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from huggingface_hub import from_pretrained_fastai
import gradio as gr
from fastai.vision.all import *
class TargetMaskConvertTransform(ItemTransform):
def __init__(self):
pass
def encodes(self, x):
img,mask = x
mask = np.array(mask)
new_mask = np.zeros_like(mask, dtype=np.uint8)
new_mask[mask==150]=1 #Clase Leaves
new_mask[(mask==29) | (mask==25)]=2 #Clase Wood
new_mask[(mask==74) | (mask==76)]=3 #Clase Pole
new_mask[mask==255]=4 #Clase Wood
mask = PILMask.create(new_mask)
return img, mask
repo_id = "joortif/Practica3"
learn = from_pretrained_fastai(repo_id)
def segment_image(img):
pred, _, _ = learn.predict(img)
return pred
interface = gr.Interface(
fn=segment_image,
inputs=gr.Image(type="pil"),
outputs=gr.Image(type="numpy"),
title="Segmentación Semántica con FastAI",
)
interface.launch() |