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import gradio as gr
import numpy as np
import torch
from PIL import Image
from skimage.feature import graycomatrix, graycoprops
from torchvision import transforms
# Load the model
model = torch.jit.load("SuSy.pt")
def process_image(image):
# Set Parameters
top_k_patches = 5
patch_size = 224
# Get the image dimensions
width, height = image.size
# Calculate the number of patches
num_patches_x = width // patch_size
num_patches_y = height // patch_size
# Divide the image in patches
patches = np.zeros((num_patches_x * num_patches_y, patch_size, patch_size, 3), dtype=np.uint8)
for i in range(num_patches_x):
for j in range(num_patches_y):
x = i * patch_size
y = j * patch_size
patch = image.crop((x, y, x + patch_size, y + patch_size))
patches[i * num_patches_y + j] = np.array(patch)
# Compute the most relevant patches (optional)
dissimilarity_scores = []
for patch in patches:
transform_patch = transforms.Compose([transforms.PILToTensor(), transforms.Grayscale()])
grayscale_patch = transform_patch(Image.fromarray(patch)).squeeze(0)
glcm = graycomatrix(grayscale_patch, [5], [0], 256, symmetric=True, normed=True)
dissimilarity_scores.append(graycoprops(glcm, "contrast")[0, 0])
# Sort patch indices by their dissimilarity score
sorted_indices = np.argsort(dissimilarity_scores)[::-1]
# Extract top k patches and convert them to tensor
top_patches = patches[sorted_indices[:top_k_patches]]
top_patches = torch.from_numpy(np.transpose(top_patches, (0, 3, 1, 2))) / 255.0
# Predict patches
model.eval()
with torch.no_grad():
preds = model(top_patches)
# Process results
classes = ['Authentic', 'DALL·E 3', 'Stable Diffusion 1.x', 'MJ V5/V6', 'MJ V1/V2', 'Stable Diffusion XL']
mean_probs = preds.mean(dim=0).numpy()
# Create a dictionary of class probabilities
class_probs = {cls: prob for cls, prob in zip(classes, mean_probs)}
# Sort probabilities in descending order
sorted_probs = dict(sorted(class_probs.items(), key=lambda item: item[1], reverse=True))
return sorted_probs
# Define Gradio interface
iface = gr.Interface(
fn=process_image,
inputs=gr.Image(type="pil"),
outputs=gr.Label(num_top_classes=6),
title="SuSy: Synthetic Image Detector",
description="""
<table style="border-collapse: collapse; border: none; padding: 20px;">
<tr style="border: none;">
<td style="border: none; vertical-align: top; padding-right: 30px; padding-left: 30px;">
<img src="https://cdn-uploads.huggingface.co/production/uploads/62f7a16192950415b637e201/NobqlpFbFkTyBi1LsT9JE.png" alt="SuSy Logo" width="120" style="margin-bottom: 10px;">
</td>
<td style="border: none; vertical-align: top; padding: 10px;">
<p style="margin-bottom: 15px;">Detect synthetic images with SuSy! SuSy can distinguish between authentic images and those generated by DALL·E, Midjourney and Stable Diffusion.</p>
<p style="margin-top: 15px;">Learn more about SuSy: <a href="https://arxiv.org/abs/2409.14128">Present and Future Generalization of Synthetic Image Detectors</a></p>
<p style="margin-top: 15px;">
Enter the SuSy-verse!
<a href="https://huggingface.co/HPAI-BSC/SuSy">Model</a> |
<a href="https://github.com/HPAI-BSC/SuSy">Code</a> |
<a href="https://huggingface.co/datasets/HPAI-BSC/SuSy-Dataset">Dataset</a>
</p>
</td>
</tr>
</table>
""",
examples=[
["example_authentic.jpg"],
["example_dalle3.jpg"],
["example_mjv5.jpg"],
["example_sdxl.jpg"]
]
)
# Launch the interface
iface.launch() |