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import gradio as gr
import torch
from sahi.prediction import ObjectPrediction
from sahi.utils.cv import visualize_object_predictions, read_image
from ultralyticsplus import YOLO, render_result
def yolov8_inference(
image: gr.Image = None,
model_path: gr.Dropdown = None,
image_size: gr.Slider = 640,
conf_threshold: gr.Slider = 0.25,
iou_threshold: gr.Slider = 0.45,
):
"""
YOLOv8 inference function
Args:
image: Input image
model_path: Path to the model
image_size: Image size
conf_threshold: Confidence threshold
iou_threshold: IOU threshold
Returns:
Rendered image
"""
model = YOLO(model_path)
model.overrides['conf'] = conf_threshold
model.overrides['iou']= iou_threshold
model.overrides['agnostic_nms'] = False # NMS class-agnostic
model.overrides['max_det'] = 1000
image = read_image(image)
results = model.predict(image)
render = render_result(model=model, image=image, result=results[0])
return render
inputs = [
gr.Image(type="filepath", label="Input Image"),
gr.Dropdown(["foduucom/thermal-image-object-detection"],
default="foduucom/thermal-image-object-detection", label="Model"),
gr.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
gr.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
gr.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
]
outputs = gr.Image(type="filepath", label="Output Image")
title = "ThermalSense: Object Detection in Thermal Images"
description ="""
π₯ Unveiling ThermalFoduu: Spot Objects with Thermal Vision! ππΈ Lost your keys in the dark? ποΈπ ThermalFoduu's got you covered! Powered by Foduu AI, our app effortlessly detects objects in thermal images. No more blurry blobs β just pinpoint accuracy! π¦
π―
Love the thermal world? Give us a thumbs up! π Questions or suggestions? Contact us at info@foduu. Let's decode the thermal universe together! π§π‘οΈ
"""
examples = [['samples/1.jpeg', 'foduucom/thermal-image-object-detection', 640, 0.25, 0.45], ['samples/2.jpg', 'foduucom/thermal-image-object-detection', 640, 0.25, 0.45]]
demo_app = gr.Interface(
fn=yolov8_inference,
inputs=inputs,
outputs=outputs,
title=title,
description=description,
examples=examples,
cache_examples=True,
theme='huggingface',
)
demo_app.queue().launch(debug=True)
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