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#
from huggingface_hub import from_pretrained_fastai
import gradio as gr
from fastai.vision.all import *
from fastai.learner import load_learner
from PIL import Image
from albumentations import (
Compose,
OneOf,
ElasticTransform,
GridDistortion,
OpticalDistortion,
HorizontalFlip,
Rotate,
Transpose,
CLAHE,
ShiftScaleRotate
)
def get_y_fn (x):
return Path(str(x).replace("Images","Labels").replace("color","gt").replace(".jpg",".png"))
class SegmentationAlbumentationsTransform(ItemTransform):
split_idx = 0
def __init__(self, aug):
self.aug = aug
def encodes(self, x):
img,mask = x
aug = self.aug(image=np.array(img), mask=np.array(mask))
return PILImage.create(aug["image"]), PILMask.create(aug["mask"])
class TargetMaskConvertTransform(ItemTransform):
def __init__(self):
pass
def encodes(self, x):
img,mask = x
#Convert to array
mask = np.array(mask)
# mask[mask!=255]=0
# Change 255 for 1
mask[mask==255]=1
mask[mask==150]=2
mask[mask==74]=3
mask[mask==76]=3
mask[mask==29]=4
mask[mask==25]=4
# mask[mask==255]=1
# Back to PILMask
mask = PILMask.create(mask)
return img, mask
# Carga el modelo después de definir la clase
repo_id = "LuisCe/Practica03"
learner = from_pretrained_fastai(repo_id)
# Carga el modelo previamente entrenado
model = learner.model
model = model.cpu()
model.eval()
import torchvision.transforms as transforms
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)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def prediccion(img):
img = Image.fromarray(img)
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]=74
mask[mask==4]=29
mask=np.reshape(mask,(480,640))
return(mask)
# Crea la interfaz Gradio
gr.Interface(prediccion,
inputs="image",
outputs="image",
title="Grape Segmentation",
description="Segment grapes in the image.",
theme="compact",
allow_flagging=False).launch(debug=True)
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