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3e95a1f 0ee278a cc6ce4e 0ee278a 3e95a1f cc6ce4e 3e95a1f 0ee278a cc6ce4e 0ee278a cc6ce4e 0ee278a 3e95a1f 1ec847b 0ee278a 1ec847b 0ee278a 3e95a1f 0ee278a cc6ce4e 3e95a1f 0ee278a 3e95a1f 703f89d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | 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 briarmbg import BriaRMBG
from PIL import Image
from fastapi import FastAPI, File, UploadFile
from fastapi.responses import FileResponse, JSONResponse
import os
app = FastAPI()
# 모델 로드
net = 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, map_location="cuda", weights_only=True))
net = net.cuda()
else:
net.load_state_dict(torch.load(model_path, map_location="cpu", weights_only=True))
net.eval()
def resize_image(image):
image = image.convert('RGB')
model_input_size = (1024, 1024)
image = image.resize(model_input_size, Image.BILINEAR)
return image
def process_image(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)
im_tensor = torch.divide(im_tensor, 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()
# 모델 추론
result = net(im_tensor)
result = torch.squeeze(F.interpolate(result[0][0], size=(h, w), mode='bilinear'), 0)
ma = torch.max(result)
mi = torch.min(result)
result = (result - mi) / (ma - mi)
# 이미지 변환
im_array = (result * 255).cpu().data.numpy().astype(np.uint8)
pil_im = Image.fromarray(np.squeeze(im_array))
new_im = Image.new("RGBA", orig_image.size, (0, 0, 0, 0))
new_im.paste(orig_image, mask=pil_im)
# 결과 이미지 저장
output_path = "output_image.png"
new_im.save(output_path)
return output_path
@app.get("/")
def read_root():
return {"message": "Welcome to the Background Removal API"}
@app.post("/remove-background/")
async def remove_background(file: UploadFile = File(...)):
image = Image.open(file.file)
output_path = process_image(image)
return FileResponse(output_path, media_type="image/png", filename="output_image.png")
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=7860)
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