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| from fastai.vision.all import * | |
| from icevision.all import * | |
| from fastai.basics import * | |
| from fastai.callback import * | |
| from icevision import models | |
| import PIL | |
| import gradio as gr | |
| class_map=ClassMap(['kangaroo']) | |
| # Cargamos el learner | |
| model = models.torchvision.faster_rcnn.model(backbone=models.torchvision.faster_rcnn.backbones.resnet18_fpn(pretrained=True), | |
| num_classes=len(class_map)) | |
| state_dict = torch.load('fasterRCNNkangaroo.pth', map_location = torch.device('cpu')) | |
| model.load_state_dict(state_dict) | |
| # Definimos una función que se encarga de llevar a cabo las predicciones | |
| infer_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(384),tfms.A.Normalize()]) | |
| def predict(img): | |
| img = PILImage.create(img) | |
| pred_dict = models.torchvision.faster_rcnn.end2end_detect(img, infer_tfms, model.to("cpu"), class_map=ClassMap(['kangaroo']), detection_threshold=0.5) | |
| return pred_dict['img'] | |
| # Creamos la interfaz y la lanzamos. | |
| gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(128, 128)), outputs=gr.outputs.Image(),examples=['00014.jpg','00167.jpg']).launch(share=False) | |