File size: 6,279 Bytes
2aee9e8 95fc39c 2aee9e8 95fc39c 2aee9e8 95fc39c 2aee9e8 d1a4e61 2aee9e8 d1a4e61 2aee9e8 95fc39c 2aee9e8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 | import os
import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import (Input, Conv2D, LeakyReLU, BatchNormalization,
Conv2DTranspose, Add, Activation, Flatten, Dense,
InstanceNormalization, GlobalAveragePooling2D)
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
import cv2
def residual_block(x, filters):
res = Conv2D(filters, kernel_size=3, strides=1, padding='same',
kernel_initializer=tf.keras.initializers.RandomNormal(mean=0.0, stddev=0.02))(x)
res = InstanceNormalization()(res)
res = Activation('relu')(res)
res = Conv2D(filters, kernel_size=3, strides=1, padding='same',
kernel_initializer=tf.keras.initializers.RandomNormal(mean=0.0, stddev=0.02))(res)
res = InstanceNormalization()(res)
return Add()([x, res])
def build_generator():
inputs = Input(shape=(256, 256, 3))
x = Conv2D(64, kernel_size=7, strides=1, padding='same',
kernel_initializer=tf.keras.initializers.RandomNormal(mean=0.0, stddev=0.02))(inputs)
x = InstanceNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(128, kernel_size=3, strides=2, padding='same',
kernel_initializer=tf.keras.initializers.RandomNormal(mean=0.0, stddev=0.02))(x)
x = InstanceNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(256, kernel_size=3, strides=2, padding='same',
kernel_initializer=tf.keras.initializers.RandomNormal(mean=0.0, stddev=0.02))(x)
x = InstanceNormalization()(x)
x = Activation('relu')(x)
for _ in range(9):
x = residual_block(x, 256)
x = Conv2DTranspose(128, kernel_size=3, strides=2, padding='same',
kernel_initializer=tf.keras.initializers.RandomNormal(mean=0.0, stddev=0.02))(x)
x = InstanceNormalization()(x)
x = Activation('relu')(x)
x = Conv2DTranspose(64, kernel_size=3, strides=2, padding='same',
kernel_initializer=tf.keras.initializers.RandomNormal(mean=0.0, stddev=0.02))(x)
x = InstanceNormalization()(x)
x = Activation('relu')(x)
outputs = Conv2D(3, kernel_size=7, strides=1, padding='same', activation='tanh',
kernel_initializer=tf.keras.initializers.RandomNormal(mean=0.0, stddev=0.02))(x)
return Model(inputs, outputs, name="Generator")
def build_discriminator():
inputs = Input(shape=(256, 256, 3))
x = Conv2D(64, kernel_size=4, strides=2, padding='same',
kernel_initializer=tf.keras.initializers.RandomNormal(mean=0.0, stddev=0.02))(inputs)
x = LeakyReLU(0.2)(x)
x = Conv2D(128, kernel_size=4, strides=2, padding='same',
kernel_initializer=tf.keras.initializers.RandomNormal(mean=0.0, stddev=0.02))(x)
x = InstanceNormalization()(x)
x = LeakyReLU(0.2)(x)
x = Conv2D(256, kernel_size=4, strides=2, padding='same',
kernel_initializer=tf.keras.initializers.RandomNormal(mean=0.0, stddev=0.02))(x)
x = InstanceNormalization()(x)
x = LeakyReLU(0.2)(x)
x = Conv2D(512, kernel_size=4, strides=2, padding='same',
kernel_initializer=tf.keras.initializers.RandomNormal(mean=0.0, stddev=0.02))(x)
x = InstanceNormalization()(x)
x = LeakyReLU(0.2)(x)
x = GlobalAveragePooling2D()(x)
outputs = Dense(1)(x)
return Model(inputs, outputs, name="Discriminator")
def wasserstein_loss(y_true, y_pred):
return -tf.reduce_mean(y_true * y_pred)
def load_images(folder):
images = []
for filename in os.listdir(folder):
if filename.lower().endswith(('.png', '.jpg', '.jpeg')):
img = cv2.imread(os.path.join(folder, filename))
img = cv2.resize(img, (256, 256)) / 255.0 # Normalize to [0, 1]
images.append(img)
return np.array(images)
def train(generator, discriminator, blurred_images, clear_images, epochs, batch_size):
optimizer_g = Adam(learning_rate=0.0001, beta_1=0.5, beta_2=0.999)
optimizer_d = Adam(learning_rate=0.0002, beta_1=0.5, beta_2=0.999)
for epoch in range(epochs):
print(f"Epoch {epoch + 1}/{epochs}")
for i in range(0, len(blurred_images), batch_size):
blurred_batch = blurred_images[i:i + batch_size]
clear_batch = clear_images[i:i + batch_size]
fake_images = generator.predict(blurred_batch)
real_labels = -np.ones((len(clear_batch), 1))
fake_labels = np.ones((len(fake_images), 1))
d_loss_real = discriminator.train_on_batch(clear_batch, real_labels)
d_loss_fake = discriminator.train_on_batch(fake_images, fake_labels)
d_loss = d_loss_real + d_loss_fake
misleading_labels = -np.ones((len(blurred_batch), 1))
g_loss = generator.train_on_batch(blurred_batch, misleading_labels)
print(f"Batch {i // batch_size + 1}: D Loss: {d_loss:.4f}, G Loss: {g_loss:.4f}")
def blur_images(input_folder, output_folder):
"""
Apply Gaussian blur to images in the input folder and save them to the output folder.
"""
os.makedirs(output_folder, exist_ok=True)
for filename in os.listdir(input_folder):
if filename.lower().endswith(('.png', '.jpg', '.jpeg')):
img_path = os.path.join(input_folder, filename)
image = cv2.imread(img_path)
if image is not None:
blurred = cv2.GaussianBlur(image, (3, 3), 0)
output_path = os.path.join(output_folder, f"blurred_{filename}")
cv2.imwrite(output_path, blurred)
print(f"Blurred image saved: {output_path}")
else:
print(f"Failed to load image: {img_path}")
blurred_folder = "blurred_sketches"
clear_folder = "clear_sketches"
blur_images(clear_folder, blurred_folder) # Create blurred images
blurred_images = load_images(blurred_folder)
clear_images = load_images(clear_folder)
generator = build_generator()
discriminator = build_discriminator()
discriminator.compile(optimizer=Adam(0.0002, 0.5, 0.999), loss=wasserstein_loss)
train(generator, discriminator, blurred_images, clear_images, epochs=500, batch_size=16)
|