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# built-in dependencies
from typing import Any, Dict, List, Union
# 3rd party dependencies
import numpy as np
from tqdm import tqdm
# project dependencies
from deepface.modules import modeling, detection, preprocessing
from deepface.extendedmodels import Gender, Race, Emotion
def analyze(
img_path: Union[str, np.ndarray],
actions: Union[tuple, list] = ("emotion", "age", "gender", "race"),
enforce_detection: bool = True,
detector_backend: str = "opencv",
align: bool = True,
expand_percentage: int = 0,
silent: bool = False,
) -> List[Dict[str, Any]]:
"""
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.
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', 'centerface' or 'skip'
(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).
expand_percentage (int): expand detected facial area with a percentage (default is 0).
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."
- '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."
- 'emotion' (dict): Confidence scores for each emotion category.
- 'sad': Confidence score for sadness.
- 'angry': Confidence score for anger.
- 'surprise': Confidence score for surprise.
- 'fear': Confidence score for fear.
- '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.
- 'latino hispanic': Confidence score for Latino/Hispanic ethnicity.
- 'black': Confidence score for Black ethnicity.
- 'middle eastern': Confidence score for Middle Eastern ethnicity.
- 'white': Confidence score for White ethnicity.
"""
# if actions is passed as tuple with single item, interestingly it becomes str here
if isinstance(actions, str):
actions = (actions,)
# check if actions is not an iterable or empty.
if not hasattr(actions, "__getitem__") or not actions:
raise ValueError("`actions` must be a list of strings.")
actions = list(actions)
# For each action, check if it is valid
for action in actions:
if action not in ("emotion", "age", "gender", "race"):
raise ValueError(
f"Invalid action passed ({repr(action)})). "
"Valid actions are `emotion`, `age`, `gender`, `race`."
)
# ---------------------------------
resp_objects = []
img_objs = detection.extract_faces(
img_path=img_path,
detector_backend=detector_backend,
grayscale=False,
enforce_detection=enforce_detection,
align=align,
expand_percentage=expand_percentage,
)
for img_obj in img_objs:
img_content = img_obj["face"]
img_region = img_obj["facial_area"]
img_confidence = img_obj["confidence"]
if img_content.shape[0] == 0 or img_content.shape[1] == 0:
continue
# rgb to bgr
img_content = img_content[:, :, ::-1]
# resize input image
img_content = preprocessing.resize_image(img=img_content, target_size=(224, 224))
obj = {}
# facial attribute analysis
pbar = tqdm(
range(0, len(actions)),
desc="Finding actions",
disable=silent if len(actions) > 1 else True,
)
for index in pbar:
action = actions[index]
pbar.set_description(f"Action: {action}")
if action == "emotion":
emotion_predictions = modeling.build_model("Emotion").predict(img_content)
sum_of_predictions = emotion_predictions.sum()
obj["emotion"] = {}
for i, emotion_label in enumerate(Emotion.labels):
emotion_prediction = 100 * emotion_predictions[i] / sum_of_predictions
obj["emotion"][emotion_label] = emotion_prediction
obj["dominant_emotion"] = Emotion.labels[np.argmax(emotion_predictions)]
elif action == "age":
apparent_age = modeling.build_model("Age").predict(img_content)
# int cast is for exception - object of type 'float32' is not JSON serializable
obj["age"] = int(apparent_age)
elif action == "gender":
gender_predictions = modeling.build_model("Gender").predict(img_content)
obj["gender"] = {}
for i, gender_label in enumerate(Gender.labels):
gender_prediction = 100 * gender_predictions[i]
obj["gender"][gender_label] = gender_prediction
obj["dominant_gender"] = Gender.labels[np.argmax(gender_predictions)]
elif action == "race":
race_predictions = modeling.build_model("Race").predict(img_content)
sum_of_predictions = race_predictions.sum()
obj["race"] = {}
for i, race_label in enumerate(Race.labels):
race_prediction = 100 * race_predictions[i] / sum_of_predictions
obj["race"][race_label] = race_prediction
obj["dominant_race"] = Race.labels[np.argmax(race_predictions)]
# -----------------------------
# mention facial areas
obj["region"] = img_region
# include image confidence
obj["face_confidence"] = img_confidence
resp_objects.append(obj)
return resp_objects
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