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#app.py:
# from huggingface_hub import from_pretrained_fastai
import gradio as gr
from fastai import *
from fastai.data.block import DataBlock
from fastai.data.transforms import get_image_files, FuncSplitter, Normalize
from fastai.layers import Mish
from fastai.losses import BaseLoss
from fastai.optimizer import ranger
from fastai.torch_core import tensor
from fastai.vision.augment import aug_transforms
from fastai.vision.core import PILImage, PILMask
from fastai.vision.data import ImageBlock, MaskBlock, imagenet_stats
from fastai.vision.learner import unet_learner
from PIL import Image
import numpy as np
from torch import nn
import torch
import torch.nn.functional as F
#from __future__ import annotations
#from nbdev.showdoc import *
from fastcore.test import *
from fastcore.nb_imports import *
# # repo_id = "YOUR_USERNAME/YOUR_LEARNER_NAME"
# repo_id = "islasher/segm-grapes"
# repo_id='islasher/segm-grapes'
# # Definimos una función que se encarga de llevar a cabo las predicciones
# from fastai.learner import load_learner
# # Cargar el modelo y el tokenizador
# learn = load_learner(repo_id)
#learner = from_pretrained_fastai(repo_id)
class ItemTransform(Transform):
"A transform that always take tuples as items"
_retain = True
def __call__(self, x, **kwargs): return self._call1(x, '__call__', **kwargs)
def decode(self, x, **kwargs): return self._call1(x, 'decode', **kwargs)
def _call1(self, x, name, **kwargs):
if not _is_tuple(x): return getattr(super(), name)(x, **kwargs)
y = getattr(super(), name)(list(x), **kwargs)
if not self._retain: return y
if is_listy(y) and not isinstance(y, tuple): y = tuple(y)
return retain_type(y, x)
from huggingface_hub import from_pretrained_fastai
import torchvision.transforms as transforms
# from Transform import ItemTransform
from albumentations import (
Compose,
OneOf,
ElasticTransform,
GridDistortion,
OpticalDistortion,
HorizontalFlip,
Rotate,
Transpose,
CLAHE,
ShiftScaleRotate
)
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)
# Changes: (codes= array(['Background', 'Leaves', 'Wood', 'Pole', 'Grape'], dtype='<U10'))
mask[mask==150]=1 #leaves
mask[mask==76]=3 #pole
mask[mask==74]=3 #pole
mask[mask==29]=2 #wood
mask[mask==25]=2 #wood
mask[mask==255]=4 #grape
mask[mask==0]=0
# Back to PILMask
mask = PILMask.create(mask)
return img, mask
learn = from_pretrained_fastai("islasher/segm-grapes")
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):
image = transforms.Resize((480,640))(img)
tensor = transform_image(image=image)
with torch.no_grad():
outputs = learn.model(tensor)
outputs = torch.argmax(outputs,1)
mask = np.array(outputs)
mask[mask==1]=150
mask[mask==3]=76 #pole # y no 74
# mask[mask==5]=74 #pole
mask[mask==2]=29 #wood # y no 25
# mask[mask==6]=25 #wood
mask[mask==4]=255 #grape
mask=np.reshape(mask,(480,640)) #en modo matriz
return Image.fromarray(mask.astype('uint8'))
# Creamos la interfaz y la lanzamos.
gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(128, 128)), outputs=gr.outputs.Image(shape=(480,640)),examples=['color_154.jpg','color_155.jpg']).launch(share=False) |