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import os
from fastapi import FastAPI, UploadFile, File, Form, HTTPException, Query
from fastapi.responses import StreamingResponse, HTMLResponse
from PIL import Image
import torch
import numpy as np
from transformers import AutoModelForImageSegmentation
from io import BytesIO
import requests
import uvicorn
# -------------------------
# Optional HEIC/HEIF Support
# -------------------------
try:
import pillow_heif
pillow_heif.register_heif_opener()
print("✅ HEIC/HEIF format supported.")
except ImportError:
print("⚠️ Install pillow-heif for HEIC support: pip install pillow-heif")
# -------------------------
# Model Setup
# -------------------------
MODEL_DIR = "models/BiRefNet"
os.makedirs(MODEL_DIR, exist_ok=True)
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16 if torch.cuda.is_available() else torch.float32
print("Loading BiRefNet model...")
birefnet = AutoModelForImageSegmentation.from_pretrained(
"ZhengPeng7/BiRefNet",
cache_dir=MODEL_DIR,
trust_remote_code=True,
revision="main"
)
birefnet.to(device, dtype=dtype).eval()
print("Model loaded successfully.")
# -------------------------
# FastAPI App
# -------------------------
app = FastAPI(title="Background Remover API")
# -------------------------
# Utility Functions
# -------------------------
def load_image_from_url(url: str) -> Image.Image:
try:
response = requests.get(url, timeout=10)
response.raise_for_status()
return Image.open(BytesIO(response.content)).convert("RGB")
except Exception as e:
raise HTTPException(status_code=400, detail=f"Error loading image from URL: {str(e)}")
def transform_image(image: Image.Image, resolution: int = 512) -> torch.Tensor:
image = image.resize((resolution, resolution))
arr = np.array(image).astype(np.float32) / 255.0
mean = np.array([0.485, 0.456, 0.406], dtype=np.float32)
std = np.array([0.229, 0.224, 0.225], dtype=np.float32)
arr = (arr - mean) / std
arr = np.transpose(arr, (2, 0, 1)) # HWC -> CHW
tensor = torch.from_numpy(arr).unsqueeze(0).to(dtype).to(device)
return tensor
def process_image(image: Image.Image, resolution: int = 512) -> Image.Image:
orig_size = image.size
input_tensor = transform_image(image, resolution)
with torch.no_grad():
preds = birefnet(input_tensor)[-1].sigmoid().cpu()
pred = preds[0, 0]
mask = Image.fromarray((pred.numpy() * 255).astype(np.uint8)).resize(orig_size)
image = image.convert("RGBA")
image.putalpha(mask)
return image
# -------------------------
# /remove-background Endpoint
# -------------------------
@app.post("/remove-background")
async def remove_background(
file: UploadFile = File(None),
image_url: str = Form(None),
resolution: int = Form(512)
):
"""
Remove background from an image.
Accepts a file upload or image URL.
Optional resolution (default 512) for faster inference.
Returns PNG with transparent background.
"""
try:
if file:
image = Image.open(BytesIO(await file.read())).convert("RGB")
elif image_url:
image = load_image_from_url(image_url)
else:
raise HTTPException(status_code=400, detail="Provide either 'file' or 'image_url'.")
result = process_image(image, resolution)
buf = BytesIO()
result.save(buf, format="PNG")
buf.seek(0)
return StreamingResponse(buf, media_type="image/png")
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
# -------------------------
# Developer Test Page (Bootstrap)
# -------------------------
@app.get("/", response_class=HTMLResponse)
async def index():
html = """
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>Background Remover API Test</title>
<link href="https://cdn.jsdelivr.net/npm/bootstrap@5.3.2/dist/css/bootstrap.min.css" rel="stylesheet">
<style>
body { background-color: #f8f9fa; padding-top: 40px; }
.container { max-width: 700px; }
img { max-width: 100%; margin-top: 20px; border-radius: 10px; }
</style>
</head>
<body>
<div class="container text-center">
<h2 class="mb-4">Background Remover API Tester</h2>
<form id="uploadForm" class="mb-4" enctype="multipart/form-data">
<div class="mb-3">
<label for="fileInput" class="form-label">Upload Image (any format, e.g. JPG, PNG, HEIC):</label>
<input class="form-control" type="file" id="fileInput" name="file" accept="image/*">
</div>
<div class="mb-3">
<label for="resInput" class="form-label">Resolution (default 512):</label>
<input class="form-control" type="number" id="resInput" name="resolution" value="512" min="64" max="2048">
</div>
<button class="btn btn-primary" type="submit">Remove Background</button>
</form>
<div class="mb-4">OR</div>
<form id="urlForm" class="mb-4">
<div class="mb-3">
<label for="urlInput" class="form-label">Enter Image URL:</label>
<input class="form-control" type="text" id="urlInput" placeholder="https://example.com/image.jpg">
</div>
<div class="mb-3">
<label for="urlResInput" class="form-label">Resolution (default 512):</label>
<input class="form-control" type="number" id="urlResInput" name="resolution" value="512" min="64" max="2048">
</div>
<button class="btn btn-success" type="submit">Remove Background</button>
</form>
<div id="resultContainer" class="mt-4">
<h5>Result:</h5>
<img id="resultImg" src="" alt="">
</div>
</div>
<script>
const uploadForm = document.getElementById("uploadForm");
const urlForm = document.getElementById("urlForm");
const resultImg = document.getElementById("resultImg");
uploadForm.addEventListener("submit", async e => {
e.preventDefault();
const fileInput = document.getElementById("fileInput");
const res = document.getElementById("resInput").value || 512;
if (!fileInput.files.length) return alert("Please select a file!");
const formData = new FormData();
formData.append("file", fileInput.files[0]);
formData.append("resolution", res);
const response = await fetch("/remove-background", { method: "POST", body: formData });
const blob = await response.blob();
resultImg.src = URL.createObjectURL(blob);
});
urlForm.addEventListener("submit", async e => {
e.preventDefault();
const url = document.getElementById("urlInput").value.trim();
const res = document.getElementById("urlResInput").value || 512;
if (!url) return alert("Please enter an image URL!");
const formData = new FormData();
formData.append("image_url", url);
formData.append("resolution", res);
const response = await fetch("/remove-background", { method: "POST", body: formData });
const blob = await response.blob();
resultImg.src = URL.createObjectURL(blob);
});
</script>
</body>
</html>
"""
return HTMLResponse(html)
# -------------------------
# Run App
# -------------------------
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=7860)
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