| import os | |
| import gradio as gr | |
| import PIL.Image as Image | |
| from ultralytics import YOLO | |
| model = YOLO("best.pt") | |
| def predict_image(img, conf_threshold, iou_threshold, image_size): | |
| """Predicts objects in an image using a YOLOv8 model with adjustable confidence and IOU thresholds.""" | |
| results = model.predict( | |
| source=img, | |
| conf=conf_threshold, | |
| iou=iou_threshold, | |
| show_labels=True, | |
| show_conf=True, | |
| imgsz=image_size, | |
| ) | |
| for r in results: | |
| im_array = r.plot() | |
| im = Image.fromarray(im_array[..., ::-1]) | |
| return im | |
| example_list = [["examples/" + example] for example in os.listdir("examples")] | |
| 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=0.45, label="IoU threshold"), | |
| gr.Slider( | |
| label="Image Size", | |
| minimum=320, | |
| maximum=1280, | |
| step=32, | |
| value=640, | |
| ) | |
| ], | |
| outputs=gr.Image(type="pil", label="Result"), | |
| title="YOLOv10: Real-Time Fire and Smoke Detection", | |
| description="This project utilizes the YOLOv10 model to detect Fire and Smoke in Real-Time. Adjust the confidence and IoU thresholds for optimal detection performance. Upload an image to see the detection results.\n [Github](https://github.com/X-Men01/YOLOv10-Fire-and-Smoke-Detection)", | |
| examples=[ | |
| [example_list[0][0], 0.25, 0.45, 640], | |
| [example_list[1][0], 0.25, 0.45, 960], | |
| [example_list[2][0], 0.25, 0.45, 640], | |
| ], | |
| allow_flagging="never", | |
| submit_btn="Run Inference", | |
| article = "Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)." | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() | |