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from groundingdino.util.inference import load_model, load_image, predict, annotate |
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import cv2 |
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import gradio as gr |
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import re |
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def dino_inference(image, text): |
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print("Image: ", image) |
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print("Text: ", text) |
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new_path = re.sub(r'^.+/([^/]+)$', r'.asset/train/\1', image) |
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new_path = re.sub(r'\.jpeg$', '.jpg', new_path) |
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model = load_model("groundingdino/config/GroundingDINO_SwinT_OGC.py", |
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"weights/groundingdino_swint_ogc.pth") |
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IMAGE_PATH = new_path |
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TEXT_PROMPT = text |
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BOX_TRESHOLD = 0.35 |
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TEXT_TRESHOLD = 0.25 |
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image_source, image = load_image(IMAGE_PATH) |
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boxes, logits, phrases = predict( |
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model=model, |
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image=image, |
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caption=TEXT_PROMPT, |
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box_threshold=BOX_TRESHOLD, |
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text_threshold=TEXT_TRESHOLD |
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) |
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annotated_frame = annotate(image_source=image_source, boxes=boxes, logits=logits, phrases=phrases) |
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print("Annotated frame shape: ", annotated_frame.shape) |
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cv2.imwrite("annotated_image.jpg", annotated_frame) |
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return annotated_frame |
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def app(): |
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with gr.Blocks(): |
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with gr.Row(): |
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with gr.Column(): |
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image = gr.Image(type="filepath", label="Image", visible=True) |
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text = gr.Textbox(label="Text", placeholder="Enter text here") |
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yolov10_infer = gr.Button(value="Detect Objects") |
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with gr.Column(): |
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output_image = gr.Image(width=1024, height=768, type="numpy", label="Annotated Image", visible=True) |
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def run_inference(image, text): |
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return dino_inference(image, text) |
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yolov10_infer.click( |
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fn=run_inference, |
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inputs=[image, text], |
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outputs=[output_image], |
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) |
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gradio_app = gr.Blocks() |
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with gradio_app: |
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with gr.Row(): |
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with gr.Column(): |
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app() |
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if __name__ == '__main__': |
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gradio_app.launch(share=True) |
