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import gradio as gr
from ultralytics import YOLO
model = YOLO('./best.pt') # load your custom trained model
import torch
#from ultralyticsplus import render_result
from render import custom_render_result
def yoloV8_func(image: gr.Image = None,
image_size: int = 640,
conf_threshold: float = 0.4,
iou_threshold: float = 0.5):
"""This function performs YOLOv8 object detection on the given image.
Args:
image (gr.Image, optional): Input image to detect objects on. Defaults to None.
image_size (int, optional): Desired image size for the model. Defaults to 640.
conf_threshold (float, optional): Confidence threshold for object detection. Defaults to 0.4.
iou_threshold (float, optional): Intersection over Union threshold for object detection. Defaults to 0.50.
"""
# Load the YOLOv8 model from the 'best.pt' checkpoint
model_path = "yolov8n.pt"
# model = torch.hub.load('ultralytics/yolov8', 'custom', path='/content/best.pt', force_reload=True, trust_repo=True)
# 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 = custom_render_result(model=model, image=image, result=results[0])
return render
inputs = [
gr.Image(type="filepath", label="Input Image"),
gr.Slider(minimum=320, maximum=1280, step=32, label="Image Size", value=640),
gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label="Confidence Threshold"),
gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label="IOU Threshold"),
]
outputs = gr.Image(type="filepath", label="Output Image")
title = "YOLOv8 101: Custom Object Detection pupil on eye"
examples = [['img1.jpg', 640, 0.5, 0.7],
['img2.jpg', 800, 0.5, 0.6],
['img3.jpg', 900, 0.5, 0.8]]
yolo_app = gr.Interface(
fn=yoloV8_func,
inputs=inputs,
outputs=outputs,
title=title,
examples=examples,
cache_examples=False,
)
# Launch the Gradio interface in debug mode with queue enabled
yolo_app.launch(debug=True,share=True).queue()
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