Spaces:
Sleeping
Sleeping
File size: 5,681 Bytes
f0d53ce bdda6af f0d53ce 6984dd2 f0d53ce |
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 |
import os
import numpy as np
from requests import get
import streamlit as st
import cv2
from ultralytics import YOLO
import shutil
import easyocr
import imutils
PREDICTION_PATH = os.path.join('.', 'predictions')
@st.cache_resource
def load_od_model():
finetuned_model = YOLO('cc_detect_best.pt')
return finetuned_model
@st.cache_resource
def load_easyocr():
reader = easyocr.Reader(['en'])
return reader
def decode_text(type: str):
reader = load_easyocr()
output_crop_path = os.path.join(PREDICTION_PATH, 'predict', 'crops', type)
ocr_txt = ''
if os.path.exists(output_crop_path):
crop_file = os.listdir(output_crop_path)[0]
crop_img_path = os.path.join(output_crop_path, crop_file)
crop_img = cv2.imread(crop_img_path)
increase = cv2.resize(crop_img, None, fx = 2, fy = 2, interpolation = cv2.INTER_CUBIC)
if type == 'card_number':
increase = cv2.resize(crop_img, None, fx = 5, fy = 5, interpolation = cv2.INTER_CUBIC)
gray = cv2.cvtColor(increase, cv2.COLOR_BGR2GRAY)
value, thresh = cv2.threshold(gray,0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2,2))
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=1)
# Find contours and remove small noise
cnts = cv2.findContours(opening, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
area = cv2.contourArea(c)
if area < 50:
cv2.drawContours(opening, [c], -1, 0, -1)
# Invert
result = 255 - opening
cleaned_image = result
crop_ocr = reader.readtext(cleaned_image)
cleaned_image = cv2.resize(cleaned_image, None, fx = 0.5, fy = 0.5, interpolation = cv2.INTER_CUBIC)
if type == 'card_number':
cleaned_image = cv2.resize(cleaned_image, None, fx = 0.2, fy = 0.2, interpolation = cv2.INTER_CUBIC)
cv2.imwrite(crop_img_path, cleaned_image)
ocr_txt = ''.join([t for _, t, _ in crop_ocr])
ocr_txt_conf = np.round(np.mean([p for _, _, p in crop_ocr]), 4)
if type == 'card_number':
ocr_txt = ocr_txt.replace(' ', '')
col1, col2 = st.columns(2, gap='small')
with col1:
st.markdown(f"<h5>{type.replace('_', ' ').upper()}</h5>", unsafe_allow_html=True)
st.text(f"{ocr_txt.upper()} ({str(ocr_txt_conf)})")
with col2:
st.text(' ')
if type == 'card_number':
st.text(' ')
st.image(crop_img_path)
def inference(input_image_path: str):
finetuned_model = load_od_model()
results = finetuned_model.predict(input_image_path,
show=False,
save=True,
save_crop=True,
imgsz=640,
conf=0.6,
save_txt=True,
project= PREDICTION_PATH,
show_labels=True,
show_conf=True,
line_width=2,
exist_ok=True)
decode_text('card_number')
decode_text('holder_name')
decode_text('exp_date')
st.image(os.path.join(PREDICTION_PATH, 'predict', 'input.jpg'))
def files_cleanup(path_: str):
if os.path.exists(path_):
os.remove(path_)
if os.path.exists(PREDICTION_PATH):
shutil.rmtree(PREDICTION_PATH)
# @st.cache_resource
def get_upload_path():
upload_file_path = os.path.join('.', 'uploads')
if not os.path.exists(upload_file_path):
os.makedirs(upload_file_path)
upload_filename = "input.jpg"
upload_file_path = os.path.join(upload_file_path, upload_filename)
return upload_file_path
def process_input_image(img_url):
upload_file_path = get_upload_path()
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/102.0.0.0 Safari/537.36'}
r = get(img_url, headers=headers)
arr = np.frombuffer(r.content, np.uint8)
input_image = cv2.imdecode(arr, cv2.IMREAD_UNCHANGED)
input_image = cv2.cvtColor(input_image, cv2.COLOR_BGR2RGB)
input_image = cv2.resize(input_image, (640, 640))
cv2.imwrite(upload_file_path, cv2.cvtColor(input_image, cv2.COLOR_RGB2BGR))
return upload_file_path
try:
files_cleanup(get_upload_path())
st.markdown("<h3>Credit Card Detection</h3>", unsafe_allow_html=True)
desc = '''YOLOv8 is fine-tuned to detect credit card number, holder's name and expiry date. Dataset used to fine-tune YOLOv8
can be found <a href="https://universe.roboflow.com/credit-cards-detection/credit_card_detect-wjmlc/dataset/2" target="_blank">
here</a>. The detected objects are cropped, processed and passed as inputs to EasyOCR for text recognition.
'''
st.markdown(desc, unsafe_allow_html=True)
img_url = st.text_input("Paste the image URL of a credit card:", "")
placeholder = st.empty()
if img_url:
placeholder.empty()
img_path = process_input_image(img_url)
inference(img_path)
files_cleanup(get_upload_path())
except Exception as e:
files_cleanup(get_upload_path())
st.error(f'An unexpected error occured: \n{e}') |