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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):
if not boxes:
return image
bboxes = []
for row in boxes.split("\n"):
x0, y0, x1, y1 = [int(float(i.split(".")[0])) for i in boxes.split(",")]
if box_type == "xywh":
x0 = x0 * image.size[0]
y0 = y0 * image.size[1]
x1 = x0 + (x1 * image.size[0])
y1 = y0 + (y1 * image.size[1])
bbox = [x0, y0, x1, y1]
bboxes.append(bbox)
detections = sv.Detections(
xyxy=np.array(bboxes),
class_id=np.array([0] * len(bboxes)),
confidence=np.array([1.0] * len(bboxes)),
)
image = np.array(image)
bounding_box_annotator = sv.BoundingBoxAnnotator(thickness=3)
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 Box (separate values by comma, like '100, 200, 300, 400')", 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()
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