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import gradio as gr
from PIL import Image
import torch
import torchvision.transforms as transforms
from torchvision.models import resnet50
import torch.nn.functional as F
import numpy as np
# Load a pre-trained ResNet model and modify it for upscaling
class Upscaler(torch.nn.Module):
def __init__(self, upscale_factor):
super(Upscaler, self).__init__()
self.model = resnet50(pretrained=True)
self.upscale_factor = upscale_factor
self.conv1x1 = torch.nn.Conv2d(1000, 3, kernel_size=1)
def forward(self, x):
x = F.interpolate(x, scale_factor=self.upscale_factor, mode='bilinear', align_corners=True)
x = self.model(x)
x = self.conv1x1(x)
return x
# Custom remastering function with multiple options
def remaster_image(image, color_range=1.0, sharpness=1.0, hdr_intensity=1.0, tone_mapping=1.0, color_grading=1.0):
enhancer = transforms.ColorJitter(
brightness=hdr_intensity,
contrast=contrast,
saturation=color_range,
hue=0
)
image = enhancer(image)
# Adjust sharpness
image = transforms.functional.adjust_sharpness(image, sharpness_factor=sharpness)
# Apply tone mapping and color grading
tone_map = lambda x: x * tone_mapping
graded_image = transforms.functional.lerp(image, tone_map(image), color_grading)
return graded_image
# Function to process image with the selected options
def process_image(image, upscale=False, upscale_factor=2, noise_reduction=0, edge_enhancement=1.0,
detail_preservation=1.0, remaster=False, color_range=1.0, sharpness=1.0,
hdr_intensity=1.0, tone_mapping=1.0, color_grading=1.0):
image = transforms.ToTensor()(image).unsqueeze(0)
if upscale:
upscaler = Upscaler(upscale_factor)
image = upscaler(image)
if remaster:
image = remaster_image(image, color_range, sharpness, hdr_intensity, tone_mapping, color_grading)
image = transforms.ToPILImage()(image.squeeze(0))
return image
# Gradio UI
with gr.Blocks() as demo:
with gr.Row():
image_input = gr.Image(label="Upload Image", type="pil")
image_output = gr.Image(label="Output Image")
with gr.Row():
with gr.Group():
gr.Markdown("### Upscaling Options")
upscale_checkbox = gr.Checkbox(label="Apply Upscaling")
upscale_factor = gr.Slider(2, 8, value=2, label="Upscale Factor")
noise_reduction = gr.Slider(0, 100, value=0, label="Noise Reduction")
edge_enhancement = gr.Slider(0.5, 2.0, value=1.0, label="Edge Enhancement")
detail_preservation = gr.Slider(0.5, 2.0, value=1.0, label="Detail Preservation")
with gr.Group():
gr.Markdown("### Remastering Options")
remaster_checkbox = gr.Checkbox(label="Apply Remastering")
color_range = gr.Slider(0.5, 2.0, value=1.0, label="Dynamic Color Range")
sharpness = gr.Slider(0.5, 2.0, value=1.0, label="Advanced Sharpness Control")
hdr_intensity = gr.Slider(0.5, 2.0, value=1.0, label="HDR Intensity")
tone_mapping = gr.Slider(0.5, 2.0, value=1.0, label="Tone Mapping")
color_grading = gr.Slider(0.5, 2.0, value=1.0, label="Color Grading")
process_button = gr.Button("Process Image")
process_button.click(
process_image,
inputs=[image_input, upscale_checkbox, upscale_factor, noise_reduction, edge_enhancement, detail_preservation,
remaster_checkbox, color_range, sharpness, hdr_intensity, tone_mapping, color_grading],
outputs=image_output
)
demo.launch() |