main.py

import numpy as np
import torch
import torch.nn.functional as F
from torchvision.transforms.functional import normalize
from huggingface_hub import hf_hub_download
from fastapi import FastAPI, HTTPException
from PIL import Image
import httpx
import io
import briarmbg  # Importando o modelo de remoção de fundo

app = FastAPI()

# Carregar modelo BRIA RMBG
net = briarmbg.BriaRMBG()
model_path = hf_hub_download("briaai/RMBG-1.4", "model.pth")

if torch.cuda.is_available():
    net.load_state_dict(torch.load(model_path))
    net = net.cuda()
else:
    net.load_state_dict(torch.load(model_path, map_location="cpu"))

net.eval()

# Função para redimensionar a imagem antes de processar
def resize_image(image):
    image = image.convert("RGB")
    model_input_size = (1024, 1024)
    return image.resize(model_input_size, Image.BILINEAR)

# Função para remover o fundo da imagem
def remove_bg(image: Image.Image):
    orig_image = image
    w, h = orig_image.size
    image = resize_image(orig_image)
    
    im_np = np.array(image)
    im_tensor = torch.tensor(im_np, dtype=torch.float32).permute(2, 0, 1)
    im_tensor = torch.unsqueeze(im_tensor, 0) / 255.0
    im_tensor = normalize(im_tensor, [0.5, 0.5, 0.5], [1.0, 1.0, 1.0])
    
    if torch.cuda.is_available():
        im_tensor = im_tensor.cuda()

    # Inference do modelo
    result = net(im_tensor)[0][0]
    result = torch.squeeze(F.interpolate(result, size=(h, w), mode="bilinear"), 0)

    # Normalizar para 0-255
    ma, mi = torch.max(result), torch.min(result)
    result = (result - mi) / (ma - mi)
    
    im_array = (result * 255).cpu().data.numpy().astype(np.uint8)
    pil_im = Image.fromarray(np.squeeze(im_array))

    # Criar imagem sem fundo
    new_im = Image.new("RGBA", pil_im.size, (0, 0, 0, 0))
    new_im.paste(orig_image, mask=pil_im)

    return new_im

# Função para baixar imagem de uma URL
async def download_image(image_url: str) -> Image.Image:
    async with httpx.AsyncClient() as client:
        response = await client.get(image_url)
        if response.status_code != 200:
            raise HTTPException(status_code=400, detail="Erro ao baixar imagem")
        
        return Image.open(io.BytesIO(response.content))

# Endpoint para remover fundo
@app.get("/remove-bg/")
async def remove_bg_from_url(image_url: str):
    try:
        image = await download_image(image_url)
        output_image = remove_bg(image)

        # Salvar a imagem temporariamente na memória
        img_io = io.BytesIO()
        output_image.save(img_io, format="PNG")
        img_io.seek(0)

        return {
            "message": "Fundo removido com sucesso!",
            "image": img_io.getvalue()
        }
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))