File size: 1,008 Bytes
b996089 4591b3e 2801bb3 dc98b0f 22e178c b8120f7 22e178c fa95918 b8120f7 22e178c fa95918 22e178c b8120f7 22e178c b8120f7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 |
from fastai.vision.all import *
from icevision.all import *
from fastai.basics import *
from fastai.callback import *
from icevision import models
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,num_classes=len(class_map))
state_dict = torch.load('fasterRCNNkangaroo.pth')
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=class_map, 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=['00004.jpg','00014.jpg']).launch(share=False) |