| import gradio as gr |
| import cv2 |
| import numpy as np |
| from PIL import Image |
| from typing import get_args |
|
|
| from fast_alpr import ALPR |
| from fast_alpr.default_detector import PlateDetectorModel |
| from fast_alpr.default_ocr import OcrModel |
|
|
| |
| DETECTOR_MODELS = list(get_args(PlateDetectorModel)) |
| OCR_MODELS = list(get_args(OcrModel)) |
| |
| OCR_MODELS.remove("cct-s-v1-global-model") |
| OCR_MODELS.insert(0, "cct-s-v1-global-model") |
|
|
| def process_image(image, detector_model, ocr_model): |
| """ |
| Process an image with ALPR system |
| |
| Args: |
| image: PIL Image or numpy array |
| detector_model: Selected detector model |
| ocr_model: Selected OCR model |
| |
| Returns: |
| tuple: (annotated_image, results_text) |
| """ |
| if image is None: |
| return None, "Please upload an image to continue." |
| |
| try: |
| |
| if isinstance(image, Image.Image): |
| img_array = np.array(image.convert("RGB")) |
| else: |
| img_array = image |
| |
| |
| alpr = ALPR(detector_model=detector_model, ocr_model=ocr_model) |
| |
| |
| results = alpr.predict(img_array) |
| |
| |
| annotated_img_array = alpr.draw_predictions(img_array) |
| |
| |
| annotated_img = Image.fromarray(annotated_img_array) |
| |
| |
| if results: |
| results_text = "**OCR Results:**\n" |
| for i, result in enumerate(results): |
| plate_text = result.ocr.text if result.ocr else "N/A" |
| plate_confidence = result.ocr.confidence if result.ocr else 0.0 |
| results_text += f"{i+1}. Detected Plate: `{plate_text}` with confidence `{plate_confidence:.2f}`\n" |
| else: |
| results_text = "No license plate detected." |
| |
| return annotated_img, results_text |
| |
| except Exception as e: |
| return None, f"Error processing image: {str(e)}" |
|
|
| |
| with gr.Blocks( |
| title="Automatic License Plate Recognition (ALPR)", |
| theme=gr.themes.Soft( |
| primary_hue="green", |
| secondary_hue="blue", |
| neutral_hue="slate" |
| ) |
| ) as demo: |
| |
| gr.Markdown(""" |
| # Automatic License Plate Recognition (ALPR) |
| |
| An automatic license plate recognition (ALPR) system with customizable detector and OCR models. |
| |
| This system uses the FastALPR library to detect and recognize license plates in images. |
| """) |
| |
| with gr.Row(): |
| with gr.Column(scale=1): |
| gr.Markdown("### Model Selection") |
| detector_dropdown = gr.Dropdown( |
| choices=DETECTOR_MODELS, |
| value=DETECTOR_MODELS[0], |
| label="Choose Detector Model", |
| info="Select the model for license plate detection" |
| ) |
| |
| ocr_dropdown = gr.Dropdown( |
| choices=OCR_MODELS, |
| value=OCR_MODELS[0], |
| label="Choose OCR Model", |
| info="Select the model for text recognition" |
| ) |
| |
| gr.Markdown("### Upload Image") |
| image_input = gr.Image( |
| label="Upload an image of a vehicle with a license plate", |
| type="pil", |
| height=300 |
| ) |
| |
| process_btn = gr.Button( |
| "Process Image", |
| variant="primary", |
| size="lg" |
| ) |
| |
| with gr.Column(scale=1): |
| gr.Markdown("### Results") |
| image_output = gr.Image( |
| label="Annotated Image with OCR Results", |
| height=300 |
| ) |
| |
| text_output = gr.Markdown( |
| label="OCR Results", |
| value="Upload an image and click 'Process Image' to see results." |
| ) |
| |
| |
| gr.Markdown("### Example Images") |
| gr.Examples( |
| examples=[], |
| inputs=image_input, |
| label="Try with these examples (if available)" |
| ) |
| |
| |
| process_btn.click( |
| fn=process_image, |
| inputs=[image_input, detector_dropdown, ocr_dropdown], |
| outputs=[image_output, text_output] |
| ) |
| |
| gr.Markdown(""" |
| --- |
| ### How to Use |
| |
| 1. **Select Models**: Choose your preferred detector and OCR models from the dropdowns |
| 2. **Upload Image**: Upload an image containing a vehicle with a license plate |
| 3. **Process**: Click the "Process Image" button to run ALPR |
| 4. **View Results**: See the annotated image and OCR text results |
| |
| ### Supported Models |
| |
| - **Detector Models**: Various YOLO-based detection models |
| - **OCR Models**: CCT OCR models for text recognition including global and specialized models |
| |
| ### Features |
| |
| - Real-time license plate detection |
| - Customizable model selection |
| - Visual annotations with bounding boxes |
| - Confidence scores for OCR results |
| - Support for multiple image formats |
| """) |
|
|
| |
| if __name__ == "__main__": |
| demo.launch(share=False) |
