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767dcae | 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 | import gradio as gr
import os
from inference import Pipeline
# Initialize the pipeline
# This will load the model onto the GPU (if available)
pipeline = Pipeline(model_path="models for IML/APSC-Net.pth")
def predict(image):
"""
Function to be called by the Gradio interface.
Takes a PIL image and returns the mask.
"""
# The pipeline returns a dictionary with a base64 encoded image string
# We don't need to decode it as Gradio's Image component can handle it.
result = pipeline(image)
# The output format for gr.Image can be a PIL Image, numpy array, or file path.
# We can directly return the base64 string with a data URI scheme.
return f"data:image/png;base64,{result['image']}"
# --- Gradio Interface ---
title = "APSC-Net: Image Manipulation Localization"
description = """
Official Gradio Demo for **APSC-Net**, from the paper *"Towards Modern Image Manipulation Localization: A Large-Scale Dataset and Novel Methods"*.
Upload an image to detect manipulated regions. The model will output a binary mask where white indicates tampered regions.
\nFor more details, visit the [project repository](https://github.com/qcf-568/MIML).
"""
# Example images from the 'models for IML' directory (assuming you have a test folder)
# Add your own example images here
example_paths = [
os.path.join(os.path.dirname(__file__), "models/for_IML/test_data/CASIA1/imgs/Sp_D_CRN_A_cha0001_cha0001_0101_1.jpg"),
os.path.join(os.path.dirname(__file__), "models/for_IML/test_data/IMD20/imgs/0000_000_02.png"),
]
# Filter out examples that don't exist
examples = [path for path in example_paths if os.path.exists(path)]
iface = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil", label="Input Image"),
outputs=gr.Image(type="pil", label="Predicted Tamper Mask"),
title=title,
description=description,
examples=examples,
cache_examples=True,
allow_flagging="never",
article="<p style='text-align: center'>Paper: <a href='https://openaccess.thecvf.com/content/CVPR2024/papers/Qu_Towards_Modern_Image_Manipulation_Localization_A_Large-Scale_Dataset_and_Novel_CVPR_2024_paper.pdf' target='_blank'>Towards Modern Image Manipulation Localization: A Large-Scale Dataset and Novel Methods</a></p>"
)
if __name__ == "__main__":
iface.launch() |