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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 | |
| from ultralytics import YOLO | |
| # Images | |
| try: | |
| torch.hub.download_url_to_file("https://image.jimcdn.com/app/cms/image/transf/none/path/sb7e051baffe289da/image/i98db96643a3b080e/version/1416825261/image.jpg", "mg.jpg") | |
| except: | |
| torch.hub.download_url_to_file('https://raw.githubusercontent.com/obss/sahi/main/tests/data/small-vehicles1.jpeg', 'mg.jpg') | |
| # torch.hub.download_url_to_file("https://ikiwiki.iki.fi/_media/jot-email-1612-fi-iki.png", "fi.jpg") | |
| # torch.hub.download_url_to_file("https://www.geekculture.com/joyoftech/joyimages/1612.gif", "en.jpg") | |
| # torch.hub.download_url_to_file('https://user-images.githubusercontent.com/34196005/142742872-1fefcc4d-d7e6-4c43-bbb7-6b5982f7e4ba.jpg', 'highway1.jpg') | |
| # torch.hub.download_url_to_file('https://raw.githubusercontent.com/obss/sahi/main/tests/data/small-vehicles1.jpeg', 'small-vehicles1.jpeg') | |
| 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(""+model_path+"/train/weights/best.onnx", task="detect") | |
| model = YOLO("https://huggingface.co/"+model_path+"/resolve/main/train/weights/best.onnx", task="detect") | |
| model.conf = conf_threshold | |
| model.iou = iou_threshold | |
| # results = model.predict(image, imgsz=image_size, return_outputs=True) | |
| results = model.predict(image) | |
| object_prediction_list = [] | |
| print("*", len(results)) | |
| for _box in results: | |
| for box in _box: | |
| xyxy = [int(x) for x in box.boxes.xyxy[0]] | |
| conf = float(box.boxes.conf[0]) | |
| cls = int(box.boxes.cls[0]) | |
| label = box.names[cls] | |
| #label = list(map(lambda x: box.names[int(x)], cls)) | |
| #for xyxy, conf, cls, label in zip(xyxy,conf,cls,label): | |
| object_prediction_list.append( | |
| ObjectPrediction( | |
| bbox=xyxy, | |
| category_id=cls, | |
| score=conf, | |
| category_name=label, | |
| ) | |
| ) | |
| print(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(["kadirnar/yolov8n-v8.0", "kadirnar/yolov8m-v8.0", "kadirnar/yolov8l-v8.0", "kadirnar/yolov8x-v8.0", "kadirnar/yolov8x6-v8.0"], | |
| # default="kadirnar/yolov8m-v8.0", label="Model"), | |
| # gr.inputs.Dropdown(["jongkook90/yolov8_comicbook"], default="jongkook90/yolov8_comicbook", label="Model"), | |
| gr.inputs.Dropdown(["jongkook90/yolov8_comicbook"], default="jongkook90/yolov8_comicbook", 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 = "Ultralytics YOLOv8: State-of-the-Art YOLO Models" | |
| examples = [ | |
| ['mg.jpg', 'jongkook90/yolov8_comicbook', 640, 0.25, 0.45], | |
| #['fi.jpg', 'jongkook90/yolov8_comicbook', 640, 0.25, 0.45], | |
| #['en.jpg', 'jongkook90/yolov8_comicbook', 640, 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) | |