Grounded-Segment-Anything本地部署

• 源码链接：https://github.com/IDEA-Research/Grounded-Segment-Anything
• 介绍
  • Segment Anything Model，简称 SAM。
  • SAM 已经学会了关于物体的一般概念，可以为任何图像或视频中的任何物体生成 mask，甚至包括在训练过程中没有遇到过的物体和图像类型。
  • SAM 足够通用，可以涵盖广泛的用例，并且可以在新的图像领域即开即用，无需额外的训练

1.本地部署源码

1.1 克隆源码

• 克隆命令

  git clone https://github.com/IDEA-Research/Grounded-Segment-Anything.git
  

• 问题一

  • 克隆下来后发现下面的三个文件夹是空的
    

  • 在源码链接中有这两行命令，应该是把这三个文件夹文件下载下来，但是我的报错
    

  • 解决 ：手动下载，然后放在相应的文件夹
    

• 问题二【手动放在相应的文件夹后，调用里面的文件路径是错的】
  • 解决：修改相关路径,运行代码将不对的路径全部改掉
  • 修改路径 【segment_anything】
    
  • 修改路径 【GroundingDINO】
    

1.2 下载初始参数

• 参数链接
  • groundingdino_swint_ogc.pth
  • sam_vit_h_4b8939.pth
  • sam_hq_vit_h.pth
  • ram_swin_large_14m.pth
  • tag2text_swin_14m.pth
• 初始参数文件存放目录：项目根目录
  

2. 创建虚拟环境

• 创建虚拟环境

  conda create -n env_grounded_segment_anything python==3.8.10
  

• 进入虚拟环境

  conda activate env_grounded_segment_anything
  

• 安装pytorch

  pip install torch==1.10.0+cu113 torchvision==0.11.0+cu113 torchaudio==0.10.0 -f https://download.pytorch.org/whl/torch_stable.html
  

• 安装requirements.txt库

  pip install -r requirements.txt 
  

• 使用pycharm打开项目
  
• 选择虚拟环境
  

3. 测试相关文件

3.1 运行grounding_dino_demo.py文件

• 无gpu，将DEVICE 值改为 cpu
• 有gpu,无需修改参数
• 生成标注图片
  

3.2 运行grounded_sam_demo.py文件

• 添加参数，电脑没有gpu，device 参数使用默认的cpu

  --config GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py  
  --grounded_checkpoint groundingdino_swint_ogc.pth  
  --sam_checkpoint sam_vit_h_4b8939.pth 
  --input_image assets/demo1.jpg  
  --output_dir "outputs"  
  --box_threshold 0.3  
  --text_threshold 0.25  
  --text_prompt "bear"  
  

• 输出结果
  • 文件说明
    
  • 图片显示
    

3.3 运行grounded_sam_simple_demo.py文件

• 无cuda报错
  
• inference.py修改device值改为cpu
  
• 测试demo4.jpg【无需修改代码】
  
• 测试demo7.jpg
  • 修改关于demo.jpg信息
    
  • 结果
    

3.4 grounded_sam_inpainting_demo.py文件

• 修复图片文件

• 添加参数

  --config
  GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py
  --grounded_checkpoint
  groundingdino_swint_ogc.pth
  --sam_checkpoint
  sam_vit_h_4b8939.pth
  --input_image
  assets/inpaint_demo.jpg
  --output_dir
  "outputs"
  --box_threshold
  0.3
  --text_threshold
  0.25
  --det_prompt
  "bench"
  --inpaint_prompt
  "A sofa, high quality, detailed"
  

  

• 报错 【下载远程文件失败】
  

• 解决：手动下载

  • 下载链接：https://huggingface.co/runwayml/stable-diffusion-inpainting/tree/main
  • 将下载好的文件放在config_data文件夹中
    
  • 修改为本地路径
    

• 重新运行:报错 【原因：我没有gpu】
  

• 解决，将cuda换为cpu
  

• 重新运行:报错

  • RuntimeError: "LayerNormKernelImpl" not implemented for 'Half'
    

  • 解决方式：解决链接

  • 将float16改为float32
    

• 结果
  

3.5 运行automatic_label_ram_demo.py`文件

• 添加参数

  --ram_checkpoint
  ram_swin_large_14m.pth
  --grounded_checkpoint
  groundingdino_swint_ogc.pth
  --sam_checkpoint
  sam_vit_h_4b8939.pth
  --input_image
  assets/demo9.jpg
  --output_dir
  "outputs"
  --box_threshold
  0.25
  --text_threshold
  0.2
  --iou_threshold
  0.5
  

• 结果
  

3.6 运行automatic_label_demo.py文件

• 自动标注文件

• 添加参数

  --config
  GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py
  --grounded_checkpoint
  groundingdino_swint_ogc.pth
  --sam_checkpoint
  sam_vit_h_4b8939.pth
  --input_image
  assets/demo9.jpg
  --output_dir
  "outputs"
  --box_threshold
  0.25
  --text_threshold
  0.2
  --iou_threshold
  0.5
  

  

• 报错
  

• 原因：下载文件失败，手动下载

  • 下载链接：https://huggingface.co/Salesforce/blip-image-captioning-large/tree/main
  • 将文件放在config_data中
    
  • 修改为本地路径
    

• 报错Resource punkt not found. Please use the NLTK Downloader to obtain the resources

  • 手动下载链接：http://www.nltk.org/nltk_data/
  • 下载wordnet,punkt,averaged_perceptron_tagger
  • 放到相应目录，zip及解压文件都要有
    

• 结果
  

3.7. 批量自动标注图片

• 修改automatic_label_demo.py文件
• 文件太长，其他的地方还要改

  if __name__ == "__main__":
      root_path=''  # 根目录
      images_name='images'   # 图片文件夹名
      images_path=os.path.join(root_path,images_name)
      images_outputs_path=os.path.join(root_path,'grounded_segment_anything_images')
      output_json = os.path.join(images_outputs_path,'json')
      output_orig = os.path.join(images_outputs_path,'orig')
      output_mask = os.path.join(images_outputs_path,'mask')
      output_automatic_label = os.path.join(images_outputs_path,'automatic_label')
      for i in [output_json,output_mask,output_orig,output_automatic_label]:
          os.makedirs(i, exist_ok=True)
      images_list=os.listdir(images_path)
      parser = argparse.ArgumentParser("Grounded-Segment-Anything Demo", add_help=True)
      parser.add_argument("--config", type=str, default='GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py',
                          help="path to config file")
      parser.add_argument("--grounded_checkpoint", type=str, default='groundingdino_swint_ogc.pth', help="path to checkpoint file")
      parser.add_argument("--sam_checkpoint", type=str, default='sam_vit_h_4b8939.pth', help="path to checkpoint file")
      parser.add_argument("--split", default=",", type=str, help="split for text prompt")
      parser.add_argument("--openai_key", type=str, help="key for chatgpt")
      parser.add_argument("--openai_proxy", default=None, type=str, help="proxy for chatgpt")
      parser.add_argument("--box_threshold", type=float, default=0.25, help="box threshold")
      parser.add_argument("--text_threshold", type=float, default=0.2, help="text threshold")
      parser.add_argument("--iou_threshold", type=float, default=0.5, help="iou threshold")
      parser.add_argument("--device", type=str, default="cpu", help="running on cpu only!, default=False")
      args = parser.parse_args()
  
      # cfg
      config_file = args.config  # change the path of the model config file
      grounded_checkpoint = args.grounded_checkpoint  # change the path of the model
      sam_checkpoint = args.sam_checkpoint
      # image_path = args.input_image
      split = args.split
      openai_key = args.openai_key
      openai_proxy = args.openai_proxy
      box_threshold = args.box_threshold
      text_threshold = args.text_threshold
      iou_threshold = args.iou_threshold
      device = args.device
      openai.api_key = openai_key
      if openai_proxy:
          openai.proxy = {"http": openai_proxy, "https": openai_proxy}
  
      # load model
      model = load_model(config_file, grounded_checkpoint, device=device)
   
      processor = BlipProcessor.from_pretrained("config_data/blip-image-captioning-large")
      if device == "cuda":
          blip_model = BlipForConditionalGeneration.from_pretrained("config_data/blip-image-captioning-large",
                                                                    torch_dtype=torch.float16).to("cuda")
      else:
          blip_model = BlipForConditionalGeneration.from_pretrained("config_data/blip-image-captioning-large")
      for img_name in images_list:
   
          image_path=os.path.join(images_path,img_name)
          image_pil, image = load_image(image_path)
          image_pil.save(os.path.join(output_orig, img_name))
          args = parser.parse_args()
          caption = generate_caption(image_pil, device=device)
          text_prompt = generate_tags(caption, split=split)
          print(f"Caption: {caption}")
          print(f"Tags: {text_prompt}")
          # visualize raw image
          image_pil.save(os.path.join(output_orig,img_name ))
          # run grounding dino model
          boxes_filt, scores, pred_phrases = get_grounding_output(
              model, image, text_prompt, box_threshold, text_threshold, device=device
          )
  
          # initialize SAM
          predictor = SamPredictor(build_sam(checkpoint=sam_checkpoint).to(device))
          image = cv2.imread(image_path)
          image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
          predictor.set_image(image)
  
          size = image_pil.size
          H, W = size[1], size[0]
          for i in range(boxes_filt.size(0)):
              boxes_filt[i] = boxes_filt[i] * torch.Tensor([W, H, W, H])
              boxes_filt[i][:2] -= boxes_filt[i][2:] / 2
              boxes_filt[i][2:] += boxes_filt[i][:2]
  
          boxes_filt = boxes_filt.cpu()
          # use NMS to handle overlapped boxes
          print(f"Before NMS: {boxes_filt.shape[0]} boxes")
          nms_idx = torchvision.ops.nms(boxes_filt, scores, iou_threshold).numpy().tolist()
          boxes_filt = boxes_filt[nms_idx]
          pred_phrases = [pred_phrases[idx] for idx in nms_idx]
          print(f"After NMS: {boxes_filt.shape[0]} boxes")
          caption = check_caption(caption, pred_phrases)
          print(f"Revise caption with number: {caption}")
  
          transformed_boxes = predictor.transform.apply_boxes_torch(boxes_filt, image.shape[:2]).to(device)
  
          masks, _, _ = predictor.predict_torch(
              point_coords = None,
              point_labels = None,
              boxes = transformed_boxes.to(device),
              multimask_output = False,
          )
  
          # draw output image
          plt.figure(figsize=(10, 10))
          plt.imshow(image)
          for mask in masks:
              show_mask(mask.cpu().numpy(), plt.gca(), random_color=True)
          for box, label in zip(boxes_filt, pred_phrases):
              show_box(box.numpy(), plt.gca(), label)
  
          plt.title(caption)
          plt.axis('off')
          plt.savefig(
              os.path.join(output_automatic_label,img_name),
              bbox_inches="tight", dpi=300, pad_inches=0.0
          )
  
          save_mask_data(output_mask,output_json,img_name, caption, masks, boxes_filt, pred_phrases)
  

5. 总结

• 文件太多了，其他的不像测试了，因该都没有很大的问题
• 文件初始化参数太大了，一个项目占了几十个G
• 效果也不好，不想做了，摆烂