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import supervision as sv
import gradio as gr
from ultralytics import YOLO
import sahi
import numpy as np
# Images
sahi.utils.file.download_from_url(
"https://transform.roboflow.com/zZuu207UOVOOJKuuCpmV/3512b3839afacecec643949bef398e99/thumb.jpg",
"tu1.jpg",
)
sahi.utils.file.download_from_url(
"https://transform.roboflow.com/zZuu207UOVOOJKuuCpmV/5b8b940fae2f9e4952395bcced0688aa/thumb.jpg",
"tu2.jpg",
)
sahi.utils.file.download_from_url(
"https://transform.roboflow.com/zZuu207UOVOOJKuuCpmV/347e10ab7aa2b399ec546f2037d8c786/thumb.jpg",
"tu3.jpg",
)
annotatorbbox = sv.BoxAnnotator()
annotatormask=sv.MaskAnnotator()
def yolov8_inference(
image: gr.inputs.Image = None,
conf_threshold: gr.inputs.Slider = 0.25,
iou_threshold: gr.inputs.Slider = 0.45,
):
image=image[:, :, ::-1].astype(np.uint8)
model = YOLO("https://huggingface.co/spaces/devisionx/Fifth_demo/blob/main/best_weigh.pt")
results = model(image,imgsz=360)[0]
image=image[:, :, ::-1].astype(np.uint8)
detections = sv.Detections.from_yolov8(results)
annotated_image = annotatorbbox.annotate(scene=image, detections=detections)
return annotated_image
# image_input = gr.inputs.Image() # Adjust the shape according to your requirements
# inputs = [
# gr.inputs.Image(label="Input Image"),
# gr.Slider(
# minimum=0.0, maximum=1.0, value=0.25, step=0.05, label="Confidence Threshold"
# ),
# gr.Slider(minimum=0.0, maximum=1.0, value=0.45, step=0.05, label="IOU Threshold"),
# ]
# outputs = gr.Image(type="filepath", label="Output Image")
# title = "Brain Tumor Demo"
import os
examples = [
["tu1.jpg", 0.6, 0.45],
["tu2.jpg", 0.25, 0.45],
["tu3.jpg", 0.25, 0.45],
]
outputs_images = [
["1.jpg"], # First example: an output image for the cat example
["2.jpg"] # Second example: an output image for the dog example
,["3.jpg"]
]
# demo_app = gr.Interface(examples=examples,
# fn=yolov8_inference,
# inputs=inputs,
# outputs=outputs,
# title=title,
# cache_examples=True,
# theme="default",
# )
# demo_app.launch(debug=False, enable_queue=True)
# gr.Examples(examples) # Add the examples to the app
with gr.Blocks() as demo:
gr.Markdown(
"""
# Tuba Brain Tumor Demo
[Tuba](https://Tuba.ai)
"""
)
# Define the input components and add them to the layout
with gr.Row():
image_input = gr.inputs.Image()
outputs = gr.Image(type="filepath", label="Output Image")
# Define the output component and add it to the layout
with gr.Row():
conf_slider=gr.Slider(minimum=0.0, maximum=1.0, value=0.25, step=0.05, label="Confidence Threshold" )
with gr.Row():
IOU_Slider=gr.Slider(minimum=0.0, maximum=1.0, value=0.45, step=0.05, label="IOU Threshold")
button = gr.Button("Run")
# Define the event listener that connects the input and output components and triggers the function
button.click(fn=yolov8_inference, inputs=[image_input, conf_slider,IOU_Slider], outputs=outputs, api_name="yolov8_inference")
gr.Examples(
fn=yolov8_inference,
examples=examples,
inputs=[image_input, conf_slider,IOU_Slider],
outputs=[outputs]
)
# gr.Examples(inputs=examples, outputs=outputs_images)
# Add the description below the layout
gr.Markdown(description_html)
# Launch the app
demo.launch(share=False)
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