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0200dd6 7612bbc 2a9e469 7612bbc 0200dd6 7612bbc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 | import gradio as gr
import torch
import torch.nn as nn
import torchvision.transforms as transforms
from PIL import Image
import numpy as np
import os
# Check if CUDA is available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Using device: {device}")
class DeblurNet(nn.Module):
def __init__(self):
super(DeblurNet, self).__init__()
# Encoder
self.enc_conv1 = self.conv_block(3, 64)
self.enc_conv2 = self.conv_block(64, 128)
self.enc_conv3 = self.conv_block(128, 256)
# Bottleneck
self.bottleneck = self.conv_block(256, 512)
# Decoder with residual connections
self.dec_conv1 = self.conv_block(512 + 256, 256)
self.dec_conv2 = self.conv_block(256 + 128, 128)
self.dec_conv3 = self.conv_block(128 + 64, 64)
self.final_conv = nn.Conv2d(64, 3, kernel_size=3, padding=1)
# Pooling and upsampling
self.pool = nn.MaxPool2d(2, 2)
self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
def conv_block(self, in_channels, out_channels):
return nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
nn.ReLU(inplace=True)
)
def forward(self, x):
# Encoder
x1 = self.enc_conv1(x)
x2 = self.pool(x1)
x2 = self.enc_conv2(x2)
x3 = self.pool(x2)
x3 = self.enc_conv3(x3)
x4 = self.pool(x3)
# Bottleneck
x4 = self.bottleneck(x4)
# Decoder with skip connections
x = self.upsample(x4)
x = torch.cat([x, x3], dim=1)
x = self.dec_conv1(x)
x = self.upsample(x)
x = torch.cat([x, x2], dim=1)
x = self.dec_conv2(x)
x = self.upsample(x)
x = torch.cat([x, x1], dim=1)
x = self.dec_conv3(x)
x = self.final_conv(x)
return torch.tanh(x)
# Load model
model = DeblurNet().to(device)
model_path = os.path.join('model', 'best_deblur_model.pth')
model.load_state_dict(torch.load(model_path, map_location=device))
model.eval()
# Image processing functions
transform = transforms.Compose([
transforms.Resize((256, 256)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
def postprocess_image(tensor):
tensor = tensor * 0.5 + 0.5
tensor = torch.clamp(tensor, 0, 1)
image = tensor.cpu().detach().numpy()
image = np.transpose(image, (1, 2, 0))
return (image * 255).astype(np.uint8)
def deblur_image(input_image):
if input_image is None:
return None
try:
# Convert to PIL Image
if isinstance(input_image, np.ndarray):
input_image = Image.fromarray(input_image)
# Save original size
original_size = input_image.size
# Preprocess
input_tensor = transform(input_image).unsqueeze(0).to(device)
# Inference
with torch.no_grad():
output_tensor = model(input_tensor)
# Postprocess
output_image = postprocess_image(output_tensor[0])
# Resize back to original size
output_image = Image.fromarray(output_image).resize(original_size)
return np.array(output_image)
except Exception as e:
print(f"Error processing image: {e}")
return None
# Create Gradio interface
demo = gr.Interface(
fn=deblur_image,
inputs=gr.Image(type="numpy", label="Upload Blurry Image"),
outputs=gr.Image(type="numpy", label="Deblurred Result"),
title="Image Deblurring",
description="Upload a blurry image and get it deblurred using deep learning.",
examples=[
["examples/example1.jpg"]
]
)
if __name__ == "__main__":
demo.launch() |