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使用 ESRGAN 进行图像超解析

View on TensorFlow.org View on GitHub Download notebook 查看 TF Hub 模型

此 Colab 演示了在 Enhanced Super Resolution Generative Adversarial Network(由 Xintao Wang 等人撰写)[论文] [代码] 中如何使用 TensorFlow Hub 模块

进行图像增强。(最好使用双三次降采样的图像。)

模型在 DIV2K 数据集(双三次降采样的图像)中大小为 128 x 128 的图像块上进行了训练。

准备环境

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
--2021-08-13 20:23:45--  https://user-images.githubusercontent.com/12981474/40157448-eff91f06-5953-11e8-9a37-f6b5693fa03f.png
Resolving user-images.githubusercontent.com (user-images.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
Connecting to user-images.githubusercontent.com (user-images.githubusercontent.com)|185.199.108.133|: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 0.004s  

2021-08-13 20:23:45 (7.82 MB/s) - ‘original.png’ saved [34146/34146]
# Declaring Constants
IMAGE_PATH = "original.png"
SAVED_MODEL_PATH = "https://hub.tensorflow.google.cn/captain-pool/esrgan-tf2/1"

定义辅助函数

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 to.
  """
  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)

对从路径加载的图像执行超解析

hr_image = preprocess_image(IMAGE_PATH)
2021-08-13 20:23:46.801813: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-13 20:23:46.810097: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-13 20:23:46.810983: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-13 20:23:46.813090: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-08-13 20:23:46.813678: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-13 20:23:46.814575: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-13 20:23:46.815442: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-13 20:23:47.383720: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-13 20:23:47.384636: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-13 20:23:47.385498: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-13 20:23:47.386370: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 14648 MB memory:  -> device: 0, name: Tesla V100-SXM2-16GB, pci bus id: 0000:00:05.0, compute capability: 7.0
# 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://hub.tensorflow.google.cn/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))
2021-08-13 20:23:54.396402: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)
2021-08-13 20:23:55.546863: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8100
2021-08-13 20:23:56.066721: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory
Time Taken: 2.586854
# 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

评估模型的性能

!wget "https://lh4.googleusercontent.com/-Anmw5df4gj0/AAAAAAAAAAI/AAAAAAAAAAc/6HxU8XFLnQE/photo.jpg64" -O test.jpg
IMAGE_PATH = "test.jpg"
--2021-08-13 20:23:57--  https://lh4.googleusercontent.com/-Anmw5df4gj0/AAAAAAAAAAI/AAAAAAAAAAc/6HxU8XFLnQE/photo.jpg64
Resolving lh4.googleusercontent.com (lh4.googleusercontent.com)... 108.177.97.132, 2404:6800:4008:c00::84
Connecting to lh4.googleusercontent.com (lh4.googleusercontent.com)|108.177.97.132|: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  

2021-08-13 20:23:57 (83.4 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: 1.169853
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

并排比较输出大小。

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