Độ phân giải hình ảnh siêu cao sử dụng ESRGAN

Xem trên TensorFlow.org Chạy trong Google Colab Xem trên GitHub Tải xuống sổ ghi chép Xem mô hình TF Hub

Colab này chứng tỏ sử dụng TensorFlow Hub Module cho Enhanced Siêu Nghị quyết Generative gây tranh cãi Network (bởi Xintao Wang et.al.) [ Giấy ] [ Mã ]

để nâng cao hình ảnh. (Tốt hơn là hình ảnh được lấy mẫu hai chiều).

Mô hình được đào tạo trên Tập dữ liệu DIV2K (trên hình ảnh được lấy mẫu hai lần) trên các bản vá hình ảnh có kích thước 128 x 128.

Môi trường chuẩn bị

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

--2021-11-05 12:46:51--  https://user-images.githubusercontent.com/12981474/40157448-eff91f06-5953-11e8-9a37-f6b5693fa03f.png
Resolving user-images.githubusercontent.com (user-images.githubusercontent.com)... 185.199.109.133, 185.199.108.133, 185.199.111.133, ...
Connecting to user-images.githubusercontent.com (user-images.githubusercontent.com)|185.199.109.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 34146 (33K) [image/png]
Saving to: ‘original.png’

original.png        100%[===================>]  33.35K  --.-KB/s    in 0.002s  

2021-11-05 12:46:51 (13.2 MB/s) - ‘original.png’ saved [34146/34146]

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

Xác định chức năng của người trợ giúp

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)

Thực hiện độ phân giải siêu cao của hình ảnh được tải từ đường dẫn

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")

Saved as Original Image.jpg

png

model = hub.load(SAVED_MODEL_PATH)

Downloaded https://tfhub.dev/captain-pool/esrgan-tf2/1, Total size: 20.60MB

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

Time Taken: 2.695235

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

Saved as Super Resolution.jpg

png

Đánh giá hiệu suất của mô hình

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

--2021-11-05 12:47:03--  https://lh4.googleusercontent.com/-Anmw5df4gj0/AAAAAAAAAAI/AAAAAAAAAAc/6HxU8XFLnQE/photo.jpg64
Resolving lh4.googleusercontent.com (lh4.googleusercontent.com)... 64.233.188.132, 2404:6800:4008:c06::84
Connecting to lh4.googleusercontent.com (lh4.googleusercontent.com)|64.233.188.132|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 84897 (83K) [image/jpeg]
Saving to: ‘test.jpg’

test.jpg            100%[===================>]  82.91K  --.-KB/s    in 0.001s  

2021-11-05 12:47:04 (94.8 MB/s) - ‘test.jpg’ saved [84897/84897]

# 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")

png

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))

Time Taken: 1.161794

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)

PSNR Achieved: 28.029171

png

So sánh kích thước đầu ra cạnh nhau.

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)

PSNR: 28.029171

png