import gradio as gr import numpy as np import supervision as sv from gradio.components import Image, Radio, Textbox def plot_bounding_boxes(image, boxes, box_type): x0, y0, x1, y1 = [int(i.strip()) for i in boxes.split(",")] detections = sv.Detections( xyxy=np.array([[x0, y0, x1, y1]]), class_id=np.array([0]), confidence=np.array([1.0]), ) # convert to cv2 image = np.array(image) bounding_box_annotator = sv.BoundingBoxAnnotator() annotated_image = bounding_box_annotator.annotate( scene=image, detections=detections ) return annotated_image iface = gr.Interface( fn=plot_bounding_boxes, inputs=[ Image(type="pil", label="Image"), Textbox(label="Bounding Boxes", lines=3), Radio(["xyxy", "xywh"], label="Bounding Box Type"), ], outputs=Image(type="pil"), title="Plot Bounding Boxes", description="Plot bounding boxes on an image. Useful for testing object detection models without writing bounding box code. Powered by [supervision](https://github.com/roboflow/supervision).", ) iface.launch()