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推荐整理分享【图像分割】Meta分割一切(SAM)模型环境配置和使用教程(图像分割最新算法),希望有所帮助,仅作参考,欢迎阅读内容。
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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-anything1 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 git@github.com:facebookresearch/segment-anything.gitcd 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-mainpip install -e .1.2 安装依赖库:pip install opencv-python pycocotools matplotlib onnxruntime onnxmatplotlib 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 cv2import matplotlib.pyplot as pltimport numpy as npfrom segment_anything import sam_model_registry, SamPredictordef 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()
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, SamPredictorimport cv2import matplotlib.pyplot as pltimport numpy as npdef 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
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