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【图像分割】Meta分割一切(SAM)模型环境配置和使用教程

注意:

python>=3.8

,

pytorch>=1.7,
torchvision>=0.8

Feel free to ask any question. 遇到问题欢迎评论区讨论.

官方教程:

https://github.com/facebookresearch/segment-anything

1 环境配置

1.1 安装主要库:

(1)pip:

有可能出现错误,需要配置好Git。

pip install git+https://github.com/facebookresearch/segment-anything.git

(2)本地安装:

有可能出现错误,需要配置好Git。

git clone [email protected]:facebookresearch/segment-anything.git
cd segment-anything; pip install -e .

(3)手动下载+手动本地安装:

zip文件:

链接:https://pan.baidu.com/s/1dQ--kTTJab5eloKm6nMYrg 
提取码:1234 

解压后运行:

cd segment-anything-main
pip install -e .

1.2 安装依赖库:

pip install opencv-python pycocotools matplotlib onnxruntime onnx

matplotlib 3.7.1和3.7.0可能报错

如果报错:pip install matplotlib==3.6.2

1.3 下载权重文件:

下载三个权重文件中的一个,我用的第一个。

  • default or vit_h: ViT-H SAM model.
  • vit_l: ViT-L SAM model.
  • vit_b: ViT-B SAM model.

如果下载过慢:

链接:https://pan.baidu.com/s/11wZUcjYWNL6kxOH5MFGB-g 
提取码:1234 

2 使用教程

2.1 根据在图片上选择的点扣出物体

原始图像:

导入依赖库和展示相关的函数:

import cv2
import matplotlib.pyplot as plt
import numpy as np
from segment_anything import sam_model_registry, SamPredictor

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_points(coords, labels, ax, marker_size=375):
    pos_points = coords[labels == 1]
    neg_points = coords[labels == 0]
    ax.scatter(pos_points[:, 0], pos_points[:, 1], color='green', marker='*', s=marker_size, edgecolor='white',
               linewidth=1.25)
    ax.scatter(neg_points[:, 0], neg_points[:, 1], color='red', marker='*', s=marker_size, edgecolor='white',
               linewidth=1.25)

确定使用的权重文件位置和是否使用cuda等:

sam_checkpoint = "F:\sam_vit_h_4b8939.pth"
device = "cuda"
model_type = "default"

模型实例化:

sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
sam.to(device=device)
predictor = SamPredictor(sam)

读取图像并选择抠图点:

image = cv2.imread(r"F:\Dataset\Tomato_Appearance\Tomato_Xishi\images\xs_1.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

predictor.set_image(image)

input_point = np.array([[1600, 1000]])
input_label = np.array([1])

plt.figure(figsize=(10,10))
plt.imshow(image)
show_points(input_point, input_label, plt.gca())
plt.axis('on')
plt.show()

扣取图像(会同时提供多个扣取结果):

masks, scores, logits = predictor.predict(
    point_coords=input_point,
    point_labels=input_label,
    multimask_output=True,
)

# 遍历读取每个扣出的结果
for i, (mask, score) in enumerate(zip(masks, scores)):
    plt.figure(figsize=(10,10))
    plt.imshow(image)
    show_mask(mask, plt.gca())
    show_points(input_point, input_label, plt.gca())
    plt.title(f"Mask {i+1}, Score: {score:.3f}", fontsize=18)
    plt.axis('off')
    plt.show()
 ![](https://img-blog.csdnimg.cn/8eae8225ff1140ab9ac24a732dc55188.png)![](https://img-blog.csdnimg.cn/3852212accbb49308bb057084eda28b8.png)

尝试扣取其他位置:

2.2 扣取图像中的所有物体

官方教程:

https://github.com/facebookresearch/segment-anything/blob/main/notebooks/automatic_mask_generator_example.ipynb

依赖库和函数导入:

from segment_anything import sam_model_registry, SamAutomaticMaskGenerator, SamPredictor
import cv2
import matplotlib.pyplot as plt
import numpy as np

def show_anns(anns):
    if len(anns) == 0:
        return
    sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)
    ax = plt.gca()
    ax.set_autoscale_on(False)
    polygons = []
    color = []
    for ann in sorted_anns:
        m = ann['segmentation']
        img = np.ones((m.shape[0], m.shape[1], 3))
        color_mask = np.random.random((1, 3)).tolist()[0]
        for i in range(3):
            img[:,:,i] = color_mask[i]
        ax.imshow(np.dstack((img, m*0.35)))

读取图片:

image = cv2.imread(r"F:\Dataset\Tomato_Appearance\Tomato_Xishi\images\xs_1.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

实例化模型:

sam_checkpoint = "F:\sam_vit_h_4b8939.pth"
model_type = "default"
device = "cuda"

sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
sam.to(device=device)

分割并展示(速度有点慢):

mask_generator = SamAutomaticMaskGenerator(sam)
masks = mask_generator.generate(image)

plt.figure(figsize=(20,20))
plt.imshow(image)
show_anns(masks)
plt.axis('off')
plt.show()

2.3 根据文字扣取物体

配置另外一个库:

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

后续更新细节


本文转载自: https://blog.csdn.net/Father_of_Python/article/details/130004935
版权归原作者 Father_of_Python 所有, 如有侵权,请联系我们删除。

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