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# Copyright (C) 2025 Anthony Casagrande
# Ultralytics YOLO 🚀, AGPL-3.0 license
import gradio as gr
import PIL.Image as Image
import cv2
from ultralytics import ASSETS, YOLO
model = None
def predict_image(img, conf_threshold, iou_threshold, model_name):
"""Predicts objects in an image using a YOLOv11 model with adjustable confidence and IOU thresholds."""
model = YOLO("train29s.pt")
results = model.predict(
source=img,
conf=conf_threshold,
iou=iou_threshold,
show_labels=True,
show_conf=True,
imgsz=1280,
)
for r in results:
im_array1 = r.plot(img=img.copy(), labels=False, line_width=4)
im1 = Image.fromarray(cv2.cvtColor(im_array1, cv2.COLOR_BGR2RGB))
for r in results:
im_array2 = r.plot(img=img.copy(), line_width=4, font_size=10)
im2 = Image.fromarray(cv2.cvtColor(im_array2, cv2.COLOR_BGR2RGB))
# Calculate the dimensions for the output image
total_height = im1.height + im2.height
total_width = im1.width
# Create a blank image with the combined height
im = Image.new("RGB", (total_width, total_height))
# Paste the images one above the other
im.paste(im1, (0, 0))
im.paste(im2, (0, im1.height))
return im
iface = gr.Interface(
fn=predict_image,
inputs=[
gr.Image(type="pil", label="Upload Image"),
gr.Slider(minimum=0, maximum=1, value=0.25, label="Confidence threshold"),
gr.Slider(minimum=0, maximum=1, value=0.80, label="IoU threshold"),
# gr.Radio(choices=["yolo11n", "yolo11s", "yolo11n-seg", "yolo11s-seg", "yolo11n-pose", "yolo11s-pose"], label="Model Name", value="yolo11n"),
],
outputs=gr.Image(type="pil", label="Result"),
title="RC Race Vision YOLO11 Gradio Application 🚀",
description="Upload images of RC Race Cars for inference. \nCurrently supports 1/8th Buggies, and 1/10th Scale Buggies, Stadium Trucks, and Short Course Trucks.\nNot intended to work with close-up shots, works better on a wide view of the cars on a track.",
examples=[
["rc1.jpg", 0.25, 0.8],
["rc2.jpg", 0.25, 0.8],
],
)
iface.launch(share=True)
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