from keras.layers import Conv2D, Input,MaxPool2D, Reshape,Activation,Flatten, Dense, Permute from keras.models import Model, Sequential import tensorflow as tf from keras.layers.advanced_activations import PReLU import numpy as np def create_Kao_Onet( weight_path = 'model48.h5'): input = Input(shape = [48,48,3]) x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv1')(input) x = PReLU(shared_axes=[1,2],name='prelu1')(x) x = MaxPool2D(pool_size=3, strides=2, padding='same')(x) x = Conv2D(64, (3, 3), strides=1, padding='valid', name='conv2')(x) x = PReLU(shared_axes=[1,2],name='prelu2')(x) x = MaxPool2D(pool_size=3, strides=2)(x) x = Conv2D(64, (3, 3), strides=1, padding='valid', name='conv3')(x) x = PReLU(shared_axes=[1,2],name='prelu3')(x) x = MaxPool2D(pool_size=2)(x) x = Conv2D(128, (2, 2), strides=1, padding='valid', name='conv4')(x) x = PReLU(shared_axes=[1,2],name='prelu4')(x) x = Permute((3,2,1))(x) x = Flatten()(x) x = Dense(256, name='conv5') (x) x = PReLU(name='prelu5')(x) classifier = Dense(2, activation='softmax',name='conv6-1')(x) bbox_regress = Dense(4,name='conv6-2')(x) landmark_regress = Dense(10,name='conv6-3')(x) model = Model([input], [classifier, bbox_regress, landmark_regress]) model.load_weights(weight_path, by_name=True) return model def create_Kao_Rnet (weight_path = 'model24.h5'): input = Input(shape=[24, 24, 3]) # change this shape to [None,None,3] to enable arbitraty shape input x = Conv2D(28, (3, 3), strides=1, padding='valid', name='conv1')(input) x = PReLU(shared_axes=[1, 2], name='prelu1')(x) x = MaxPool2D(pool_size=3,strides=2, padding='same')(x) x = Conv2D(48, (3, 3), strides=1, padding='valid', name='conv2')(x) x = PReLU(shared_axes=[1, 2], name='prelu2')(x) x = MaxPool2D(pool_size=3, strides=2)(x) x = Conv2D(64, (2, 2), strides=1, padding='valid', name='conv3')(x) x = PReLU(shared_axes=[1, 2], name='prelu3')(x) x = Permute((3, 2, 1))(x) x = Flatten()(x) x = Dense(128, name='conv4')(x) x = PReLU( name='prelu4')(x) classifier = Dense(2, activation='softmax', name='conv5-1')(x) bbox_regress = Dense(4, name='conv5-2')(x) model = Model([input], [classifier, bbox_regress]) model.load_weights(weight_path, by_name=True) return model def create_Kao_Pnet( weight_path = 'model12old.h5'): input = Input(shape=[None, None, 3]) x = Conv2D(10, (3, 3), strides=1, padding='valid', name='conv1')(input) x = PReLU(shared_axes=[1,2],name='PReLU1')(x) x = MaxPool2D(pool_size=2)(x) x = Conv2D(16, (3, 3), strides=1, padding='valid', name='conv2')(x) x = PReLU(shared_axes=[1,2],name='PReLU2')(x) x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv3')(x) x = PReLU(shared_axes=[1,2],name='PReLU3')(x) classifier = Conv2D(2, (1, 1), activation='softmax', name='conv4-1')(x) bbox_regress = Conv2D(4, (1, 1), name='conv4-2')(x) model = Model([input], [classifier, bbox_regress]) model.load_weights(weight_path, by_name=True) return model