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https://github.com/serengil/deepface.git
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412 lines
17 KiB
Python
412 lines
17 KiB
Python
from typing import List
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import os
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import gdown
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import tensorflow as tf
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import numpy as np
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from deepface.commons import package_utils, folder_utils
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from deepface.commons.logger import Logger
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from deepface.models.FacialRecognition import FacialRecognition
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logger = Logger(module="basemodels.OpenFace")
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tf_version = package_utils.get_tf_major_version()
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if tf_version == 1:
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from keras.models import Model
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from keras.layers import Conv2D, ZeroPadding2D, Input, concatenate
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from keras.layers import Dense, Activation, Lambda, Flatten, BatchNormalization
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from keras.layers import MaxPooling2D, AveragePooling2D
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from keras import backend as K
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else:
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from tensorflow.keras.models import Model
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from tensorflow.keras.layers import Conv2D, ZeroPadding2D, Input, concatenate
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from tensorflow.keras.layers import Dense, Activation, Lambda, Flatten, BatchNormalization
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from tensorflow.keras.layers import MaxPooling2D, AveragePooling2D
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from tensorflow.keras import backend as K
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# pylint: disable=unnecessary-lambda
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# ---------------------------------------
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# pylint: disable=too-few-public-methods
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class OpenFaceClient(FacialRecognition):
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"""
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OpenFace model class
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"""
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def __init__(self):
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self.model = load_model()
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self.model_name = "OpenFace"
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self.input_shape = (96, 96)
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self.output_shape = 128
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def find_embeddings(self, img: np.ndarray) -> List[float]:
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"""
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find embeddings with OpenFace model
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Args:
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img (np.ndarray): pre-loaded image in BGR
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Returns
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embeddings (list): multi-dimensional vector
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"""
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# model.predict causes memory issue when it is called in a for loop
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# embedding = model.predict(img, verbose=0)[0].tolist()
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return self.model(img, training=False).numpy()[0].tolist()
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def load_model(
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url="https://github.com/serengil/deepface_models/releases/download/v1.0/openface_weights.h5",
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) -> Model:
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"""
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Consturct OpenFace model, download its weights and load
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Returns:
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model (Model)
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"""
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myInput = Input(shape=(96, 96, 3))
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x = ZeroPadding2D(padding=(3, 3), input_shape=(96, 96, 3))(myInput)
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x = Conv2D(64, (7, 7), strides=(2, 2), name="conv1")(x)
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x = BatchNormalization(axis=3, epsilon=0.00001, name="bn1")(x)
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x = Activation("relu")(x)
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x = ZeroPadding2D(padding=(1, 1))(x)
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x = MaxPooling2D(pool_size=3, strides=2)(x)
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x = Lambda(lambda x: tf.nn.lrn(x, alpha=1e-4, beta=0.75), name="lrn_1")(x)
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x = Conv2D(64, (1, 1), name="conv2")(x)
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x = BatchNormalization(axis=3, epsilon=0.00001, name="bn2")(x)
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x = Activation("relu")(x)
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x = ZeroPadding2D(padding=(1, 1))(x)
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x = Conv2D(192, (3, 3), name="conv3")(x)
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x = BatchNormalization(axis=3, epsilon=0.00001, name="bn3")(x)
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x = Activation("relu")(x)
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x = Lambda(lambda x: tf.nn.lrn(x, alpha=1e-4, beta=0.75), name="lrn_2")(x) # x is equal added
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x = ZeroPadding2D(padding=(1, 1))(x)
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x = MaxPooling2D(pool_size=3, strides=2)(x)
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# Inception3a
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inception_3a_3x3 = Conv2D(96, (1, 1), name="inception_3a_3x3_conv1")(x)
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inception_3a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3a_3x3_bn1")(
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inception_3a_3x3
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)
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inception_3a_3x3 = Activation("relu")(inception_3a_3x3)
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inception_3a_3x3 = ZeroPadding2D(padding=(1, 1))(inception_3a_3x3)
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inception_3a_3x3 = Conv2D(128, (3, 3), name="inception_3a_3x3_conv2")(inception_3a_3x3)
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inception_3a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3a_3x3_bn2")(
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inception_3a_3x3
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)
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inception_3a_3x3 = Activation("relu")(inception_3a_3x3)
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inception_3a_5x5 = Conv2D(16, (1, 1), name="inception_3a_5x5_conv1")(x)
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inception_3a_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3a_5x5_bn1")(
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inception_3a_5x5
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)
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inception_3a_5x5 = Activation("relu")(inception_3a_5x5)
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inception_3a_5x5 = ZeroPadding2D(padding=(2, 2))(inception_3a_5x5)
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inception_3a_5x5 = Conv2D(32, (5, 5), name="inception_3a_5x5_conv2")(inception_3a_5x5)
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inception_3a_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3a_5x5_bn2")(
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inception_3a_5x5
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)
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inception_3a_5x5 = Activation("relu")(inception_3a_5x5)
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inception_3a_pool = MaxPooling2D(pool_size=3, strides=2)(x)
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inception_3a_pool = Conv2D(32, (1, 1), name="inception_3a_pool_conv")(inception_3a_pool)
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inception_3a_pool = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3a_pool_bn")(
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inception_3a_pool
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)
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inception_3a_pool = Activation("relu")(inception_3a_pool)
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inception_3a_pool = ZeroPadding2D(padding=((3, 4), (3, 4)))(inception_3a_pool)
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inception_3a_1x1 = Conv2D(64, (1, 1), name="inception_3a_1x1_conv")(x)
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inception_3a_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3a_1x1_bn")(
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inception_3a_1x1
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)
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inception_3a_1x1 = Activation("relu")(inception_3a_1x1)
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inception_3a = concatenate(
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[inception_3a_3x3, inception_3a_5x5, inception_3a_pool, inception_3a_1x1], axis=3
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)
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# Inception3b
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inception_3b_3x3 = Conv2D(96, (1, 1), name="inception_3b_3x3_conv1")(inception_3a)
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inception_3b_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3b_3x3_bn1")(
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inception_3b_3x3
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)
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inception_3b_3x3 = Activation("relu")(inception_3b_3x3)
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inception_3b_3x3 = ZeroPadding2D(padding=(1, 1))(inception_3b_3x3)
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inception_3b_3x3 = Conv2D(128, (3, 3), name="inception_3b_3x3_conv2")(inception_3b_3x3)
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inception_3b_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3b_3x3_bn2")(
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inception_3b_3x3
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)
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inception_3b_3x3 = Activation("relu")(inception_3b_3x3)
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inception_3b_5x5 = Conv2D(32, (1, 1), name="inception_3b_5x5_conv1")(inception_3a)
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inception_3b_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3b_5x5_bn1")(
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inception_3b_5x5
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)
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inception_3b_5x5 = Activation("relu")(inception_3b_5x5)
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inception_3b_5x5 = ZeroPadding2D(padding=(2, 2))(inception_3b_5x5)
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inception_3b_5x5 = Conv2D(64, (5, 5), name="inception_3b_5x5_conv2")(inception_3b_5x5)
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inception_3b_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3b_5x5_bn2")(
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inception_3b_5x5
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)
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inception_3b_5x5 = Activation("relu")(inception_3b_5x5)
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inception_3b_pool = Lambda(lambda x: x**2, name="power2_3b")(inception_3a)
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inception_3b_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(inception_3b_pool)
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inception_3b_pool = Lambda(lambda x: x * 9, name="mult9_3b")(inception_3b_pool)
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inception_3b_pool = Lambda(lambda x: K.sqrt(x), name="sqrt_3b")(inception_3b_pool)
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inception_3b_pool = Conv2D(64, (1, 1), name="inception_3b_pool_conv")(inception_3b_pool)
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inception_3b_pool = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3b_pool_bn")(
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inception_3b_pool
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)
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inception_3b_pool = Activation("relu")(inception_3b_pool)
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inception_3b_pool = ZeroPadding2D(padding=(4, 4))(inception_3b_pool)
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inception_3b_1x1 = Conv2D(64, (1, 1), name="inception_3b_1x1_conv")(inception_3a)
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inception_3b_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3b_1x1_bn")(
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inception_3b_1x1
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)
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inception_3b_1x1 = Activation("relu")(inception_3b_1x1)
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inception_3b = concatenate(
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[inception_3b_3x3, inception_3b_5x5, inception_3b_pool, inception_3b_1x1], axis=3
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)
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# Inception3c
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inception_3c_3x3 = Conv2D(128, (1, 1), strides=(1, 1), name="inception_3c_3x3_conv1")(
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inception_3b
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)
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inception_3c_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3c_3x3_bn1")(
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inception_3c_3x3
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)
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inception_3c_3x3 = Activation("relu")(inception_3c_3x3)
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inception_3c_3x3 = ZeroPadding2D(padding=(1, 1))(inception_3c_3x3)
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inception_3c_3x3 = Conv2D(256, (3, 3), strides=(2, 2), name="inception_3c_3x3_conv" + "2")(
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inception_3c_3x3
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)
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inception_3c_3x3 = BatchNormalization(
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axis=3, epsilon=0.00001, name="inception_3c_3x3_bn" + "2"
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)(inception_3c_3x3)
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inception_3c_3x3 = Activation("relu")(inception_3c_3x3)
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inception_3c_5x5 = Conv2D(32, (1, 1), strides=(1, 1), name="inception_3c_5x5_conv1")(
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inception_3b
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)
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inception_3c_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_3c_5x5_bn1")(
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inception_3c_5x5
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)
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inception_3c_5x5 = Activation("relu")(inception_3c_5x5)
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inception_3c_5x5 = ZeroPadding2D(padding=(2, 2))(inception_3c_5x5)
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inception_3c_5x5 = Conv2D(64, (5, 5), strides=(2, 2), name="inception_3c_5x5_conv" + "2")(
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inception_3c_5x5
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)
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inception_3c_5x5 = BatchNormalization(
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axis=3, epsilon=0.00001, name="inception_3c_5x5_bn" + "2"
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)(inception_3c_5x5)
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inception_3c_5x5 = Activation("relu")(inception_3c_5x5)
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inception_3c_pool = MaxPooling2D(pool_size=3, strides=2)(inception_3b)
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inception_3c_pool = ZeroPadding2D(padding=((0, 1), (0, 1)))(inception_3c_pool)
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inception_3c = concatenate([inception_3c_3x3, inception_3c_5x5, inception_3c_pool], axis=3)
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# inception 4a
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inception_4a_3x3 = Conv2D(96, (1, 1), strides=(1, 1), name="inception_4a_3x3_conv" + "1")(
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inception_3c
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)
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inception_4a_3x3 = BatchNormalization(
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axis=3, epsilon=0.00001, name="inception_4a_3x3_bn" + "1"
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)(inception_4a_3x3)
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inception_4a_3x3 = Activation("relu")(inception_4a_3x3)
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inception_4a_3x3 = ZeroPadding2D(padding=(1, 1))(inception_4a_3x3)
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inception_4a_3x3 = Conv2D(192, (3, 3), strides=(1, 1), name="inception_4a_3x3_conv" + "2")(
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inception_4a_3x3
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)
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inception_4a_3x3 = BatchNormalization(
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axis=3, epsilon=0.00001, name="inception_4a_3x3_bn" + "2"
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)(inception_4a_3x3)
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inception_4a_3x3 = Activation("relu")(inception_4a_3x3)
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inception_4a_5x5 = Conv2D(32, (1, 1), strides=(1, 1), name="inception_4a_5x5_conv1")(
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inception_3c
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)
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inception_4a_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_4a_5x5_bn1")(
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inception_4a_5x5
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)
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inception_4a_5x5 = Activation("relu")(inception_4a_5x5)
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inception_4a_5x5 = ZeroPadding2D(padding=(2, 2))(inception_4a_5x5)
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inception_4a_5x5 = Conv2D(64, (5, 5), strides=(1, 1), name="inception_4a_5x5_conv" + "2")(
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inception_4a_5x5
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)
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inception_4a_5x5 = BatchNormalization(
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axis=3, epsilon=0.00001, name="inception_4a_5x5_bn" + "2"
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)(inception_4a_5x5)
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inception_4a_5x5 = Activation("relu")(inception_4a_5x5)
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inception_4a_pool = Lambda(lambda x: x**2, name="power2_4a")(inception_3c)
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inception_4a_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(inception_4a_pool)
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inception_4a_pool = Lambda(lambda x: x * 9, name="mult9_4a")(inception_4a_pool)
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inception_4a_pool = Lambda(lambda x: K.sqrt(x), name="sqrt_4a")(inception_4a_pool)
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inception_4a_pool = Conv2D(128, (1, 1), strides=(1, 1), name="inception_4a_pool_conv" + "")(
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inception_4a_pool
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)
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inception_4a_pool = BatchNormalization(
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axis=3, epsilon=0.00001, name="inception_4a_pool_bn" + ""
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)(inception_4a_pool)
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inception_4a_pool = Activation("relu")(inception_4a_pool)
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inception_4a_pool = ZeroPadding2D(padding=(2, 2))(inception_4a_pool)
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inception_4a_1x1 = Conv2D(256, (1, 1), strides=(1, 1), name="inception_4a_1x1_conv" + "")(
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inception_3c
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)
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inception_4a_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_4a_1x1_bn" + "")(
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inception_4a_1x1
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)
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inception_4a_1x1 = Activation("relu")(inception_4a_1x1)
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inception_4a = concatenate(
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[inception_4a_3x3, inception_4a_5x5, inception_4a_pool, inception_4a_1x1], axis=3
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)
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# inception4e
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inception_4e_3x3 = Conv2D(160, (1, 1), strides=(1, 1), name="inception_4e_3x3_conv" + "1")(
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inception_4a
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)
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inception_4e_3x3 = BatchNormalization(
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axis=3, epsilon=0.00001, name="inception_4e_3x3_bn" + "1"
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)(inception_4e_3x3)
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inception_4e_3x3 = Activation("relu")(inception_4e_3x3)
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inception_4e_3x3 = ZeroPadding2D(padding=(1, 1))(inception_4e_3x3)
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inception_4e_3x3 = Conv2D(256, (3, 3), strides=(2, 2), name="inception_4e_3x3_conv" + "2")(
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inception_4e_3x3
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)
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inception_4e_3x3 = BatchNormalization(
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axis=3, epsilon=0.00001, name="inception_4e_3x3_bn" + "2"
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)(inception_4e_3x3)
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inception_4e_3x3 = Activation("relu")(inception_4e_3x3)
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inception_4e_5x5 = Conv2D(64, (1, 1), strides=(1, 1), name="inception_4e_5x5_conv" + "1")(
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inception_4a
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)
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inception_4e_5x5 = BatchNormalization(
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axis=3, epsilon=0.00001, name="inception_4e_5x5_bn" + "1"
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)(inception_4e_5x5)
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inception_4e_5x5 = Activation("relu")(inception_4e_5x5)
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inception_4e_5x5 = ZeroPadding2D(padding=(2, 2))(inception_4e_5x5)
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inception_4e_5x5 = Conv2D(128, (5, 5), strides=(2, 2), name="inception_4e_5x5_conv" + "2")(
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inception_4e_5x5
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)
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inception_4e_5x5 = BatchNormalization(
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axis=3, epsilon=0.00001, name="inception_4e_5x5_bn" + "2"
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)(inception_4e_5x5)
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inception_4e_5x5 = Activation("relu")(inception_4e_5x5)
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inception_4e_pool = MaxPooling2D(pool_size=3, strides=2)(inception_4a)
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inception_4e_pool = ZeroPadding2D(padding=((0, 1), (0, 1)))(inception_4e_pool)
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inception_4e = concatenate([inception_4e_3x3, inception_4e_5x5, inception_4e_pool], axis=3)
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# inception5a
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inception_5a_3x3 = Conv2D(96, (1, 1), strides=(1, 1), name="inception_5a_3x3_conv" + "1")(
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inception_4e
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)
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inception_5a_3x3 = BatchNormalization(
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axis=3, epsilon=0.00001, name="inception_5a_3x3_bn" + "1"
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)(inception_5a_3x3)
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inception_5a_3x3 = Activation("relu")(inception_5a_3x3)
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inception_5a_3x3 = ZeroPadding2D(padding=(1, 1))(inception_5a_3x3)
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inception_5a_3x3 = Conv2D(384, (3, 3), strides=(1, 1), name="inception_5a_3x3_conv" + "2")(
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inception_5a_3x3
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)
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inception_5a_3x3 = BatchNormalization(
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axis=3, epsilon=0.00001, name="inception_5a_3x3_bn" + "2"
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)(inception_5a_3x3)
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inception_5a_3x3 = Activation("relu")(inception_5a_3x3)
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inception_5a_pool = Lambda(lambda x: x**2, name="power2_5a")(inception_4e)
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inception_5a_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(inception_5a_pool)
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inception_5a_pool = Lambda(lambda x: x * 9, name="mult9_5a")(inception_5a_pool)
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inception_5a_pool = Lambda(lambda x: K.sqrt(x), name="sqrt_5a")(inception_5a_pool)
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|
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inception_5a_pool = Conv2D(96, (1, 1), strides=(1, 1), name="inception_5a_pool_conv" + "")(
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|
inception_5a_pool
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|
)
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inception_5a_pool = BatchNormalization(
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axis=3, epsilon=0.00001, name="inception_5a_pool_bn" + ""
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)(inception_5a_pool)
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inception_5a_pool = Activation("relu")(inception_5a_pool)
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|
inception_5a_pool = ZeroPadding2D(padding=(1, 1))(inception_5a_pool)
|
|
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|
inception_5a_1x1 = Conv2D(256, (1, 1), strides=(1, 1), name="inception_5a_1x1_conv" + "")(
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|
inception_4e
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|
)
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|
inception_5a_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_5a_1x1_bn" + "")(
|
|
inception_5a_1x1
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|
)
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|
inception_5a_1x1 = Activation("relu")(inception_5a_1x1)
|
|
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|
inception_5a = concatenate([inception_5a_3x3, inception_5a_pool, inception_5a_1x1], axis=3)
|
|
|
|
# inception_5b
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|
inception_5b_3x3 = Conv2D(96, (1, 1), strides=(1, 1), name="inception_5b_3x3_conv" + "1")(
|
|
inception_5a
|
|
)
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|
inception_5b_3x3 = BatchNormalization(
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|
axis=3, epsilon=0.00001, name="inception_5b_3x3_bn" + "1"
|
|
)(inception_5b_3x3)
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|
inception_5b_3x3 = Activation("relu")(inception_5b_3x3)
|
|
inception_5b_3x3 = ZeroPadding2D(padding=(1, 1))(inception_5b_3x3)
|
|
inception_5b_3x3 = Conv2D(384, (3, 3), strides=(1, 1), name="inception_5b_3x3_conv" + "2")(
|
|
inception_5b_3x3
|
|
)
|
|
inception_5b_3x3 = BatchNormalization(
|
|
axis=3, epsilon=0.00001, name="inception_5b_3x3_bn" + "2"
|
|
)(inception_5b_3x3)
|
|
inception_5b_3x3 = Activation("relu")(inception_5b_3x3)
|
|
|
|
inception_5b_pool = MaxPooling2D(pool_size=3, strides=2)(inception_5a)
|
|
|
|
inception_5b_pool = Conv2D(96, (1, 1), strides=(1, 1), name="inception_5b_pool_conv" + "")(
|
|
inception_5b_pool
|
|
)
|
|
inception_5b_pool = BatchNormalization(
|
|
axis=3, epsilon=0.00001, name="inception_5b_pool_bn" + ""
|
|
)(inception_5b_pool)
|
|
inception_5b_pool = Activation("relu")(inception_5b_pool)
|
|
|
|
inception_5b_pool = ZeroPadding2D(padding=(1, 1))(inception_5b_pool)
|
|
|
|
inception_5b_1x1 = Conv2D(256, (1, 1), strides=(1, 1), name="inception_5b_1x1_conv" + "")(
|
|
inception_5a
|
|
)
|
|
inception_5b_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name="inception_5b_1x1_bn" + "")(
|
|
inception_5b_1x1
|
|
)
|
|
inception_5b_1x1 = Activation("relu")(inception_5b_1x1)
|
|
|
|
inception_5b = concatenate([inception_5b_3x3, inception_5b_pool, inception_5b_1x1], axis=3)
|
|
|
|
av_pool = AveragePooling2D(pool_size=(3, 3), strides=(1, 1))(inception_5b)
|
|
reshape_layer = Flatten()(av_pool)
|
|
dense_layer = Dense(128, name="dense_layer")(reshape_layer)
|
|
norm_layer = Lambda(lambda x: K.l2_normalize(x, axis=1), name="norm_layer")(dense_layer)
|
|
|
|
# Final Model
|
|
model = Model(inputs=[myInput], outputs=norm_layer)
|
|
|
|
# -----------------------------------
|
|
|
|
home = folder_utils.get_deepface_home()
|
|
|
|
if os.path.isfile(home + "/.deepface/weights/openface_weights.h5") != True:
|
|
logger.info("openface_weights.h5 will be downloaded...")
|
|
|
|
output = home + "/.deepface/weights/openface_weights.h5"
|
|
gdown.download(url, output, quiet=False)
|
|
|
|
# -----------------------------------
|
|
|
|
model.load_weights(home + "/.deepface/weights/openface_weights.h5")
|
|
|
|
# -----------------------------------
|
|
|
|
return model
|