import gradio as gr import sahi import torch from ultralytics import YOLO # Download images model_names = [ "yolov8n.pt", "yolov8s.pt", "yolov8m.pt", "yolov8l.pt", "yolov8x.pt", ] current_model_name = "yolov8m.pt" model = YOLO(current_model_name) def yolov8_inference( image: gr.Image = None, model_name: gr.Dropdown = None, image_size: gr.Slider = 640, conf_threshold: gr.Slider = 0.25, iou_threshold: gr.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: Bounding box coordinates in xyxy format """ 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(image) print(results) outputs=[] for i,box in enumerate(results[0].boxes): label = results[0].names[box.cls[0].item()] bbox = box.xyxy[0] outputs.append({"label": label, "bbox_coords": bbox}) return outputs 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.JSON(label="Bounding Boxes (xyxy format)") title = "YOLOv8 Bounding Box Extraction Demo" demo_app = gr.Interface( fn=yolov8_inference, inputs=inputs, outputs=outputs, title=title, theme="default" ) demo_app.queue().launch(debug=True)