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import gradio as gr
import sahi
import torch
from ultralyticsplus import YOLO, render_model_output
# Images
sahi.utils.file.download_from_url(
"https://raw.githubusercontent.com/kadirnar/dethub/main/data/images/highway.jpg",
"highway.jpg",
)
sahi.utils.file.download_from_url(
"https://raw.githubusercontent.com/obss/sahi/main/tests/data/small-vehicles1.jpeg",
"small-vehicles1.jpeg",
)
sahi.utils.file.download_from_url(
"https://raw.githubusercontent.com/ultralytics/yolov5/master/data/images/zidane.jpg",
"zidane.jpg",
)
model_names = [
"yolov8n-seg.pt",
"yolov8s-seg.pt",
"yolov8m-seg.pt",
"yolov8l-seg.pt",
"yolov8x-seg.pt",
]
current_model_name = "yolov8m-seg.pt"
model = YOLO(current_model_name)
def yolov8_inference(
image: gr.inputs.Image = None,
model_name: gr.inputs.Dropdown = None,
image_size: gr.inputs.Slider = 640,
conf_threshold: gr.inputs.Slider = 0.25,
iou_threshold: gr.inputs.Slider = 0.45,
):
"""
YOLOv8 inference function
Args:
image: Input image
model_name: Name of the model
image_size: Image size
conf_threshold: Confidence threshold
iou_threshold: IOU threshold
Returns:
Rendered image
"""
global model
global current_model_name
if model_name != current_model_name:
model = YOLO(model_name)
current_model_name = model_name
model.overrides["conf"] = conf_threshold
model.overrides["iou"] = iou_threshold
results = model.predict(image, imgsz=image_size, return_outputs=True)
renders = []
for image_results in model.predict(image, imgsz=image_size, return_outputs=True):
render = render_model_output(
model=model, image=image, model_output=image_results
)
renders.append(render)
return renders[0]
inputs = [
gr.Image(type="filepath", label="Input Image"),
gr.Dropdown(
model_names,
value=current_model_name,
label="Model type",
),
gr.Slider(minimum=320, maximum=1280, value=640, step=32, label="Image Size"),
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"
examples = [
["zidane.jpg", "yolov8m-seg.pt", 640, 0.6, 0.45],
["highway.jpg", "yolov8m-seg.pt", 640, 0.25, 0.45],
["small-vehicles1.jpeg", "yolov8m-seg.pt", 640, 0.25, 0.45],
]
demo_app = gr.Interface(
fn=yolov8_inference,
inputs=inputs,
outputs=outputs,
title=title,
examples=examples,
cache_examples=True,
theme="default",
)
demo_app.launch(debug=True, enable_queue=True)
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