| # import gradio as gr | |
| # import spaces | |
| # from huggingface_hub import hf_hub_download | |
| # # Import YOLOv9 | |
| # import yolov9 | |
| # def download_models(model_id): | |
| # hf_hub_download("SakshiRathi77/void-space-detection/weights", filename=f"{model_id}", local_dir=f"./") | |
| # return f"./{model_id}" | |
| # def download_models(model_id): | |
| # hf_hub_download("merve/yolov9", filename=f"{model_id}", local_dir=f"./") | |
| # return f"./{model_id}" | |
| # def yolov9_inference(img_path, image_size, conf_threshold, iou_threshold): | |
| # """ | |
| # Load a YOLOv9 model, configure it, perform inference on an image, and optionally adjust | |
| # the input size and apply test time augmentation. | |
| # :param model_path: Path to the YOLOv9 model file. | |
| # :param conf_threshold: Confidence threshold for NMS. | |
| # :param iou_threshold: IoU threshold for NMS. | |
| # :param img_path: Path to the image file. | |
| # :param size: Optional, input size for inference. | |
| # :return: A tuple containing the detections (boxes, scores, categories) and the results object for further actions like displaying. | |
| # """ | |
| # # Load the model | |
| # model_path = download_models() | |
| # # model = yolov9.load("./best.pt") | |
| # # Set model parameters | |
| # model.conf = conf_threshold | |
| # model.iou = iou_threshold | |
| # # Perform inference | |
| # results = model(img_path, size=image_size) | |
| # # Optionally, show detection bounding boxes on image | |
| # output = results.render() | |
| # return output[0] | |
| # def app(): | |
| # with gr.Blocks(): | |
| # with gr.Row(): | |
| # with gr.Column(): | |
| # img_path = gr.Image(type="filepath", label="Image") | |
| # image_size = gr.Slider( | |
| # label="Image Size", | |
| # minimum=320, | |
| # maximum=1280, | |
| # step=32, | |
| # value=640, | |
| # ) | |
| # conf_threshold = gr.Slider( | |
| # label="Confidence Threshold", | |
| # minimum=0.1, | |
| # maximum=1.0, | |
| # step=0.1, | |
| # value=0.4, | |
| # ) | |
| # iou_threshold = gr.Slider( | |
| # label="IoU Threshold", | |
| # minimum=0.1, | |
| # maximum=1.0, | |
| # step=0.1, | |
| # value=0.5, | |
| # ) | |
| # yolov9_infer = gr.Button(value="Inference") | |
| # with gr.Column(): | |
| # output_numpy = gr.Image(type="numpy",label="Output") | |
| # yolov9_infer.click( | |
| # fn=yolov9_inference, | |
| # inputs=[ | |
| # img_path, | |
| # # model_path, | |
| # image_size, | |
| # conf_threshold, | |
| # iou_threshold, | |
| # ], | |
| # outputs=[output_numpy], | |
| # ) | |
| # gradio_app = gr.Blocks() | |
| # with gradio_app: | |
| # gr.HTML( | |
| # """ | |
| # <h1 style='text-align: center'> | |
| # YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information | |
| # </h1> | |
| # """) | |
| # gr.HTML( | |
| # """ | |
| # <h3 style='text-align: center'> | |
| # Follow me for more! | |
| # </h3> | |
| # """) | |
| # with gr.Row(): | |
| # with gr.Column(): | |
| # app() | |
| # gradio_app.launch(debug=True) | |
| # make sure you have the following dependencies | |
| # import gradio as gr | |
| # import torch | |
| # from torchvision import transforms | |
| # from PIL import Image | |
| # # Load the YOLOv9 model | |
| # model_path = "best.pt" # Replace with the path to your YOLOv9 model | |
| # model = torch.load(model_path) | |
| # # Define preprocessing transforms | |
| # preprocess = transforms.Compose([ | |
| # transforms.Resize((640, 640)), # Resize image to model input size | |
| # transforms.ToTensor(), # Convert image to tensor | |
| # ]) | |
| # # Define a function to perform inference | |
| # def detect_void(image): | |
| # # Preprocess the input image | |
| # image = Image.fromarray(image) | |
| # image = preprocess(image).unsqueeze(0) # Add batch dimension | |
| # # Perform inference | |
| # with torch.no_grad(): | |
| # output = model(image) | |
| # # Post-process the output if needed | |
| # # For example, draw bounding boxes on the image | |
| # # Convert the image back to numpy array | |
| # # and return the result | |
| # return output.squeeze().numpy() | |
| # # Define Gradio interface components | |
| # input_image = gr.inputs.Image(shape=(640, 640), label="Input Image") | |
| # output_image = gr.outputs.Image(label="Output Image") | |
| # # Create Gradio interface | |
| # gr.Interface(fn=detect_void, inputs=input_image, outputs=output_image, title="Void Detection App").launch() | |
| import gradio as gr | |
| import spaces | |
| from huggingface_hub import hf_hub_download | |
| # def download_models(model_id): | |
| # hf_hub_download("SakshiRathi77/void-space-detection/weights", filename=f"{model_id}", local_dir=f"./") | |
| # return f"./{model_id}" | |
| def yolov9_inference(img_path, model_id, image_size, conf_threshold, iou_threshold): | |
| """ | |
| Load a YOLOv9 model, configure it, perform inference on an image, and optionally adjust | |
| the input size and apply test time augmentation. | |
| :param model_path: Path to the YOLOv9 model file. | |
| :param conf_threshold: Confidence threshold for NMS. | |
| :param iou_threshold: IoU threshold for NMS. | |
| :param img_path: Path to the image file. | |
| :param size: Optional, input size for inference. | |
| :return: A tuple containing the detections (boxes, scores, categories) and the results object for further actions like displaying. | |
| """ | |
| # Import YOLOv9 | |
| import yolov9 | |
| # Load the model | |
| # model_path = download_models(model_id) | |
| model = yolov9.load(model_id) | |
| # Set model parameters | |
| model.conf = conf_threshold | |
| model.iou = iou_threshold | |
| # Perform inference | |
| results = model(img_path, size=image_size) | |
| # Optionally, show detection bounding boxes on image | |
| output = results.render() | |
| return output[0] | |
| def app(): | |
| with gr.Blocks(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| img_path = gr.Image(type="filepath", label="Image") | |
| model_path = gr.Dropdown( | |
| label="Model", | |
| choices=[ | |
| "best.pt", | |
| ], | |
| value="./best.pt", | |
| ) | |
| image_size = gr.Slider( | |
| label="Image Size", | |
| minimum=320, | |
| maximum=1280, | |
| step=32, | |
| value=640, | |
| ) | |
| conf_threshold = gr.Slider( | |
| label="Confidence Threshold", | |
| minimum=0.1, | |
| maximum=1.0, | |
| step=0.1, | |
| value=0.4, | |
| ) | |
| iou_threshold = gr.Slider( | |
| label="IoU Threshold", | |
| minimum=0.1, | |
| maximum=1.0, | |
| step=0.1, | |
| value=0.5, | |
| ) | |
| yolov9_infer = gr.Button(value="Inference") | |
| with gr.Column(): | |
| output_numpy = gr.Image(type="numpy",label="Output") | |
| yolov9_infer.click( | |
| fn=yolov9_inference, | |
| inputs=[ | |
| img_path, | |
| model_path, | |
| image_size, | |
| conf_threshold, | |
| iou_threshold, | |
| ], | |
| outputs=[output_numpy], | |
| ) | |
| gradio_app = gr.Blocks() | |
| with gradio_app: | |
| gr.HTML( | |
| """ | |
| <h1 style='text-align: center'> | |
| YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information | |
| </h1> | |
| """) | |
| gr.HTML( | |
| """ | |
| <h3 style='text-align: center'> | |
| Follow me for more! | |
| <a href='https://twitter.com/kadirnar_ai' target='_blank'>Twitter</a> | <a href='https://github.com/kadirnar' target='_blank'>Github</a> | <a href='https://www.linkedin.com/in/kadir-nar/' target='_blank'>Linkedin</a> | <a href='https://www.huggingface.co/kadirnar/' target='_blank'>HuggingFace</a> | |
| </h3> | |
| """) | |
| with gr.Row(): | |
| with gr.Column(): | |
| app() | |
| gradio_app.launch(debug=True) | |