Context-Encoders 2016 图像修复篇 基本框架代码复现（tf+keras)

1.文件路径

2.数据集

5k张places2中的arch数据集
shape：256×256×3

部分数据集和全部数据集均可在官网下载，也可以在我博客文件里下载
博客内下载可以访问我的资源里免费下载

3.代码实现

# context_encoders 图像修复的通用模型讲解 tf+keras
# 中航恒拓
# by Plusleft
# 2021.05.17
import glob
import cv2
import os
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras import Sequential, Model
from tensorflow.keras.layers import Conv2D, LeakyReLU, BatchNormalization, UpSampling2D
from tensorflow.keras.layers import Activation, Input, Flatten, Dense
from tensorflow.keras.optimizers import Adam
import numpy as np
# 分配GPU资源 我这里是2080的卡，分配了百分之三十显存 如果没有显卡可能会慢一些 但不影响程序运行
config = tf.compat.v1.ConfigProto(allow_soft_placement=True)
config.gpu_options.per_process_gpu_memory_fraction = 0.3
tf.compat.v1.keras.backend.set_session(tf.compat.v1.Session(config=config))

class ContextEncoder():
    def __init__(self):
        self.img_rows = 256
        self.img_cols = 256

        self.mask_height = 64
        self.mask_width = 64

        self.channels = 3

        self.sum_classes = 1

        self.img_shape = (self.img_rows, self.img_cols, self.channels)
        self.missing_shape = (self.mask_height, self.mask_width, self.channels)

        optimizer = Adam(0.0002, 0.5)

        # 生成器判别器
        self.generator = self.build_generator()

        self.discriminator = self.build_discriminator()
        self.discriminator.compile(loss='binary_crossentropy',
                                   optimizer=optimizer,
                                   metrics=['accuracy'])

        # 准备联合训练
        self.discriminator.trainable = False
        masked_img = Input(shape=self.img_shape)
        gen_missing = self.generator(masked_img)
        valid = self.discriminator(gen_missing)
        self.combined = Model(masked_img, [gen_missing, valid])
        self.combined.compile(loss=['mse', 'binary_crossentropy'],
                              loss_weights=[0.999, 0.001],
                              optimizer=optimizer)


    def build_generator(self):

        model = Sequential()

        # 先定义编码器

        # 输入256*256*3的遮挡图
        model.add(Conv2D(64, kernel_size=4, strides=2, input_shape=self.img_shape, padding="same"))
        model.add(LeakyReLU(alpha=0.2))
        model.add(BatchNormalization(momentum=0.8))
        # ——>128*12*64 特征图
        model.add(Conv2D(64, kerne