299 lines
9.6 KiB
Python
299 lines
9.6 KiB
Python
import os
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import tempfile
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from urllib.parse import urlparse
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from PIL import Image
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import torch
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from torchvision import transforms
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from transformers import AutoModelForImageSegmentation
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import time
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import warnings
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import gc
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import base64
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import asyncio
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import io
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import uuid
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import httpx
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from settings import settings
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# 关闭不必要的警告
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warnings.filterwarnings("ignore", category=UserWarning)
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warnings.filterwarnings("ignore", category=FutureWarning)
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# 设置torch精度
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torch.set_float32_matmul_precision("high")
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class RmbgService:
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def __init__(self, model_path="zhengpeng7/BiRefNet"):
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"""初始化背景移除服务"""
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self.model_path = model_path
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self.model = None
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self.device = None
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self.save_dir = settings.save_dir
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self.download_url = settings.download_url
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# 确保保存目录存在
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os.makedirs(self.save_dir, exist_ok=True)
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# 创建异步HTTP客户端（复用连接，提高性能）
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self.http_client = httpx.AsyncClient(timeout=30.0, limits=httpx.Limits(max_keepalive_connections=20))
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self._load_model()
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def _load_model(self):
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"""加载模型"""
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# 设置设备
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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t0 = time.time()
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self.model = AutoModelForImageSegmentation.from_pretrained(self.model_path, trust_remote_code=True)
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self.model = self.model.to(self.device)
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self.model.eval() # 设置为评估模式
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def _process_image_sync(self, image):
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"""同步处理图像，移除背景（内部方法，在线程池中执行）"""
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image_size = image.size
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# 转换图像
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transform_image = transforms.Compose([
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transforms.Resize((1024, 1024)),
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
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])
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input_images = transform_image(image).unsqueeze(0).to(self.device)
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# 推理
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with torch.no_grad():
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preds = self.model(input_images)[-1].sigmoid().cpu()
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# 处理预测结果
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pred = preds[0].squeeze()
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pred_pil = transforms.ToPILImage()(pred)
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mask = pred_pil.resize(image_size)
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# 添加透明通道
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image.putalpha(mask)
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# 清理显存
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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gc.collect()
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return image
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async def process_image(self, image):
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"""异步处理图像，移除背景（在线程池中执行同步操作）"""
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# 将同步的GPU操作放到线程池中执行，避免阻塞事件循环
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loop = asyncio.get_event_loop()
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return await loop.run_in_executor(None, self._process_image_sync, image)
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def image_to_base64(self, image):
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"""将PIL Image对象转换为base64字符串"""
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buffered = io.BytesIO()
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image.save(buffered, format="PNG")
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return base64.b64encode(buffered.getvalue()).decode('utf-8')
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def save_image_to_file(self, image):
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"""
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保存图片到jfile/files目录并返回URL
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Args:
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image: PIL Image对象
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Returns:
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图片URL
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"""
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# 生成唯一文件名
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filename = f"rmbg_{uuid.uuid4().hex[:10]}.png"
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file_path = os.path.join(self.save_dir, filename)
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# 保存图片
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image.save(file_path, format="PNG")
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# 构建URL
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image_url = f"{self.download_url}/{filename}"
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return image_url
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async def remove_background(self, image_path):
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"""
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移除图像背景
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Args:
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image_path: 输入图像的路径或URL
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Returns:
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处理后的图像内容
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"""
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temp_file = None
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try:
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# 检查是否是URL
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if self.is_valid_url(image_path):
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try:
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# 异步下载图片到临时文件
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temp_file = await self.download_image(image_path)
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image_path = temp_file
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except Exception as e:
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raise Exception(f"下载图片失败: {e}")
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# 验证输入文件是否存在
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if not os.path.exists(image_path):
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raise FileNotFoundError(f"输入图像不存在: {image_path}")
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# 加载图像（IO操作，在线程池中执行）
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loop = asyncio.get_event_loop()
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image = await loop.run_in_executor(
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None,
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lambda: Image.open(image_path).convert("RGB")
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)
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# 异步处理图像
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image_no_bg = await self.process_image(image)
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# 保存图片到文件并获取URL（IO操作，在线程池中执行）
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image_url = await loop.run_in_executor(
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None,
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self.save_image_to_file,
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image_no_bg
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)
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return {
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"status": "success",
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"image_url": image_url
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}
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finally:
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# 清理临时文件
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if temp_file and os.path.exists(temp_file):
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try:
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os.unlink(temp_file)
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except:
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pass
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async def remove_background_from_file(self, file_content):
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"""
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从上传的文件内容移除背景
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Args:
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file_content: 上传的文件内容
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Returns:
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处理后的图像内容
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"""
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try:
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# 从文件内容创建PIL Image对象（IO操作，在线程池中执行）
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loop = asyncio.get_event_loop()
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image = await loop.run_in_executor(
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None,
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lambda: Image.open(io.BytesIO(file_content)).convert("RGB")
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)
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# 异步处理图像
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image_no_bg = await self.process_image(image)
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# 保存图片到文件并获取URL（IO操作，在线程池中执行）
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image_url = await loop.run_in_executor(
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None,
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self.save_image_to_file,
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image_no_bg
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)
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return {
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"status": "success",
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"image_url": image_url
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}
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except Exception as e:
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raise Exception(f"处理图片失败: {e}")
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async def process_batch(self, urls):
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"""
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批量处理多个URL图像，并发处理并流式返回结果
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Args:
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urls: 图片URL列表
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Yields:
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每个图片的处理结果（按完成顺序返回）
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"""
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total = len(urls)
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success_count = 0
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error_count = 0
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# 创建并发任务
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async def process_single_url(index, url):
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"""处理单个URL的包装函数"""
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try:
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url_str = str(url)
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result = await self.remove_background(url_str)
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return {
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"index": index,
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"total": total,
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"original_url": url_str,
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"status": "success",
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"image_url": result["image_url"],
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"message": "处理成功"
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}
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except Exception as e:
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return {
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"index": index,
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"total": total,
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"original_url": str(url),
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"status": "error",
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"error": str(e),
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"message": f"处理失败: {str(e)}"
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}
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# 创建所有任务
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tasks = [
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process_single_url(i, url)
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for i, url in enumerate(urls, 1)
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]
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# 并发执行所有任务，使用as_completed按完成顺序返回
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for coro in asyncio.as_completed(tasks):
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result = await coro
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if result["status"] == "success":
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success_count += 1
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else:
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error_count += 1
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# 更新统计信息
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result["success_count"] = success_count
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result["error_count"] = error_count
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yield result
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def is_valid_url(self, url):
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"""验证URL是否有效"""
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try:
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result = urlparse(url)
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return all([result.scheme, result.netloc])
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except:
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return False
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async def download_image(self, url):
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"""异步从URL下载图片到临时文件"""
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try:
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response = await self.http_client.get(url)
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response.raise_for_status()
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# 创建临时文件并写入内容（IO操作，在线程池中执行）
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def write_temp_file(content):
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temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.png')
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temp_file.write(content)
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temp_file.close()
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return temp_file.name
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loop = asyncio.get_event_loop()
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temp_file_path = await loop.run_in_executor(
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None,
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write_temp_file,
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response.content
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)
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return temp_file_path
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except Exception as e:
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raise Exception(f"下载图片失败: {e}")
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async def cleanup(self):
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"""清理资源"""
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# 关闭HTTP客户端
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await self.http_client.aclose()
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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gc.collect()
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print("资源已清理") |