基于DeeplabV3+Xception+Unet实现遥感图像的语义分割python源码+项目说明(毕业设计).zip

# -*- coding: utf-8 -*- """ Deeplabv3+ model for Keras. This model is based on TF repo: https://github.com/tensorflow/models/tree/master/research/deeplab On Pascal VOC, original model gets to 84.56% mIOU MobileNetv2 backbone is based on this repo: https://github.com/JonathanCMitchell/mobilenet_v2_keras # Reference - [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://arxiv.org/pdf/1802.02611.pdf) - [Xception: Deep Learning with Depthwise Separable Convolutions] (https://arxiv.org/abs/1610.02357) - [Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation](https://arxiv.org/abs/1801.04381) """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from keras.models import Model,load_model from keras import layers from keras.layers import Input,Activation,Concatenate,Add,Dropout,BatchNormalization,Conv2D,MaxPooling2D,UpSampling2D from keras.layers import DepthwiseConv2D,ZeroPadding2D,AveragePooling2D from keras.engine import Layer,InputSpec from keras.engine.topology import get_source_inputs from keras.optimizers import Adam from keras import backend as K from keras.applications import imagenet_utils from keras.utils import conv_utils,plot_model from keras.utils.data_utils import get_file #WEIGHTS_PATH_X = "https://github.com/bonlime/keras-deeplab-v3-plus/releases/download/1.1/deeplabv3_xception_tf_dim_ordering_tf_kernels.h5" #WEIGHTS_PATH_MOBILE = "https://github.com/bonlime/keras-deeplab-v3-plus/releases/download/1.1/deeplabv3_mobilenetv2_tf_dim_ordering_tf_kernels.h5" smooth = 1e-12 class BilinearUpsampling(Layer): """Just a simple bilinear upsampling layer. Works only with TF. Args: upsampling: tuple of 2 numbers > 0. The upsampling ratio for h and w output_size: used instead of upsampling arg if passed! """ def __init__(self, upsampling=(2, 2), output_size=None, data_format=None, **kwargs): super(BilinearUpsampling, self).__init__(**kwargs) self.data_format = conv_utils.normalize_data_format(data_format) self.input_spec = InputSpec(ndim=4) if output_size: self.output_size = conv_utils.normalize_tuple(output_size, 2, 'output_size') self.upsampling = None else: self.output_size = None self.upsampling = conv_utils.normalize_tuple(upsampling, 2, 'upsampling') def compute_output_shape(self, input_shape): if self.upsampling: height = self.upsampling[0] * input_shape[1] if input_shape[1] is not None else None width = self.upsampling[1] * input_shape[2] if input_shape[2] is not None else None else: height = self.output_size[0] width = self.output_size[1] return (input_shape[0],height,width,input_shape[3]) def call(self, inputs): if self.upsampling: return K.tf.image.resize_bilinear(inputs, (inputs.shape[1] * self.upsampling[0],inputs.shape[2] * self.upsampling[1]),align_corners=True) else: return K.tf.image.resize_bilinear(inputs, (self.output_size[0],self.output_size[1]),align_corners=True) def get_config(self): config = {'upsampling': self.upsampling,'output_size': self.output_size,'data_format': self.data_format} base_config = super(BilinearUpsampling, self).get_config() return dict(list(base_config.items()) + list(config.items())) def SepConv_BN(x, filters, prefix, stride=1, kernel_size=3, rate=1, depth_activation=False, epsilon=1e-3): """ SepConv with BN between depthwise & pointwise. Optionally add activation after BN Implements right "same" padding for even kernel sizes Args: x: input tensor filters: num of filters in pointwise convolution prefix: prefix before name stride: stride at depthwise conv kernel_size: kernel size for depthwise convolution rate: atrous rate for depthwise convolution depth_activation: flag to use activation between depthwise & poinwise convs epsilon: epsilon to use in BN layer """ if stride == 1: depth_padding = 'same' else: kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1) pad_total = kernel_size_effective - 1 pad_beg = pad_total // 2 pad_end = pad_total - pad_beg x = ZeroPadding2D((pad_beg, pad_end))(x) depth_padding = 'valid' if not depth_activation: x = Activation('relu')(x) x = DepthwiseConv2D((kernel_size, kernel_size), strides=(stride, stride), dilation_rate=(rate, rate), padding=depth_padding, use_bias=False, name=prefix + '_depthwise')(x) x = BatchNormalization(name=prefix + '_depthwise_BN', epsilon=epsilon)(x) if depth_activation: x = Activation('relu')(x) x = Conv2D(filters, (1, 1), padding='same', use_bias=False, name=prefix + '_pointwise')(x) x = BatchNormalization(name=prefix + '_pointwise_BN', epsilon=epsilon)(x) if depth_activation: x = Activation('relu')(x) return x def jaccard_coef(y_true, y_pred): intersection = K.sum(y_true * y_pred, axis=[0, -1, -2]) sum_ = K.sum(y_true + y_pred, axis=[0, -1, -2]) jac = (intersection + smooth) / (sum_ - intersection + smooth) jac_mean=K.mean(jac) print(jac_mean) return jac_mean def _conv2d_same(x, filters, prefix, stride=1, kernel_size=3, rate=1): """Implements right 'same' padding for even kernel sizes Without this there is a 1 pixel drift when stride = 2 Args: x: input tensor filters: num of filters in pointwise convolution prefix: prefix before name stride: stride at depthwise conv kernel_size: kernel size for depthwise convolution rate: atrous rate for depthwise convolution """ if stride == 1: return Conv2D(filters,(kernel_size, kernel_size),strides=(stride, stride), padding='same', use_bias=False,dilation_rate=(rate, rate),name=prefix)(x) else: kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1) pad_total = kernel_size_effective - 1 pad_beg = pad_total // 2 pad_end = pad_total - pad_beg x = ZeroPadding2D((pad_beg, pad_end))(x) return Conv2D(filters,(kernel_size, kernel_size),strides=(stride, stride), padding='valid', use_bias=False,dilation_rate=(rate, rate),name=prefix)(x) def _xception_block(inputs, depth_list, prefix, skip_connection_type, stride,rate=1, depth_activation=False, return_skip=False): """ Basic building block of modified Xception network Args: inputs: input tensor depth_list: number of filters in each SepConv layer. len(depth_list) == 3 prefix: prefix before name skip_connection_type: one of {'conv','sum','none'} stride: stride at last depthwise conv rate: atrous rate for depthwise convolution depth_activation: flag to use activation between depthwise & pointwise convs return_skip: flag to return additional tensor after 2 SepConvs for decoder """ residual = inputs for i in range(3): residual = SepConv_BN(residual,depth_list[i],prefix + '_separable_conv{}'.format(i + 1), stride=stride if i == 2 else 1,rate=rate,depth_activation=depth_activation) if i == 1: skip = residual if skip_connection_type == 'conv': shortcut = _conv2d_same(inputs, depth_list[-1], prefix + '_shortcut',kernel_size=1,stride=stride) shortcut = BatchNormalization(name=prefix + '_shortcut_BN')(shortcut) outputs = layers.add([residual, shortcut]) elif skip_connection_type == 'sum':