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| 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) | |
| # Ensure pred1 and pred2 are numpy arrays before proceeding | |
| if isinstance(pred1, dict): | |
| print("pred1 is dict!") | |
| pred1 = pred1[next(iter(pred1))] | |
| if isinstance(pred2, dict): | |
| pred2 = pred2[next(iter(pred2))] | |
| 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_path1 = "IMVIP_Supplementary_Material/models/model1/" | |
| model_path2 = "IMVIP_Supplementary_Material/models/model2/" | |
| tfsm_layer1 = tf.keras.layers.TFSMLayer(model_path1, call_endpoint='serving_default') | |
| tfsm_layer2 = tf.keras.layers.TFSMLayer(model_path2, call_endpoint='serving_default') | |
| input_shape = (256, 256, 3) | |
| inputs = Input(shape=input_shape) | |
| #create the model | |
| outputs1 = tfsm_layer1(inputs) | |
| model_M1 = Model(inputs, outputs1) | |
| outputs2 = tfsm_layer2(inputs) | |
| model_M2 = Model(inputs, outputs2) | |
| #model_M1 = tf.keras.layers.TFSMLayer("IMVIP_Supplementary_Material/models/model1/") #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) | |