File size: 1,511 Bytes
772b566 86b80d6 772b566 86b80d6 772b566 65f41de 4763157 65f41de 706b9bb 65f41de 772b566 eeab346 f348f34 772b566 f348f34 eae290b 65f41de a186b92 772b566 a186b92 772b566 0dcb86b |
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 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 |
import gradio as gr
from fastai.basics import *
from fastai.vision import models
from fastai.vision.all import *
from fastai.metrics import *
from fastai.data.all import *
from fastai.callback import *
import PIL
import torchvision.transforms as transforms
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = torch.jit.load("pract3.pth")
model = model.cpu()
def transform_image(image):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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)
def predict(img):
img = PIL.Image.fromarray(img, "RGB")
image = transforms.Resize((480,640))(img)
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[mask==2]=150
mask[mask==3]=76
mask[mask==4]=29
mask=np.reshape(mask,(480,640))
return Image.fromarray(mask.astype('uint8'))
gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(128, 128)), outputs=[gr.outputs.Image(type="pil", label="Predicción")], examples=['color_155.jpg','color_154.jpg']).launch(share=False) |