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| import gradio as gr | |
| import torch | |
| from sahi.prediction import ObjectPrediction | |
| from sahi.utils.cv import visualize_object_predictions, read_image | |
| from ultralyticsplus import YOLO | |
| def yolov8_inference( | |
| image: gr.inputs.Image = None, | |
| model_path: gr.inputs.Dropdown = None, | |
| image_size: gr.inputs.Slider = 640, | |
| conf_threshold: gr.inputs.Slider = 0.25, | |
| iou_threshold: gr.inputs.Slider = 0.45, | |
| ): | |
| """ | |
| YOLOv8 inference function | |
| Args: | |
| image: Input image | |
| model_path: Path to the model | |
| image_size: Image size | |
| conf_threshold: Confidence threshold | |
| iou_threshold: IOU threshold | |
| Returns: | |
| Rendered image | |
| """ | |
| model = YOLO(f'{model_path}.pt') | |
| # set model parameters | |
| model.overrides['conf'] = conf_threshold # NMS confidence threshold | |
| model.overrides['iou'] = iou_threshold # NMS IoU threshold | |
| model.overrides['agnostic_nms'] = False # NMS class-agnostic | |
| model.overrides['max_det'] = 1000 # maximum number of detections per image | |
| results = model.predict(image, imgsz=image_size, return_outputs=True) | |
| object_prediction_list = [] | |
| for _, image_results in enumerate(results): | |
| if len(image_results)!=0: | |
| image_predictions_in_xyxy_format = image_results['det'] | |
| for pred in image_predictions_in_xyxy_format: | |
| x1, y1, x2, y2 = ( | |
| int(pred[0]), | |
| int(pred[1]), | |
| int(pred[2]), | |
| int(pred[3]), | |
| ) | |
| bbox = [x1, y1, x2, y2] | |
| score = pred[4] | |
| category_name = model.model.names[int(pred[5])] | |
| category_id = pred[5] | |
| object_prediction = ObjectPrediction( | |
| bbox=bbox, | |
| category_id=int(category_id), | |
| score=score, | |
| category_name=category_name, | |
| ) | |
| object_prediction_list.append(object_prediction) | |
| image = read_image(image) | |
| output_image = visualize_object_predictions(image=image, object_prediction_list=object_prediction_list) | |
| return output_image['image'] | |
| inputs = [ | |
| gr.inputs.Image(type="filepath", label="Input Image"), | |
| gr.inputs.Dropdown(["yolov8n", "yolov8m", "yolov8l", "yolov8x"], | |
| default="yolov8m", label="Model"), | |
| 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 = "State-of-the-Art YOLO Models for Object detection" | |
| # examples = [['demo_01.jpg', 'yolov8n', 640, 0.25, 0.45], ['demo_02.jpg', 'yolov8l', 640, 0.25, 0.45], ['demo_03.jpg', 'yolov8x', 1280, 0.25, 0.45]] | |
| demo_app = gr.Interface( | |
| fn=yolov8_inference, | |
| inputs=inputs, | |
| outputs=outputs, | |
| title=title, | |
| examples=examples, | |
| cache_examples=True, | |
| theme='huggingface', | |
| ) | |
| demo_app.launch(debug=True, enable_queue=True) |