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from huggingface_hub import from_pretrained_fastai
import gradio as gr
from fastai.vision.all import *
import PIL
import torchvision.transforms as transforms
#repo_id = "Ignaciobfp/segmentacion-dron-marras"
#learner = from_pretrained_fastai(repo_id)
device = torch.device("cpu")
#model = learner.model
model = torch.jit.load("pr3.pth")
model = model.cpu()
def transform_image(image):
my_transforms = transforms.Compose([transforms.ToTensor(),
transforms.Normalize(
[0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])])
image_aux = image
return my_transforms(image_aux).unsqueeze(0).to(device)
# Definimos una función que se encarga de llevar a cabo las predicciones
def predict(img):
img_pil = PIL.Image.fromarray(img, 'RGB')
image = transforms.Resize((400,400))(img_pil)
tensor = transform_image(image=image)
model.to(device)
with torch.no_grad():
outputs = model(tensor)
outputs = torch.argmax(outputs,1)
mask = np.array(outputs.cpu())
mask[mask==1]=255
mask=np.reshape(mask,(400,400))
return Image.fromarray(mask.astype('uint8'))
# Creamos la interfaz y la lanzamos.
gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(400, 400)), outputs=gr.outputs.Image(type="pil"),
examples=['examplesB/color_180.jpg', 'examplesB/color_179.jpg', 'examplesB/color_156.jpg', 'examplesB/color_155.jpg', 'examplesB/color_154.jpg']).launch(share=False)
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