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| import gradio as gr | |
| import torch | |
| from ultralyticsplus import YOLO, render_result | |
| # torch.hub.download_url_to_file( | |
| # 'https://en.wikipedia.org/wiki/Human_eye#/media/File:Human_eye,_anterior_view.jpg', 'one.jpg') | |
| # torch.hub.download_url_to_file( | |
| # 'https://glaucoma.org/wp-content/uploads/2024/01/amazing-eye-close-up_900-585x540.jpg', 'two.jpg') | |
| # torch.hub.download_url_to_file( | |
| # 'https://www.nvisioncenters.com/wp-content/uploads//woman-blue-eye-close-up-352x235.jpg', 'three.jpg') | |
| def yoloV8_func(image: gr.Image = None, | |
| image_size: int = 640, | |
| conf_threshold: float = 0.70, | |
| iou_threshold: float = 0.50): | |
| """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 0.7. | |
| 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 = "EyesCareEyeDetectModel.pt" | |
| model = YOLO("yolov8n.pt") | |
| # 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.Image(type="filepath", label="Input Image"), | |
| gr.Slider(minimum=320, maximum=1280, value=640, | |
| step=32, label="Image Size"), | |
| gr.Slider(minimum=0.0, maximum=1.0, value=0.25, | |
| step=0.05, label="Confidence Threshold"), | |
| gr.Slider(minimum=0.0, maximum=1.0, value=0.45, | |
| step=0.05, label="IOU Threshold"), | |
| ] | |
| outputs = gr.Image(type="filepath", label="Output Image") | |
| title = "YOLOv9 101: Custom Object Detection on Eye" | |
| examples = [['one.jpg', 640, 0.5, 0.7], | |
| ['two.jpg', 640, 0.5, 0.6], | |
| ['three.jpg', 640, 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) | |