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hf-similarity-check

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Mitul Mohammad Abdullah Al Mukit commited on Jul 3, 2023

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+import streamlit as st
+import similarity_check as sc
+import cv2
+from PIL import Image
+import numpy as np
+import tempfile
+from streamlit_webrtc import VideoTransformerBase, webrtc_streamer
+import demo
+import time
+import streamlit as st
+import requests
+import json
+import request_json.sbt_request_generator as sbt
+global data
+data = {}
+def main():
+    # st.title("SBT Web Application")
+    # today's date = get_today_date
+    # global data
+    html_temp = """
+        <body style="background-color:red;">
+        <div style="background-color:teal ;padding:10px">
+        <h2 style="color:white;text-align:center;">SBT Web Application</h2>
+        </div>
+        </body>
+        """
+    st.markdown(html_temp, unsafe_allow_html=True)
+    st.header("I. Similarity Check")
+    image_file = st.file_uploader("Upload Image", type=['jpg', 'png', 'jpeg'], accept_multiple_files=True)
+    if len(image_file) == 1:
+        # print(image_file[0].name)
+        image1 = Image.open(image_file[0])
+        st.text("HKID card")
+        st.image(image1)
+    elif len(image_file) == 2:
+        image1 = Image.open(image_file[0])
+        st.text("HKID card")
+        st.image(image1)
+        image2 = Image.open(image_file[1])
+        file_name = image_file[1].name
+        st.text("Bank statement")
+        st.image(image2)
+    # if image_file2 is not None:
+    #     image2 = Image.open(image_file)
+    #     st.text("Bank statement")
+    #     st.image(image2)
+    # path1 = 'IMG_4495.jpg'
+    # path2 = 'hangseng_page-0001.jpg'
+    # image1 = save_image(image1)
+    # image2 = save_image(image2)
+    data = {}
+    if st.button("Recognise"):
+        with st.spinner('Wait for it...'):
+            # global data
+            data = sc.get_data(image1, image2, file_name)
+            with open('data1.txt', 'w') as f:
+                f.write(json.dumps(data))
+            # data.update(sc.get_data(image1, image2, file_name))
+            print(f'data inside {data}')
+            # sbt.split_data(data)
+        st.success('Done!')
+        score = data["similarity_score"]
+        #print(score)
+        st.text(f'score: {score}')
+        if (score>85):
+            st.text(f'matched')
+        else:
+            st.text(f'unmatched')
+        st.header("IIa. HKID Data Extraction")
+        st.text(f'Name: {data["name_on_id"]}') # name is without space
+        st.text(f'HKID: {data["hkid"]} and validity: {data["validity"]}')
+        st.text(f'Date of issue: {data["issue_date"]}')
+        st.header("IIb. Bank Statement Data Extraction")
+        # st.write('------------From bank statement------------')
+        st.text(f'Name: {data["name_on_bs"]}')
+        st.text(f'Address: {data["address"]}')
+        st.text(f'Bank: {data["bank"]}')
+        st.text(f'Date: {data["date"]}')
+        st.text(f'Asset: {data["asset"]} hkd')
+        st.text(f'Liabilities: {data["liabilities"]} hkd')
+        # result_img= detect_faces(our_image)
+        # st.image(result_img)
+    # print(f'data outside 1 {data}')
+    st.header("II. Facial Recognition")
+    run = st.checkbox('Run')
+    # webrtc_streamer(key="example")
+    # 1. Web Rtc
+    # webrtc_streamer(key="jhv", video_frame_callback=video_frame_callback)
+    # # init the camera
+    face_locations = []
+    # face_encodings = []
+    face_names = []
+    process_this_frame = True
+    score = []
+    faces = 0
+    FRAME_WINDOW = st.image([])
+    camera = cv2.VideoCapture(0)
+    while run:
+        # Capture frame-by-frame
+        # Grab a single frame of video
+        ret, frame = camera.read()
+        result, process_this_frame, face_locations, faces, face_names, score = demo.process_frame(frame, process_this_frame, face_locations, faces, face_names, score)
+        # Display the resulting image
+        FRAME_WINDOW.image(result)
+        print(score)
+        if len(score) > 20:
+            avg_score =  sum(score) / len(score)
+            st.write(f'{avg_score}')
+            with open('data1.txt', 'w') as f:
+                data_raw = f.read()
+                data = json.loads(data_raw)
+                data['avg_score'] = str(avg_score)
+                f.write(json.dumps(data))
+        # update_text(f'{demo.convert_distance_to_percentage(score, 0.45)}')
+    else:
+        st.write('Stopped')
+    # print(f'the data is {data}')
+    # st.header("IIIa. HKID Data Extraction")
+    # st.text(f'Name: {data["name_on_id"]}') # name is without space
+    # st.text(f'HKID: {data["hkid"]} and validity: {data["validity"]}')
+    # st.text(f'Date of issue: {data["issue_date"]}')
+    # st.header("IIIb. Bank Statement Data Extraction")
+    # # st.write('------------From bank statement------------')
+    # st.text(f'Name: {data["name_on_bs"]}')
+    # st.text(f'Address: {data["address"]}')
+    # st.text(f'Bank: {data["bank"]}')
+    # st.text(f'Date: {data["date"]}')
+    # st.text(f'Asset: {data["asset"]} hkd')
+    # st.text(f'Liabilities: {data["liabilities"]} hkd')
+    # print(f'data outside 2 {data}')
+    if st.button("Confirm"):
+        # print(f'data outside 3 {data}')
+        with st.spinner('Sending data...'):
+            sbt.split_data(data)
+        st.success('Done!')
+if __name__ == '__main__':
+    main()
+    # def save_image(image):
+#     try:
+#         temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.jpg')
+#         Image.save(temp_file.name)
+#         return temp_file.name
+#     except IOError:
+#         print("Unable to save image to temporary file")
+#         return None
+    # json_file = 'request json\request_legalDocument.json'
+    # file = open(json_file, 'r')
+    # data = json.load(file)
+    # file.close()
+    # # Update data
+    # data.update(new_data)
+    # file = open(json_file, 'w')
+    # for item in data['request']['body']['formdata']:
+    #     if item["key"] == "requestId":
+    #         item["value"] = ""
+    #     elif item["key"] == "userId":
+    #         item["value"] = generate_token_id(2048)
+    #     elif item["key"] == "endpoint":
+    #         item["value"] = ""
+    #     elif item["key"] == "apiType":
+    #         item["value"] = ""
+    #     elif item["key"] == "docType":
+    #         item["value"] = "HKID"
+    #     elif item["key"] == "nameDoc":
+    #         item["value"] = new_data["name_on_id"]
+    #     elif item["key"] == "docID":
+    #         item["value"] = new_data["name_on_id"]
+    #     elif item["key"] == "docValidity":
+    #         item["value"] = new_data["validity"]
+    #     elif item["key"] == "dateOfIssue":
+    #         item["value"] = new_data["date_issue"]
+    #     elif item["key"] == "matchingScore":
+    #         item["value"] = new_data["similarity_score"]
+    # json.dump(data, file)
+    # file.close()