, 224)).
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' (default is opencv)
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' (default is opencv).
enforce_detection (boolean): If no face is detected in an image, raise an exception.
Set to False to avoid the exception for low-resolution images (default is True).
align (bool): Flag to enable face alignment (default is True).
grayscale (boolean): Flag to convert the image to grayscale before
processing (default is False).
Returns:
results (List[Dict[str, Any]]): A list of dictionaries, where each dictionary contains:
- "face" (np.ndarray): The detected face as a NumPy array.
- "facial_area" (List[float]): The detected face's regions represented as a list of floats.
- "confidence" (float): The confidence score associated with the detected face.
"""

2
deepface/detectors/Ssd.py
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@ -12,7 +12,7 @@ from deepface.commons.logger import Logger
logger = Logger(module="detectors.SsdWrapper")
# pylint: disable=line-too-long
# pylint: disable=line-too-long, c-extension-no-member
class SsdClient(Detector):

9
deepface/detectors/YuNet.py
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@ -21,6 +21,13 @@ class YuNetClient(Detector):
Returns:
model (Any)
"""
opencv_version = cv2.__version__.split(".")
if len(opencv_version) > 2 and int(opencv_version[0]) == 4 and int(opencv_version[1]) < 8:
# min requirement: https://github.com/opencv/opencv_zoo/issues/172
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"
@ -67,7 +74,7 @@ class YuNetClient(Detector):
"""
# FaceDetector.detect_faces does not support score_threshold parameter.
# We can set it via environment variable.
score_threshold = os.environ.get("yunet_score_threshold", "0.9")
score_threshold = float(os.environ.get("yunet_score_threshold", "0.9"))
resp = []
detected_face = None
img_region = [0, 0, img.shape[1], img.shape[0]]

26
deepface/modules/recognition.py
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@ -1,7 +1,7 @@
# built-in dependencies
import os
import pickle
from typing import List, Union
from typing import List, Union, Optional
import time
# 3rd party dependencies
@ -25,6 +25,7 @@ def find(
enforce_detection: bool = True,
detector_backend: str = "opencv",
align: bool = True,
threshold: Optional[float] = None,
normalization: str = "base",
silent: bool = False,
) -> List[pd.DataFrame]:
@ -53,6 +54,11 @@ def find(
align (boolean): Perform alignment based on the eye positions.
threshold (float): Specify a threshold to determine whether a pair represents the same
person or different individuals. This threshold is used for comparing distances.
If left unset, default pre-tuned threshold values will be applied based on the specified
model name and distance metric (default is None).
normalization (string): Normalize the input image before feeding it to the model.
Default is base. Options: base, raw, Facenet, Facenet2018, VGGFace, VGGFace2, ArcFace
@ -64,11 +70,16 @@ def find(
The DataFrame columns include:
- 'identity': Identity label of the detected individual.
- 'target_x', 'target_y', 'target_w', 'target_h': Bounding box coordinates of the
target face in the database.
- 'source_x', 'source_y', 'source_w', 'source_h': Bounding box coordinates of the
detected face in the source image.
- '{model_name}_{distance_metric}': Similarity score between the faces based on the
- 'threshold': threshold to determine a pair whether same person or different persons
- 'distance': Similarity score between the faces based on the
specified model and distance metric
"""
@ -248,16 +259,15 @@ def find(
distances.append(distance)
# ---------------------------
target_threshold = threshold or dst.findThreshold(model_name, distance_metric)
result_df[f"{model_name}_{distance_metric}"] = distances
result_df["threshold"] = target_threshold
result_df["distance"] = distances
threshold = dst.findThreshold(model_name, distance_metric)
result_df = result_df.drop(columns=[f"{model_name}_representation"])
# pylint: disable=unsubscriptable-object
result_df = result_df[result_df[f"{model_name}_{distance_metric}"] <= threshold]
result_df = result_df.sort_values(
by=[f"{model_name}_{distance_metric}"], ascending=True
).reset_index(drop=True)
result_df = result_df[result_df["distance"] <= target_threshold]
result_df = result_df.sort_values(by=["distance"], ascending=True).reset_index(drop=True)
resp_obj.append(result_df)

2
setup.py
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@ -8,7 +8,7 @@ with open("requirements.txt", "r", encoding="utf-8") as f:
setuptools.setup(
name="deepface",
version="0.0.82",
version="0.0.83",
author="Sefik Ilkin Serengil",
author_email="serengil@gmail.com",
description="A Lightweight Face Recognition and Facial Attribute Analysis Framework (Age, Gender, Emotion, Race) for Python",

6
tests/test_find.py
View File

@ -21,7 +21,7 @@ def test_find_with_exact_path():
assert identity_df.shape[0] > 0
# validate reproducability
assert identity_df["VGG-Face_cosine"].values[0] < threshold
assert identity_df["distance"].values[0] < threshold
df = df[df["identity"] != img_path]
logger.debug(df.head())
@ -42,7 +42,7 @@ def test_find_with_array_input():
assert identity_df.shape[0] > 0
# validate reproducability
assert identity_df["VGG-Face_cosine"].values[0] < threshold
assert identity_df["distance"].values[0] < threshold
df = df[df["identity"] != img_path]
logger.debug(df.head())
@ -65,7 +65,7 @@ def test_find_with_extracted_faces():
assert identity_df.shape[0] > 0
# validate reproducability
assert identity_df["VGG-Face_cosine"].values[0] < threshold
assert identity_df["distance"].values[0] < threshold
df = df[df["identity"] != img_path]
logger.debug(df.head())

2
tests/visual-test.py
View File

@ -20,7 +20,7 @@ model_names = [
"SFace",
]
detector_backends = ["opencv", "ssd", "dlib", "mtcnn", "retinaface"]
detector_backends = ["opencv", "ssd", "dlib", "mtcnn", "retinaface", "yunet"]
# verification