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app.py

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+import streamlit as st
+import torch
+import torch.nn as nn
+from PIL import Image
+import albumentations as A
+from albumentations.pytorch import ToTensorV2
+import timm
+import numpy as np
+import onnxruntime as ort
+
+import warnings
+warnings.filterwarnings("ignore")
+
+# Khởi tạo mô hình
+class SiameseNetwork(nn.Module):
+    def __init__(self, model_name='resnet18', pretrained=True):
+        super(SiameseNetwork, self).__init__()
+        self.encoder = timm.create_model(model_name, pretrained=pretrained, num_classes=0)
+
+        self.fc = nn.Sequential(
+            nn.Linear(self.encoder.num_features, 256),
+            nn.ReLU(inplace=True),
+            nn.Linear(256, 128)
+        )
+
+    def forward_once(self, x):
+        output = self.encoder(x)
+        output = self.fc(output)
+        return output
+
+    def forward(self, img1, img2):
+        output1 = self.forward_once(img1)
+        output2 = self.forward_once(img2)
+        return output1, output2
+
+# Load the model
+@st.cache_resource
+def load_model():
+    backbone = 'efficientnet_b2'
+    onnx_file_path = f"weights/model_{backbone}.onnx"
+    session = ort.InferenceSession(onnx_file_path)
+    return session
+
+# Function to run inference
+def inference_with_real_set(session, img, real_imgs, threshold=0.5):
+    distances = []
+    for real_img in real_imgs:
+        input_dict = {
+            session.get_inputs()[0].name: img.numpy(),
+            session.get_inputs()[1].name: real_img.numpy()
+        }
+        outputs = session.run(None, input_dict)
+        
+        euclidean_distance = np.linalg.norm(outputs[0] - outputs[1])
+        
+        distances.append(euclidean_distance)
+
+    avg_distance = sum(distances) / len(distances)
+    
+    return "Real" if avg_distance < threshold else "Fake"
+
+# Image preprocessing function
+def preprocess_image(image):
+    transform = A.Compose([
+        A.Resize(256, 256),
+        A.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
+        ToTensorV2(),
+    ])
+    image = np.array(image)/255.0
+    image = transform(image=image)['image'].unsqueeze(0)
+    return image
+
+# Hide Streamlit warnings and footers
+hide_warning = """
+    <style>
+    .stAlert {display: none;}
+    .css-18e3th9 {padding-top: 2rem; padding-bottom: 2rem;}  /* Adjust top and bottom padding */
+    .css-1d391kg {max-width: 100% !important; padding-left: 1rem; padding-right: 1rem;}  /* Adjust the max-width and side padding */
+    </style>
+    """
+    
+st.markdown(hide_warning, unsafe_allow_html=True)
+
+# Streamlit interface
+st.title("Forge Signature Siamese")
+
+# Load the model
+model = load_model()
+
+# Chia màn hình thành 2 cột
+col1, col2 = st.columns([1, 1])  # Cột bên trái để input, cột bên phải để hiển thị kết quả
+
+with col1:
+    # Upload image to compare
+    uploaded_image = st.file_uploader("Upload an image to verify", type=["png", "jpg", "jpeg"])
+
+    # Upload real reference images
+    uploaded_real_images = st.file_uploader("Upload real reference images", type=["png", "jpg", "jpeg"], accept_multiple_files=True)
+
+    # Threshold slider
+    threshold = st.slider("Threshold", 0.0, 1.0, 0.5)
+
+# Nếu cả hình ảnh tải lên và hình ảnh thật đã được tải lên
+if uploaded_image is not None and uploaded_real_images:
+    # Xử lý ảnh
+    img = preprocess_image(Image.open(uploaded_image).convert('RGB'))
+    real_imgs = [preprocess_image(Image.open(img).convert('RGB')) for img in uploaded_real_images]
+
+    # Chạy inference
+    result = inference_with_real_set(model, img, real_imgs, threshold)
+
+    with col2:
+        # Hiển thị nút kết quả với màu sắc tùy chỉnh
+        if result == "Real":
+            button_color = "background-color: green; color: white; font-weight: bold;"
+            button_label = "Real"
+        else:
+            button_color = "background-color: red; color: white; font-weight: bold;"
+            button_label = "Fake"
+        
+        st.markdown(
+            f"""
+            <div style="display: flex; justify-content: center; margin-top: 20px;">
+                <button style="padding: 10px 20px; font-size: 18px; {button_color}">{button_label}</button>
+            </div>
+            """, 
+            unsafe_allow_html=True
+        )
+        
+        # Hiển thị hình ảnh và tham chiếu
+        sub_col1, sub_col2 = st.columns([1, 1])
+
+        # Cột nhỏ bên trái: Hình ảnh tải lên
+        with sub_col1:
+            st.write("\n")
+            st.write("**Input Image**")
+            st.image(uploaded_image, caption="Uploaded Image", use_column_width=True)
+
+        # Cột nhỏ bên phải: Hình ảnh tham chiếu
+        with sub_col2:
+            st.write("\n")
+            st.write("**Real Reference Images**")
+            for real_image in uploaded_real_images:
+                st.image(real_image, caption="Real Image", use_column_width=True)
+
+
+
+        

requirements.txt

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+streamlit
+torch
+albumentations
+timm
+numpy
+onnxruntime

weights/best_efficientnet_b2.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e43e5d65c1ac1c3aeb99d2366a7a3bfd81c103f5c3fd96a6e837f7c1e5e9ec82
+size 32841296

weights/model_efficientnet_b2.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b91a9fc692af66d06cddad847ad68fe9b5983c883f4057b04315c198ac26d85d
+size 32433908