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import gradio as gr
import torch
from sahi.prediction import ObjectPrediction
from sahi.utils.cv import visualize_object_predictions, read_image
from ultralyticsplus import YOLO, render_result
def yolov8_inference(
image,
model_path,
image_size,
conf_threshold,
iou_threshold,
):
"""
YOLOv8 inference function
Args:
image: Input image
model_path: Path to the model
image_size: Image size
conf_threshold: Confidence threshold
iou_threshold: IOU threshold
Returns:
Rendered image
"""
model = YOLO(f'kadirnar/{Ultralytics/YOLOv8/blob/main/yolov8n.pt}-v8.')
# set model parameters
model.overrides['conf'] = conf_threshold # NMS confidence threshold
model.overrides['iou'] = iou_threshold # NMS IoU threshold
model.overrides['agnostic_nms'] = False # NMS class-agnostic
model.overrides['max_det'] = 1000 # maximum number of detections per image
results = model.predict(image, imgsz=image_size)
render = render_result(model=model, image=image, result=results[0])
return render
inputs = [
gr.Image(type="filepath", label="Input Image"),
gr.Dropdown(["yolov8n", "yolov8m", "yolov8l", "yolov8x"],
value="yolov8m", label="Model"),
gr.Slider(minimum=320, maximum=1280, value=640, step=320, 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 = "State-of-the-Art YOLO Models for Object detection"
examples = [['demo_01.jpg', 'yolov8n', 640, 0.25, 0.45], ['demo_02.jpg', 'yolov8l', 640, 0.25, 0.45], ['demo_03.jpg', 'yolov8x', 1280, 0.25, 0.45]]
demo_app = gr.Interface(
fn=yolov8_inference,
inputs=inputs,
outputs=outputs,
title=title,
examples=examples,
cache_examples=True,
)
demo_app.launch(debug=True) |