Spaces:
Sleeping
Sleeping
File size: 6,745 Bytes
d357ee3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 |
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() |