real_esrgan_mod / app.py
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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
# Define model parameters
if model_name == 'RealESRGAN_x4plus': # x4 RRDBNet model
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': # x4 RRDBNet model
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': # x4 RRDBNet model with 6 blocks
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': # x2 RRDBNet model
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': # x4 VGG-style model (S size)
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'
]
# Determine model paths
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 will be updated
model_path = load_file_from_url(
url=url, model_dir=os.path.join(ROOT_DIR, 'weights'), progress=True, file_name=None)
# Use dni to control the denoise strength
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]
# Restorer Class
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
)
# Use GFPGAN for face enhancement
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)
# Convert the input PIL image to cv2 image, so that it can be processed by realesrgan
cv_img = numpy.array(img)
img = cv2.cvtColor(cv_img, cv2.COLOR_RGBA2BGRA)
# Apply restoration
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:
# Save restored image and return it to the output Image component
if img_mode == 'RGBA': # RGBA images should be saved in png format
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(number):
"""Returns a string of 'number' random characters
"""
characters = "abcdefghijklmnopqrstuvwxyz_0123456789"
result = "".join((random.choice(characters)) for x in range(number))
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"Width: {img.size[0]}, Height: {img.size[1]} | Color Mode: {img_mode}"
return properties
# Gradio Interface
with gr.Blocks(title="Real-ESRGAN Gradio Demo") as demo:
gr.Markdown(
"""# <div align="center"> Real-ESRGAN Demo for Image Restoration and Upscaling </div>
<div align="center"><img width="200" height="74" src="https://github.com/xinntao/Real-ESRGAN/raw/master/assets/realesrgan_logo.png"></div>
This Gradio Demo was built as my Final Project for **CS50's Introduction to Programming with Python**.
Please visit the [Real-ESRGAN GitHub page](https://github.com/xinntao/Real-ESRGAN) for detailed information about the project.
"""
)
with gr.Accordion("Options/Parameters"):
with gr.Row():
model_name = gr.Dropdown(label="Real-ESRGAN model to be used",
choices=["RealESRGAN_x4plus", "RealESRNet_x4plus", "RealESRGAN_x4plus_anime_6B",
"RealESRGAN_x2plus", "realesr-general-x4v3"],
value="realesr-general-x4v3", show_label=True)
denoise_strength = gr.Slider(label="Denoise Strength (Used only with the realesr-general-x4v3 model)",
minimum=0, maximum=1, step=0.1, value=0.5)
outscale = gr.Slider(label="Image Upscaling Factor",
minimum=1, maximum=10, step=1, value=2, show_label=True)
face_enhance = gr.Checkbox(label="Face Enhancement using GFPGAN (Doesn't work for anime images)",
value=False, show_label=True)
with gr.Row():
with gr.Group():
input_image = gr.Image(label="Source Image", type="pil", image_mode="RGBA")
input_image_properties = gr.Textbox(label="Image Properties", max_lines=1)
output_image = gr.Image(label="Restored Image", image_mode="RGBA")
with gr.Row():
restore_btn = gr.Button("Restore Image")
reset_btn = gr.Button("Reset")
# Event listeners:
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])
# reset_btn.click(None, inputs=[], outputs=[input_image], _js="() => (null)\n")
# Undocumented method to clear a component's value using Javascript
demo.launch()