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import gradio as gr
from ultralytics import YOLO
from PIL import Image, ImageDraw
import numpy as np
# Load your YOLOv8 model
model = YOLO("my_yolo_model.onnx") # or "yolov8n.pt"
def predict(image):
# Convert Gradio's numpy array to PIL Image
pil_image = Image.fromarray(image)
# Run YOLOv8 inference
results = model(pil_image)
# Extract bounding boxes and labels
boxes = results[0].boxes.xyxy.cpu().numpy() # Coordinates
classes = results[0].boxes.cls.cpu().numpy() # Class IDs
confidences = results[0].boxes.conf.cpu().numpy() # Confidence scores
# Draw bounding boxes on the image (PIL)
draw = ImageDraw.Draw(pil_image)
for box, cls, conf in zip(boxes, classes, confidences):
x1, y1, x2, y2 = box
label = f"{model.names[int(cls)]} {conf:.2f}"
# Draw rectangle and label
draw.rectangle([x1, y1, x2, y2], outline="blue", width=2)
draw.text((x1, y1), label, fill="red")
return pil_image # Return PIL Image (Gradio handles RGB)
# Gradio Interface
demo = gr.Interface(
fn=predict,
inputs=gr.Image(label="Input Image"),
outputs=gr.Image(label="Detected Objects"),
title="Pathole Detection by Yunusa Jibrin ",
examples=["example1.jpg", "example2.jpg"], # Optional
)
demo.launch() |