Spaces:
Runtime error
Runtime error
File size: 3,182 Bytes
0f1bb12 4d26a0d 0f1bb12 4d26a0d 0f1bb12 4d26a0d 6ac1fcf f7a089e 4d26a0d 0f1bb12 35eddcc 0f1bb12 35eddcc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 |
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],
]
# 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")
gr.Examples(examples) # Add the examples to
with gr.Row():
button = gr.Button("Run")
# Define the event listener that connects the input and output components and triggers the function
button.click(fn=get_results, inputs=[image_input, conf_slider,IOU_Slider], outputs=output, api_name="yolov8_inference")
# Add the description below the layout
gr.Markdown(description_html)
# Launch the app
demo.launch(share=False)
|