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test of the first version of the code - zero-shot detection only
Browse files- .gitignore +2 -0
- README.md +3 -3
- app.py +59 -0
- requirements.txt +3 -0
.gitignore
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venv/
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.idea/
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README.md
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---
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title: YOLO World
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colorFrom:
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sdk: gradio
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sdk_version: 4.19.0
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app_file: app.py
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---
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title: YOLO World
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emoji: π₯
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colorFrom: purple
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colorTo: green
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sdk: gradio
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sdk_version: 4.19.0
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app_file: app.py
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app.py
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from typing import List
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import gradio as gr
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import numpy as np
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import supervision as sv
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from inference.models import YOLOWorld
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MARKDOWN = """
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# YOLO-World π
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Powered by Roboflow [Inference](https://github.com/roboflow/inference) and [Supervision](https://github.com/roboflow/supervision).
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"""
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MODEL = YOLOWorld(model_id="yolo_world/l")
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BOUNDING_BOX_ANNOTATOR = sv.BoundingBoxAnnotator()
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LABEL_ANNOTATOR = sv.LabelAnnotator(text_color=sv.Color.BLACK)
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def process_categories(categories: str) -> List[str]:
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return [category.strip() for category in categories.split(',')]
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def process_image(input_image: np.ndarray, categories: str) -> np.ndarray:
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categories = process_categories(categories)
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MODEL.set_classes(categories)
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results = MODEL.infer(input_image, confidence=0.003)
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detections = sv.Detections.from_inference(results).with_nms(0.1)
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output_image = input_image.copy()
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output_image = BOUNDING_BOX_ANNOTATOR.annotate(output_image, detections)
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output_image = LABEL_ANNOTATOR.annotate(output_image, detections)
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return output_image
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with gr.Blocks() as demo:
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gr.Markdown(MARKDOWN)
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with gr.Row():
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input_image_component = gr.Image(
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type='numpy',
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label='Input Image'
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)
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output_image_component = gr.Image(
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type='numpy',
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label='Output Image'
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)
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with gr.Row():
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categories_text_component = gr.Textbox(
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label='Categories',
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placeholder='comma separated list of categories',
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scale=5
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)
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submit_button_component = gr.Button('Submit', scale=1)
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submit_button_component.click(
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fn=process_image,
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inputs=[input_image_component, categories_text_component],
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outputs=output_image_component
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)
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demo.launch(debug=False, show_error=True)
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requirements.txt
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inference-gpu[yolo-world]==0.9.12
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supervision==0.19.0rc3
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gradio==4.19.0
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