gfp_gan.py

# Clone GFPGAN and enter the GFPGAN folder
%cd /content
!rm -rf GFPGAN
!git clone https://github.com/TencentARC/GFPGAN.git
%cd GFPGAN

# Set up the environment
# Install basicsr - https://github.com/xinntao/BasicSR
# We use BasicSR for both training and inference
!pip install basicsr
# Install facexlib - https://github.com/xinntao/facexlib
# We use face detection and face restoration helper in the facexlib package
!pip install facexlib
# Install other depencencies
!pip install -r requirements.txt
!python setup.py develop
!pip install realesrgan  # used for enhancing the background (non-face) regions
# Download the pre-trained model
!wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P experiments/pretrained_models
  
  # upload your own images
import os
from google.colab import files
import shutil

upload_folder = 'inputs/upload'

if os.path.isdir(upload_folder):
    shutil.rmtree(upload_folder)
os.mkdir(upload_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)
  
# Now we use the GFPGAN to restore the above low-quality images
# We use [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) for enhancing the background (non-face) regions
!rm -rf results
!python inference_gfpgan.py --upscale 2 --test_path inputs/upload --save_root results --model_path experiments/pretrained_models/GFPGANCleanv1-NoCE-C2.pth --bg_upsampler realesrgan

!ls results/cmp

# We first visualize the cropped faces
# The left are the inputs images; the right are the results of GFPGAN

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('GFPGAN 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 = 'results/cropped_faces'
result_folder = 'results/restored_faces'
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 the result
!ls results
print('Download results')
os.system('zip -r download.zip results')
files.download("download.zip")