|
import gradio as gr |
|
import cv2 |
|
import numpy |
|
import os |
|
import random |
|
from basicsr.archs.rrdbnet_arch import RRDBNet |
|
from basicsr.utils.download_util import load_file_from_url |
|
|
|
from realesrgan import RealESRGANer |
|
from realesrgan.archs.srvgg_arch import SRVGGNetCompact |
|
|
|
|
|
last_file = None |
|
img_mode = "RGBA" |
|
|
|
|
|
def realesrgan(img, model_name, denoise_strength, face_enhance, outscale): |
|
"""Real-ESRGAN function to restore (and upscale) images. |
|
""" |
|
if not img: |
|
return |
|
|
|
|
|
if model_name == 'RealESRGAN_x4plus': |
|
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4) |
|
netscale = 4 |
|
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth'] |
|
elif model_name == 'RealESRNet_x4plus': |
|
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4) |
|
netscale = 4 |
|
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth'] |
|
elif model_name == 'RealESRGAN_x4plus_anime_6B': |
|
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4) |
|
netscale = 4 |
|
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth'] |
|
elif model_name == 'RealESRGAN_x2plus': |
|
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2) |
|
netscale = 2 |
|
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth'] |
|
elif model_name == 'realesr-general-x4v3': |
|
model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu') |
|
netscale = 4 |
|
file_url = [ |
|
'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth', |
|
'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth' |
|
] |
|
|
|
|
|
model_path = os.path.join('weights', model_name + '.pth') |
|
if not os.path.isfile(model_path): |
|
ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) |
|
for url in file_url: |
|
|
|
model_path = load_file_from_url( |
|
url=url, model_dir=os.path.join(ROOT_DIR, 'weights'), progress=True, file_name=None) |
|
|
|
|
|
dni_weight = None |
|
if model_name == 'realesr-general-x4v3' and denoise_strength != 1: |
|
wdn_model_path = model_path.replace('realesr-general-x4v3', 'realesr-general-wdn-x4v3') |
|
model_path = [model_path, wdn_model_path] |
|
dni_weight = [denoise_strength, 1 - denoise_strength] |
|
|
|
|
|
upsampler = RealESRGANer( |
|
scale=netscale, |
|
model_path=model_path, |
|
dni_weight=dni_weight, |
|
model=model, |
|
tile=0, |
|
tile_pad=10, |
|
pre_pad=10, |
|
half=False, |
|
gpu_id=None |
|
) |
|
|
|
|
|
if face_enhance: |
|
from gfpgan import GFPGANer |
|
face_enhancer = GFPGANer( |
|
model_path='https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth', |
|
upscale=outscale, |
|
arch='clean', |
|
channel_multiplier=2, |
|
bg_upsampler=upsampler) |
|
|
|
|
|
cv_img = numpy.array(img) |
|
img = cv2.cvtColor(cv_img, cv2.COLOR_RGBA2BGRA) |
|
|
|
|
|
try: |
|
if face_enhance: |
|
_, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True) |
|
else: |
|
output, _ = upsampler.enhance(img, outscale=outscale) |
|
except RuntimeError as error: |
|
print('Error', error) |
|
print('If you encounter CUDA out of memory, try to set --tile with a smaller number.') |
|
else: |
|
|
|
if img_mode == 'RGBA': |
|
extension = 'png' |
|
else: |
|
extension = 'jpg' |
|
|
|
out_filename = f"output_{rnd_string(8)}.{extension}" |
|
cv2.imwrite(out_filename, output) |
|
global last_file |
|
last_file = out_filename |
|
return out_filename |
|
|
|
|
|
def rnd_string(x): |
|
"""Returns a string of 'x' random characters |
|
""" |
|
characters = "abcdefghijklmnopqrstuvwxyz_0123456789" |
|
result = "".join((random.choice(characters)) for i in range(x)) |
|
return result |
|
|
|
|
|
def reset(): |
|
"""Resets the Image components of the Gradio interface and deletes |
|
the last processed image |
|
""" |
|
global last_file |
|
if last_file: |
|
print(f"Deleting {last_file} ...") |
|
os.remove(last_file) |
|
last_file = None |
|
return gr.update(value=None), gr.update(value=None) |
|
|
|
|
|
def has_transparency(img): |
|
"""This function works by first checking to see if a "transparency" property is defined |
|
in the image's info -- if so, we return "True". Then, if the image is using indexed colors |
|
(such as in GIFs), it gets the index of the transparent color in the palette |
|
(img.info.get("transparency", -1)) and checks if it's used anywhere in the canvas |
|
(img.getcolors()). If the image is in RGBA mode, then presumably it has transparency in |
|
it, but it double-checks by getting the minimum and maximum values of every color channel |
|
(img.getextrema()), and checks if the alpha channel's smallest value falls below 255. |
|
https://stackoverflow.com/questions/43864101/python-pil-check-if-image-is-transparent |
|
""" |
|
if img.info.get("transparency", None) is not None: |
|
return True |
|
if img.mode == "P": |
|
transparent = img.info.get("transparency", -1) |
|
for _, index in img.getcolors(): |
|
if index == transparent: |
|
return True |
|
elif img.mode == "RGBA": |
|
extrema = img.getextrema() |
|
if extrema[3][0] < 255: |
|
return True |
|
return False |
|
|
|
|
|
def image_properties(img): |
|
"""Returns the dimensions (width and height) and color mode of the input image and |
|
also sets the global img_mode variable to be used by the realesrgan function |
|
""" |
|
global img_mode |
|
if img: |
|
if has_transparency(img): |
|
img_mode = "RGBA" |
|
else: |
|
img_mode = "RGB" |
|
properties = f"Resolution: Width: {img.size[0]}, Height: {img.size[1]} | Color Mode: {img_mode}" |
|
return properties |
|
|
|
|
|
def main(): |
|
|
|
with gr.Blocks(title="Real-ESRGAN Gradio Demo", theme="ParityError/Interstellar") as demo: |
|
|
|
gr.Markdown( |
|
""" |
|
""" |
|
) |
|
|
|
with gr.Accordion("Upscaling option"): |
|
with gr.Row(): |
|
model_name = gr.Dropdown(label="Upscaler model", |
|
choices=["RealESRGAN_x4plus", "RealESRNet_x4plus", "RealESRGAN_x4plus_anime_6B", |
|
"RealESRGAN_x2plus", "realesr-general-x4v3"], |
|
value="RealESRGAN_x4plus_anime_6B", show_label=True) |
|
denoise_strength = gr.Slider(label="Denoise Strength", |
|
minimum=0, maximum=1, step=0.1, value=0.5) |
|
outscale = gr.Slider(label="Resolution upscale", |
|
minimum=1, maximum=6, step=1, value=4, show_label=True) |
|
face_enhance = gr.Checkbox(label="Face Enhancement (GFPGAN)", |
|
) |
|
|
|
with gr.Row(): |
|
with gr.Group(): |
|
input_image = gr.Image(label="Input Image", type="pil", image_mode="RGBA") |
|
input_image_properties = gr.Textbox(label="Image Properties", max_lines=1) |
|
output_image = gr.Image(label="Output Image", image_mode="RGBA") |
|
with gr.Row(): |
|
reset_btn = gr.Button("Remove images") |
|
restore_btn = gr.Button("Upscale") |
|
|
|
|
|
input_image.change(fn=image_properties, inputs=input_image, outputs=input_image_properties) |
|
restore_btn.click(fn=realesrgan, |
|
inputs=[input_image, model_name, denoise_strength, face_enhance, outscale], |
|
outputs=output_image) |
|
reset_btn.click(fn=reset, inputs=[], outputs=[output_image, input_image]) |
|
|
|
|
|
|
|
gr.Markdown( |
|
""" |
|
""" |
|
) |
|
|
|
demo.launch() |
|
|
|
|
|
if __name__ == "__main__": |
|
main() |