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import gradio as gr
import spaces
import supervision as sv
from rfdetr import RFDETRBase
from rfdetr.util.coco_classes import COCO_CLASSES
MARKDOWN = """
# RF-DETR 🔥
<div style="display: flex; align-items: center; gap: 8px;">
<a href="https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-finetune-rf-detr-on-detection-dataset.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="colab" />
</a>
<a href="https://blog.roboflow.com/rf-detr">
<img src="https://raw.githubusercontent.com/roboflow-ai/notebooks/main/assets/badges/roboflow-blogpost.svg" alt="roboflow" />
</a>
<a href="https://github.com/roboflow/rf-detr">
<img src="https://badges.aleen42.com/src/github.svg" alt="roboflow" />
</a>
</div>
RF-DETR is a real-time, transformer-based object detection model architecture developed
by [Roboflow](https://roboflow.com/) and released under the Apache 2.0 license.
"""
COLOR = sv.ColorPalette.from_hex([
"#ffff00", "#ff9b00", "#ff8080", "#ff66b2", "#ff66ff", "#b266ff",
"#9999ff", "#3399ff", "#66ffff", "#33ff99", "#66ff66", "#99ff00"
])
MODEL = RFDETRBase()
@spaces.GPU()
def inference(image, confidence):
detections = MODEL.predict(image, threshold=confidence)
text_scale = sv.calculate_optimal_text_scale(resolution_wh=image.size)
thickness = sv.calculate_optimal_line_thickness(resolution_wh=image.size)
bbox_annotator = sv.BoxAnnotator(color=COLOR, thickness=thickness)
label_annotator = sv.LabelAnnotator(
color=COLOR,
text_color=sv.Color.BLACK,
text_scale=text_scale,
smart_position=True
)
labels = [
f"{COCO_CLASSES[class_id]} {confidence:.2f}"
for class_id, confidence
in zip(detections.class_id, detections.confidence)
]
annotated_image = image.copy()
annotated_image = bbox_annotator.annotate(annotated_image, detections)
annotated_image = label_annotator.annotate(annotated_image, detections, labels)
return annotated_image
with gr.Blocks() as demo:
gr.Markdown(MARKDOWN)
with gr.Row():
with gr.Column():
input_image = gr.Image(
label="Input Image",
image_mode='RGB',
type='pil',
height=600
)
confidence_slider = gr.Slider(
label="Confidence",
minimum=0.0,
maximum=1.0,
step=0.05,
value=0.5,
)
submit_button = gr.Button("Submit")
with gr.Column():
output_image = gr.Image(
label="Input Image",
image_mode='RGB',
type='pil',
height=600
)
submit_button.click(
inference,
inputs=[input_image, confidence_slider],
outputs=output_image
)
demo.launch(debug=False, show_error=True)
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