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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/aHClLv0V9gWdgkEi3TZOcyGv4zZ2/2b24b3f5ef9330424b9fda06ad38f98a/thumb.jpg",
"m1.jpg",
)
sahi.utils.file.download_from_url(
"https://transform.roboflow.com/aHClLv0V9gWdgkEi3TZOcyGv4zZ2/751a6fca76be162856174c24048b293d/thumb.jpg",
"m2.jpg",
)
sahi.utils.file.download_from_url(
"https://transform.roboflow.com/aHClLv0V9gWdgkEi3TZOcyGv4zZ2/9373f5662c0ea03977182aa9ebe37695/thumb.jpg",
"m3.jpg",
)
annotatorbbox = sv.BoxAnnotator()
annotatormask=sv.MaskAnnotator()
def yolov8_inference(
image: gr.inputs.Image = None,
conf_threshold: gr.inputs.Slider = 0.5,
iou_threshold: gr.inputs.Slider = 0.45,
):
image=image[:, :, ::-1].astype(np.uint8)
model = YOLO("https://huggingface.co/spaces/devisionx/Final_demo/blob/main/best_weights.pt")
results = model(image,imgsz=360)[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 = "Ultralytics YOLOv8 Segmentation Demo"
import os
examples = [
["m1.jpg", 0.6, 0.45],
["m2.jpg", 0.25, 0.45],
["m3.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) |