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import cv2 as cv |
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import numpy as np |
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import gradio as gr |
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from lpd_yunet import LPD_YuNet |
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from huggingface_hub import hf_hub_download |
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model_path = hf_hub_download( |
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repo_id="opencv/license_plate_detection_yunet", |
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filename="license_plate_detection_lpd_yunet_2023mar.onnx" |
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) |
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model = LPD_YuNet( |
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modelPath=model_path, |
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confThreshold=0.9, |
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nmsThreshold=0.3, |
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topK=5000, |
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keepTopK=750, |
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backendId=cv.dnn.DNN_BACKEND_OPENCV, |
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targetId=cv.dnn.DNN_TARGET_CPU |
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) |
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def visualize(image, dets, line_color=(0, 255, 0), text_color=(0, 0, 255)): |
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output = image.copy() |
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for det in dets: |
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bbox = det[:-1].astype(np.int32) |
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x1, y1, x2, y2, x3, y3, x4, y4 = bbox |
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cv.line(output, (x1, y1), (x2, y2), line_color, 2) |
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cv.line(output, (x2, y2), (x3, y3), line_color, 2) |
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cv.line(output, (x3, y3), (x4, y4), line_color, 2) |
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cv.line(output, (x4, y4), (x1, y1), line_color, 2) |
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return output |
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def detect_license_plates(input_image): |
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input_image = cv.cvtColor(input_image, cv.COLOR_RGB2BGR) |
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h, w, _ = input_image.shape |
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model.setInputSize([w, h]) |
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results = model.infer(input_image) |
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if results is None or len(results) == 0: |
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return cv.cvtColor(input_image, cv.COLOR_BGR2RGB) |
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output = visualize(input_image, results) |
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output = cv.cvtColor(output, cv.COLOR_BGR2RGB) |
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return output |
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with gr.Blocks(css='''.example * { |
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font-style: italic; |
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font-size: 18px !important; |
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color: #0ea5e9 !important; |
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}''') as demo: |
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gr.Markdown("### License Plate Detection (LPD-YuNet)") |
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gr.Markdown("Upload a vehicle image to detect license plates using OpenCV's ONNX-based LPD-YuNet model.") |
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with gr.Row(): |
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input_image = gr.Image(type="numpy", label="Upload Vehicle Image") |
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output_image = gr.Image(type="numpy", label="Detected License Plates") |
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input_image.change(fn=lambda: (None), outputs=output_image) |
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with gr.Row(): |
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submit_btn = gr.Button("Submit", variant="primary") |
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clear_btn = gr.Button("Clear") |
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submit_btn.click(fn=detect_license_plates, inputs=input_image, outputs=output_image) |
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clear_btn.click(fn=lambda:(None, None), outputs=[input_image, output_image]) |
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gr.Markdown("Click on any example to try it.", elem_classes=["example"]) |
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gr.Examples( |
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examples=[ |
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["examples/licenseplate1.jpg"], |
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["examples/licenseplate2.jpg"] |
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], |
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inputs=input_image |
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) |
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gr.Markdown("Example images credit: https://unsplash.com/") |
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if __name__ == "__main__": |
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demo.launch() |
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