app.py

from PIL import Image
import streamlit as st
import cv2
import numpy as np
import os
import tensorflow as tf

from IMVIP_Supplementary_Material.scripts import dfutils #methods used for DF-Net

DESCRIPTION = """# DF-Net
The Digital Forensics Network is designed and trained to detect and locate image manipulations. 
More information can be found in this [publication](https://zenodo.org/record/8214996)
"""

IMG_SIZE=256

tf.experimental.numpy.experimental_enable_numpy_behavior()
#np.warnings.filterwarnings('error', category=np.VisibleDeprecationWarning) 



def check_forgery_df(img):
    shape_original = img.shape
    img = cv2.resize(img, (IMG_SIZE,IMG_SIZE))
    x = np.expand_dims( img.astype('float32')/255., axis=0 )
    
    pred1 = model_M1.predict(x, verbose=0)
    pred2= model_M2.predict(x, verbose=0)    
    pred = np.max([pred1,pred2], axis=0)   
    
    pred = dfutils.create_mask(pred)
    pred = pred.reshape(pred.shape[-3:-1])
    resized_image = cv2.resize(pred, (shape_original[1],shape_original[0]), interpolation=cv2.INTER_LINEAR)

    return resized_image



def evaluate(img):
    pre_t = check_forgery_df(img)
    st.image(pre_t, caption="White area indicates potential image manipulations.")



st.markdown(DESCRIPTION)

uploaded_file = st.file_uploader("Please upload an image", type=["jpeg", "jpg", "png"])

if uploaded_file is not None:
    #load models
    model_M1 = tf.keras.models.load_model("IMVIP_Supplementary_Material/models/model1/")
    model_M2 = tf.keras.models.load_model("IMVIP_Supplementary_Material/models/model2/")

    # Convert the file to an opencv image.
    file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
    opencv_image = cv2.imdecode(file_bytes, 1)
    reversed_image = opencv_image[:, :, ::-1]
    st.image(reversed_image, caption="Input Image")
    evaluate(reversed_image)