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import os |
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import cv2 |
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import keras |
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import warnings |
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import numpy as np |
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from PIL import Image |
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import matplotlib.pyplot as plt |
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from tensorflow.keras.optimizers import Adam |
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from tensorflow.keras.models import Sequential, Model |
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from tensorflow.keras.layers import Dense, LeakyReLU, Reshape, Flatten, Input |
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from tensorflow.keras.layers import Conv2D, MaxPooling2D, Activation, Dropout, Conv2DTranspose |
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from tensorflow.compat.v1.keras.layers import BatchNormalization |
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images = [] |
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def load_images(size=(64,64)): |
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pixed_faces = os.listdir("kaggle/working/results/pixed_faces") |
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images_Path = "kaggle/working/results/pixed_faces" |
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for i in pixed_faces: |
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try: |
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image = cv2.imread(f"{images_Path}/{i}") |
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image = cv2.resize(image,size) |
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images.append(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) |
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except: |
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pass |
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load_images() |
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def __init__(self): |
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self.img_shape = (64, 64, 3) |
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self.noise_size = 100 |
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optimizer = Adam(0.0002,0.5) |
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self.discriminator = self.build_discriminator() |
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self.discriminator.compile(loss='binary_crossentropy', |
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optimizer=optimizer, |
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metrics=['accuracy']) |
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self.generator = self.build_generator() |
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self.generator.compile(loss='binary_crossentropy', optimizer=optimizer) |
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self.combined = Sequential() |
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self.combined.add(self.generator) |
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self.combined.add(self.discriminator) |
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self.discriminator.trainable = False |
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self.combined.compile(loss='binary_crossentropy', optimizer=optimizer) |
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self.combined.summary() |
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def build_generator(self): |
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epsilon = 0.00001 |
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noise_shape = (self.noise_size,) |
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model = Sequential() |
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model.add(Dense(4*4*512, activation='linear', input_shape=noise_shape)) |
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model.add(LeakyReLU(alpha=0.2)) |
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model.add(Reshape((4, 4, 512))) |
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model.add(Conv2DTranspose(512, kernel_size=[4,4], strides=[2,2], padding="same", |
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kernel_initializer= keras.initializers.TruncatedNormal(stddev=0.02))) |
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model.add(BatchNormalization(momentum=0.9, epsilon=epsilon)) |
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model.add(LeakyReLU(alpha=0.2)) |
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model.add(Conv2DTranspose(256, kernel_size=[4,4], strides=[2,2], padding="same", |
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kernel_initializer= keras.initializers.TruncatedNormal(stddev=0.02))) |
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model.add(BatchNormalization(momentum=0.9, epsilon=epsilon)) |
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model.add(LeakyReLU(alpha=0.2)) |
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model.add(Conv2DTranspose(128, kernel_size=[4,4], strides=[2,2], padding="same", |
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kernel_initializer= keras.initializers.TruncatedNormal(stddev=0.02))) |
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model.add(BatchNormalization(momentum=0.9, epsilon=epsilon)) |
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model.add(LeakyReLU(alpha=0.2)) |
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model.add(Conv2DTranspose(64, kernel_size=[4,4], strides=[2,2], padding="same", |
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kernel_initializer= keras.initializers.TruncatedNormal(stddev=0.02))) |
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model.add(BatchNormalization(momentum=0.9, epsilon=epsilon)) |
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model.add(LeakyReLU(alpha=0.2)) |
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model.add(Conv2DTranspose(3, kernel_size=[4,4], strides=[1,1], padding="same", |
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kernel_initializer= keras.initializers.TruncatedNormal(stddev=0.02))) |
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model.add(Activation("tanh")) |
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model.summary() |
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noise = Input(shape=noise_shape) |
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img = model(noise) |
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return Model(noise, img) |
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def build_discriminator(self): |
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model = Sequential() |
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model.add(Conv2D(128, (3,3), padding='same', input_shape=self.img_shape)) |
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model.add(LeakyReLU(alpha=0.2)) |
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model.add(BatchNormalization()) |
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model.add(Conv2D(128, (3,3), padding='same')) |
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model.add(LeakyReLU(alpha=0.2)) |
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model.add(BatchNormalization()) |
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model.add(MaxPooling2D(pool_size=(3,3))) |
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model.add(Dropout(0.2)) |
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model.add(Conv2D(128, (3,3), padding='same')) |
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model.add(LeakyReLU(alpha=0.2)) |
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model.add(BatchNormalization()) |
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model.add(Conv2D(128, (3,3), padding='same')) |
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model.add(LeakyReLU(alpha=0.2)) |
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model.add(BatchNormalization()) |
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model.add(MaxPooling2D(pool_size=(3,3))) |
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model.add(Dropout(0.3)) |
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model.add(Flatten()) |
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model.add(Dense(128)) |
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model.add(LeakyReLU(alpha=0.2)) |
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model.add(Dense(128)) |
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model.add(LeakyReLU(alpha=0.2)) |
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model.add(Dense(1, activation='sigmoid')) |
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model.summary() |
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img = Input(shape=self.img_shape) |
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validity = model(img) |
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return Model(img, validity) |
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def train(self, epochs, batch_size=128, metrics_update=50, save_images=100, save_model=2000): |
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X_train = np.array(images) |
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X_train = (X_train.astype(np.float32) - 127.5) / 127.5 |
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half_batch = int(batch_size / 2) |
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mean_d_loss=[0,0] |
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mean_g_loss=0 |
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for epoch in range(epochs): |
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idx = np.random.randint(0, X_train.shape[0], half_batch) |
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imgs = X_train[idx] |
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noise = np.random.normal(0, 1, (half_batch, self.noise_size)) |
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gen_imgs = self.generator.predict(noise) |
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d_loss = 0.5 * np.add(self.discriminator.train_on_batch(imgs, np.ones((half_batch, 1))), |
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self.discriminator.train_on_batch(gen_imgs, np.zeros((half_batch, 1)))) |
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noise = np.random.normal(0, 1, (batch_size, self.noise_size)) |
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valid_y = np.array([1] * batch_size) |
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g_loss = self.combined.train_on_batch(noise, valid_y) |
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mean_d_loss[0] += d_loss[0] |
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mean_d_loss[1] += d_loss[1] |
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mean_g_loss += g_loss |
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if epoch % metrics_update == 0: |
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print ("%d [Discriminator loss: %f, acc.: %.2f%%] [Generator loss: %f]" % (epoch, mean_d_loss[0]/metrics_update, 100*mean_d_loss[1]/metrics_update, mean_g_loss/metrics_update)) |
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mean_d_loss=[0,0] |
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mean_g_loss=0 |
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if epoch % save_images == 0: |
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self.save_images(epoch) |
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if epoch % save_model == 0: |
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self.generator.save("kaggle/working/results/generators/generator_%d" % epoch) |
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self.discriminator.save("kaggle/working/results/discriminators/discriminator_%d" % epoch) |
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def save_images(self, epoch): |
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noise = np.random.normal(0, 1, (25, self.noise_size)) |
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gen_imgs = self.generator.predict(noise) |
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gen_imgs = 0.5 * gen_imgs + 0.5 |
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fig, axs = plt.subplots(5,5, figsize = (8,8)) |
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for i in range(5): |
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for j in range(5): |
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axs[i,j].imshow(gen_imgs[5*i+j]) |
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axs[i,j].axis('off') |
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plt.show() |
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fig.savefig("kaggle/working/results/pandaS_%d.png" % epoch) |
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plt.close() |
