Image Super Resolution using ESRGAN

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

 --2024-03-09 12:57:57-- 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 0.003s 2024-03-09 12:57:57 (9.94 MB/s) - ‘original.png’ saved [34146/34146] 

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

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) 

hr_image = preprocess_image(IMAGE_PATH) 

 2024-03-09 12:57:57.917967: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:282] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected 

# 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 

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: 1.146020 

# 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 

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

 --2024-03-09 12:58:05-- https://lh4.googleusercontent.com/-Anmw5df4gj0/AAAAAAAAAAI/AAAAAAAAAAc/6HxU8XFLnQE/photo.jpg64 Resolving lh4.googleusercontent.com (lh4.googleusercontent.com)... 173.194.216.132, 2607:f8b0:400c:c10::84 Connecting to lh4.googleusercontent.com (lh4.googleusercontent.com)|173.194.216.132|: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 2024-03-09 12:58:05 (92.9 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") 

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: 1.151733 

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 

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