【计算机视觉 | 目标检测 | 图像分割】Grounding DINO + Segment Anything Model (SAM)源代码分享（含源代码）

男娘i 2023-10-16 11:05 36阅读 0赞

#### 文章目录 ####

*  一、Grounding DINO + Segment Anything Model (SAM)
 *   *  1.1 开始
     *  1.2 Install Grounding DINO and Segment Anything Model
     *  1.3 Download Grounding DINO Model Weights
     *  1.4 Download Segment Anything Model (SAM) Weights
     *  1.5 Load models
     *   *  1.5.1 Load Grounding DINO Model
         *  1.5.2 Load Segment Anything Model (SAM)
     *  1.6 Download Example Data
     *  1.7 Single Image Mask Auto Annotation
     *   *  1.7.1 Zero-Shot Object Detection with Grounding DINO
         *  1.7.2 Prompting SAM with detected boxes
     *  1.8 Full Dataset Mask Auto Annotation
     *   *  1.8.1 Extract labels from images
         *  1.8.2 Save labels in Pascal VOC XML
         *  1.8.3 Upload annotations to Roboflow
         *  1.8.4 Convert Object Detection to Instance Segmentation Dataset
         *  1.8.5 Download Object Detection Dataset from Roboflow
         *  1.8.6 Load and Visualize Object Detection Dataset with Supervision
         *  1.8.7 Run SAM convert Boxes into Masks

## 一、Grounding DINO + Segment Anything Model (SAM) ##

在本教程中，我们将学习如何使用两个突破性的模型自动注释图像 - Grounding DINO 和 Segment Anything Model (SAM)。

然后，我们可以使用此数据集来训练实时对象检测或实例分割模型。 以传统方式使用多边形对图像进行注释极其耗时且昂贵。 借助 Grounding DINO 和 SAM，初始注释仅需几分钟，我们的工作将简化为手动验证所获得的标签。

![在这里插入图片描述][ef8e3e44b8ec4dce89fd95de7c9359c5.png]

### 1.1 开始 ###

让我们确保我们可以访问 GPU。 我们可以使用 nvidia-smi 命令来做到这一点。

!nvidia-smi

Tue Sep 19 21:10:12 2023       
    +-----------------------------------------------------------------------------+
    | NVIDIA-SMI 525.85.12    Driver Version: 525.85.12    CUDA Version: 12.0     |
    |-------------------------------+----------------------+----------------------+
    | GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
    | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
    |                               |                      |               MIG M. |
    |===============================+======================+======================|
    |   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |
    | N/A   53C    P8    10W /  70W |      0MiB / 15360MiB |      0%      Default |
    |                               |                      |                  N/A |
    +-------------------------------+----------------------+----------------------+
                                                                                   
    +-----------------------------------------------------------------------------+
    | Processes:                                                                  |
    |  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
    |        ID   ID                                                   Usage      |
    |=============================================================================|
    |  No running processes found                                                 |
    +-----------------------------------------------------------------------------+

注意：为了让我们更容易管理数据集、图像和模型，我们创建了一个 HOME 常量。

import os
    HOME = os.getcwd()
    print("HOME:", HOME)

输出为：

HOME: /content

### 1.2 Install Grounding DINO and Segment Anything Model ###

我们的项目将使用两种突破性的设计：Grounding DINO（用于零样本检测）和 Segment Anything Model（SAM）（用于将盒子转换为分段）。 我们必须先安装它们。

%cd {
        HOME}
    !git clone https://github.com/IDEA-Research/GroundingDINO.git
    %cd {
        HOME}/GroundingDINO
    !git checkout -q 57535c5a79791cb76e36fdb64975271354f10251
    !pip install -q -e .

部分输出结果为：

/content
    Cloning into 'GroundingDINO'...
    remote: Enumerating objects: 267, done.
    remote: Counting objects: 100% (41/41), done.
    remote: Compressing objects: 100% (25/25), done.
    remote: Total 267 (delta 18), reused 16 (delta 16), pack-reused 226
    Receiving objects: 100% (267/267), 12.33 MiB | 22.16 MiB/s, done.
    Resolving deltas: 100% (122/122), done.
    /content/GroundingDINO
      Preparing metadata (setup.py) ... done
         ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 7.0/7.0 MB 40.4 MB/s eta 0:00:00
         ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 200.9/200.9 kB 14.2 MB/s eta 0:00:00
         ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 549.1/549.1 kB 32.1 MB/s eta 0:00:00
         ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 200.1/200.1 kB 16.4 MB/s eta 0:00:00
         ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 7.8/7.8 MB 62.7 MB/s eta 0:00:00

%cd {
        HOME}
    
    import sys
    !{
        sys.executable} -m pip install 'git+https://github.com/facebookresearch/segment-anything.git'

部分输出结果为：

/content
    Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
    Collecting git+https://github.com/facebookresearch/segment-anything.git
      Cloning https://github.com/facebookresearch/segment-anything.git to /tmp/pip-req-build-e2789hn3
      Running command git clone --filter=blob:none --quiet https://github.com/facebookresearch/segment-anything.git /tmp/pip-req-build-e2789hn3
      Resolved https://github.com/facebookresearch/segment-anything.git to commit 567662b0fd33ca4b022d94d3b8de896628cd32dd
      Preparing metadata (setup.py) ... done
    Building wheels for collected packages: segment-anything
      Building wheel for segment-anything (setup.py) ... done
      Created wheel for segment-anything: filename=segment_anything-1.0-py3-none-any.whl size=36610 sha256=ed90a8a550d5948a1b3b9c5f2a173970657a518009512fb20e89dc5ec5240160
      Stored in directory: /tmp/pip-ephem-wheel-cache-xtm37yc6/wheels/d5/11/03/7aca746a2c0e09f279b10436ced7175926bc38f650b736a648
    Successfully built segment-anything
    Installing collected packages: segment-anything
    Successfully installed segment-anything-1.0

注意：为了将演示的所有元素粘合在一起，我们将使用监督 pip 包，它将帮助我们处理、过滤和可视化我们的检测以及保存我们的数据集。

然而，在这个演示中，我们需要最新版本中引入的功能。 因此，我们卸载当前的supervsion版本并安装0.6.0版本。

!pip uninstall -y supervision
    !pip install -q supervision==0.6.0
    
    import supervision as sv
    print(sv.__version__)

输出结果为：

Found existing installation: supervision 0.4.0
    Uninstalling supervision-0.4.0:
      Successfully uninstalled supervision-0.4.0
    ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
    groundingdino 0.1.0 requires supervision==0.4.0, but you have supervision 0.6.0 which is incompatible.
    0.6.0

让我们安装 roboflow pip 包。

!pip install -q roboflow

部分结果如下所示：

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 56.2/56.2 kB 2.6 MB/s eta 0:00:00
         ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 67.8/67.8 kB 8.0 MB/s eta 0:00:00
      Preparing metadata (setup.py) ... done
         ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 54.5/54.5 kB 4.9 MB/s eta 0:00:00
         ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 58.8/58.8 kB 6.2 MB/s eta 0:00:00
      Building wheel for wget (setup.py) ... done

### 1.3 Download Grounding DINO Model Weights ###

要运行 Grounding DINO，我们需要两个文件 - 配置和模型权重。 配置文件是 Grounding DINO 存储库的一部分，我们已经克隆了它。 另一方面，我们需要下载权重文件。 我们将这两个文件的路径写入 GROUNDING\_DINO\_CONFIG\_PATH 和 GROUNDING\_DINO\_CHECKPOINT\_PATH 变量，并验证路径是否正确以及文件是否存在于磁盘上。

import os
    
    GROUNDING_DINO_CONFIG_PATH = os.path.join(HOME, "GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py")
    print(GROUNDING_DINO_CONFIG_PATH, "; exist:", os.path.isfile(GROUNDING_DINO_CONFIG_PATH))

输出结果为：

/content/GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py ; exist: True

%cd {
        HOME}
    !mkdir -p {
        HOME}/weights
    %cd {
        HOME}/weights
    
    !wget -q https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth

输出结果为：

/content
    /content/weights

import os
    
    GROUNDING_DINO_CHECKPOINT_PATH = os.path.join(HOME, "weights", "groundingdino_swint_ogc.pth")
    print(GROUNDING_DINO_CHECKPOINT_PATH, "; exist:", os.path.isfile(GROUNDING_DINO_CHECKPOINT_PATH))

输出结果为：

/content/weights/groundingdino_swint_ogc.pth ; exist: True

### 1.4 Download Segment Anything Model (SAM) Weights ###

与 Grounding DINO 一样，为了运行 SAM，我们需要一个权重文件，我们必须首先下载该文件。 我们将本地权重文件的路径写入 SAM\_CHECKPOINT\_PATH 变量，并验证路径是否正确以及文件是否存在于磁盘上。

%cd {
        HOME}
    !mkdir -p {
        HOME}/weights
    %cd {
        HOME}/weights
    
    !wget -q https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth

输出为：

/content
    /content/weights

import os
    
    SAM_CHECKPOINT_PATH = os.path.join(HOME, "weights", "sam_vit_h_4b8939.pth")
    print(SAM_CHECKPOINT_PATH, "; exist:", os.path.isfile(SAM_CHECKPOINT_PATH))

输出结果为：

/content/weights/sam_vit_h_4b8939.pth ; exist: True

### 1.5 Load models ###

import torch
    
    DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

#### 1.5.1 Load Grounding DINO Model ####

%cd {
        HOME}/GroundingDINO
    
    from groundingdino.util.inference import Model
    
    grounding_dino_model = Model(model_config_path=GROUNDING_DINO_CONFIG_PATH, model_checkpoint_path=GROUNDING_DINO_CHECKPOINT_PATH)

![在这里插入图片描述][487741a5bfaf49f29098be1123369664.png]

#### 1.5.2 Load Segment Anything Model (SAM) ####

SAM_ENCODER_VERSION = "vit_h"

from segment_anything import sam_model_registry, SamPredictor
    
    sam = sam_model_registry[SAM_ENCODER_VERSION](checkpoint=SAM_CHECKPOINT_PATH).to(device=DEVICE)
    sam_predictor = SamPredictor(sam)

### 1.6 Download Example Data ###

f"{
          HOME}/data"

输出为：

/content/data

%cd {
        HOME}
    !mkdir {
        HOME}/data
    %cd {
        HOME}/data
    
    !wget -q https://media.roboflow.com/notebooks/examples/dog.jpeg
    !wget -q https://media.roboflow.com/notebooks/examples/dog-2.jpeg
    !wget -q https://media.roboflow.com/notebooks/examples/dog-3.jpeg
    !wget -q https://media.roboflow.com/notebooks/examples/dog-4.jpeg
    !wget -q https://media.roboflow.com/notebooks/examples/dog-5.jpeg
    !wget -q https://media.roboflow.com/notebooks/examples/dog-6.jpeg
    !wget -q https://media.roboflow.com/notebooks/examples/dog-7.jpeg
    !wget -q https://media.roboflow.com/notebooks/examples/dog-8.jpeg

输出结果为：

/content
    /content/data

### 1.7 Single Image Mask Auto Annotation ###

在我们自动注释整个数据集之前，让我们先关注单个图像。

SOURCE_IMAGE_PATH = f"{
          HOME}/data/dog-3.jpeg"
    CLASSES = ['car', 'dog', 'person', 'nose', 'chair', 'shoe', 'ear']
    BOX_TRESHOLD = 0.35
    TEXT_TRESHOLD = 0.25

#### 1.7.1 Zero-Shot Object Detection with Grounding DINO ####

注意：为了获得更好的Grounding DINO检测，我们将使用下面定义的enhance\_class\_name函数来利用一些提示工程。

from typing import List
    
    def enhance_class_name(class_names: List[str]) -> List[str]:
        return [
            f"all {
          class_name}s"
            for class_name
            in class_names
        ]

import cv2
    import supervision as sv
    
    # load image
    image = cv2.imread(SOURCE_IMAGE_PATH)
    
    # detect objects
    detections = grounding_dino_model.predict_with_classes(
        image=image,
        classes=enhance_class_name(class_names=CLASSES),
        box_threshold=BOX_TRESHOLD,
        text_threshold=TEXT_TRESHOLD
    )
    
    # annotate image with detections
    box_annotator = sv.BoxAnnotator()
    labels = [
        f"{
          CLASSES[class_id]} {
          confidence:0.2f}" 
        for _, _, confidence, class_id, _ 
        in detections]
    annotated_frame = box_annotator.annotate(scene=image.copy(), detections=detections, labels=labels)
    
    %matplotlib inline
    sv.plot_image(annotated_frame, (16, 16))

输出结果为：  
![在这里插入图片描述][5fd5e43480e647ff8162ebfdd42b20a9.png]

#### 1.7.2 Prompting SAM with detected boxes ####

import numpy as np
    from segment_anything import SamPredictor
    
    
    def segment(sam_predictor: SamPredictor, image: np.ndarray, xyxy: np.ndarray) -> np.ndarray:
        sam_predictor.set_image(image)
        result_masks = []
        for box in xyxy:
            masks, scores, logits = sam_predictor.predict(
                box=box,
                multimask_output=True
            )
            index = np.argmax(scores)
            result_masks.append(masks[index])
        return np.array(result_masks)

import cv2
    
    # convert detections to masks
    detections.mask = segment(
        sam_predictor=sam_predictor,
        image=cv2.cvtColor(image, cv2.COLOR_BGR2RGB),
        xyxy=detections.xyxy
    )
    
    # annotate image with detections
    box_annotator = sv.BoxAnnotator()
    mask_annotator = sv.MaskAnnotator()
    labels = [
        f"{
          CLASSES[class_id]} {
          confidence:0.2f}" 
        for _, _, confidence, class_id, _ 
        in detections]
    annotated_image = mask_annotator.annotate(scene=image.copy(), detections=detections)
    annotated_image = box_annotator.annotate(scene=annotated_image, detections=detections, labels=labels)
    
    %matplotlib inline
    sv.plot_image(annotated_image, (16, 16))

![在这里插入图片描述][5b81c20e43164a6d92cd90f0b29ca761.png]

import math
    
    grid_size_dimension = math.ceil(math.sqrt(len(detections.mask)))
    
    titles = [
        CLASSES[class_id]
        for class_id
        in detections.class_id
    ]
    
    sv.plot_images_grid(
        images=detections.mask,
        titles=titles,
        grid_size=(grid_size_dimension, grid_size_dimension),
        size=(16, 16)
    )

![在这里插入图片描述][b4cf510366ef4564a363394540cddee7.png]

### 1.8 Full Dataset Mask Auto Annotation ###

import os
    
    IMAGES_DIRECTORY = os.path.join(HOME, 'data')
    IMAGES_EXTENSIONS = ['jpg', 'jpeg', 'png']
    
    CLASSES = ['car', 'dog', 'person', 'nose', 'chair', 'shoe', 'ear', 'coffee', 'backpack', 'cap']
    BOX_TRESHOLD = 0.35
    TEXT_TRESHOLD = 0.25

#### 1.8.1 Extract labels from images ####

import cv2
    from tqdm.notebook import tqdm
    
    images = {
        }
    annotations = {
        }
    
    image_paths = sv.list_files_with_extensions(
        directory=IMAGES_DIRECTORY, 
        extensions=IMAGES_EXTENSIONS)
    
    for image_path in tqdm(image_paths):
        image_name = image_path.name
        image_path = str(image_path)
        image = cv2.imread(image_path)
    
        detections = grounding_dino_model.predict_with_classes(
            image=image,
            classes=enhance_class_name(class_names=CLASSES),
            box_threshold=BOX_TRESHOLD,
            text_threshold=TEXT_TRESHOLD
        )
        detections = detections[detections.class_id != None]
        detections.mask = segment(
            sam_predictor=sam_predictor,
            image=cv2.cvtColor(image, cv2.COLOR_BGR2RGB),
            xyxy=detections.xyxy
        )
        images[image_name] = image
        annotations[image_name] = detections

输出结果为：

![在这里插入图片描述][37dcad87c21448e48953ab55e83d2743.png]  
注意：在以 Pascal VOC XML 格式保存检测之前，让我们看一下我们获得的注释。 此步骤是可选的，请随意跳过。

plot_images = []
    plot_titles = []
    
    box_annotator = sv.BoxAnnotator()
    mask_annotator = sv.MaskAnnotator()
    
    for image_name, detections in annotations.items():
        image = images[image_name]
        plot_images.append(image)
        plot_titles.append(image_name)
    
        labels = [
            f"{
          CLASSES[class_id]} {
          confidence:0.2f}" 
            for _, _, confidence, class_id, _ 
            in detections]
        annotated_image = mask_annotator.annotate(scene=image.copy(), detections=detections)
        annotated_image = box_annotator.annotate(scene=annotated_image, detections=detections, labels=labels)
        plot_images.append(annotated_image)
        title = " ".join(set([
            CLASSES[class_id]
            for class_id
            in detections.class_id
        ]))
        plot_titles.append(title)
    
    sv.plot_images_grid(
        images=plot_images,
        titles=plot_titles,
        grid_size=(len(annotations), 2),
        size=(2 * 4, len(annotations) * 4)
    )

![在这里插入图片描述][b1025109f4fe413191f8f2141ccea06e.png]

#### 1.8.2 Save labels in Pascal VOC XML ####

在将注释上传到 Roboflow 之前，我们必须首先将它们保存到硬盘上。 为此，我们将使用最新的监督功能之一（在 0.6.0 更新中提供） - 数据集保存。

ANNOTATIONS_DIRECTORY = os.path.join(HOME, 'annotations')
    
    MIN_IMAGE_AREA_PERCENTAGE = 0.002
    MAX_IMAGE_AREA_PERCENTAGE = 0.80
    APPROXIMATION_PERCENTAGE = 0.75

sv.Dataset(
        classes=CLASSES,
        images=images,
        annotations=annotations
    ).as_pascal_voc(
        annotations_directory_path=ANNOTATIONS_DIRECTORY,
        min_image_area_percentage=MIN_IMAGE_AREA_PERCENTAGE,
        max_image_area_percentage=MAX_IMAGE_AREA_PERCENTAGE,
        approximation_percentage=APPROXIMATION_PERCENTAGE
    )

#### 1.8.3 Upload annotations to Roboflow ####

现在我们准备使用 API 将注释上传到 Roboflow。

PROJECT_NAME = "auto-generated-dataset-7"
    PROJECT_DESCRIPTION = "auto-generated-dataset-7"

import roboflow
    from roboflow import Roboflow
    
    roboflow.login()
    
    workspace = Roboflow().workspace()
    new_project = workspace.create_project(
        project_name=PROJECT_NAME,
        project_license="MIT",
        project_type="instance-segmentation", 
        annotation=PROJECT_DESCRIPTION)

部分结果如下：

visit https://app.roboflow.com/auth-cli to get your authentication token.
    Paste the authentication token here: ··········
    loading Roboflow workspace...
    loading Roboflow project...

import os
    
    for image_path in tqdm(image_paths):
        image_name = image_path.name
        annotation_name = f"{
          image_path.stem}.xml"
        image_path = str(image_path)
        annotation_path = os.path.join(ANNOTATIONS_DIRECTORY, annotation_name)
        new_project.upload(
            image_path=image_path, 
            annotation_path=annotation_path, 
            split="train", 
            is_prediction=True, 
            overwrite=True, 
            tag_names=["auto-annotated-with-grounded-sam"],
            batch_name="auto-annotated-with-grounded-sam"
        )

![在这里插入图片描述][393ce21114574a8b9faae2a76c001064.png]

#### 1.8.4 Convert Object Detection to Instance Segmentation Dataset ####

如果您已经拥有数据集，但它带有边界框注释，您可以快速轻松地将其转换为实例分割数据集。 SAM 允许使用框作为提示。

#### 1.8.5 Download Object Detection Dataset from Roboflow ####

要使用本教程中的数据集，请确保以 Pascal VOC XML 格式下载。 我们将使用 BlueBerries 数据集作为示例。

%cd {
        HOME}
    
    import roboflow
    from roboflow import Roboflow
    
    roboflow.login()
    
    rf = Roboflow()
    
    project = rf.workspace("inkyu-sa-e0c78").project("blueberries-u0e84")
    dataset = project.version(1).download("voc")

![在这里插入图片描述][3f338c1c3c4d4446a9a45819178ea1d9.png]

dataset.location

位置为：`/content/BlueBerries-1`

!ls {
        dataset.location}

输出为：`README.dataset.txt README.roboflow.txt test train valid`

#### 1.8.6 Load and Visualize Object Detection Dataset with Supervision ####

object_detection_dataset = sv.Dataset.from_pascal_voc(
        images_directory_path=f"{
          dataset.location}/train",
        annotations_directory_path=f"{
          dataset.location}/train"
    )

import random
    random.seed(9001)

注意：重新运行下面的单元格以查看数据集中的不同图像。

image_names = list(object_detection_dataset.images.keys())
    image_name = random.choice(image_names)
    
    image = object_detection_dataset.images[image_name]
    detections = object_detection_dataset.annotations[image_name]
    
    box_annotator = sv.BoxAnnotator()
    
    annotated_image = box_annotator.annotate(scene=image.copy(), detections=detections, skip_label=True)
    
    %matplotlib inline
    sv.plot_image(annotated_image, (16, 16))

![在这里插入图片描述][ece1c3e8231547b48d9d2550638ecbbf.png]

#### 1.8.7 Run SAM convert Boxes into Masks ####

from tqdm.notebook import tqdm
    
    for image_name, image in tqdm(object_detection_dataset.images.items()):
        detections = object_detection_dataset.annotations[image_name]
        detections.mask = segment(
            sam_predictor=sam_predictor,
            image=cv2.cvtColor(image, cv2.COLOR_BGR2RGB),
            xyxy=detections.xyxy
        )

![在这里插入图片描述][0e6f4e1414cc4b82a78e1603a52b216b.png]

image_names = list(object_detection_dataset.images.keys())
    image_name = random.choice(image_names)
    
    image = object_detection_dataset.images[image_name]
    detections = object_detection_dataset.annotations[image_name]
    
    mask_annotator = sv.MaskAnnotator()
    
    annotated_image = mask_annotator.annotate(scene=image.copy(), detections=detections)
    
    %matplotlib inline
    sv.plot_image(annotated_image, (16, 16))

![在这里插入图片描述][515ca6b4985e4390b87c5023a2071698.png]

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