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from predict_pipeline import DetectionPipeline | |
import streamlit as st | |
st.title('Automatic Vechile LICENSE Plate detection') | |
st.write('Detects the License plate of a car and predicts the digits present in it! \nPowered by YOLOv8 Medium model') | |
st.write('') | |
detect_pipeline = DetectionPipeline() | |
st.info('License Plate Detector MODEL loaded successfully!') | |
uploaded_file = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"]) | |
if uploaded_file is not None: | |
with st.container(): | |
col1, col2 = st.columns([3, 3]) | |
col1.header('Input Image') | |
col1.image(uploaded_file, caption='Uploaded Image', use_column_width=True) | |
col1.text('') | |
col1.text('') | |
if st.button('Detect!'): | |
preprocessed_img_array = detect_pipeline.preprocess_image(uploaded_file=uploaded_file) | |
detections = detect_pipeline.detectLicensePlates(input_array=preprocessed_img_array) | |
detections_img = detect_pipeline.detections2Image(preprocess_image=preprocessed_img_array, detections=detections) | |
col2.header('Detections') | |
col2.image(detections_img, caption='Predictions by model', use_column_width=True) | |