Low Graphic image to HD image

Real-ESRGAN Inference Demo

https://colab.research.google.com/drive/1k2Zod6kSHEvraybHl50Lys0LerhyTMCo?usp=sharing#scrollTo=lHNHoP8PZJQ7








# Clone Real-ESRGAN and enter the Real-ESRGAN
!git clone https://github.com/xinntao/Real-ESRGAN.git
%cd Real-ESRGAN
# Set up the environment
!pip install basicsr
!pip install facexlib
!pip install gfpgan
!pip install -r requirements.txt
!python setup.py develop

# Upload Image
import os
from google.colab import files
import shutil

upload_folder = 'upload'
result_folder = 'results'

if os.path.isdir(upload_folder):
    shutil.rmtree(upload_folder)
if os.path.isdir(result_folder):
    shutil.rmtree(result_folder)
os.mkdir(upload_folder)
os.mkdir(result_folder)

# upload images
uploaded = files.upload()
for filename in uploaded.keys():
  dst_path = os.path.join(upload_folder, filename)
  print(f'move {filename} to {dst_path}')
  shutil.move(filename, dst_path)
  
# Inference

# if it is out of memory, try to use the `--tile` option
# We upsample the image with the scale factor X3.5
!python inference_realesrgan.py -n RealESRGAN_x4plus -i upload --outscale 3.5 --face_enhance
# Arguments
# -n, --model_name: Model names
# -i, --input: input folder or image
# --outscale: Output scale, can be arbitrary scale factore. 

# Visualization

# utils for visualization
import cv2
import matplotlib.pyplot as plt
def display(img1, img2):
  fig = plt.figure(figsize=(25, 10))
  ax1 = fig.add_subplot(1, 2, 1) 
  plt.title('Input image', fontsize=16)
  ax1.axis('off')
  ax2 = fig.add_subplot(1, 2, 2)
  plt.title('Real-ESRGAN output', fontsize=16)
  ax2.axis('off')
  ax1.imshow(img1)
  ax2.imshow(img2)
def imread(img_path):
  img = cv2.imread(img_path)
  img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
  return img

# display each image in the upload folder
import os
import glob

input_folder = 'upload'
result_folder = 'results'
input_list = sorted(glob.glob(os.path.join(input_folder, '*')))
output_list = sorted(glob.glob(os.path.join(result_folder, '*')))
for input_path, output_path in zip(input_list, output_list):
  img_input = imread(input_path)
  img_output = imread(output_path)
  display(img_input, img_output)
  
# Download Result
# Download the results
zip_filename = 'Real-ESRGAN_result.zip'
if os.path.exists(zip_filename):
  os.remove(zip_filename)
os.system(f"zip -r -j {zip_filename} results/*")
files.download(zip_filename)