SAM:Segment Anything 代码复现和测试 基本使用_sam代码复现

相关地址

代码：
https://github.com/facebookresearch/segment-anything
在线网站：
https://segment-anything.com/demo

环境配置

建议可以clone下来学习相关代码，安装可以不依赖与这个库

git clone https://github.com/facebookresearch/segment-anything.git
1

1.创建environment.yaml

name: sam
channels:
  - pytorch
  - conda-forge
dependencies:
  - python=3.8
  - pytorch=1.9.0
  - torchvision=0.10.0
  - cudatoolkit=11.1
  - pip
1
2
3
4
5
6
7
8
9
10

conda env create -f environment.yaml
conda activate raptor
1
2

2.安装

pip install git+https://github.com/facebookresearch/segment-anything.git
1

或从源代码构建

git clone https://github.com/facebookresearch/segment-anything.git
cd segment-anything; pip install -e .
1
2

3.其他库

pip install opencv-python pycocotools matplotlib onnxruntime onnx
1

目前安装的版本

Successfully installed coloredlogs-15.0.1 contourpy-1.1.1
cycler-0.12.1 flatbuffers-23.5.26 fonttools-4.43.1 humanfriendly-10.0
importlib-resources-6.1.0 kiwisolver-1.4.5 matplotlib-3.7.3
mpmath-1.3.0 numpy-1.24.4 onnx-1.15.0 onnxruntime-1.16.1
opencv-python-4.8.1.78 packaging-23.2 protobuf-4.24.4
pycocotools-2.0.7 pyparsing-3.1.1 python-dateutil-2.8.2 six-1.16.0
sympy-1.12 zipp-3.17.0

初阶测试

1.下载模型
https://github.com/facebookresearch/segment-anything#model-checkpoints

2.测试代码

import numpy as np
import torch
import matplotlib.pyplot as plt
import cv2

import sys
sys.path.append("..")
from segment_anything import sam_model_registry, SamAutomaticMaskGenerator, SamPredictor


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)

    img = np.ones((sorted_anns[0]['segmentation'].shape[0], sorted_anns[0]['segmentation'].shape[1], 4))
    img[:,:,3] = 0
    for ann in sorted_anns:
        m = ann['segmentation']
        color_mask = np.concatenate([np.random.random(3), [0.35]])
        img[m] = color_mask
    ax.imshow(img)

sam_checkpoint = "./checkpoints/sam_vit_h_4b8939.pth"
model_type = "vit_h"

device = "cuda"

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

mask_generator = SamAutomaticMaskGenerator(sam)

img_path = '/data/qinl/code/segment-anything/notebooks/images/dog.jpg'
image = cv2.imread(img_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

masks = mask_generator.generate(image)

'''
Mask generation returns a list over masks, where each mask is a dictionary containing various data about the mask. These keys are:
* `segmentation` : the mask
* `area` : the area of the mask in pixels
* `bbox` : the boundary box of the mask in XYWH format
* `predicted_iou` : the model's own prediction for the quality of the mask
* `point_coords` : the sampled input point that generated this mask
* `stability_score` : an additional measure of mask quality
* `crop_box` : the crop of the image used to generate this mask in XYWH format
'''

print(len(masks))
print(masks[0].keys())

plt.figure(figsize=(20,20))
plt.imshow(image)
show_anns(masks)
plt.axis('off')
plt.show() 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60

3.输出

65
dict_keys(['segmentation', 'area', 'bbox', 'predicted_iou', 'point_coords', 'stability_score', 'crop_box'])
1
2

进阶测试

图片预处理部分

其他instruction，都是在这个基础上进行处理

import numpy as np
import torch
import matplotlib.pyplot as plt
import cv2

import sys
sys.path.append("..")
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)   
    
def show_box(box, ax):
    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))   


sam_checkpoint = "./checkpoints/sam_vit_h_4b8939.pth"
model_type = "vit_h"

device = "cuda"

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

predictor = SamPredictor(sam)

img_path = '/data/qinl/code/segment-anything/notebooks/images/truck.jpg'
image = cv2.imread(img_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# 预处理输入图片
predictor.set_image(image)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46

输入的instruction为point的情况

# 输入为point的情况
    input_point = np.array([[500, 375]])
    input_label = np.array([1])

    # 可以用来显示一下点的位置
    # plt.figure(figsize=(10,10))
    # plt.imshow(image)
    # show_points(input_point, input_label, plt.gca())
    # plt.axis('on')
    # plt.show()  

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

    print('masks.shape',masks.shape)  # (number_of_masks) x H x W

    # 输出3个mask,分别有不同的score
    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()  
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28

多点输入（都视为前景点）

# 输入为多个point的情况（前景点）
    input_point = np.array([[500, 375]])
    input_label = np.array([1])

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

    # additional points
    input_point = np.array([[500, 375], [1125, 625]])
    input_label = np.array([1, 1])

    mask_input = logits[np.argmax(scores), :, :]  # Choose the model's best mask

    masks, _, _ = predictor.predict(
        point_coords=input_point,
        point_labels=input_label,
        mask_input=mask_input[None, :, :],
        multimask_output=False,
    )
    
    print('masks.shape',masks.shape) # only 1 x H x W

    plt.figure(figsize=(10,10))
    plt.imshow(image)
    show_mask(masks, plt.gca())
    show_points(input_point, input_label, plt.gca())
    plt.axis('off')
    plt.show() 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31

多点输入（前景点加后景点）

决定这个点是前景点还是后景点的就是label，0就是背景的意思

修改标签，得到不一样的结果

    # input_point = np.array([[500, 375], [1125, 625]])
    # input_label = np.array([1, 1])

    input_point = np.array([[500, 375], [1125, 625]])
    input_label = np.array([1, 0])
1
2
3
4
5

使用box框具体物体

# 输入为additional points
    input_box = np.array([425, 600, 700, 875])
    masks, _, _ = predictor.predict(
        point_coords=None,
        point_labels=None,
        box=input_box[None, :],
        multimask_output=False,
    )
    plt.figure(figsize=(10, 10))
    plt.imshow(image)
    show_mask(masks[0], plt.gca())
    show_box(input_box, plt.gca())
    plt.axis('off')
    plt.show()
1
2
3
4
5
6
7
8
9
10
11
12
13
14

结合points和box

    # 输入为point和box
    input_box = np.array([425, 600, 700, 875])
    input_point = np.array([[575, 750]])
    input_label = np.array([0])

    masks, _, _ = predictor.predict(
        point_coords=input_point,
        point_labels=input_label,
        box=input_box,
        multimask_output=False,
    )

    plt.figure(figsize=(10, 10))
    plt.imshow(image)
    show_mask(masks[0], plt.gca())
    show_box(input_box, plt.gca())
    show_points(input_point, input_label, plt.gca())
    plt.axis('off')
    plt.show()
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19

batch prompt inputs

    # batch prompt inputs
    input_boxes = torch.tensor([
        [75, 275, 1725, 850],
        [425, 600, 700, 875],
        [1375, 550, 1650, 800],
        [1240, 675, 1400, 750],
    ], device=predictor.device)

    transformed_boxes = predictor.transform.apply_boxes_torch(input_boxes, image.shape[:2])
    masks, _, _ = predictor.predict_torch(
        point_coords=None,
        point_labels=None,
        boxes=transformed_boxes,
        multimask_output=False,
    )

    print(masks.shape)  # (batch_size) x (num_predicted_masks_per_input) x H x W

    plt.figure(figsize=(10, 10))
    plt.imshow(image)
    for mask in masks:
        show_mask(mask.cpu().numpy(), plt.gca(), random_color=True)
    for box in input_boxes:
        show_box(box.cpu().numpy(), plt.gca())
    plt.axis('off')
    plt.show()
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26

End-to-end batched inference

    ## End-to-end batched inference
    image1 = image  # truck.jpg from above
    image1_boxes = torch.tensor([
        [75, 275, 1725, 850],
        [425, 600, 700, 875],
        [1375, 550, 1650, 800],
        [1240, 675, 1400, 750],
    ], device=sam.device)

    image2 = cv2.imread('./notebooks/images/groceries.jpg')
    image2 = cv2.cvtColor(image2, cv2.COLOR_BGR2RGB)
    image2_boxes = torch.tensor([
        [450, 170, 520, 350],
        [350, 190, 450, 350],
        [500, 170, 580, 350],
        [580, 170, 640, 350],
    ], device=sam.device)

    # Both images and prompts are input as PyTorch tensors that are already transformed to the correct frame. 
    # Inputs are packaged as a list over images, which each element is a dict that takes the following keys:
    # * `image`: The input image as a PyTorch tensor in CHW format.
    # * `original_size`: The size of the image before transforming for input to SAM, in (H, W) format.
    # * `point_coords`: Batched coordinates of point prompts.
    # * `point_labels`: Batched labels of point prompts.
    # * `boxes`: Batched input boxes.
    # * `mask_inputs`: Batched input masks.

    from segment_anything.utils.transforms import ResizeLongestSide
    resize_transform = ResizeLongestSide(sam.image_encoder.img_size)

    def prepare_image(image, transform, device):
        image = transform.apply_image(image)
        image = torch.as_tensor(image, device=device.device) 
        return image.permute(2, 0, 1).contiguous()
    
    batched_input = [
        {
            'image': prepare_image(image1, resize_transform, sam),
            'boxes': resize_transform.apply_boxes_torch(image1_boxes, image1.shape[:2]),
            'original_size': image1.shape[:2]
        },
        {
            'image': prepare_image(image2, resize_transform, sam),
            'boxes': resize_transform.apply_boxes_torch(image2_boxes, image2.shape[:2]),
            'original_size': image2.shape[:2]
        }
    ]

    batched_output = sam(batched_input, multimask_output=False)

    # The output is a list over results for each input image, where list elements are dictionaries with the following keys:
    # * `masks`: A batched torch tensor of predicted binary masks, the size of the original image.
    # * `iou_predictions`: The model's prediction of the quality for each mask.
    # * `low_res_logits`: Low res logits for each mask, which can be passed back to the model as mask input on a later iteration.

    print('batched_output[0].keys()',batched_output[0].keys())

    fig, ax = plt.subplots(1, 2, figsize=(20, 20))

    ax[0].imshow(image1)
    for mask in batched_output[0]['masks']:
        show_mask(mask.cpu().numpy(), ax[0], random_color=True)
    for box in image1_boxes:
        show_box(box.cpu().numpy(), ax[0])
    ax[0].axis('off')

    ax[1].imshow(image2)
    for mask in batched_output[1]['masks']:
        show_mask(mask.cpu().numpy(), ax[1], random_color=True)
    for box in image2_boxes:
        show_box(box.cpu().numpy(), ax[1])
    ax[1].axis('off')

    plt.tight_layout()
    plt.show()
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75

高阶测试

模型训练（waiting）
https://github.com/bnsreenu/python_for_microscopists/blob/master/331_fine_tune_SAM_mito.ipynb