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| import streamlit as st | |
| import cv2 | |
| import numpy as np | |
| # Import your chosen deep learning framework (TensorFlow or PyTorch) | |
| # ... | |
| import tensorflow | |
| # Load the pre-trained object detection model | |
| model = cv2.dnn_DetectionModel("path/to/model.weights", "path/to/model.cfg") | |
| model.setInputParams(size=(416, 416), scale=1/255) | |
| # Optional: Load EasyOCR model if using | |
| reader = EasyOCR("en") # Change "en" to your desired language code | |
| def detect_plates(image): | |
| # Preprocess image for model input (resizing, normalization, etc.) | |
| # ... | |
| classes, confidences, boxes = model.detect(image) | |
| for (class_id, confidence, box) in zip(classes.flatten(), confidences.flatten(), boxes): | |
| if class_id == (class_index for class_index in range(len(model.names)) if model.names[class_index] == "license_plate"): # Adjust class index based on your model | |
| x_min, y_min, x_max, y_max = box | |
| plate_roi = image[y_min:y_max, x_min:x_max] | |
| # Perform character recognition (if not using EasyOCR, implement your own) | |
| plate_text = "..." | |
| if reader is not None: | |
| result = reader.readtext(plate_roi) | |
| plate_text = result[0][1] | |
| # Display bounding box and plate text (or confidence score if not using OCR) | |
| cv2.rectangle(image, (x_min, y_min), (x_max, y_max), (255, 0, 0), 2) | |
| if reader is not None: | |
| cv2.putText(image, plate_text, (x_min, y_min - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 0, 0), 2) | |
| else: | |
| cv2.putText(image, f"Confidence: {confidence:.2f}", (x_min, y_min - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 0, 0), 2) | |
| return image | |
| def main(): | |
| """Streamlit app""" | |
| st.title("Number Plate Detection App") | |
| uploaded_file = st.file_uploader("Choose an image", type=["jpg", "jpeg", "png"]) | |
| if uploaded_file is not None: | |
| image = cv2.imdecode(np.fromstring(uploaded_file.read(), np.uint8), cv2.IMREAD_COLOR) | |
| results = detect_plates |