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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 = "paascorb/practica3_Segmentation"
learner = from_pretrained_fastai(repo_id)
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)
def predict(img):
img = PIL.Image.fromarray(img, "RGB")
image = transforms.Resize((480,640))(img)
tensor = transform_image(image=image)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
learner.to(device)
with torch.no_grad():
outputs = learner(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) |