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://raw.githubusercontent.com/mensss/vvvvv/main/MRI_of_Human_Brain.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,conf=conf_threshold,iou=iou_threshold)[0] image=image[:, :, ::-1].astype(np.uint8) detections = sv.Detections.from_yolov8(results) annotated_image = annotatormask.annotate(scene=image, detections=detections) annotated_image = annotatorbbox.annotate(scene=annotated_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 readme_html = """

More details:

We present a demo for performing object segmentation with training a Yolov8-seg on Brain tumor dataset. The model was trained on 236 training images and validated on 28 images.

Usage:

You can upload Brain tumor images, and the demo will provide you with your segmented image.

Dataset:

This dataset comprises a total of 278 images, which are divided into three distinct sets for various purposes:

License: This dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

To access and download this dataset, please follow this link: Dataset Download

""" with gr.Blocks() as demo: gr.Markdown( """

Brain Tumor Segmentation Demo

Powered by Tuba
""" ) # 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(readme_html) # Launch the app demo.launch(share=False)