SAM2：环境安装&代码调试

引子 时隔大半年，SAM 2代终于来了，之前写过一篇《Segment Anything（SAM）环境安装&代码调试》，感兴趣童鞋请移步https://blog.csdn.net/zzq1989_/article/details/135479818?spm=1001.2014.3001.5501，OK，让我们开始吧。 一、模型介绍 Meta 公司去年发布了 SAM 1 基础模型，已经可以在图像上分割对象。而最新发布的 SAM 2 可用于图片和视频，并可以实现实时、可提示的对象分割。SAM 2 在图像分割准确性方面超越了以往的能力，在视频分割性能方面优于现有成果，同时所需的交互时间减少了三倍。SAM 2 还可以分割任何视频或图像中的任何对象（通常称为零镜头泛化），这意味着它可以应用于以前未见过的视觉内容，而无需进行自定义调整。 二、环境搭建 1、模型下载 https://github.com/facebookresearch/segment-anything-2?tab=readme-ov-file 代码下载　 git clone https://github.com/facebookresearch/segment-anything-2.git 2、环境安装 docker pull pytorch/pytorch:2.3.1-cuda12.1-cudnn8-devel docker run -it --rm --gpus=all -v /datas/work/zzq:/workspace pytorch/pytorch:2.3.1-cuda12.1-cudnn8-devel bash cd /workspace/SAM2/segment-anything-2-main pip install -e .(PS:安装时间巨长，要有耐心) apt-get update && apt-get install libgl1 apt-get install libglib2.0-0 三、推理测试

import numpy as np
import torch
import matplotlib.pyplot as plt
from PIL import Image
import cv2

# use bfloat16 for the entire notebook
torch.autocast(device_type="cuda", dtype=torch.float16).__enter__()

if torch.cuda.get_device_properties(0).major >= 8:
    # turn on tfloat32 for Ampere GPUs (https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices)
    torch.backends.cuda.matmul.allow_tf32 = True
    torch.backends.cudnn.allow_tf32 = True

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 show_anns(anns, borders=True):
    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), [0.5]])
        img[m] = color_mask 
        if borders:
            import cv2
            contours, _ = cv2.findContours(m.astype(np.uint8),cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) 
            # Try to smooth contours
            contours = [cv2.approxPolyDP(contour, epsilon=0.01, closed=True) for contour in contours]
            cv2.drawContours(img, contours, -1, (0,0,1,0.4), thickness=1) 

    ax.imshow(img)

image = Image.open('images/cars.png')
image = np.array(image.convert("RGB"))
from sam2.build_sam import build_sam2
from sam2.automatic_mask_generator import SAM2AutomaticMaskGenerator
sam2_checkpoint = "models/sam2_hiera_large.pt"
model_cfg = "sam2_hiera_l.yaml"
sam2 = build_sam2(model_cfg, sam2_checkpoint, device ='cuda', apply_postprocessing=False)
mask_generator = SAM2AutomaticMaskGenerator(sam2)
masks = mask_generator.generate(image)

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

# plt.figure(figsize=(20,20))
# plt.imshow(image)
# show_anns(masks)
# plt.axis('off')
# plt.show() 
image_select = image.copy()
for i in range(len(masks)):
    color = tuple(np.random.randint(0, 256, 3).tolist())#随机列表颜色，就是
    selected_mask=masks[i]['segmentation']
    selected_image = apply_color_mask(image_select,selected_mask, color)
cv2.imwrite("res.jpg", selected_image)