用魔法打败魔法！用AI制作AI分割数据集！

news2024/11/14 14:01:22

本节内容我们使用SAM将边界框转换为分割数据集，这对于实例分割数据集的制作非常有用，下面我会一步步给出我的代码，希望对你有用。

有兴趣的朋友可以研究一下这本书，详细的介绍了数据集制作到分割的实际项目应用！

步骤 1. 安装与设置

import torch          
import torchvision          
print("PyTorch version:", torch.__version__)          
print("Torchvision version:", torchvision.__version__)          
print("CUDA is available:", torch.cuda.is_available())          
import sys          
!{sys.executable} -m pip install opencv-python matplotlib          
!{sys.executable} -m pip install 'git+https://github.com/facebookresearch/segment-anything.git'          
         
!mkdir images          
!wget -P images https://raw.githubusercontent.com/facebookresearch/segment-anything/main/notebooks/images/truck.jpg          
!wget -P images https://raw.githubusercontent.com/facebookresearch/segment-anything/main/notebooks/images/groceries.jpg          
         
!wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth


import numpy as np          
import torch          
import matplotlib.pyplot as plt          
import cv2          
         
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))           
         
         
import sys          
sys.path.append("..")          
from segment_anything import sam_model_registry, SamPredictor          
         
sam_checkpoint = "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)


image = cv2.imread('/notebooks/images/000027.jpg')          
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

图像分割

步骤二、获取mask

通过将 yolo 格式转换为 SAM 期望的边界框格式，我们得到了这些掩码的边界框。这是这张图片的边界框。对yolo熟悉的朋友对这个应该非常熟悉！

bounding_boxes = [[209, 532, 262, 626], [256, 444, 300, 531], [213, 258, 362, 401], [200, 96, 376, 623], [247, 172, 371, 265], [397, 409, 496, 484], [184, 261, 253, 327]]


用于将这些框输入到 SAM 预测器中。我们将它们转换为张量。


input_boxes = torch.tensor(bounding_boxes, device=predictor.device)


这样做之后提取新的mask


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,          
)          
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()