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import os
import cv2
import gradio as gr
import torch
from basicsr.archs.srvgg_arch import SRVGGNetCompact
from gfpgan.utils import GFPGANer
from realesrgan.utils import RealESRGANer
os.system("pip freeze")
#os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P .")
#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth -P .")
#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P .")
# background enhancer with RealESRGAN
model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
model_path = 'realesr-general-x4v3.pth'
half = True if torch.cuda.is_available() else False
upsampler = RealESRGANer(scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
# Use GFPGAN for face enhancement
face_enhancer_v3 = GFPGANer(
model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
face_enhancer_v2 = GFPGANer(
model_path='GFPGANv1.2.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
os.makedirs('output', exist_ok=True)
def inference(img, version, scale):
print(img, version, scale)
try:
img = cv2.imread(img, cv2.IMREAD_UNCHANGED)
if len(img.shape) == 3 and img.shape[2] == 4:
img_mode = 'RGBA'
else:
img_mode = None
h, w = img.shape[0:2]
if h < 300:
img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
if version == 'v1.2':
face_enhancer = face_enhancer_v2
else:
face_enhancer = face_enhancer_v3
try:
_, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
except RuntimeError as error:
print('Error', error)
else:
extension = 'png'
try:
if scale != 2:
interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4
h, w = img.shape[0:2]
output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation)
except Exception as error:
print('wrong scale input.', error)
if img_mode == 'RGBA': # RGBA images should be saved in png format
extension = 'png'
else:
extension = 'jpg'
save_path = f'output/out.{extension}'
cv2.imwrite(save_path, output)
output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
return output, save_path
except Exception as error:
print('global exception', error)
return None, None
gr.Interface(
inference, [
gr.inputs.Image(type="filepath", label="Input"),
], [
gr.outputs.Image(type="numpy", label="Output (The whole image)"),
gr.outputs.File(label="Download the output image")
],
examples=[['1.jpg', 'v1.3', 2]]).launch()
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