python -m venv 仮想環境名　#仮想環境の作成

仮想環境名/Scripts/activate #仮想環境の有効化(Windows version)
source 仮想環境名/bin/activate #仮想環境の有効化(Mac/Linux　version)

cd 仮想環境名/Scripts #Scriptsディレクトリまで移動

echo. > ファイル名.py #Pythonファイルを作成

code . #vsCodeを開く

cd Scripts #Scriptsディレクトリに移動
pip install janome　#janomeをインストール

from janome.tokenizer import Tokenizer

# テキストファイルのパス
file_path = 'テキストデータが入ったファイル名.txt'

# JanomeのTokenizerオブジェクトを作成
tokenizer = Tokenizer()

# テキストデータを読み込み、形態素解析を行う関数
def analyze_text(text):
    tokens = tokenizer.tokenize(text)
    for token in tokens:
        print(token)

# テキストファイルを読み込み、形態素解析を行う
with open(file_path, 'r', encoding='utf-8') as file:
    text_data = file.read()
    analyze_text(text_data)

【      記号,括弧開,*,*,*,*,【,【,【
感傷    名詞,一般,*,*,*,*,感傷,カンショウ,カンショー
に      助詞,格助詞,一般,*,*,*,に,ニ,ニ
浸る    動詞,自立,*,*,五段・ラ行,基本形,浸る,ヒタル,ヒタル
の      名詞,非自立,一般,*,*,*,の,ノ,ノ
は      助詞,係助詞,*,*,*,*,は,ハ,ワ
楽      名詞,形容動詞語幹,*,*,*,*,楽,ラク,ラク
。      記号,句点,*,*,*,*,。,。,。
そんな  連体詞,*,*,*,*,*,そんな,ソンナ,ソンナ
こと    名詞,非自立,一般,*,*,*,こと,コト,コト
より    助詞,格助詞,一般,*,*,*,より,ヨリ,ヨリ
も      助詞,係助詞,*,*,*,*,も,モ,モ
やれよ  動詞,自立,*,*,一段,命令ｙｏ,やれる,ヤレヨ,ヤレヨ
！      記号,一般,*,*,*,*,！,！,！
】      記号,括弧閉,*,*,*,*,】,】,】

        記号,空白,*,*,*,*,
,*,*
感傷    名詞,一般,*,*,*,*,感傷,カンショウ,カンショー
に      助詞,格助詞,一般,*,*,*,に,ニ,ニ
浸る    動詞,自立,*,*,五段・ラ行,基本形,浸る,ヒタル,ヒタル
の      名詞,非自立,一般,*,*,*,の,ノ,ノ
は      助詞,係助詞,*,*,*,*,は,ハ,ワ
誰      名詞,代名詞,一般,*,*,*,誰,ダレ,ダレ
に      助詞,格助詞,一般,*,*,*,に,ニ,ニ
でも    助詞,副助詞,*,*,*,*,でも,デモ,デモ
できる  動詞,自立,*,*,一段,基本形,できる,デキル,デキル
現実    名詞,一般,*,*,*,*,現実,ゲンジツ,ゲンジツ
逃避    名詞,サ変接続,*,*,*,*,逃避,トウヒ,トーヒ
の      助詞,連体化,*,*,*,*,の,ノ,ノ
手段    名詞,一般,*,*,*,*,手段,シュダン,シュダン
。      記号,句点,*,*,*,*,。,。,。
そんな  連体詞,*,*,*,*,*,そんな,ソンナ,ソンナ
もの    名詞,非自立,一般,*,*,*,もの,モノ,モノ
に      助詞,格助詞,一般,*,*,*,に,ニ,ニ
浸っ    動詞,自立,*,*,五段・ラ行,連用タ接続,浸る,ヒタッ,ヒタッ

from janome.tokenizer import Tokenizer

# テキストファイルのパス
file_path = 'テキストデータが入ったファイル名.txt'

# JanomeのTokenizerオブジェクトを作成
tokenizer = Tokenizer(wakati=True)

# テキストデータを読み込み、形態素解析を行う関数
def analyze_text(text):
    tokens = tokenizer.tokenize(text)
    for token in tokens:
        print(token)

# テキストファイルを読み込み、形態素解析を行う
with open(file_path, 'r', encoding='utf-8') as file:
    text_data = file.read()
    analyze_text(text_data)

【
感傷
に
浸る
の
は
楽
。
そんな
こと
より
も
やれよ
！
】


感傷
に
浸る
の
は

from janome.tokenizer import Tokenizer
from janome.analyzer import Analyzer
from janome.tokenfilter import POSKeepFilter

# テキストファイルのパス
file_path = 'テキストデータが入ったファイル名.txt'

# JanomeのTokenizerオブジェクトを作成
tokenizer = Tokenizer()

# POSKeepFilterを使って動詞のみを抽出する関数
def extract_verbs(text):
    # Tokenizerオブジェクトを使用して形態素解析を行い、POSKeepFilterで品詞を絞り込む
    token_filters = [POSKeepFilter(['動詞'])]
    analyzer = Analyzer(tokenizer=tokenizer, token_filters=token_filters)
    verbs = []

    # 形態素解析を行い、動詞を抽出する
    for token in analyzer.analyze(text):
        verbs.append(token.surface)
    
    return verbs

# テキストファイルを読み込み、動詞を抽出する
with open(file_path, 'r', encoding='utf-8') as file:
    text_data = file.read()
    verbs = extract_verbs(text_data)
    print("抽出された動詞:")
    print(verbs)

抽出された動詞:
['浸る', 'やれよ', '浸る', 'できる', '浸っ', 'い', '起き', 'やれ', '落ち込ん', 'でる', '奮い立た', 'せろ', 'でき', 'できる', 'なれ', '備わっ', 'てる', 'わかっ', 'てる', 'やれ', '頼む', '動い', 'くれ']


おまけ（名詞の場合）；
['感傷', 'の', '楽', 'こと', '感傷', 'の', '誰', '現実', '逃避', '手段', 'もの', '何', '暇', 'お前', '時間', '勿体', '自分', 'ぁ', ' ー', 'よう', 'お前', '力', 'メンタル', 'それ', '俺', '自信', '一番']

import matplotlib.pyplot as plt
import numpy as np
from keras.datasets import mnist
import os
from keras.layers import Dense, Flatten, Reshape, LeakyReLU, Dropout
from keras.models import Sequential
from keras.optimizers import Adam
from keras.layers import BatchNormalization, Activation
from keras.layers import Activation, BatchNormalization
from keras.layers import Conv2D, Conv2DTranspose
import cv2

width = 144
height = 144
channels = 1
shape = (width, height, channels)
noise_dim = 100



def generator_model(noise_dim):
    model = Sequential()
    model.add(Dense(256 * 18 * 18, input_dim=noise_dim))
    model.add(Reshape((18, 18, 256)))

    model.add(Conv2DTranspose(128, kernel_size=3, strides=2, padding='same'))
    model.add(LeakyReLU(alpha=0.01))

    model.add(Conv2DTranspose(64, kernel_size=3, strides=1, padding='same'))
    model.add(LeakyReLU(alpha=0.01))
    
    model.add(Conv2DTranspose(32, kernel_size=3, strides=2, padding='same'))
    model.add(LeakyReLU(alpha=0.01))

    model.add(Conv2DTranspose(1, kernel_size=3, strides=2, padding='same'))
    model.add(Activation('tanh'))
    
    return model


def discriminator_model(shape):
    model = Sequential()

    model.add(Conv2D(32, kernel_size=3, strides=2, input_shape=shape, padding="same"))
    model.add(LeakyReLU(alpha=0.2))

    model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dropout(0.25))

    model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dropout(0.25))

    model.add(Conv2D(256, kernel_size=3, strides=2, padding="same"))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dropout(0.25))

    model.add(Conv2D(512, kernel_size=3, strides=2, padding="same"))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dropout(0.25))

    model.add(Conv2D(1024, kernel_size=3, strides=2, padding="same"))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dropout(0.25))

    model.add(Flatten())
    model.add(Dense(1, activation='sigmoid'))

    return model



def gan_model(generator, discriminator):
    model = Sequential()
    model.add(generator)
    model.add(discriminator)
    return model

discriminator = discriminator_model(shape)
discriminator.compile(loss='binary_crossentropy',
                      optimizer=Adam(lr=0.0001, beta_1=0.5),#0.0001→0.0002,
                      metrics=['accuracy'])

generator = generator_model(noise_dim)
generator.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0007, beta_1=0.5))#0.0003→0.0004

discriminator.trainable = False

gan = gan_model(generator, discriminator)
gan.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0007, beta_1=0.5))

losses = []
accuracies = []
iteration_checkpoints = []

def train(iterations, batch_size, sample_interval):
    X_train = load_images("./gray_144_face")
    X_train = X_train / 127.5 - 1.0
    X_train=np.expand_dims(X_train,axis=3)#グレースケールだから
    real_label = np.ones((batch_size, 1))  # 修正
    fake_label = np.zeros((batch_size, 1))  # 修正

    for iteration in range(iterations):
        idx = np.random.randint(0, X_train.shape[0], batch_size)
        batch_images = X_train[idx]
        z = np.random.normal(0, 1, (batch_size, noise_dim))
        gene_imgs = generator.predict(z)

        d_loss_real = discriminator.train_on_batch(batch_images, real_label)
        d_loss_fake = discriminator.train_on_batch(gene_imgs, fake_label)
        d_loss, accuracy = 0.5 * np.add(d_loss_real, d_loss_fake)

        z = np.random.normal(0, 1, (batch_size, noise_dim))
        g_loss = gan.train_on_batch(z, real_label)


        if (iteration + 1) % sample_interval == 0:
            losses.append((d_loss, g_loss))
            accuracies.append(100.0 * accuracy)
            iteration_checkpoints.append(iteration + 1)

            print("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (iteration + 1, d_loss, 100.0 * accuracy, g_loss))
            save_images(generator, iteration + 1)


def save_images(generator, iteration, directory='144x144x_face_gray_images', image_grid_rows=4, image_grid_columns=4):
    if not os.path.exists(directory):
        os.makedirs(directory)

    z = np.random.normal(0, 1, (image_grid_rows * image_grid_columns, noise_dim))
    gene_imgs = generator.predict(z)
    gene_imgs = 0.5 * gene_imgs + 0.5
    fig, axs = plt.subplots(image_grid_rows, image_grid_columns, figsize=(4, 4), sharey=True, sharex=True)
    cnt = 0
    for row in range(image_grid_rows):
        for col in range(image_grid_columns):
            axs[row, col].imshow(gene_imgs[cnt, :, :, 0], cmap='gray')
            axs[row, col].axis('off')
            cnt += 1

    fig.savefig(f"{directory}/iteration_{iteration}.png")
    plt.close(fig)

def load_images(directory):#グレースケール用に変換する
    images = []
    for filename in os.listdir(directory):
        img = cv2.imread(os.path.join(directory, filename))
        if img is not None:  
            img = cv2.resize(img, (width, height))
            if len(img.shape) == 3:  # チャンネルが3の場合はグレースケールに変換
                img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
            images.append(img)
        else:
            print(f"Warning: Failed to load image {filename}")
    if len(images) == 0:
        print("Error: No images loaded")
        return None
    else:
        return np.array(images)



iterations = 20000
batch_size = 128
sample_interval = 1  

train(iterations, batch_size, sample_interval)
generator.save('Face-gene.keras')
discriminator.save('Face-dis.keras')
gan.save('Face-gan.keras')

import matplotlib.pyplot as plt
import numpy as np
from keras.layers import Dense, Flatten,Reshape, LeakyReLU 
from keras.models import Sequential
from keras.optimizers import Adam
import os
from keras.layers import Activation,Dropout,BatchNormalization
from keras.layers import Conv2D
from keras.layers import UpSampling2D
import cv2
#上記↑は『衝撃簡単7』と同じ
from keras.layers import Conv2DTranspose

width = 144
height = 144
channels = 1
shape = (width, height, channels)
noise_dim = 100

def generator_model(noise_dim):
    model = Sequential()
    model.add(Dense(256 * 18 * 18, input_dim=noise_dim))
    model.add(Reshape((18, 18, 256)))

    model.add(Conv2DTranspose(128, kernel_size=3, strides=2, padding='same'))
    model.add(LeakyReLU(alpha=0.01))

    model.add(Conv2DTranspose(64, kernel_size=3, strides=1, padding='same'))
    model.add(LeakyReLU(alpha=0.01))
    
    model.add(Conv2DTranspose(32, kernel_size=3, strides=2, padding='same'))
    model.add(LeakyReLU(alpha=0.01))

    model.add(Conv2DTranspose(1, kernel_size=3, strides=2, padding='same'))
    model.add(Activation('tanh'))
    
    return model

def discriminator_model(shape):
    model = Sequential()

    model.add(Conv2D(32, kernel_size=3, strides=2, input_shape=shape, padding="same"))
    model.add(LeakyReLU(alpha=0.2))

    model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dropout(0.25))

    model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dropout(0.25))

    model.add(Conv2D(256, kernel_size=3, strides=2, padding="same"))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dropout(0.25))

    model.add(Conv2D(512, kernel_size=3, strides=2, padding="same"))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dropout(0.25))

    model.add(Conv2D(1024, kernel_size=3, strides=2, padding="same"))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dropout(0.25))

    model.add(Flatten())
    model.add(Dense(1, activation='sigmoid'))

    return model

def gan_model(generator, discriminator):
    model = Sequential()
    model.add(generator)
    model.add(discriminator)
    return model

discriminator = discriminator_model(shape)
discriminator.compile(loss='binary_crossentropy',
                      optimizer=Adam(lr=0.0001, beta_1=0.5),metrics=['accuracy'])

generator = generator_model(noise_dim)
generator.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0007, beta_1=0.5))

discriminator.trainable = False

gan = gan_model(generator, discriminator)
gan.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0007, beta_1=0.5))

losses = []
accuracies = []
iteration_checkpoints = []

def train(iterations, batch_size, sample_interval):
    X_train = load_images("./gray_144_face")
    X_train = X_train / 127.5 - 1.0
    X_train=np.expand_dims(X_train,axis=3)#グレースケールだから
    real_label = np.ones((batch_size, 1))  # 修正
    fake_label = np.zeros((batch_size, 1))  # 修正

    for iteration in range(iterations):
        idx = np.random.randint(0, X_train.shape[0], batch_size)
        batch_images = X_train[idx]
        z = np.random.normal(0, 1, (batch_size, noise_dim))
        gene_imgs = generator.predict(z)

        d_loss_real = discriminator.train_on_batch(batch_images, real_label)
        d_loss_fake = discriminator.train_on_batch(gene_imgs, fake_label)
        d_loss, accuracy = 0.5 * np.add(d_loss_real, d_loss_fake)

        z = np.random.normal(0, 1, (batch_size, noise_dim))
        g_loss = gan.train_on_batch(z, real_label)


        if (iteration + 1) % sample_interval == 0:
            losses.append((d_loss, g_loss))
            accuracies.append(100.0 * accuracy)
            iteration_checkpoints.append(iteration + 1)

            print("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (iteration + 1, d_loss, 100.0 * accuracy, g_loss))
            save_images(generator, iteration + 1)

def load_images(directory):#グレースケール用に変換する
    images = []
    for filename in os.listdir(directory):
        img = cv2.imread(os.path.join(directory, filename))
        if img is not None:  
            img = cv2.resize(img, (width, height))
            if len(img.shape) == 3:  # チャンネルが3の場合はグレースケールに変換
                img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
            images.append(img)
        else:
            print(f"Warning: Failed to load image {filename}")
    if len(images) == 0:
        print("Error: No images loaded")
        return None
    else:
        return np.array(images)

def save_images(generator, iteration, directory='144x144x_face_gray_images', image_grid_rows=4, image_grid_columns=4):
    if not os.path.exists(directory):
        os.makedirs(directory)

    z = np.random.normal(0, 1, (image_grid_rows * image_grid_columns, noise_dim))
    gene_imgs = generator.predict(z)
    gene_imgs = 0.5 * gene_imgs + 0.5
    fig, axs = plt.subplots(image_grid_rows, image_grid_columns, figsize=(4, 4), sharey=True, sharex=True)
    cnt = 0
    for row in range(image_grid_rows):
        for col in range(image_grid_columns):
            axs[row, col].imshow(gene_imgs[cnt, :, :, 0], cmap='gray')
            axs[row, col].axis('off')
            cnt += 1

    fig.savefig(f"{directory}/iteration_{iteration}.png")
    plt.close(fig)

iterations = 20000
batch_size = 128
sample_interval = 100 

train(iterations, batch_size, sample_interval)
generator.save('144Face-gene.keras')
discriminator.save('144Face-dis.keras')
gan.save('144Face-gan.keras')

ファイル名.py

import numpy as np
import matplotlib.pyplot as plt
from keras.models import load_model
import os

noise_dim=100
def generate_and_save_images(generator, noise_dim, save_directory='generated_images', num_images=10):
    if not os.path.exists(save_directory):
        os.makedirs(save_directory)
    
    for i in range(num_images):
        noise = np.random.normal(0, 1, (1, noise_dim))
        generated_image = generator.predict(noise)
        generated_image = 0.5 * generated_image + 0.5  # 画像のスケーリングを元に戻す
        generated_image = np.squeeze(generated_image)  # 不要な次元を削除
        plt.imshow(generated_image, cmap='gray')
        plt.axis('off')
        plt.savefig(f"{save_directory}/generated_image_{i+1}.png")
        plt.close()

# 生成器モデルをロードする
generator = load_model('144Face-gene.keras')

# 10枚の画像を生成して保存する
generate_and_save_images(generator, noise_dim)

from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers import LeakyReLU
from keras.layers import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam
import matplotlib.pyplot as plt
import os
import cv2
import numpy as np

width = 40
height = 40
channels = 3
shape = (width, height, channels)
noise_dim = 100


def generator_model(noise_dim):
    model = Sequential()
    model.add(Dense(10 * 10 * 256, activation="relu", input_dim=noise_dim))
    model.add(Reshape((10, 10, 256)))
    model.add(BatchNormalization(momentum=0.8))
    model.add(UpSampling2D())

    model.add(Conv2D(128, kernel_size=3, padding="same"))
    model.add(Activation("relu"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(UpSampling2D())


    model.add(Conv2D(64, kernel_size=3, padding="same"))
    model.add(Activation("relu"))
    model.add(BatchNormalization(momentum=0.8))

    model.add(Conv2D(3, kernel_size=3, padding="same"))
    model.add(Activation("tanh"))
    return model



def discriminator_model(shape):
    model = Sequential()
    model.add(Conv2D(32, kernel_size=3, strides=2, input_shape=shape, padding="same"))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dropout(0.25))
    model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dropout(0.25))
    model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dropout(0.25))
    model.add(Flatten())
    model.add(Dense(1, activation='sigmoid'))
    return model




def gan_model(generator, discriminator):
    model = Sequential()
    model.add(generator)
    model.add(discriminator)
    return model


discriminator = discriminator_model(shape)
discriminator.compile(loss='binary_crossentropy',
                      optimizer=Adam(lr=0.0001, beta_1=0.5),
                      metrics=['accuracy'])

generator = generator_model(noise_dim)
generator.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0003, beta_1=0.5))

discriminator.trainable = False

gan = gan_model(generator, discriminator)
gan.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0003, beta_1=0.5))



losses = []
accuracies = []
iteration_checkpoints = []



def train(iterations, batch_size, sample_interval):
    X_train = load_images("./resized_images_40")
    X_train = X_train / 127.5 - 1.0
    real_label = np.ones((batch_size, 1))  # 修正
    fake_label = np.zeros((batch_size, 1))  # 修正

    for iteration in range(iterations):
        idx_real = np.random.randint(0, X_train.shape[0] - 1, batch_size)  # 修正
        idx_fake = np.random.randint(0, X_train.shape[0] - 1, batch_size)  # 修正
        batch_images_real = X_train[idx_real]
        batch_images_fake = X_train[idx_fake]
        
        # ノイズの生成時に正しい次元数を使用する
        z = np.random.normal(0, 1, (batch_size, noise_dim))  # 修正
        gene_imgs = generator.predict(z)

        d_loss_real = discriminator.train_on_batch(batch_images_real, real_label)  # 修正
        d_loss_fake = discriminator.train_on_batch(gene_imgs, fake_label)
        d_loss, accuracy = 0.5 * np.add(d_loss_real, d_loss_fake)

        z = np.random.normal(0, 1, (batch_size, noise_dim))
        g_loss = gan.train_on_batch(z, real_label)

        if (iteration + 1) % sample_interval == 0:
            losses.append((d_loss, g_loss))
            accuracies.append(100.0 * accuracy)
            iteration_checkpoints.append(iteration + 1)

            print("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (iteration + 1, d_loss, 100.0 * accuracy, g_loss))
            save_images(generator, iteration + 1)


def save_images(generator, iteration, directory='face-gan_images', image_grid_rows=4, image_grid_columns=4):
    if not os.path.exists(directory):
        os.makedirs(directory)

    z = np.random.normal(0, 1, (image_grid_rows * image_grid_columns, noise_dim))
    gene_imgs = generator.predict(z)
    gene_imgs = 0.5 * gene_imgs + 0.5
    fig, axs = plt.subplots(image_grid_rows, image_grid_columns, figsize=(4, 4), sharey=True, sharex=True)
    cnt = 0
    for row in range(image_grid_rows):
        for col in range(image_grid_columns):
            axs[row, col].imshow(gene_imgs[cnt])
            axs[row, col].axis('off')
            cnt += 1

    fig.savefig(f"{directory}/iteration_{iteration}.png")
    plt.close(fig)


def load_images(directory):
    images = []
    for filename in os.listdir(directory):
        img = cv2.imread(os.path.join(directory, filename))
        if img is not None:  # 画像の読み込みが成功した場合のみ処理を続行
            img = cv2.resize(img, (width, height))
            images.append(img)
        else:
            print(f"Warning: Failed to load image {filename}")
    if len(images) == 0:
        print("Error: No images loaded")
        return None
    else:
        return np.array(images)


iterations = 20000
batch_size = 128
sample_interval = 1  
train(iterations, batch_size, sample_interval)
generator.save('40x40color-Face-gene.keras')
discriminator.save('40x40color-Face-dis.keras')
gan.save('40x40color-Face-gan.keras')

import matplotlib.pyplot as plt
import numpy as np
from keras.layers import Dense, Flatten,Reshape, LeakyReLU 
from keras.models import Sequential
from keras.optimizers import Adam
import os
from keras.layers import Activation,Dropout,BatchNormalization
#上記↑は『衝撃簡単５』と同じ
from keras.layers import Conv2D
from keras.layers import UpSampling2D
import cv2

width = 40
height = 40
channels = 3
shape = (width, height, channels)
noise_dim = 100

def generator_model(noise_dim):
    model = Sequential()
    model.add(Dense(10 * 10 * 256, activation="relu", input_dim=noise_dim))
    model.add(Reshape((10, 10, 256)))
    model.add(BatchNormalization(momentum=0.8))
    model.add(UpSampling2D())

    model.add(Conv2D(128, kernel_size=3, padding="same"))
    model.add(Activation("relu"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(UpSampling2D())


    model.add(Conv2D(64, kernel_size=3, padding="same"))
    model.add(Activation("relu"))
    model.add(BatchNormalization(momentum=0.8))

    model.add(Conv2D(3, kernel_size=3, padding="same"))
    model.add(Activation("tanh"))
    return model

def discriminator_model(shape):
    model = Sequential()
    model.add(Conv2D(32, kernel_size=3, strides=2, input_shape=shape, padding="same"))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dropout(0.25))
    model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dropout(0.25))
    model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dropout(0.25))
    model.add(Flatten())
    model.add(Dense(1, activation='sigmoid'))
    return model

def gan_model(generator, discriminator):
    model = Sequential()
    model.add(generator)
    model.add(discriminator)
    return model

discriminator = discriminator_model(shape)
discriminator.compile(loss='binary_crossentropy',
                      optimizer=Adam(lr=0.0001, beta_1=0.5),
                      metrics=['accuracy'])

generator = generator_model(noise_dim)
generator.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0003, beta_1=0.5))

discriminator.trainable = False

gan = gan_model(generator, discriminator)
gan.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0003, beta_1=0.5))

losses = []
accuracies = []
iteration_checkpoints = []

def train(iterations, batch_size, sample_interval):
    X_train = load_images("./resized_images_40")
    X_train = X_train / 127.5 - 1.0
    real_label = np.ones((batch_size, 1))  # 修正
    fake_label = np.zeros((batch_size, 1))  # 修正

    for iteration in range(iterations):
        idx_real = np.random.randint(0, X_train.shape[0] - 1, batch_size)  # 修正
        idx_fake = np.random.randint(0, X_train.shape[0] - 1, batch_size)  # 修正
        batch_images_real = X_train[idx_real]
        batch_images_fake = X_train[idx_fake]
        
        # ノイズの生成時に正しい次元数を使用する
        z = np.random.normal(0, 1, (batch_size, noise_dim))  # 修正
        gene_imgs = generator.predict(z)

        d_loss_real = discriminator.train_on_batch(batch_images_real, real_label)  # 修正
        d_loss_fake = discriminator.train_on_batch(gene_imgs, fake_label)
        d_loss, accuracy = 0.5 * np.add(d_loss_real, d_loss_fake)

        z = np.random.normal(0, 1, (batch_size, noise_dim))
        g_loss = gan.train_on_batch(z, real_label)

        if (iteration + 1) % sample_interval == 0:
            losses.append((d_loss, g_loss))
            accuracies.append(100.0 * accuracy)
            iteration_checkpoints.append(iteration + 1)

            print("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (iteration + 1, d_loss, 100.0 * accuracy, g_loss))
            save_images(generator, iteration + 1)

def load_images(directory):
    images = []
    for filename in os.listdir(directory):
        img = cv2.imread(os.path.join(directory, filename))
        if img is not None:  # 画像の読み込みが成功した場合のみ処理を続行
            img = cv2.resize(img, (width, height))
            images.append(img)
        else:
            print(f"Warning: Failed to load image {filename}")
    if len(images) == 0:
        print("Error: No images loaded")
        return None
    else:
        return np.array(images)

def save_images(generator, iteration, directory='face-gan_images', image_grid_rows=4, image_grid_columns=4):
    if not os.path.exists(directory):
        os.makedirs(directory)

    z = np.random.normal(0, 1, (image_grid_rows * image_grid_columns, noise_dim))
    gene_imgs = generator.predict(z)
    gene_imgs = 0.5 * gene_imgs + 0.5
    fig, axs = plt.subplots(image_grid_rows, image_grid_columns, figsize=(4, 4), sharey=True, sharex=True)
    cnt = 0
    for row in range(image_grid_rows):
        for col in range(image_grid_columns):
            axs[row, col].imshow(gene_imgs[cnt])
            #カラーなので前回までのgene_imgs[cnt, :, :, 0], cmap='gray'は不要
            axs[row, col].axis('off')
            cnt += 1

    fig.savefig(f"{directory}/iteration_{iteration}.png")
    plt.close(fig)

iterations = 20000
batch_size = 128
sample_interval = 100 
train(iterations, batch_size, sample_interval)
generator.save('40x40color-Face-gene.keras')
discriminator.save('40x40color-Face-dis.keras')
gan.save('40x40color-Face-gan.keras')

ファイル名.py

import numpy as np
import matplotlib.pyplot as plt
from keras.models import load_model
import os
import cv2
noise_dim=100
# 保存したGANモデルの読み込み
generator = load_model('40x40color-Face-gene.keras')

def generate_images(generator, save_directory, num_images=10):
    if not os.path.exists(save_directory):
        os.makedirs(save_directory)

    noise = np.random.normal(0, 1, (num_images, noise_dim))
    generated_images = generator.predict(noise)

    for i in range(num_images):
        img = (generated_images[i] + 1) / 2  # 画像のスケーリングを元に戻す
        plt.imshow(img)
        plt.axis('off')
        plt.savefig(f"{save_directory}/generated_image_{i+1}.png")
        plt.close()

# 画像を保存するディレクトリと生成する画像の枚数を指定して実行
generate_images(generator, "generated_images", num_images=40)

import os
import requests
from bs4 import BeautifulSoup
from urllib.parse import urlparse, parse_qs
import time

def is_valid_url(url):
    # URLが有効なHTTPまたはHTTPSスキームを持っているかどうかを確認する
    return url.startswith("http://") or url.startswith("https://")

def download_images(query, num_images):
    save_dir = './woman_images'#収集した画像の保存先ディレクトリ
    if not os.path.exists(save_dir):
        os.makedirs(save_dir)
    
    headers = {'User-Agent': 'Mozilla/5.0'}
    downloaded = 0
    while downloaded < num_images:
        # キーワードを含むURLを構築
        url = f"https://www.google.com/search?q={query}&tbm=isch&start={downloaded}"
        # ヘッダーを設定してGoogleにリクエストを送信
        response = requests.get(url, headers=headers)
        # HTMLをパース
        soup = BeautifulSoup(response.text, 'html.parser')
        # 画像のURLを抽出
        image_urls = []
        for img in soup.find_all('img'):
            image_url = img.get('src')
            if image_url and is_valid_url(image_url):
                image_urls.append(image_url)
        # 画像をダウンロード
        for i, image_url in enumerate(image_urls):
            try:
                if downloaded >= num_images:
                    break
                image_data = requests.get(image_url).content
                with open(f'{save_dir}/{query}_{downloaded}.jpg', 'wb') as f:
                    f.write(image_data)
                print(f"Downloaded image {downloaded+1}/{num_images}")
                downloaded += 1
            except Exception as e:
                print(f"Error downloading image {downloaded+1}: {e}")
        time.sleep(1)  # Googleへの負荷を軽減するために1秒待つ

# 使用例
query = "ジヒョ　高画質"  # 検索キーワード
num_images = 100  # ダウンロードする画像の数
download_images(query, num_images)

from PIL import Image
import os

def resize_images(input_folder, output_folder, target_size):
    if not os.path.exists(output_folder):
        os.makedirs(output_folder)

    for filename in os.listdir(input_folder):
        try:
            # 画像を開く
            img_path = os.path.join(input_folder, filename)
            img = Image.open(img_path)
            # 画像を指定されたサイズにリサイズする
            resized_img = img.resize(target_size)
            # リサイズした画像を保存する
            output_path = os.path.join(output_folder, filename)
            resized_img.save(output_path)
            print(f"Resized image: {output_path}")
        except Exception as e:
            print(f"Error resizing image {filename}: {e}")

# 使用例
input_folder = './woman_images'  # 元の画像が保存されているフォルダ
output_folder = './resized_images_144'    # リサイズされた画像を保存するフォルダ
target_size=(144,144)# 目標の画像サイズ (幅, 高さ)
resize_images(input_folder, output_folder, target_size)

import os
import random
import string

# ディレクトリのパスを指定
directory = './resized_images_144'

# ディレクトリ内のファイルを取得
files = os.listdir(directory)

# ファイルごとにループ
for filename in files:
    # 拡張子を取得
    _, ext = os.path.splitext(filename)
    
    # 新しいファイル名を生成
    new_filename = ''.join(random.choices(string.digits, k=8)) + ext
    
    # 新しいファイル名のパスを作成
    new_filepath = os.path.join(directory, new_filename)
    
    # ファイル名を変更
    os.rename(os.path.join(directory, filename), new_filepath)

import cv2
import os

# 入力画像が保存されているフォルダパス
input_folder = 'resized_images_144'

# 出力フォルダパス
output_folder = 'gray_144_face'

# 入力フォルダ内のすべての画像ファイルを処理
for filename in os.listdir(input_folder):
    # 画像ファイルのパスを取得
    input_path = os.path.join(input_folder, filename)
    
    # 画像をグレースケールで読み込む
    img = cv2.imread(input_path)
    gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    
    # 出力ファイルのパスを指定
    output_path = os.path.join(output_folder, filename)
    
    # グレースケール画像を保存
    cv2.imwrite(output_path, gray_img)

print("変換が完了しました。")

import matplotlib.pyplot as plt
import numpy as np
from keras.datasets import mnist
import os
from keras.layers import Dense, Flatten, Reshape, LeakyReLU, Dropout
from keras.models import Sequential
from keras.optimizers import Adam

from keras.layers import Activation, BatchNormalization
from keras.layers import Conv2D, Conv2DTranspose

width = 28
height = 28
channels = 1
shape = (width, height, channels)
noise_dim = 100

def generator_model(noise_dim):
    model = Sequential()
    model.add(Dense(256 * 7 * 7, input_dim=noise_dim))
    model.add(Reshape((7, 7, 256)))
    model.add(Conv2DTranspose(128, kernel_size=3, strides=2, padding='same'))
    model.add(BatchNormalization())

    model.add(LeakyReLU(alpha=0.01))

    model.add(Conv2DTranspose(64, kernel_size=3, strides=1, padding='same'))
    model.add(BatchNormalization())
    model.add(LeakyReLU(alpha=0.01))
    model.add(Conv2DTranspose(1, kernel_size=3, strides=2, padding='same'))
    model.add(Activation('tanh'))
    return model



def discriminator_model(shape):
    model = Sequential()
    model.add(Conv2D(32, kernel_size=3, strides=2, input_shape=shape, padding='same'))
    model.add(LeakyReLU(alpha=0.01))
    
    model.add(Dropout(0.25))
    model.add(Conv2D(64, kernel_size=3, strides=2, padding='same'))
    model.add(LeakyReLU(alpha=0.01))
    model.add(Conv2D(128, kernel_size=3, strides=2, padding='same'))
    model.add(LeakyReLU(alpha=0.01))
    model.add(Flatten())
    model.add(Dense(1, activation='sigmoid'))
    return model

def gan_model(generator, discriminator):
    model = Sequential()
    model.add(generator)
    model.add(discriminator)
    return model

discriminator = discriminator_model(shape)
discriminator.compile(loss='binary_crossentropy',
                      optimizer=Adam(lr=0.0001, beta_1=0.5),
                      metrics=['accuracy'])

generator = generator_model(noise_dim)
generator.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0003, beta_1=0.5))

discriminator.trainable = False

gan = gan_model(generator, discriminator)
gan.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0003, beta_1=0.5))

losses = []
accuracies = []
iteration_checkpoints = []

def train(iterations, batch_size, sample_interval):
    (X_train, _), (_, _) = mnist.load_data()
    X_train = X_train / 127.5 - 1.0
    X_train = np.expand_dims(X_train, axis=3)
    real_label = np.ones((batch_size, 1))
    fake_label = np.zeros((batch_size, 1))

    for iteration in range(iterations):
        idx = np.random.randint(0, X_train.shape[0], batch_size)
        batch_images = X_train[idx]
        z = np.random.normal(0, 1, (batch_size, noise_dim))
        gene_imgs = generator.predict(z)

        d_loss_real = discriminator.train_on_batch(batch_images, real_label)
        d_loss_fake = discriminator.train_on_batch(gene_imgs, fake_label)
        d_loss, accuracy = 0.5 * np.add(d_loss_real, d_loss_fake)

        z = np.random.normal(0, 1, (batch_size, noise_dim))
        g_loss = gan.train_on_batch(z, real_label)


        if (iteration + 1) % sample_interval == 0:
            losses.append((d_loss, g_loss))
            accuracies.append(100.0 * accuracy)
            iteration_checkpoints.append(iteration + 1)

            print("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (iteration + 1, d_loss, 100.0 * accuracy, g_loss))
            save_images(generator, iteration + 1)



def save_images(generator, iteration, directory='dcgan_images', image_grid_rows=4, image_grid_columns=4):
    if not os.path.exists(directory):
        os.makedirs(directory)

    z = np.random.normal(0, 1, (image_grid_rows * image_grid_columns, noise_dim))
    gene_imgs = generator.predict(z)
    gene_imgs = 0.5 * gene_imgs + 0.5
    fig, axs = plt.subplots(image_grid_rows, image_grid_columns, figsize=(4, 4), sharey=True, sharex=True)
    cnt = 0
    for row in range(image_grid_rows):
        for col in range(image_grid_columns):
            axs[row, col].imshow(gene_imgs[cnt, :, :, 0], cmap='gray')
            axs[row, col].axis('off')
            cnt += 1

    fig.savefig(f"{directory}/iteration_{iteration}.png")
    plt.close(fig)

iterations = 20000
batch_size = 128
sample_interval = 50  

train(iterations, batch_size, sample_interval)
generator.save('DC_generator.keras')
discriminator.save('DC_discriminator.keras')
gan.save('dcgan_model.keras')

import matplotlib.pyplot as plt
import numpy as np
from keras.datasets import mnist

from keras.layers import Dense, Flatten, Reshape,LeakyReLU
from keras.models import Sequential
from keras.optimizers import Adam
import os
#上記↑は前回と同じ
from keras.layers import Activation, Dropout,BatchNormalization
from keras.layers import Conv2D, Conv2DTranspose

width=28
height=28
channels=1
shape=(width,height,channels)
noise_dim=100

def generator_model(noise_dim):
    model = Sequential()
    model.add(Dense(256 * 7 * 7, input_dim=noise_dim))
    model.add(Reshape((7, 7, 256)))
    model.add(Conv2DTranspose(128, kernel_size=3, strides=2, padding='same'))
    model.add(BatchNormalization())

    model.add(LeakyReLU(alpha=0.01))

    model.add(Conv2DTranspose(64, kernel_size=3, strides=1, padding='same'))
    model.add(BatchNormalization())
    model.add(LeakyReLU(alpha=0.01))
    model.add(Conv2DTranspose(1, kernel_size=3, strides=2, padding='same'))
    model.add(Activation('tanh'))
    return model

def discriminator_model(shape):
    model = Sequential()
    model.add(Conv2D(32, kernel_size=3, strides=2, input_shape=shape, padding='same'))
    model.add(LeakyReLU(alpha=0.01))
    model.add(Dropout(0.25))
    model.add(Conv2D(64, kernel_size=3, strides=2, padding='same'))
    model.add(LeakyReLU(alpha=0.01))
    model.add(Conv2D(128, kernel_size=3, strides=2, padding='same'))
    model.add(LeakyReLU(alpha=0.01))
    model.add(Flatten())
    model.add(Dense(1, activation='sigmoid'))
    return model

def gan_model(generator,discriminator):
    model=Sequential()
    model.add(generator)
    model.add(discriminator)
    return model