mirror of
https://github.com/serengil/deepface.git
synced 2025-06-07 03:55:21 +00:00
112 lines
3.3 KiB
Python
112 lines
3.3 KiB
Python
# stdlib dependencies
|
|
from typing import List, Union
|
|
|
|
# 3rd party dependencies
|
|
import numpy as np
|
|
|
|
# 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
|
|
|
|
# pylint: disable=line-too-long
|
|
|
|
# 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/race_model_single_batch.h5"
|
|
)
|
|
# Labels for the ethnic phenotypes that can be detected by the model.
|
|
labels = ["asian", "indian", "black", "white", "middle eastern", "latino hispanic"]
|
|
|
|
logger = Logger()
|
|
|
|
# pylint: disable=too-few-public-methods
|
|
class RaceClient(Demography):
|
|
"""
|
|
Race model class
|
|
"""
|
|
|
|
def __init__(self):
|
|
self.model = load_model()
|
|
self.model_name = "Race"
|
|
|
|
def predict(self, img: Union[np.ndarray, List[np.ndarray]]) -> Union[np.ndarray, np.ndarray]:
|
|
"""
|
|
Predict race probabilities 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:
|
|
Single prediction as np.ndarray (n_races,) [race_probs] or
|
|
Multiple predictions as np.ndarray (n, n_races)
|
|
where n_races is the number of race categories
|
|
"""
|
|
# Convert to numpy array if input is list
|
|
if isinstance(img, list):
|
|
imgs = np.array(img)
|
|
else:
|
|
imgs = img
|
|
|
|
# Remove batch dimension if exists
|
|
imgs = imgs.squeeze()
|
|
|
|
# Check input dimension
|
|
if len(imgs.shape) == 3:
|
|
# Single image - add batch dimension
|
|
imgs = np.expand_dims(imgs, axis=0)
|
|
is_single = True
|
|
else:
|
|
is_single = False
|
|
|
|
# Batch prediction
|
|
predictions = self.model.predict_on_batch(imgs)
|
|
|
|
# Return single prediction for single image
|
|
if is_single:
|
|
return predictions[0]
|
|
return predictions
|
|
|
|
|
|
def load_model(
|
|
url=WEIGHTS_URL,
|
|
) -> Model:
|
|
"""
|
|
Construct race model, download its weights and load
|
|
"""
|
|
|
|
model = VGGFace.base_model()
|
|
|
|
# --------------------------
|
|
|
|
classes = 6
|
|
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)
|
|
|
|
# --------------------------
|
|
|
|
race_model = Model(inputs=model.input, outputs=base_model_output)
|
|
|
|
# --------------------------
|
|
|
|
# load weights
|
|
weight_file = weight_utils.download_weights_if_necessary(
|
|
file_name="race_model_single_batch.h5", source_url=url
|
|
)
|
|
|
|
race_model = weight_utils.load_model_weights(model=race_model, weight_file=weight_file)
|
|
|
|
return race_model
|