Update app.py

app.py

@@ -1,43 +1,58 @@
 import gradio as gr
+from PIL import Image
 import torch
-from torchvision import transforms
-from PIL import Image, ImageEnhance
-import rembg
+import torchvision.transforms as transforms
+from torchvision.models import resnet50
+import torch.nn.functional as F
+import numpy as np
 
-# Upscaling function using a basic upscaling approach
-def upscale_image(image, upscale_factor=2, sharpness=1.0, contrast=1.0, brightness=1.0):
-    # Resize the image
-    width, height = image.size
-    new_size = (int(width * upscale_factor), int(height * upscale_factor))
-    upscaled_image = image.resize(new_size, Image.BICUBIC)
-    
-    # Apply sharpness, contrast, and brightness adjustments
-    upscaled_image = ImageEnhance.Sharpness(upscaled_image).enhance(sharpness)
-    upscaled_image = ImageEnhance.Contrast(upscaled_image).enhance(contrast)
-    upscaled_image = ImageEnhance.Brightness(upscaled_image).enhance(brightness)
-    
-    return upscaled_image
+# 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
 
-# Remastering function with background removal and enhancements
-def remaster_image(image, remove_background=False, color_balance=1.0, detail_enhancement=1.0):
-    if remove_background:
-        image = rembg.remove(image)
-    
-    # Apply color balance and detail enhancement
-    image = ImageEnhance.Color(image).enhance(color_balance)
-    image = ImageEnhance.Sharpness(image).enhance(detail_enhancement)
-    
-    return image
+# 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)
 
-# Main function combining upscaling and remastering
-def process_image(image, apply_upscale=False, upscale_factor=2, sharpness=1.0, contrast=1.0, brightness=1.0,
-                  apply_remaster=False, remove_background=False, color_balance=1.0, detail_enhancement=1.0):
-    if apply_upscale:
-        image = upscale_image(image, upscale_factor, sharpness, contrast, brightness)
+    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 apply_remaster:
-        image = remaster_image(image, remove_background, color_balance, detail_enhancement)
+    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
@@ -50,24 +65,26 @@ with gr.Blocks() as demo:
         with gr.Group():
             gr.Markdown("### Upscaling Options")
             upscale_checkbox = gr.Checkbox(label="Apply Upscaling")
-            upscale_factor = gr.Slider(1, 4, value=2, label="Upscale Factor")
-            sharpness = gr.Slider(0.5, 2.0, value=1.0, label="Sharpness")
-            contrast = gr.Slider(0.5, 2.0, value=1.0, label="Contrast")
-            brightness = gr.Slider(0.5, 2.0, value=1.0, label="Brightness")
+            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")
-            remove_background = gr.Checkbox(label="Remove Background")
-            color_balance = gr.Slider(0.5, 2.0, value=1.0, label="Color Balance")
-            detail_enhancement = gr.Slider(0.5, 2.0, value=1.0, label="Detail Enhancement")
+            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, sharpness, contrast, brightness,
-                remaster_checkbox, remove_background, color_balance, detail_enhancement],
+        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
     )