test

app.py

@@ -5,13 +5,13 @@ import numpy as np
 import PIL.Image
 
 
-def pil_to_torch(image, ref_size=256):
+def pil_to_torch(image, ref_size=512):
     image = np.array(image)
     image = image.transpose((2, 0, 1))
     image = torch.tensor(image).float() / 255
     image = image.unsqueeze(0)
 
-    if ref_size == 128:
+    if ref_size == 256:
         size = (ref_size, ref_size)
     elif image.shape[2] > image.shape[3]:
         size = (ref_size, ref_size * image.shape[3]//image.shape[2])
@@ -31,7 +31,7 @@ def torch_to_pil(image):
 
 
 def image_mod(image, noise_level, denoiser):
-    image = pil_to_torch(image, ref_size=128 if denoiser == 'DiffUNet' else 256)
+    image = pil_to_torch(image, ref_size=256 if denoiser == 'DiffUNet' else 512)
     if denoiser == 'DnCNN':
         den = dinv.models.DnCNN()
         sigma0 = 2/255
@@ -62,17 +62,17 @@ output_images = gr.Image(label='Denoised Image')
 noise_image = gr.Image(label='Noisy Image')
 input_image_output = gr.Image(label='Input Image')
 
-noise_levels = gr.Dropdown(choices=[0.1, 0.2, 0.3, 0.5, 1], value=0.1, label='Noise Level')
+noise_levels = gr.Dropdown(choices=[0.05, 0.1, 0.2, 0.3, 0.5, 1], value=0.1, label='Noise Level')
 
-denoiser = gr.Dropdown(choices=['DnCNN', 'DRUNet', 'DiffUNet', 'BM3D', 'MedianFilter', 'TV', 'TGV', 'Wavelets'], value='DnCNN', label='Denoiser')
+denoiser = gr.Dropdown(choices=['DnCNN', 'DRUNet', 'DiffUNet', 'BM3D', 'MedianFilter', 'TV', 'TGV', 'Wavelets'], value='DRUNet', label='Denoiser')
 
 demo = gr.Interface(
     image_mod,
     inputs=[input_image, noise_levels, denoiser],
-    examples=[['https://deepinv.github.io/deepinv/_static/deepinv_logolarge.png', 0.1, 'DnCNN']],
+    examples=[['https://upload.wikimedia.org/wikipedia/commons/b/b4/Lionel-Messi-Argentina-2022-FIFA-World-Cup_%28cropped%29.jpg', 0.1, 'DRUNet']],
     outputs=[noise_image, output_images],
     title="Image Denoising with DeepInverse",
-    description="Denoise an image using a variety of denoisers and noise levels using the deepinverse library (https://deepinv.github.io/). We only include lightweight models like DnCNN and MedianFilter as this example is intended to be run on a CPU. We also automatically resize the input image to 256 pixels to reduce the computation time. For more advanced models, please run the code locally.",
+    description="Denoise an image using a variety of denoisers and noise levels using the deepinverse library (https://deepinv.github.io/). We only include lightweight models like DnCNN and MedianFilter as this example is intended to be run on a CPU. We also automatically resize the input image to 512 pixels to reduce the computation time. For more advanced models, please run the code locally.",
 )
 
 demo.launch()