117 lines
3.4 KiB
Python
117 lines
3.4 KiB
Python
# stdlib dependencies
|
|
from typing import List, Union, Any, cast
|
|
|
|
# 3rd party dependencies
|
|
import numpy as np
|
|
from numpy.typing import NDArray
|
|
|
|
# project dependencies
|
|
from deepface.models.facial_recognition import VGGFace
|
|
from deepface.commons import package_utils, weight_utils
|
|
from deepface.models.Demography import Demography
|
|
from deepface.commons.logger import Logger
|
|
|
|
logger = Logger()
|
|
|
|
# dependency configurations
|
|
|
|
tf_version = package_utils.get_tf_major_version()
|
|
|
|
if tf_version == 1:
|
|
from keras.models import Model, Sequential
|
|
from keras.layers import Convolution2D, Flatten, Activation
|
|
else:
|
|
from tensorflow.keras.models import Model, Sequential
|
|
from tensorflow.keras.layers import Convolution2D, Flatten, Activation
|
|
|
|
WEIGHTS_URL = (
|
|
"https://github.com/serengil/deepface_models/releases/download/v1.0/age_model_weights.h5"
|
|
)
|
|
|
|
|
|
# pylint: disable=too-few-public-methods
|
|
class ApparentAgeClient(Demography):
|
|
"""
|
|
Age model class
|
|
"""
|
|
|
|
def __init__(self) -> None:
|
|
self.model = load_model()
|
|
self.model_name = "Age"
|
|
|
|
def predict(
|
|
self, img: Union[NDArray[Any], List[NDArray[Any]]]
|
|
) -> Union[np.float64, NDArray[Any]]:
|
|
"""
|
|
Predict apparent age(s) for single or multiple faces
|
|
Args:
|
|
img: Single image as np.ndarray (224, 224, 3) or
|
|
List of images as List[np.ndarray] or
|
|
Batch of images as np.ndarray (n, 224, 224, 3)
|
|
Returns:
|
|
np.ndarray (age_classes,) if single image,
|
|
np.ndarray (n, age_classes) if batched images.
|
|
"""
|
|
# Preprocessing input image or image list.
|
|
imgs = self._preprocess_batch_or_single_input(img)
|
|
|
|
# Prediction from 3 channels image
|
|
age_predictions = self._predict_internal(imgs)
|
|
|
|
# Calculate apparent ages
|
|
if len(age_predictions.shape) == 1: # Single prediction list
|
|
return find_apparent_age(age_predictions)
|
|
|
|
return np.array([find_apparent_age(age_prediction) for age_prediction in age_predictions])
|
|
|
|
|
|
def load_model(
|
|
url: str = WEIGHTS_URL,
|
|
) -> Model:
|
|
"""
|
|
Construct age model, download its weights and load
|
|
Returns:
|
|
model (Model)
|
|
"""
|
|
|
|
model = VGGFace.base_model()
|
|
|
|
# --------------------------
|
|
|
|
classes = 101
|
|
base_model_output = Sequential()
|
|
base_model_output = Convolution2D(classes, (1, 1), name="predictions")(model.layers[-4].output)
|
|
base_model_output = Flatten()(base_model_output)
|
|
base_model_output = Activation("softmax")(base_model_output)
|
|
|
|
# --------------------------
|
|
|
|
age_model = Model(inputs=model.inputs, outputs=base_model_output)
|
|
|
|
# --------------------------
|
|
|
|
# load weights
|
|
weight_file = weight_utils.download_weights_if_necessary(
|
|
file_name="age_model_weights.h5", source_url=url
|
|
)
|
|
|
|
age_model = weight_utils.load_model_weights(model=age_model, weight_file=weight_file)
|
|
|
|
return age_model
|
|
|
|
|
|
def find_apparent_age(age_predictions: NDArray[Any]) -> np.float64:
|
|
"""
|
|
Find apparent age prediction from a given probas of ages
|
|
Args:
|
|
age_predictions (age_classes,)
|
|
Returns:
|
|
apparent_age (float)
|
|
"""
|
|
assert (
|
|
len(age_predictions.shape) == 1
|
|
), f"Input should be a list of predictions, not batched. Got shape: {age_predictions.shape}"
|
|
output_indexes = np.arange(0, 101)
|
|
apparent_age = cast(np.float64, np.sum(age_predictions * output_indexes))
|
|
return apparent_age
|