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from huggingface_hub import from_pretrained_fastai
import gradio as gr
from fastai.vision.all import *
import torchvision.transforms as transforms
import torchvision.transforms as transforms
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 *
from pathlib import Path
import random
import PIL
#Primero definimos todas las funciones, clases y variables que sopn necesarias para que esto funcione
device = torch.device("cuda" if torch.cuda.is_available() else "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)
class TargetMaskConvertTransform(ItemTransform):
def __init__(self):
pass
def encodes(self, x):
img,mask = x
#Convert to array
mask = np.array(mask)
mask[(mask!=255) & (mask!=150) & (mask!=76) & (mask!=74) & (mask!=29) & (mask!=25)]=0
mask[mask==255]=1
mask[mask==150]=2
mask[mask==76]=4
mask[mask==74]=4
mask[mask==29]=3
mask[mask==25]=3
# Back to PILMask
mask = PILMask.create(mask)
return img, mask
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"])
#Cargamos el modelo
repo_id = "luisvarona/Practica3"
learn = from_pretrained_fastai(repo_id)
model = learn.model
model = model.cpu()
# Definimos una función que se encarga de llevar a cabo las predicciones
def predict(img_ruta):
# img = PIL.Image.open(img_ruta) #esto si el parámetro de entrada es una ruta a una imagen
# img = img_ruta # esto si el parámetro de entrada es una imagen
img = PIL.Image.fromarray(img_ruta)
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]=29
mask[mask==4]=74
mask = np.reshape(mask,(480,640))
return Image.fromarray(mask.astype('uint8'))
#img = PILImage.create(img) #igual hay que usar esto en vez de PIL.Image.open
# Creamos la interfaz y la lanzamos.
gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(480, 640)), outputs=gr.inputs.Image(shape=(480, 640)), examples=['color_155.jpg','color_154 (1).jpg']).launch(share=False) |