198 lines
6.7 KiB
Python
198 lines
6.7 KiB
Python
import os
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import argparse
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import requests
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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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from concurrent.futures import ThreadPoolExecutor
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import gc
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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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# 示例图片URL
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image_url = "http://test001.jingrow.com/files/3T8A4426.JPG"
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def is_valid_url(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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def download_image(url):
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"""从URL下载图片到临时文件"""
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response = requests.get(url, stream=True)
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response.raise_for_status()
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# 创建临时文件
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temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.png')
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with open(temp_file.name, 'wb') as f:
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for chunk in response.iter_content(chunk_size=8192):
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f.write(chunk)
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return temp_file.name
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def process_image(image, model, device):
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"""处理图像，移除背景"""
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image_size = image.size
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# 转换图像
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t0 = time.time()
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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(device)
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print(f"图像预处理耗时: {time.time()-t0:.3f}秒")
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# 使用半精度加速
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t0 = time.time()
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with torch.cuda.amp.autocast():
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with torch.no_grad():
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preds = model(input_images)[-1].sigmoid().cpu()
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print(f"模型推理耗时: {time.time()-t0:.3f}秒")
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# 处理预测结果
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t0 = time.time()
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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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print(f"后处理耗时: {time.time()-t0:.3f}秒")
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return image
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def remove_background(image_path, output_dir=None, model=None, device=None):
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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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output_dir: 输出目录，如果不指定则使用当前目录
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model: 模型实例
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device: 设备
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Returns:
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处理后的图像路径
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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 is_valid_url(image_path):
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try:
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# 下载图片到临时文件
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temp_file = 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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# 如果输出目录未指定，则使用当前目录
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if output_dir is None:
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output_dir = os.getcwd()
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# 确保输出目录存在
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os.makedirs(output_dir, exist_ok=True)
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# 加载并处理图像
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image = Image.open(image_path).convert("RGB")
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image_no_bg = process_image(image, model, device)
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# 保存结果
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filename = os.path.basename(image_path)
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name, ext = os.path.splitext(filename)
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output_file_path = os.path.join(output_dir, f"{name}_nobg.png")
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image_no_bg.save(output_file_path)
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return output_file_path
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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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def process_batch(image_paths, output_dir, model, device, max_workers=4):
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"""批量处理图片"""
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with ThreadPoolExecutor(max_workers=max_workers) as executor:
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futures = []
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for image_path in image_paths:
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future = executor.submit(remove_background, image_path, output_dir, model, device)
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futures.append((image_path, future))
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results = []
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for image_path, future in futures:
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try:
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output_path = future.result()
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results.append((image_path, output_path, None))
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except Exception as e:
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results.append((image_path, None, str(e)))
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return results
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def main():
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parser = argparse.ArgumentParser(description='移除图像背景')
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parser.add_argument('--image', nargs='+', help='输入图像的路径或URL（支持多个）')
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parser.add_argument('--output-dir', default='output', help='输出目录')
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parser.add_argument('--model-path', default='briaai/RMBG-2.0', help='模型路径')
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parser.add_argument('--batch-size', type=int, default=4, help='批处理大小')
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args = parser.parse_args()
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# 设置设备
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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print(f"使用设备: {device}")
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if torch.cuda.is_available():
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print(f"GPU: {torch.cuda.get_device_name(0)}")
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print(f"显存总量: {torch.cuda.get_device_properties(0).total_memory / 1024**3:.1f}GB")
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try:
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# 加载模型
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print("正在加载模型...")
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t0 = time.time()
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model = AutoModelForImageSegmentation.from_pretrained(args.model_path, trust_remote_code=True)
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model = model.to(device)
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model.eval() # 设置为评估模式
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print(f"模型加载完成，用时 {time.time()-t0:.1f} 秒")
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# 处理图片
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image_paths = args.image if args.image else [image_url]
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if len(image_paths) > 1:
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print(f"开始批量处理 {len(image_paths)} 张图片...")
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results = process_batch(image_paths, args.output_dir, model, device, args.batch_size)
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for image_path, output_path, error in results:
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if error:
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print(f"处理失败: {image_path}, 错误: {error}")
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else:
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print(f"处理成功: {image_path} -> {output_path}")
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else:
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print(f"处理图片: {image_paths[0]}")
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t1 = time.time()
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output_path = remove_background(image_paths[0], args.output_dir, model, device)
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print(f"处理完成，用时 {time.time()-t1:.1f} 秒")
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print(f"输出文件: {output_path}")
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except Exception as e:
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print(f"错误: {e}")
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finally:
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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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if __name__ == "__main__":
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main()
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