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import gradio as gr
import torch
from ultralyticsplus import YOLO, render_result
torch.hub.download_url_to_file(
'https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg',
'one.jpg')
torch.hub.download_url_to_file(
'https://www.state.gov/wp-content/uploads/2022/01/shutterstock_248799484-scaled.jpg',
'two.jpg')
torch.hub.download_url_to_file(
'https://cdn.theatlantic.com/thumbor/xoh2WVVSx4F2uboG9xbT5BDprtM=/0x0:4939x2778/960x540/media/img/mt/2023/11/LON68717_copy/original.jpg',
'three.jpg')
def yoloV8_func(image: gr.inputs.Image = None,
image_size: gr.inputs.Slider = 640,
conf_threshold: gr.inputs.Slider = 0.4,
iou_threshold: gr.inputs.Slider = 0.50):
"""This function performs YOLOv8 object detection on the given image.
Args:
image (gr.inputs.Image, optional): Input image to detect objects on. Defaults to None.
image_size (gr.inputs.Slider, optional): Desired image size for the model. Defaults to 640.
conf_threshold (gr.inputs.Slider, optional): Confidence threshold for object detection. Defaults to 0.4.
iou_threshold (gr.inputs.Slider, optional): Intersection over Union threshold for object detection. Defaults to 0.50.
"""
# Load the YOLOv8 model from the 'best.pt' checkpoint
model_path = "YOLO-best.pt"
model = YOLO(model_path)
# Perform object detection on the input image using the YOLOv8 model
results = model.predict(image,
conf=conf_threshold,
iou=iou_threshold,
imgsz=image_size)
# Print the detected objects' information (class, coordinates, and probability)
box = results[0].boxes
print("Object type:", box.cls)
print("Coordinates:", box.xyxy)
print("Probability:", box.conf)
# Render the output image with bounding boxes around detected objects
render = render_result(model=model, image=image, result=results[0])
return render
inputs = [
gr.inputs.Image(type="filepath", label="Input Image"),
gr.inputs.Slider(minimum=320, maximum=1280, default=640,
step=32, label="Image Size"),
gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25,
step=0.05, label="Confidence Threshold"),
gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45,
step=0.05, label="IOU Threshold"),
]
outputs = gr.outputs.Image(type="filepath", label="Output Image")
title = "YOLOv8 101: Custom Object Detection on Objects in Big Cities"
examples = [['one.jpg', 640, 0.5, 0.7],
['two.jpg', 800, 0.5, 0.6],
['three.jpg', 900, 0.5, 0.8]]
yolo_app = gr.Interface(
fn=yoloV8_func,
inputs=inputs,
outputs=outputs,
title=title,
examples=examples,
cache_examples=True,
)
# Launch the Gradio interface in debug mode with queue enabled
yolo_app.launch(debug=True, enable_queue=True) |