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66e8682 87160fd 7526718 6282d95 66e8682 87160fd 66e8682 87160fd b0ba1e3 87160fd b0ba1e3 66e8682 87160fd b0ba1e3 87160fd b0ba1e3 87160fd b0ba1e3 87160fd 66e8682 87160fd | 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 | from ultralytics import YOLO
import numpy as np
import cv2
from PIL import Image
import streamlit as st
# Model labels
model1Labels = {0: 'single_number_plate', 1: 'double_number_plate'}
model2Labels = {
0: '0', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5', 6: '6', 7: '7', 8: '8', 9: '9', 10: 'A', 11: 'B', 12: 'C',
13: 'D', 14: 'E', 15: 'F', 16: 'G', 17: 'H', 18: 'I', 19: 'J', 20: 'K', 21: 'L', 22: 'M', 23: 'N', 24: 'O',
25: 'P', 26: 'Q', 27: 'R', 28: 'S', 29: 'T', 30: 'U', 31: 'V', 32: 'W', 33: 'X', 34: 'Y', 35: 'Z'
}
# Load models
model = YOLO("models/LP-detection.pt")
model2 = YOLO("models/Charcter-LP.pt")
def prediction(image):
result = model.predict(source=image, conf=0.5)
boxes = result[0].boxes
height = boxes.xywh
crd = boxes.data
n = len(crd)
lp_number = []
img_lp_final = None
for i in range(n):
ht = int(height[i][3])
c = int(crd[i][5])
xmin = int(crd[i][0])
ymin = int(crd[i][1])
xmax = int(crd[i][2])
ymax = int(crd[i][3])
img_lp = image[ymin:ymax, xmin:xmax]
img_lp_final = img_lp.copy() # Store the cropped image for display
cv2.rectangle(image, (xmin, ymin), (xmax, ymax), (0, 255, 0), 2)
h = np.median(ht)
# Second Model Prediction
result2 = model2.predict(source=img_lp, conf=0.25)
boxes_ocr = result2[0].boxes
data2 = boxes_ocr.data
n2 = len(data2)
xaxis0, xaxis11, xaxis12 = [], [], []
label0, label11, label12 = [], [], []
numberPlate = ""
if c == 0: # Single line license plate
for i in range(n2):
x = int(data2[i][2])
xaxis0.append(x)
l = int(data2[i][5])
label0.append(l)
# Sort characters by x-axis for single line
sorted_labels = [label0[i] for i in np.argsort(xaxis0)]
numberPlate = ''.join([model2Labels.get(l) for l in sorted_labels])
lp_number.append(numberPlate)
elif c == 1: # Double line license plate
for i in range(n2):
x = int(data2[i][0])
y = int(data2[i][3])
l = int(data2[i][5])
if y < (h / 2):
xaxis11.append(x)
label11.append(l)
else:
xaxis12.append(x)
label12.append(l)
# Sort characters by x-axis for double line (upper and lower separately)
sorted_labels11 = [label11[i] for i in np.argsort(xaxis11)]
sorted_labels12 = [label12[i] for i in np.argsort(xaxis12)]
numberPlate = ''.join([model2Labels.get(l) for l in sorted_labels11 + sorted_labels12])
lp_number.append(numberPlate)
return lp_number, img_lp_final
st.title('License Plate Recognition 🚗')
st.header('Upload an image of a license plate to get the License number.')
# File uploader
uploaded_file = st.file_uploader("Choose an image...", type="jpg")
image = None
if uploaded_file is not None:
image = np.array(Image.open(uploaded_file))
if image is not None:
c1, c2, c3 = st.columns(3)
with c1:
st.image(image, caption='Uploaded Image', use_container_width=True)
license_plate_text, img_lp = prediction(image)
with c2:
if img_lp is not None:
st.image(img_lp, caption='Cropped License Plate', use_container_width=True)
else:
st.write('No License Plate Detected')
with c3:
st.success(', '.join(license_plate_text))
st.write('License Plate Text')
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