前言
Segment Anything Model（SAM）是一种先进的图像分割模型，它基于Facebook AI在2020年发布的Foundation Model3，能够根据简单的输入提示（如点或框）准确地分割图像中的任何对象，并且无需额外训练就能适应不熟悉的对象和图像4。它利用了传统的计算机视觉技术和深度学习算法，在一个涵盖1100万张图片和11亿个掩码的庞大数据集上进行了训练，展现出了卓越的零样本性能。

1: 环境及软件
win10
Miniconda3 (自行安装，网上一堆教程,这里不介绍了)
GPU （rtx 3060TI 8G）

2：安装
官方说明

1> 下载segment-anything
下载地址: https://github.com/facebookresearch/segment-anything
解压随便放个目录下
这里放在 F:\gameai\segment-anything

2>模型数据下载地址
模型文件下载放到segment-anything 目录下就行
default or vit_h:
https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth

vit_l:
https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth

vit_b:
https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth

3>创建conda 环境
<1>点击 Anaconda Prompt(Miniconda3) 打开conda命令界面

<2> 创建虚拟环境 (环境名 segment-anything)
conda create -n segment-anything python=3.10 #创建环境并安装python3.10
conda activate segment-anything #进入环境

【1】 进入 https://pytorch.org/get-started/locally/ 根据自己的配置 生成 运行命令
本地用的是 cuda11.7
查看本地cuda版本
cmd
nvidia-smi

在segment-anything 环境下 执行
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117

测试Pytorch 是否开启了GPU
显示True 表示OK
退出 python 命令 exit()

 cd segment-anything
 pip install -e . 
 pip install opencv-python pycocotools matplotlib onnxruntime onnx

创新2个目录input output
模型也放这里

input 里随便放一章图片（这里先放入segment-anything\assets\notebook2.png）

**3：测试**

```bash
python scripts/amg.py --checkpoint sam_vit_h_4b8939.pth --model-type vit_h --input F:\gameai\segment-anything\input --output F:\gameai\segment-anything\output

网上找的一个脚本（具体地址忘了，别人写的） 点击选择区域
test.py 内容如下

import cv2
import os
import numpy as np
from segment_anything import sam_model_registry, SamPredictor

input_dir = 'input'
output_dir = 'output'
crop_mode=True#是否裁剪到最小范围
#alpha_channel是否保留透明通道
print('最好是每加一个点就按w键predict一次')
os.makedirs(output_dir, exist_ok=True)
image_files = [f for f in os.listdir(input_dir) if f.lower().endswith(('.png', '.jpg', '.jpeg','.JPG','.JPEG','.PNG'))]

sam = sam_model_registry["vit_b"](checkpoint="sam_vit_b_01ec64.pth")
_ = sam.to(device="cuda")#注释掉这一行，会用cpu运行，速度会慢很多
predictor = SamPredictor(sam)#SAM预测图像
def mouse_click(event, x, y, flags, param):#鼠标点击事件
    global input_point, input_label, input_stop#全局变量，输入点，
    if not input_stop:#判定标志是否停止输入响应了！
        if event == cv2.EVENT_LBUTTONDOWN :#鼠标左键
            input_point.append([x, y])
            input_label.append(1)#1表示前景点
        elif event == cv2.EVENT_RBUTTONDOWN :#鼠标右键
            input_point.append([x, y])
            input_label.append(0)#0表示背景点
    else:
        if event == cv2.EVENT_LBUTTONDOWN or event == cv2.EVENT_RBUTTONDOWN :#提示添加不了
            print('此时不能添加点,按w退出mask选择模式')


def apply_mask(image, mask, alpha_channel=True):#应用并且响应mask
    if alpha_channel:
        alpha = np.zeros_like(image[..., 0])#制作掩体
        alpha[mask == 1] = 255#兴趣地方标记为1，且为白色
        image = cv2.merge((image[..., 0], image[..., 1], image[..., 2], alpha))#融合图像
    else:
        image = np.where(mask[..., None] == 1, image, 0)
    return image

def apply_color_mask(image, mask, color, color_dark = 0.5):#对掩体进行赋予颜色
    for c in range(3):
        image[:, :, c] = np.where(mask == 1, image[:, :, c] * (1 - color_dark) + color_dark * color[c], image[:, :, c])
    return image

def get_next_filename(base_path, filename):#进行下一个图像
    name, ext = os.path.splitext(filename)
    for i in range(1, 101):
        new_name = f"{name}_{i}{ext}"
        if not os.path.exists(os.path.join(base_path, new_name)):
            return new_name
    return None

def save_masked_image(image, mask, output_dir, filename, crop_mode_):#保存掩盖部分的图像（感兴趣的图像）
    if crop_mode_:
        y, x = np.where(mask)
        y_min, y_max, x_min, x_max = y.min(), y.max(), x.min(), x.max()
        cropped_mask = mask[y_min:y_max+1, x_min:x_max+1]
        cropped_image = image[y_min:y_max+1, x_min:x_max+1]
        masked_image = apply_mask(cropped_image, cropped_mask)
    else:
        masked_image = apply_mask(image, mask)
    filename = filename[:filename.rfind('.')]+'.png'
    new_filename = get_next_filename(output_dir, filename)
    
    if new_filename:
        if masked_image.shape[-1] == 4:
            cv2.imwrite(os.path.join(output_dir, new_filename), masked_image, [cv2.IMWRITE_PNG_COMPRESSION, 9])
        else:
            cv2.imwrite(os.path.join(output_dir, new_filename), masked_image)
        print(f"Saved as {new_filename}")
    else:
        print("Could not save the image. Too many variations exist.")

current_index = 0

cv2.namedWindow("image")
cv2.setMouseCallback("image", mouse_click)
input_point = []
input_label = []
input_stop=False
while True:
    filename = image_files[current_index]
    image_orign = cv2.imread(os.path.join(input_dir, filename))
    image_crop = image_orign.copy()#原图裁剪
    image = cv2.cvtColor(image_orign.copy(), cv2.COLOR_BGR2RGB)#原图色彩转变
    selected_mask = None
    logit_input= None
    while True:
        #print(input_point)
        input_stop=False
        image_display = image_orign.copy()
        display_info = f'{filename} | Press s to save | Press w to predict | Press d to next image | Press a to previous image | Press space to clear | Press q to remove last point '
        cv2.putText(image_display, display_info, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2, cv2.LINE_AA)
        for point, label in zip(input_point, input_label):#输入点和输入类型
            color = (0, 255, 0) if label == 1 else (0, 0, 255)
            cv2.circle(image_display, tuple(point), 5, color, -1)
        if selected_mask is not None :
            color = tuple(np.random.randint(0, 256, 3).tolist())
            selected_image = apply_color_mask(image_display,selected_mask, color)

        cv2.imshow("image", image_display)
        key = cv2.waitKey(1)

        if key == ord(" "):
            input_point = []
            input_label = []
            selected_mask = None
            logit_input= None
        elif key == ord("w"):
            input_stop=True
            if len(input_point) > 0 and len(input_label) > 0:
                #todo 预测图像
                predictor.set_image(image)#设置输入图像
                input_point_np = np.array(input_point)#输入暗示点,需要转变array类型才可以输入
                input_label_np = np.array(input_label)#输入暗示点的类型
                #todo 输入暗示信息，将返回masks
                masks, scores, logits= predictor.predict(
                    point_coords=input_point_np,
                    point_labels=input_label_np,
                    mask_input=logit_input[None, :, :] if logit_input is not None else None,
                    multimask_output=True,
                )

                mask_idx=0
                num_masks = len(masks)#masks的数量
                while(1):
                    color = tuple(np.random.randint(0, 256, 3).tolist())#随机列表颜色，就是
                    image_select = image_orign.copy()
                    selected_mask=masks[mask_idx]#选择msks也就是,a,d切换
                    selected_image = apply_color_mask(image_select,selected_mask, color)
                    mask_info = f'Total: {num_masks} | Current: {mask_idx} | Score: {scores[mask_idx]:.2f} | Press w to confirm | Press d to next mask | Press a to previous mask | Press q to remove last point | Press s to save'
                    cv2.putText(selected_image, mask_info, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2, cv2.LINE_AA)
                    #todo 显示在当前的图片,
                    cv2.imshow("image", selected_image)

                    key=cv2.waitKey(10)
                    if key == ord('q') and len(input_point)>0:
                        input_point.pop(-1)
                        input_label.pop(-1)
                    elif key == ord('s'):
                        save_masked_image(image_crop, selected_mask, output_dir, filename, crop_mode_=crop_mode)
                    elif key == ord('a') :
                        if mask_idx>0:
                            mask_idx-=1
                        else:
                            mask_idx=num_masks-1
                    elif key == ord('d') :
                        if mask_idx<num_masks-1:
                            mask_idx+=1
                        else:
                            mask_idx=0
                    elif key == ord('w') :
                        break
                    elif key == ord(" "):
                        input_point = []
                        input_label = []
                        selected_mask = None
                        logit_input= None
                        break
                logit_input=logits[mask_idx, :, :]
                print('max score:',np.argmax(scores),' select:',mask_idx)

        elif key == ord('a'):
            current_index = max(0, current_index - 1)
            input_point = []
            input_label = []
            break
        elif key == ord('d'):
            current_index = min(len(image_files) - 1, current_index + 1)
            input_point = []
            input_label = []
            break
        elif key == 27:
            break
        elif key == ord('q') and len(input_point)>0:
            input_point.pop(-1)
            input_label.pop(-1)
        elif key == ord('s') and selected_mask is not None :
            save_masked_image(image_crop, selected_mask, output_dir, filename, crop_mode_=crop_mode)

    if key == 27:
        break

运行结果如下

操作
先按 w
鼠标左键 选择点 ，可以多点击下
鼠标右键 选择背景
根据上面提示 自行选择
轮廓闪时，按w确认/按d下个轮廓/
Score 分数越大越好
确认后 按s 保存轮廓到 output 目录下（这里选择了狗,在狗头上点了一下）

还是比较精确的，就是不太圆滑，有点提升

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