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, 0.25, 0.45], ["highway.jpg", "yolov8m-seg.pt", 640, 0.25, 0.45], ["highway1.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)