Update app.py

app.py

@@ -1,24 +1,31 @@
 import gradio as gr
 from PIL import Image, ImageEnhance
-import numpy as np
 import torch
-import torchvision.transforms as transforms
+import torch.nn.functional as F
+from torchvision import transforms
 from torchvision.models import resnet34
+from torchvision.models.segmentation import deeplabv3_resnet50
+import numpy as np
+import cv2
 
-# Load a pre-trained ResNet model
-model = resnet34(pretrained=True)
+# Load a pre-trained ResNet model for remastering
+resnet_model = resnet34(pretrained=True)
+resnet_model.eval()
 
-# Define the upscaling function
-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)
+# Load a pre-trained DeepLab model for segmentation (optional for advanced remastering)
+deeplab_model = deeplabv3_resnet50(pretrained=True)
+deeplab_model.eval()
+
+# Define the upscaling function using super-resolution techniques
+def upscale_image(image, upscale_factor=2):
+    # Convert the image to a tensor and upscale it using a neural network
+    preprocess = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Lambda(lambda x: x.unsqueeze(0))
+    ])
+    img_tensor = preprocess(image)
+    upscaled_tensor = F.interpolate(img_tensor, scale_factor=upscale_factor, mode='bicubic', align_corners=False)
+    upscaled_image = transforms.ToPILImage()(upscaled_tensor.squeeze())
     
     return upscaled_image
 
@@ -32,21 +39,33 @@ def remaster_image(image, color_range=1.0, sharpness=1.0, hdr_intensity=1.0, ton
     enhancer = ImageEnhance.Sharpness(image)
     image = enhancer.enhance(sharpness)
 
-    # For HDR simulation and tone mapping, we're using simple brightness adjustments
+    # Apply a simulated HDR effect using tone mapping
     enhancer = ImageEnhance.Brightness(image)
     image = enhancer.enhance(hdr_intensity)
-
-    # Simulate color grading by adjusting contrast
+    
     enhancer = ImageEnhance.Contrast(image)
     image = enhancer.enhance(color_grading)
-
-    return image
+    
+    # Optional: Use segmentation to remaster specific regions
+    input_tensor = transforms.ToTensor()(image).unsqueeze(0)
+    with torch.no_grad():
+        output = deeplab_model(input_tensor)['out'][0]
+    output_predictions = output.argmax(0)
+    
+    # Process each segmented region (e.g., sky, water) differently (optional)
+    # Example: Apply a slight blur to the sky region to create a dreamy effect
+    mask = output_predictions.byte().cpu().numpy()
+    segmented_image = np.array(image)
+    segmented_image[mask == 15] = cv2.GaussianBlur(segmented_image[mask == 15], (5, 5), 0)
+    
+    final_image = Image.fromarray(segmented_image)
+    
+    return final_image
 
 # Process function for Gradio
-def process_image(image, upscale=False, upscale_factor=2, sharpness=1.0, contrast=1.0, brightness=1.0,
-                  remaster=False, color_range=1.0, hdr_intensity=1.0, tone_mapping=1.0, color_grading=1.0):
+def process_image(image, upscale=False, upscale_factor=2, remaster=False, color_range=1.0, sharpness=1.0, hdr_intensity=1.0, tone_mapping=1.0, color_grading=1.0):
     if upscale:
-        image = upscale_image(image, upscale_factor, sharpness, contrast, brightness)
+        image = upscale_image(image, upscale_factor)
     
     if remaster:
         image = remaster_image(image, color_range, sharpness, hdr_intensity, tone_mapping, color_grading)
@@ -64,14 +83,12 @@ with gr.Blocks() as demo:
             gr.Markdown("### Upscaling Options")
             upscale_checkbox = gr.Checkbox(label="Apply Upscaling")
             upscale_factor = gr.Slider(1, 8, 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")
         
         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="Sharpness")
             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")
@@ -80,8 +97,7 @@ with gr.Blocks() as demo:
     
     process_button.click(
         process_image,
-        inputs=[image_input, upscale_checkbox, upscale_factor, sharpness, contrast, brightness,
-                remaster_checkbox, color_range, hdr_intensity, tone_mapping, color_grading],
+        inputs=[image_input, upscale_checkbox, upscale_factor, remaster_checkbox, color_range, sharpness, hdr_intensity, tone_mapping, color_grading],
         outputs=image_output
     )