安装

# 创建虚拟环境
conda create -n sam python=3.8
# 激活环境
conda activate sam
# 下载代码
git clone [email protected]:facebookresearch/segment-anything.git
# 安装
cd segment-anything; pip install -e .
# 常见库安装
pip install torch torchvision opencv-python pycocotools matplotlib onnxruntime onnx

下载模型，放置models文件夹，本示例使用ViT-H，

使用

SAM输入为points, boxes, text 或mask

全图分割

输入图片‘onepiece.jpg’，

输出结果如下图，

代码：

# coding=utf-8
import numpy as np
import matplotlib.pyplot as plt
import cv2
from pathlib import Path
from segment_anything import SamAutomaticMaskGenerator, sam_model_registry, 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)
    
def process_img(img_path):
    '''img_path to img(np.array)
    '''
    image = cv2.imread(img_path)
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    return image

def entire_img(img_path):
    '''whole img generate mask
    '''
    image = process_img(img_path)
    sam = sam_model_registry["vit_h"](checkpoint="./models/sam_vit_h_4b8939.pth")
    sam.to(device="cuda")
    mask_generator = SamAutomaticMaskGenerator(sam)
    masks = mask_generator.generate(image)
    plt.figure(figsize=(20,20))
    plt.imshow(image)
    show_anns(masks)
    plt.axis('off')
    plt.savefig(str(Path(img_path).name))
    
    # predictor = SamPredictor(sam)
def main():
    img_path = './notebooks/images/onepiece.jpg'
    entire_img(img_path)


if __name__ == "__main__":
    main()

点

选取绿色五角星位置[1064, 1205]

框

选取框坐标[1305, 244, 2143, 1466]

完整代码

完整代码如下，欢迎大家体验

# coding=utf-8
import numpy as np
import matplotlib.pyplot as plt
import cv2
from pathlib import Path
from segment_anything import SamAutomaticMaskGenerator, sam_model_registry, 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)
    
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)) 

def process_img(img_path):
    '''img_path to img(np.array)
    '''
    image = cv2.imread(img_path)
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    return image

def entire_img(img_path):
    '''whole img generate mask
    '''
    image = process_img(img_path)
    sam = sam_model_registry["vit_h"](checkpoint="./models/sam_vit_h_4b8939.pth")
    sam.to(device="cuda")
    mask_generator = SamAutomaticMaskGenerator(sam)
    masks = mask_generator.generate(image)
    plt.figure(figsize=(20,20))
    plt.imshow(image)
    show_anns(masks)
    plt.axis('off')
    plt.savefig(str(Path(img_path).name))

def predict(img_path, type='point'):
    image = process_img(img_path)
    sam = sam_model_registry["vit_h"](checkpoint="./models/sam_vit_h_4b8939.pth")
    sam.to(device="cuda")

    predictor = SamPredictor(sam)
    predictor.set_image(image)
    if type == 'point':
        # [X, Y]
        input_point = np.array([[1064, 1205]])
        input_label = np.array([1])
        masks, scores, logits = predictor.predict(
                point_coords=input_point,
                point_labels=input_label,
                multimask_output=True,
        )
    elif type == 'bbox':
        input_box = np.array([1305, 244, 2143, 1466])
        masks, scores, logits = predictor.predict(
            point_coords=None,
            point_labels=None,
            box=input_box[None, :],
            multimask_output=False,
        )


    index = np.argmax(scores)

    plt.figure(figsize=(10,10))
    plt.imshow(image)
    show_mask(masks[index], plt.gca())
    if type == 'point':
        show_points(input_point, input_label, plt.gca())
    elif type == 'bbox':
        show_box(input_box, plt.gca())
    plt.title(f"Score: {
      
      scores[index]:.3f}", fontsize=18)
    plt.savefig(str(Path(img_path).stem)+f'{
      
      scores[index]:.3f}.png')


    # predictor = SamPredictor(sam)
def main():
    img_path = './notebooks/images/onepiece.jpg'
    # entire_img(img_path)
    predict(img_path, type='bbox')
    # predict(img_path)


if __name__ == "__main__":
    main()