How does Google Map My Location work?(转载)

本文详细解析了Google Maps for Mobile中的MyLocation功能实现原理。该功能可在无GPS支持的情况下定位手机位置,采用Cell-ID定位技术并通过用户众包方式不断完善位置数据库。

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2008-05-11

Tag:GPS_&_GIS

版权声明:转载时请以超链接形式标明文章原始出处和作者信息及本声明
http://aleung.blogbus.com/logs/20685204.html

在今年初推出的Google Map for Mobile新版本中提供了称为My Location的功能,不需要GPS的支持,也能在地图上显示出当前手机所在的位置。这个功能着实让人眼前一亮。虽然移动网络本身就可以提供定位的能力,但是Google的My Location并不依赖于移动网络定位能力,完全绕开了运营商的控制。

My Location是怎样实现的呢?Google网站上介绍了大致的原理:手机接收到移动基站的信号,根据基站的信息可以知道当前处于哪个基站的信号覆盖范围,如果基站的位置是已知的,就可以确定当前的大概位置了。

具体点说,这是一种叫做Cell-Id Positioning Method的技术。GSM网络(3G网络也一样)覆盖由Location Area组成,(Location Area是指mobile terminal可以任意移动而不需要进行location update的区域)。Location Area由LAI(Location Area Id)来标识,LAI由MCC,MNC,LAC组成。其中MCC是3位的Mobile Country Code,中国为460;MNC是2位Mobile Network Code,在国家内分配,中国移动为00;LAC为Location Area Code,在network内分配;可见LAI是全世界唯一的。在一个location area中设置一个或多个基站,基站天线的信号覆盖一定的区域,称为cell(小区);根据天线的不同,每个基站可能包含1个或多个cell,定向天线的信号覆盖一个扇形范围,多个天线的扇区为不同的cell。每个cell有自己的Cell-Id,结合LAI和Cell-Id,就可以在全球范围内唯一确定一个cell。要进行定位,需要有一个cell坐标数据库,根据cell-id来查找位置信息。

Google的My Location表示格式为myl:MCC:MNC:LAC:CI。在Google Map for Mobile的about信息末尾可以看到。如果显示为myl:n/a,那就是手机不能提供cell-id信息。很不幸,我正在用的SonyEricsson w810c就不支持:(

下一个问题就是cell坐标数据是怎么来的?移动网络运营商提供,不是太现实。Google不可能与全球所有的运营商达成协议。特别是在中国,凡是涉及精确地理坐标的数据都属于机密范畴,然而我们发现My Location在广州是可用的,估计其他城市也可以。于是我猜Google是自己采集这些信息的,例如开辆车周围转,记录各处的cell-id。但有点奇怪的是,在广州我们发现在天河软件园得到的位置比较准确,但是在其他地方的就误差很大,并且表示精确度范围的圆圈半径非常大,将半个天河区都包含在内。难道Google只在天河软件园采集了数据,而其他地方没有?

最近,一个同事开发了一个程序,利用手机自带的GPS定位,将cell-id与坐标记录下来。我们讨论的时候才恍然大悟,Google很可能就是这样做的!有些Google Map for Mobile用户的手机是支持GPS的,当这些用户运行Google Map时打开了GPS,坐标数据以及cell-id就会发送到Google的服务器,等于有许多用户在替Google采集数据。在天河软件园的定位准确,就因为我们(当然也可能有其他人)在这里试验Google Map时打开过GPS。在我们这里,支持GPS的手机的拥有率不高,同时又使用Google Map的更少,因此Google掌握到的数据很有限。在没有采集到数据的地方,定位就不准确了。

为了求证,到Google网站上查看,在Google Mobile的Privacy Policy中找到这样一段话:If you use location-based products and services, such as Google Maps for mobile, you may be sending us location information. This information may reveal your actual location, such as GPS data, or it may not, such as when you submit a partial address to look at a map of the area. 在help中又找到一段话:Google takes geo-contextual information [from anonymous GPS-readings, etc] and associates this information with the cell at that location to develop a database of cell locations. 看来我们的猜想是成立的。其实还可以通过做实验来证明:找一台支持GPS的手机,到一个定位有非常大误差的地方,在Google Map中enable GPS。过一段时间后,例如几天,因为Google处理数据可能有延迟,在关闭GPS的状态下看My Location,如果定位变得准确了就是Google使用了用户手机GPS采集的数据。不过我没有GPS手机,无法做这个实验。

在网上查找资料的过程中发现,其实Cell-id定位并不是一项新技术,不过它与Google Map的海量高清晰度地图结合起来,给业界带来了震撼。在Treo智能手机上,2006年就有国内的爱好者开发了手机定位软件,根据cell-id查询到当前位置,不过位置信息是地名,而不是经纬度坐标;Cell-Id对照地名信息的数据库是由用户补充完善的。在网上搜索一下,也可以找到好几个开放的cell-id坐标数据库,例如OpenCellID,只不过几乎都没有中国大陆的数据。Flickr为了配合ZoneTag(用手机拍照时记录下cell-id,从而可以知道照片拍摄的位置),还提供了Cell Location API

import argparse import os import sys import numpy as np import json import torch from PIL import Image sys.path.append(os.path.join(os.getcwd(), "GroundingDINO")) sys.path.append(os.path.join(os.getcwd(), "segment_anything")) # Grounding DINO import GroundingDINO.groundingdino.datasets.transforms as T from GroundingDINO.groundingdino.models import build_model from GroundingDINO.groundingdino.util.slconfig import SLConfig from GroundingDINO.groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap # segment anything from segment_anything import sam_model_registry import cv2 import numpy as np import matplotlib.pyplot as plt def load_image(image_path): # load image image_pil = Image.open(image_path).convert("RGB") # load image transform = T.Compose( [ T.RandomResize([800], max_size=1333), T.ToTensor(), T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), ] ) image, _ = transform(image_pil, None) # 3, h, w return image_pil, image def load_model(model_config_path, model_checkpoint_path, bert_base_uncased_path, device): args = SLConfig.fromfile(model_config_path) args.device = device args.bert_base_uncased_path = bert_base_uncased_path model = build_model(args) checkpoint = torch.load(model_checkpoint_path, map_location="cpu") load_res = model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False) print(load_res) _ = model.eval() return model def get_grounding_output(model, image, caption, box_threshold, text_threshold, with_logits=True, device="cpu"): caption = caption.lower() caption = caption.strip() if not caption.endswith("."): caption = caption + "." model = model.to(device) image = image.to(device) with torch.no_grad(): outputs = model(image[None], captions=[caption]) logits = outputs["pred_logits"].cpu().sigmoid()[0] # (nq, 256) boxes = outputs["pred_boxes"].cpu()[0] # (nq, 4) logits.shape[0] # filter output logits_filt = logits.clone() boxes_filt = boxes.clone() filt_mask = logits_filt.max(dim=1)[0] > box_threshold logits_filt = logits_filt[filt_mask] # num_filt, 256 boxes_filt = boxes_filt[filt_mask] # num_filt, 4 logits_filt.shape[0] # get phrase tokenlizer = model.tokenizer tokenized = tokenlizer(caption) # build pred pred_phrases = [] for logit, box in zip(logits_filt, boxes_filt): pred_phrase = get_phrases_from_posmap(logit > text_threshold, tokenized, tokenlizer) if with_logits: pred_phrases.append(pred_phrase + f"({str(logit.max().item())[:4]})") else: pred_phrases.append(pred_phrase) return boxes_filt, pred_phrases def show_mask(mask, ax, random_color=False): if random_color: color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0) else: color = np.array([30/255, 144/255, 255/255, 0.6]) h, w = mask.shape[-2:] mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1) ax.imshow(mask_image) def show_box(box, ax, label): x0, y0 = box[0], box[1] w, h = box[2] - box[0], box[3] - box[1] ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2)) ax.text(x0, y0, label) def save_mask_data(output_dir, mask_list, box_list, label_list): value = 0 # 0 for background mask_img = torch.zeros(mask_list.shape[-2:]) for idx, mask in enumerate(mask_list): mask_img[mask.cpu().numpy()[0] == True] = value + idx + 1 plt.figure(figsize=(10, 10)) plt.imshow(mask_img.numpy()) plt.axis('off') plt.savefig(os.path.join(output_dir, 'mask.jpg'), bbox_inches="tight", dpi=300, pad_inches=0.0) json_data = [{ 'value': value, 'label': 'background' }] for label, box in zip(label_list, box_list): value += 1 name, logit = label.split('(') logit = logit[:-1] # the last is ')' json_data.append({ 'value': value, 'label': name, 'logit': float(logit), 'box': box.numpy().tolist(), }) with open(os.path.join(output_dir, 'mask.json'), 'w') as f: json.dump(json_data, f) if __name__ == "__main__": 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_version", type=str, default="vit_h", required=False, help="SAM ViT version: vit_b / vit_l / vit_h" ) parser.add_argument( "--sam_checkpoint", type=str, default="./sam_vit_h_4b8939.pth", help="path to sam checkpoint file" ) parser.add_argument( "--sam_hq_checkpoint", type=str, default=None, help="path to sam-hq checkpoint file" ) parser.add_argument( "--use_sam_hq", action="store_true", help="using sam-hq for prediction" ) parser.add_argument("--input_image", type=str, required=True, help="path to image file") parser.add_argument("--text_prompt", type=str, required=True, help="text prompt") parser.add_argument( "--output_dir", "-o", type=str, default="./outputs", help="output directory" ) parser.add_argument("--box_threshold", type=float, default=0.3, help="box threshold") parser.add_argument("--text_threshold", type=float, default=0.25, help="text threshold") parser.add_argument("--device", type=str, default="cpu", help="running on cpu only!, default=False") parser.add_argument("--bert_base_uncased_path", type=str, required=False, help="bert_base_uncased model path, 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_version = args.sam_version sam_checkpoint = args.sam_checkpoint sam_hq_checkpoint = args.sam_hq_checkpoint use_sam_hq = args.use_sam_hq image_path = args.input_image text_prompt = args.text_prompt output_dir = args.output_dir box_threshold = args.box_threshold text_threshold = args.text_threshold device = args.device bert_base_uncased_path = args.bert_base_uncased_path # make dir os.makedirs(output_dir, exist_ok=True) # load image image_pil, image = load_image(image_path) # load model model = load_model(config_file, grounded_checkpoint, bert_base_uncased_path, device=device) # visualize raw image image_pil.save(os.path.join(output_dir, "raw_image.jpg")) # run grounding dino model boxes_filt, pred_phrases = get_grounding_output( model, image, text_prompt, box_threshold, text_threshold, device=device ) # initialize SAM if use_sam_hq: predictor = SamPredictor(sam_model_registry[sam_version](checkpoint=sam_hq_checkpoint).to(device)) else: predictor = SamPredictor(sam_model_registry[sam_version](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() 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.axis('off') plt.savefig( os.path.join(output_dir, "grounded_sam_output.jpg"), bbox_inches="tight", dpi=300, pad_inches=0.0 ) save_mask_data(output_dir, masks, boxes_filt, pred_phrases) 运行时报错 C:\Users\29386\.conda\envs\grounded_sam\python.exe C:\Users\29386\segment-anything\Grounded-Segment-Anything\grounded_sam_demo.py --config GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py --grounded_checkpoint weights/groundingdino_swint_ogc.pth --sam_checkpoint weights/sam_vit_h_4b8939.pth --input_image assets/demo1.jpg --output_dir outputs --text_prompt cat C:\Users\29386\.conda\envs\grounded_sam\lib\site-packages\timm\models\layers\__init__.py:48: FutureWarning: Importing from timm.models.layers is deprecated, please import via timm.layers warnings.warn(f"Importing from {__name__} is deprecated, please import via timm.layers", FutureWarning) C:\Users\29386\.conda\envs\grounded_sam\lib\site-packages\torch\functional.py:513: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at C:\actions-runner\_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\native\TensorShape.cpp:3610.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] final text_encoder_type: bert-base-uncased C:\Users\29386\.conda\envs\grounded_sam\lib\site-packages\huggingface_hub\file_download.py:143: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\Users\29386\.cache\huggingface\hub\models--bert-base-uncased. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations. To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development warnings.warn(message) Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet` C:\Users\29386\segment-anything\Grounded-Segment-Anything\grounded_sam_demo.py:47: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature. checkpoint = torch.load(model_checkpoint_path, map_location="cpu") Traceback (most recent call last): File "C:\Users\29386\segment-anything\Grounded-Segment-Anything\grounded_sam_demo.py", line 187, in <module> model = load_model(config_file, grounded_checkpoint, bert_base_uncased_path, device=device) File "C:\Users\29386\segment-anything\Grounded-Segment-Anything\grounded_sam_demo.py", line 47, in load_model checkpoint = torch.load(model_checkpoint_path, map_location="cpu") File "C:\Users\29386\.conda\envs\grounded_sam\lib\site-packages\torch\serialization.py", line 1065, in load with _open_file_like(f, 'rb') as opened_file: File "C:\Users\29386\.conda\envs\grounded_sam\lib\site-packages\torch\serialization.py", line 468, in _open_file_like return _open_file(name_or_buffer, mode) File "C:\Users\29386\.conda\envs\grounded_sam\lib\site-packages\torch\serialization.py", line 449, in __init__ super().__init__(open(name, mode)) FileNotFoundError: [Errno 2] No such file or directory: 'weights/groundingdino_swint_ogc.pth' 如何在windows系统上解决问题
最新发布
07-25
<think>我们正在解决在Windows系统上运行Grounded-Segment-Anything代码时出现的FileNotFoundError,具体是找不到权重文件:weights/groundingdino_swint_ogc.pth。 根据引用[1]中的信息,我们知道在Python环境中,需要将权重文件(如weights.pth)放置到特定的checkpoints文件夹下。但是,用户提供的错误信息中提到的文件路径是`weights/groundingdino_swint_ogc.pth`,这是一个相对路径,而程序运行时的当前工作目录可能不是这个文件所在的目录。 解决方案思路: 1. 确定程序期望的权重文件路径。 2. 将下载的权重文件放到正确的路径下。 具体步骤: ### 步骤1:下载权重文件 根据Grounded-Segment-Anyting项目的README,我们需要下载两个权重文件: - GroundingDINO的权重:`groundingdino_swint_ogc.pth` - SAM的权重:`sam_vit_h_4b8939.pth` 我们可以从官方提供的链接下载: - GroundingDINO权重:https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth - SAM权重:https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth ### 步骤2:确定正确的存放路径 根据引用[1]中的提示,我们需要将权重文件放在Python环境下的特定路径中。但是,每个项目对权重文件的路径要求可能不同。在Grounded-Segment-Anything项目中,通常会在代码中指定一个路径,或者我们可以通过配置文件来设置。 根据项目文档(如果用户没有提供,我们可以根据常见做法推断): - 项目可能期望权重文件放在项目根目录下的`weights`文件夹中。 - 或者,我们可以通过设置环境变量或修改配置文件来指定权重文件的路径。 ### 步骤3:创建权重文件夹并放置文件 在项目根目录下创建`weights`文件夹,并将下载的两个权重文件放入其中。 ### 步骤4:修改代码或配置(如果需要) 如果项目代码中指定的路径不是相对路径`weights/`,那么我们需要修改代码或配置文件。但是,根据错误信息,它正在寻找`weights/groundingdino_swint_ogc.pth`,所以如果我们把权重文件放在项目根目录下的`weights`文件夹中,应该就可以解决。 ### 具体操作步骤: 1. **下载权重文件**: - 打开浏览器,访问上述链接下载两个权重文件。 - 或者使用命令行下载(在项目根目录下执行): ```powershell # 创建weights目录(如果不存在) mkdir weights # 下载GroundingDINO权重 Invoke-WebRequest -Uri "https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth" -OutFile "weights\groundingdino_swint_ogc.pth" # 下载SAM权重 Invoke-WebRequest -Uri "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth" -OutFile "weights\sam_vit_h_4b8939.pth" ``` 2. **验证文件路径**: - 确保项目根目录下有一个`weights`文件夹,里面包含两个文件: - `groundingdino_swint_ogc.pth` - `sam_vit_h_4b8939.pth` 3. **运行代码**: - 重新运行程序,此时应该能够找到权重文件。 ### 备选方案:修改代码中的路径 如果上述方法不起作用,可能是代码中使用了绝对路径或者不同的相对路径。我们可以通过修改代码来指定正确的路径。 在Grounded-Segment-Anything项目中,通常有一个配置文件(如`config.py`)或命令行参数来设置权重路径。我们可以搜索代码中`groundingdino_swint_ogc.pth`出现的位置,然后修改为正确的路径。 例如,如果我们在代码中找到类似这样的行: ```python config_file = "GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py" weight_file = "weights/groundingdino_swint_ogc.pth" ``` 我们可以将`weight_file`改为绝对路径,或者确保相对路径正确。 ### 注意:环境变量路径(引用[1]的提示) 引用[1]提到了Python环境变量路径下的checkpoints文件夹。但是,这个项目可能不需要放在那里,除非代码中指定了从那个位置加载。所以,我们首先尝试放在项目根目录下的weights文件夹。 ### 如果项目要求放在特定位置(如checkpoints文件夹) 如果按照引用[1]的做法,我们需要: 1. 找到Python环境下的checkpoints文件夹(如`C:\Users\<用户名>\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\checkpoints`)。 2. 将权重文件复制到该文件夹中。 但是,根据错误信息,程序是在`weights`目录下寻找,所以这种方法可能不适用,除非我们修改代码指向这个checkpoints文件夹。 ### 总结 最直接的方法是在项目根目录下创建weights文件夹,并将权重文件放入。如果问题仍然存在,则需要检查代码中关于路径的设置。 ### 相关问题 §§相关问题§§ 1. 如何在Windows系统中使用PowerShell下载大文件并验证完整性? 2. 如何修改Python项目的配置文件来改变模型权重路径? 3. 在运行深度学习项目时,如何处理常见的文件路径错误?</think>根据您描述的问题,运行 Grounded-Segment-Anything 时出现 `FileNotFoundError: weights/groundingdino_swint_ogc.pth` 错误,这通常是权重文件路径配置不正确导致的。以下是详细的解决方案: ### 解决方案分步指南 #### 1. **获取权重文件** - **官方下载地址**: - GroundingDINO 权重:[groundingdino_swint_ogc.pth](https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth) - SAM 权重:[sam_vit_h_4b8939.pth](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth) - **命令行下载**(推荐): ```powershell # 创建 weights 目录 mkdir weights # 下载 GroundingDINO 权重 Invoke-WebRequest -Uri "https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth" -OutFile "weights\groundingdino_swint_ogc.pth" # 下载 SAM 权重 Invoke-WebRequest -Uri "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth" -OutFile "weights\sam_vit_h_4b8939.pth" ``` #### 2. **正确放置权重文件** - **项目结构要求**: ``` Your_Project_Root/ ├── weights/ │ ├── groundingdino_swint_ogc.pth # GroundingDINO 权重 │ └── sam_vit_h_4b8939.pth # SAM 权重 └── main.py # 主程序 ``` - **验证路径**:确保权重文件位于项目根目录下的 `weights` 文件夹内(注意是复数形式) #### 3. **修改代码配置** 在代码中显式指定权重路径(以 Python 为例): ```python # 在加载模型前添加路径配置 import os # 设置权重路径(替换为您的实际路径) GROUNDING_DINO_CONFIG_PATH = "path/to/GroundingDINO_SwinT_OGC.py" GROUNDING_DINO_CHECKPOINT_PATH = os.path.abspath("weights/groundingdino_swint_ogc.pth") SAM_CHECKPOINT_PATH = os.path.abspath("weights/sam_vit_h_4b8939.pth") # 初始化模型 grounding_dino_model = load_model( GROUNDING_DINO_CONFIG_PATH, GROUNDING_DINO_CHECKPOINT_PATH ) ``` #### 4. **环境依赖检查** - 确保已安装必要依赖(参考引用[2]): ```powershell # 安装 Visual C++ Build Tools winget install Microsoft.VisualStudio.2022.BuildTools --override "--add Microsoft.VisualStudio.Workload.VCTools" ``` - 安装 Python 依赖: ```powershell pip install torch torchvision pip install git+https://github.com/facebookresearch/segment-anything.git pip install -e git+https://github.com/IDEA-Research/GroundingDINO.git ``` #### 5. **路径问题排查技巧** ```python # 在代码中添加路径验证 print(f"当前工作目录: {os.getcwd()}") print(f"权重文件存在: {os.path.exists('weights/groundingdino_swint_ogc.pth')}") ``` 如果输出 `False`,说明路径配置错误,需使用绝对路径: ```python # 使用绝对路径示例 checkpoint_path = r"C:\Your\Project\Path\weights\groundingdino_swint_ogc.pth" ``` ### 常见问题解决 1. **路径大小写敏感问题**: - Windows 路径不区分大小写,但 Python 字符串需与文件夹名称完全一致 - 确保代码中的路径与磁盘上的文件夹名称匹配(如 `weights` ≠ `Weights`) 2. **权限问题**: ```powershell # 以管理员身份运行 PowerShell Start-Process powershell -Verb runAs ``` 3. **路径转义问题**: - 使用原始字符串避免转义问题: ```python path = r"C:\Project\weights\groundingdino_swint_ogc.pth" ``` ### 验证是否解决 运行代码前检查: ```powershell # 在项目根目录执行 dir weights\*.pth ``` 应显示: ``` Directory: C:\Your\Project\Path\weights Mode LastWriteTime Length Name ---- ------------- ------ ---- -a---- 2023-05-01 10:23 1.2 GB groundingdino_swint_ogc.pth -a---- 2023-05-01 10:25 2.3 GB sam_vit_h_4b8939.pth ``` > **提示**:如果使用预训练模型转换工具(如引用[3]),确保转换后的文件扩展名正确(.pth 而非 .weights)[^3]。
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