image_enhancing.ipynb

Kernel: Python 3

Copyright 2019 The TensorFlow Hub Authors.

Licensed under the Apache License, Version 2.0 (the "License");

Created by @Adrish Dey for Google Summer of Code 2019

# Copyright 2019 The TensorFlow Hub Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, 
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

Super-resolução de imagens usando ESRGAN

Ver em TensorFlow.org

Executar no Google Colab

Ver no GitHub

Baixar notebook

Ver modelo do TF Hub

Este Colab demonstra o uso do módulo do TensorFlow Hub para uma Rede Adversária Generativa de Super-Resolução Aprimorada (por Xintao Wang et.al.) [Artigo] [Código]

para aprimoramento de imagens (preferencialmente, imagens que passaram por downsample de forma bicúbica).

Modelo treinado com o dataset DIV2K (imagens que passaram por downsample de forma bicúbica) em partes de imagens de tamanho 128x128.

Preparação do ambiente

import os
import time
from PIL import Image
import numpy as np
import tensorflow as tf
import tensorflow_hub as hub
import matplotlib.pyplot as plt
os.environ["TFHUB_DOWNLOAD_PROGRESS"] = "True"

!wget "https://user-images.githubusercontent.com/12981474/40157448-eff91f06-5953-11e8-9a37-f6b5693fa03f.png" -O original.png

# Declaring Constants
IMAGE_PATH = "original.png"
SAVED_MODEL_PATH = "https://tfhub.dev/captain-pool/esrgan-tf2/1"

Definição das funções helper

def preprocess_image(image_path):
  """ Loads image from path and preprocesses to make it model ready
      Args:
        image_path: Path to the image file
  """
  hr_image = tf.image.decode_image(tf.io.read_file(image_path))
  # If PNG, remove the alpha channel. The model only supports
  # images with 3 color channels.
  if hr_image.shape[-1] == 4:
    hr_image = hr_image[...,:-1]
  hr_size = (tf.convert_to_tensor(hr_image.shape[:-1]) // 4) * 4
  hr_image = tf.image.crop_to_bounding_box(hr_image, 0, 0, hr_size[0], hr_size[1])
  hr_image = tf.cast(hr_image, tf.float32)
  return tf.expand_dims(hr_image, 0)

def save_image(image, filename):
  """
    Saves unscaled Tensor Images.
    Args:
      image: 3D image tensor. [height, width, channels]
      filename: Name of the file to save.
  """
  if not isinstance(image, Image.Image):
    image = tf.clip_by_value(image, 0, 255)
    image = Image.fromarray(tf.cast(image, tf.uint8).numpy())
  image.save("%s.jpg" % filename)
  print("Saved as %s.jpg" % filename)

%matplotlib inline
def plot_image(image, title=""):
  """
    Plots images from image tensors.
    Args:
      image: 3D image tensor. [height, width, channels].
      title: Title to display in the plot.
  """
  image = np.asarray(image)
  image = tf.clip_by_value(image, 0, 255)
  image = Image.fromarray(tf.cast(image, tf.uint8).numpy())
  plt.imshow(image)
  plt.axis("off")
  plt.title(title)

Super-resolução de imagens carregadas a partir do caminho

hr_image = preprocess_image(IMAGE_PATH)

# Plotting Original Resolution image
plot_image(tf.squeeze(hr_image), title="Original Image")
save_image(tf.squeeze(hr_image), filename="Original Image")

model = hub.load(SAVED_MODEL_PATH)

start = time.time()
fake_image = model(hr_image)
fake_image = tf.squeeze(fake_image)
print("Time Taken: %f" % (time.time() - start))

# Plotting Super Resolution Image
plot_image(tf.squeeze(fake_image), title="Super Resolution")
save_image(tf.squeeze(fake_image), filename="Super Resolution")

Avaliação do desempenho do modelo

!wget "https://lh4.googleusercontent.com/-Anmw5df4gj0/AAAAAAAAAAI/AAAAAAAAAAc/6HxU8XFLnQE/photo.jpg64" -O test.jpg
IMAGE_PATH = "test.jpg"

# Defining helper functions
def downscale_image(image):
  """
      Scales down images using bicubic downsampling.
      Args:
          image: 3D or 4D tensor of preprocessed image
  """
  image_size = []
  if len(image.shape) == 3:
    image_size = [image.shape[1], image.shape[0]]
  else:
    raise ValueError("Dimension mismatch. Can work only on single image.")

  image = tf.squeeze(
      tf.cast(
          tf.clip_by_value(image, 0, 255), tf.uint8))

  lr_image = np.asarray(
    Image.fromarray(image.numpy())
    .resize([image_size[0] // 4, image_size[1] // 4],
              Image.BICUBIC))

  lr_image = tf.expand_dims(lr_image, 0)
  lr_image = tf.cast(lr_image, tf.float32)
  return lr_image

hr_image = preprocess_image(IMAGE_PATH)

lr_image = downscale_image(tf.squeeze(hr_image))

# Plotting Low Resolution Image
plot_image(tf.squeeze(lr_image), title="Low Resolution")

model = hub.load(SAVED_MODEL_PATH)

start = time.time()
fake_image = model(lr_image)
fake_image = tf.squeeze(fake_image)
print("Time Taken: %f" % (time.time() - start))

plot_image(tf.squeeze(fake_image), title="Super Resolution")
# Calculating PSNR wrt Original Image
psnr = tf.image.psnr(
    tf.clip_by_value(fake_image, 0, 255),
    tf.clip_by_value(hr_image, 0, 255), max_val=255)
print("PSNR Achieved: %f" % psnr)

Comparação das saídas lado a lado

plt.rcParams['figure.figsize'] = [15, 10]
fig, axes = plt.subplots(1, 3)
fig.tight_layout()
plt.subplot(131)
plot_image(tf.squeeze(hr_image), title="Original")
plt.subplot(132)
fig.tight_layout()
plot_image(tf.squeeze(lr_image), "x4 Bicubic")
plt.subplot(133)
fig.tight_layout()
plot_image(tf.squeeze(fake_image), "Super Resolution")
plt.savefig("ESRGAN_DIV2K.jpg", bbox_inches="tight")
print("PSNR: %f" % psnr)