Yuvnish017

style_layer_names = [
    "block1_conv1",
    "block2_conv1",
    "block3_conv1",
    "block4_conv1",
    "block5_conv1",
]
# The layer to use for the content loss.
content_layer_name = "block5_conv2"

Yuvnish017

model = vgg16.VGG16(weights="imagenet", include_top=False)

# Get the symbolic outputs of each "key" layer (we gave them unique names).
outputs_dict = dict([(layer.name, layer.output) for layer in model.layers])

# Set up a model that returns the activation values for every layer in
# VGG19 (as a dict).
feature_extractor = keras.Model(inputs=model.inputs, outputs=outputs_dict)

Yuvnish017

def gram_matrix(x):
  x = tf.transpose(x, (2, 0, 1))
  features = tf.reshape(x, (tf.shape(x)[0], -1))
  gram = tf.matmul(features, tf.transpose(features))
  return gram
  
def style_loss(style, combination):
  S = gram_matrix(style)
  C = gram_matrix(combination)
  channels = 3

Yuvnish017

def preprocess_image(image_path):
    image = cv2.imread(image_path)
    image = cv2.resize(image, (img_cols, img_rows))
    image = np.array(image)
    image = np.expand_dims(image, axis=0)
    image = vgg16.preprocess_input(image)
    return image


def deprocess_image(x):

Yuvnish017

img = cv2.imread('yuvnish_malhotra_photo.jpg')
print(img.shape)
(height, width, channels) = img.shape
img_rows = 400
img_cols = int(width * img_rows / height)
result_prefix = "neural_style_transfer_generated"

# Weights of the different loss components
total_variation_weight = 1e-6
style_weight = 5e-6

Yuvnish017

import matplotlib.pyplot as plt
import cv2
import tensorflow as tf
import numpy as np
from tensorflow import keras
from tensorflow.keras.applications import vgg16
from tensorflow import keras
from tensorflow.keras import backend as K
from tensorflow.keras.regularizers import Regularizer
from tensorflow.keras.layers import Input