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from huggingface_hub import from_pretrained_fastai
import gradio as gr
from fastai.vision.all import *
# repo_id = "YOUR_USERNAME/YOUR_LEARNER_NAME"
# repo_id = "igmarco/grapes-semanticsegmentation"
# learner = from_pretrained_fastai(repo_id)
import torchvision.transforms as transforms
import PIL
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = torch.jit.load("Pr1.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((480,640))(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==0]=0
mask[mask==1]=150
mask[mask==2]=25
mask[mask==3]=74
mask[mask==4]=255
mask=np.reshape(mask,(480,640))
return(Image.fromarray(mask.astype('uint8')))
# Creamos la interfaz y la lanzamos.
# gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(480, 640)), outputs=gr.outputs.Image(type="pil")).launch(share=False)
gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(480, 640)), outputs=gr.outputs.Image(type="pil"),examples=['grapes1.jpg','grapes2.jpg']).launch(share=False) |