Update app.py

app.py

@@ -1,132 +1,78 @@
 import cv2
-import os
-import logging
+import numpy as np
 import streamlit as st
-from sqlalchemy import text
-from preprocess import read_image, extract_id_card, save_image
-from ocr_engine import extract_text
-from postprocess import extract_information
-from face_verification import detect_and_extract_face, face_comparison, get_face_embeddings
-from mysqldb_operations import insert_records, fetch_records, check_duplicacy
-
-# {
-#   "ID": "CCNPA",
-#   "Name": "BIBEK RAUTH",
-#   "Father's Name": "AJAY RAUTH",
-#   "DOB": "14/09/1994",
-#   "ID Type": "PAN"
-# }
-
-
-logging_str = "[%(asctime)s: %(levelname)s: %(module)s]: %(message)s"
-log_dir = "logs"
-os.makedirs(log_dir, exist_ok=True)
-logging.basicConfig(filename=os.path.join(log_dir,"ekyc_logs.log"), level=logging.INFO, format=logging_str, filemode="a")
-
-# Set wider page layout
-def wider_page():
-    max_width_str = "max-width: 1200px;"
-    st.markdown(
-        f"""
-        <style>
-            .reportview-container .main .block-container{{ {max_width_str} }}
-        </style>
-        """,
-        unsafe_allow_html=True,
-    )
-    logging.info("Page layout set to wider configuration.")
-
-# Customized Streamlit theme
-def set_custom_theme():
-    st.markdown(
-        """
-        <style>
-            body {
-                background-color: #f0f2f6; /* Set background color */
-                color: #333333; /* Set text color */
-            }
-            .sidebar .sidebar-content {
-                background-color: #ffffff; /* Set sidebar background color */
-            }
-        </style>
-        """,
-        unsafe_allow_html=True,
-    )
-    logging.info("Custom theme applied to Streamlit app.")
-
-# Sidebar
-def sidebar_section():
-    st.sidebar.title("Select ID Card Type")
-    option = st.sidebar.selectbox("", ("PAN", " "))
-    logging.info(f"ID card type selected: {option}")
-    return option
-
-# Header
-def header_section(option):
-    if option == "Aadhar":
-        st.title("Registration Using Aadhar Card")
-        logging.info("Header set for Aadhar Card registration.")
-    elif option == "PAN":
-        st.title("Registration Using PAN Card")
-        logging.info("Header set for PAN Card registration.")
-
-# Main content
-def main_content(image_file, face_image_file, conn):
-    if image_file is not None:
-        face_image = read_image(face_image_file, is_uploaded=True)
-        logging.info("Face image loaded.")
-        if face_image is not None:
-            image = read_image(image_file, is_uploaded=True)
-            logging.info("ID card image loaded.")
-            image_roi, _ = extract_id_card(image)
-            logging.info("ID card ROI extracted.")
-            face_image_path2 = detect_and_extract_face(img=image_roi)
-            face_image_path1 = save_image(face_image, "face_image.jpg", path="data\\02_intermediate_data")
-            logging.info("Faces extracted and saved.")
-            is_face_verified = face_comparison(image1_path=face_image_path1, image2_path=face_image_path2)
-            logging.info(f"Face verification status: {'successful' if is_face_verified else 'failed'}.")
-
-            if is_face_verified:
-                extracted_text = extract_text(image_roi)
-                text_info = extract_information(extracted_text)
-                logging.info("Text extracted and information parsed from ID card.")
-                records = fetch_records(text_info)
-                if records.shape[0] > 0:
-                    st.write(records.shape)
-                    st.write(records)
-                is_duplicate = check_duplicacy(text_info)
-                if is_duplicate:
-                    st.write(f"User already present with ID {text_info['ID']}")
-                else: 
-                    st.write(text_info)
-                    text_info['DOB'] = text_info['DOB'].strftime('%Y-%m-%d')
-                    text_info['Embedding'] =  get_face_embeddings(face_image_path1)
-                    insert_records(text_info)
-                    logging.info(f"New user record inserted: {text_info['ID']}")
-                    
-            else:
-                st.error("Face verification failed. Please try again.")
-
-        else:
-            st.error("Face image not uploaded. Please upload a face image.")
-            logging.error("No face image uploaded.")
-
-    else:
-        st.warning("Please upload an ID card image.")
-        logging.warning("No ID card image uploaded.")
-
-# Main function setup as previously provided...
-def main():
-    # Initialize connection.
-    conn = st.connection('mysql', type='sql')
-    wider_page()
-    set_custom_theme()
-    option = sidebar_section()
-    header_section(option)
-    image_file = st.file_uploader("Upload ID Card")
-    if image_file is not None:
-        face_image_file = st.file_uploader("Upload Face Image")
-        main_content(image_file, face_image_file, conn)
-
-if __name__ == "__main__":
-    main()
+from PIL import Image
+
+# Function to display an image using Streamlit
+def show_img(img, caption=""):
+    st.image(img, caption=caption, use_column_width=True)
+
+# Function to sort points for perspective transform
+def sort_points(pts):
+    pts = pts.reshape((4, 2))
+    rect = np.zeros((4, 2), dtype="float32")
+
+    s = pts.sum(axis=1)
+    rect[0] = pts[np.argmin(s)]
+    rect[2] = pts[np.argmax(s)]
+
+    diff = np.diff(pts, axis=1)
+    rect[1] = pts[np.argmin(diff)]
+    rect[3] = pts[np.argmax(diff)]
+
+    return rect
+
+# Function to find contours
+def find_contours(edged):
+    conts, _ = cv2.findContours(edged, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
+    conts = sorted(conts, key=cv2.contourArea, reverse=True)[:5]
+    return conts
+
+# Function to transform the image
+def transform_image(image_file):
+    img = cv2.imdecode(np.fromstring(image_file.read(), np.uint8), 1)
+    original = img.copy()
+    show_img(original, caption="Original Image")
+
+    (H, W) = img.shape[:2]
+
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    blur = cv2.GaussianBlur(gray, (7, 7), 0)
+    edged = cv2.Canny(blur, 60, 160)
+    show_img(edged, caption="Edge Detection")
+
+    conts = find_contours(edged.copy())
+    for c in conts:
+        peri = cv2.arcLength(c, True)
+        aprox = cv2.approxPolyDP(c, 0.02 * peri, True)
+
+        if len(aprox) == 4:
+            larger = aprox
+            break
+
+    cv2.drawContours(img, [larger], -1, (120, 255, 0), 2)
+    show_img(img, caption="Detected Contours")
+
+    points_larger = sort_points(larger)
+    pts1 = np.float32(points_larger)
+    pts2 = np.float32([[0, 0], [W, 0], [W, H], [0, H]])
+
+    matrix = cv2.getPerspectiveTransform(pts1, pts2)
+    transform = cv2.warpPerspective(original, matrix, (W, H))
+
+    show_img(transform, caption="Transformed Image")
+    return transform
+
+# Streamlit app layout
+st.set_page_config(page_title="Image Scanner", page_icon="📄")
+
+st.title("Document Scanner using Computer Vision")
+st.write("Upload an image to transform it into a scanned document format.")
+
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    st.write("Original Image:")
+    transformed_image = transform_image(uploaded_file)
+    st.write("Transformed Image:")
+    st.image(transformed_image, caption="Scanned Image", use_column_width=True)