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import gradio as gr
import torch
import cv2
import imgproc
from imgproc import image_to_tensor
from inference import choice_device, build_model
from utils import load_state_dict
model = "srresnet_x4"
device = choice_device("cpu")
# Initialize the model
sr_model = build_model(model, device)
print(f"Build {model} model successfully.")
# Load model weights
sr_model = load_state_dict(sr_model, "weights/SRGAN_x4-ImageNet-8c4a7569.pth.tar")
print(f"Load `{model}` model weights successfully.")
# Start the verification mode of the model.
sr_model.eval()
def downscale(image):
(width, height, colors) = image.shape
new_height = int(60 * width / height)
return cv2.resize(image, (60, new_height), interpolation=cv2.INTER_AREA)
def preprocess(image):
image = image / 255.0
# Convert image data to pytorch format data
tensor = image_to_tensor(image, False, False).unsqueeze_(0)
# Transfer tensor channel image format data to CUDA device
tensor = tensor.to(device="cpu", memory_format=torch.channels_last, non_blocking=True)
return tensor
def processHighRes(image):
if image is None:
raise gr.Error("Please enter an image")
downscaled = downscale(image)
lr_tensor = preprocess(downscaled)
# Use the model to generate super-resolved images
with torch.no_grad():
sr_tensor = sr_model(lr_tensor)
# Save image
sr_image = imgproc.tensor_to_image(sr_tensor, False, False)
return [downscaled, sr_image]
def processLowRes(image):
if image is None:
raise gr.Error("Please enter an image")
(width, height, colors) = image.shape
if width > 150 or height > 150:
raise gr.Error("Image is too big")
lr_tensor = preprocess(image)
# Use the model to generate super-resolved images
with torch.no_grad():
sr_tensor = sr_model(lr_tensor)
# Save image
sr_image = imgproc.tensor_to_image(sr_tensor, False, False)
return sr_image
description = """<p style='text-align: center'> <a href='https://arxiv.org/abs/1609.04802' target='_blank'>Paper</a> | <a href=https://github.com/Lornatang/SRGAN-PyTorch target='_blank'>GitHub</a></p>"""
about = "<p style='text-align: center'>Made for the 2022-2023 Grenoble-INP Phelma Image analysis course by Thibaud CHERUY, Clément DEBUY & Yassine EL KHANOUSSI.</p>"
with gr.Blocks() as demo:
gr.Markdown("# **<p align='center'>SRGAN: Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network</p>**")
gr.Markdown(description)
with gr.Tab("From high res"):
high_res_input = gr.Image(label="High-res source image", show_label=True)
with gr.Row():
low_res_output = gr.Image(label="Low-res image")
srgan_output = gr.Image(label="SRGAN Output")
high_res_button = gr.Button("Process")
with gr.Tab("From low res"):
low_res_input = gr.Image(label="Low-res source image", show_label=True)
srgan_upscale = gr.Image(label="SRGAN Output")
low_res_button = gr.Button("Process")
gr.Examples(
examples=["examples/bird.png", "examples/butterfly.png", "examples/comic.png", "examples/gray.png",
"examples/man.png"],
inputs=[high_res_input],
outputs=[low_res_output, srgan_output],
fn=processHighRes
)
high_res_button.click(processHighRes, inputs=[high_res_input], outputs=[low_res_output, srgan_output])
low_res_button.click(processLowRes, inputs=[low_res_input], outputs=[srgan_upscale])
gr.Markdown(about)
demo.launch() |