تبدیل عکس به شخصیت های دیزنی با پایتون

import tensorflow as tf

import numpy as np

import PIL.Image

# Load the DeepDream model

model = tf.keras.applications.InceptionV3(include_top=False, weights=’imagenet’)

# Create a function to process the image

def process_image(image):

  # Convert the image to an array of floating-point values between 0 and 1

  image = tf.keras.preprocessing.image.img_to_array(image)

  image = np.expand_dims(image, axis=0)

  image = tf.keras.applications.inception_v3.preprocess_input(image)

  return image

# Create a function to deprocess the image

def deprocess_image(image):

  image = image.reshape((image.shape[1], image.shape[2], 3))

  # Undo the preprocessing step

  image /= 2.0

  image += 0.5

  image *= 255.0

  # Clip the pixel values to the range [0, 255]

  image = np.clip(image, 0, 255).astype(‘uint8’)

  return image

# Create a function to apply DeepDream to the image

def deep_dream(image, model, steps=100, step_size=0.01, octave_scale=1.3):

  # Define the names of the layers to use for the DeepDream process

  names = [‘mixed3’, ‘mixed5’, ‘mixed7’]

  # Convert the image to a TensorFlow variable

  image = tf.keras.preprocessing.image.img_to_array(image)

  image = tf.Variable(process_image(image))

  # Create the multi-scale images for the DeepDream process

  octaves = []

  for _ in range(3):

    high_freq = tf.image.resize(image, (int(image.shape[1]*octave_scale), int(image.shape[2]*octave_scale)))

    low_freq = tf.image.resize(tf.image.resize(image, (int(image.shape[1]/octave_scale), int(image.shape[2]/octave_scale))), image.shape[1:3])

    image = high_freq – low_freq

    octaves.append(image)

  # Initialize the DeepDream process

  for octave in range(len(octaves)-1, -1, -1):

    # Upscale the DeepDream image

    upscaled = tf.image.resize(octaves[octave], image.shape[1:3])

    # Create a TensorFlow variable to store the image

    image = tf.Variable(upscaled)

    # Iterate over the layers and apply DeepDream to each one

    for name in names:

      layer = model.get_layer(name).output

      # Calculate the loss and gradients for the layer

      loss = tf.math.reduce_mean(layer)

      gradients = tf.gradients(loss, image)[0]

      # Normalize the gradients

      gradients /= tf.math.reduce_std(gradients) + 1e-8

      # Apply the gradients to the image

      image.assign_add(gradients * step_size)

    # Create the final DeepDream image

    final_image = deprocess_image(image.numpy())

  return final_image

# Load the image

image_path = ‘your_image_path.jpg’

image = PIL.Image.open(image_path)

# Apply DeepDream to the image

dream_image = deep_dream(image, model)

# Save the output image

output_path = ‘disney_character.jpg’

PIL.Image.fromarray(dream_image).save(output_path)