.

Welcome.py

@@ -13,9 +13,9 @@ st.markdown(
     """
     Signfiy is an open-source project addressing core problems in signature verification with the help of deep learning.
     ### 3 Tasks
-    - Signature Verification (Siamese Model)
     - Signature Detection (Object detection with YOLO)
     - Signature Cleaning (Cycle-GAN)
+    - Signature Verification (Siamese Model)
 
     **👈 Select a demo from the sidebar** 
     ### Want to learn more?

pages/{3_Signify.py → 3_Verify.py}

@@ -18,7 +18,7 @@ YOLO_RESULT = 'results/yolov5/'
 YOLO_OP = 'crops/DLSignature/'
 
 
-st.set_page_config(page_title="Signature Detection", page_icon="📈")
+st.set_page_config(page_title="Signature Verificaton", page_icon="📈")
 
 st.markdown("# Detect Signatures")
 st.sidebar.header("Plotting Demo")
@@ -66,7 +66,7 @@ transform_valid = transforms.Compose([
     transforms.Resize((124, 124)),
 ])
 
-st.session_state.predict = st.button('Detect Signature')
+st.session_state.predict = st.button('Verify Signatures')
 if st.session_state.predict:
     device = torch.device('cpu')
     backbone = models.efficientnet_v2_s(pretrained=True)
@@ -75,6 +75,6 @@ if st.session_state.predict:
     model.load_state_dict(weights)
     img1, img2 = transform_valid(img1).unsqueeze(0), transform_valid(img2).unsqueeze(0)
     prediction = model(img1,img2)
-    st.write(f"Score: {str(prediction)}")
+    st.write(f"The signatures match with a probability of {str(round(float(prediction), 2))}%")
 
 

pages/4_Siamese.py

@@ -1,84 +0,0 @@
-"""
-
-import glob
-import os
-import time
-
-import numpy as np
-import streamlit as st
-
-MEDIA_ROOT = 'results/media/signatures/'
-
-
-st.set_page_config(page_title="Signature Detection", page_icon="📈")
-
-st.markdown("# Detect Signatures")
-st.sidebar.header("Plotting Demo")
-st.write(
-    "Detect signatures of the given image!"
-)
-
-if "predict" not in st.session_state:
-    # Initialize session state.
-    st.session_state.update({
-        # Default page.
-        "predict": False,
-    })
-
-
-def select_document():
-    '''
-        Selects the document from the dropdown menu and displays the image.
-        Returns an integer represeting the id of the document selected.
-    '''
-    left, right = st.columns(2) # Create two columns
-    # dropdown box in left column
-    selection_left = str(left.selectbox('Select document to run inference 1', [1, 2]))
-    selection_right = str(right.selectbox('Select document to run inference 2', [1, 2]))
-    # select corresponding document image from media/documents
-    selection_image_left = MEDIA_ROOT+selection_left+'.png'
-    left.image(selection_image_left, use_column_width='always')
-
-    selection_image_right = MEDIA_ROOT+selection_right+'.png'
-    right.image(selection_image_right, use_column_width='always')
-    return selection_image_left, selection_image_right
-
-def upload_document():
-    img1 = st.file_uploader("left_img")
-    img2 = st.file_uploader("right_img")
-
-def compare(img1, img2):
-    print("images are very similar .. yuhu (hard coded func not implemented yet)")
-
-
-selection = select_document()
-# call YOLOv5 detection fn on all images in the document folder.
-#detect.detect(MEDIA_ROOT)
-
-st.session_state.predict = st.button('Compare signatures')
-if st.session_state.predict:
-
-    if not st.session_state.img1 or not st.session_state.img2:
-        
-        with st.empty():
-
-            st.write("Upload both images first!!")
-            time.sleep(2)
-            st.session_state.predict = False
-    
-    compare(img1, img2)
-
-
-
-
-# get the path where last detected results are stored.
-latest_detection = max(glob.glob(os.path.join(YOLO_RESULT, '*/')), key=os.path.getmtime)
-# resize and add top and bottom padding to detected sigantures. 
-# gan model expects ips in that particular format.
-
-# selects and display the detections of the document which the user selected.
-#selection_detection =latest_detection + YOLO_OP + selection + '.jpg'
-#st.image(selection_detection)
-
-
-"""