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【Meta-AI】Sam-分割一切 测试

【什么是 SAM】

近日,Meta AI在官网发布了基础模型 Segment Anything Model(SAM)并开源,其本质是用GPT的方式(基于Transform 模型架构)让计算机具备理解了图像里面的一个个“对象”的通用能力。SAM模型建立了一个可以接受文本提示、基于海量数据(603138)训练而获得泛化能力的图像分割大模型。图像分割是计算机视觉中的一项重要任务,有助于识别和确认图像中的不同物体,把它们从背景中分离出来,这在自动驾驶(检测其他汽车、行人和障碍物)、医学成像(提取特定结构或潜在病灶)等应用中特别重要。

官网:
Segment Anything | Meta AI
github:
GitHub - facebookresearch/segment-anything: The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
官方论文:
https://arxiv.org/abs/2304.02643

【环境搭建】

首先将源码下载到pytorch环境中:
GitHub - facebookresearch/segment-anything: The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
安装依赖库:
pip install opencv-python pycocotools matplotlib onnxruntime onnx
安装SAM
cd segment-anything
pip install -e .

下载权重文件:
下载三个权重文件中的一个,我用的第一个,三个模型从大到小,8G以下显存选vit_b。
default or vit_h: ViT-H SAM model.
vit_l: ViT-L SAM model.
vit_b: ViT-B SAM model.

【推理测试】

源码的 notebooks下面提供了测试代码和图片:

automatic_mask_generator_example.ipynb : 自动识别图片所有mask
predictor_example.ipynb :手动选取范围进行识别mask
onnx_model_example.ipynb : onnx格式模型工具

下面测试使用的 py 代码:
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple jupyter
jupyter nbconvert --to script predictor_example.ipynb
jupyter nbconvert --to script automatic_mask_generator_example.ipynb

测试代码中 matplotlib 库需要使用3.6以下的低版本这里选择3.5.3:

automatic_mask_generator_example(识别所有区域):

区别主要在于引入的Sam预测器:

from segment_anything import sam_model_registry, SamPredictor
from segment_anything import sam_model_registry, SamAutomaticMaskGenerator

SamPredictor => 需要传入一个抠图点坐标,也就是 input_point,会扣出包含抠图点的mask以及可能的父mask。
masks, scores, logits = predictor.predict(
point_coords=input_point,
point_labels=input_label,
multimask_output=True,
)

代码如下:

import cv2
import matplotlib.pyplot as plt
import numpy as np
from segment_anything import sam_model_registry, SamPredictor
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)
if __name__ == '__main__':
    # 配置,vit_h、vit_l、vit_b 从大到小,8G显存选 vit_b
    sam_checkpoint = "C:\\workspace\\pycharm_workspace\\pytorch\\src\\segment-anything\\sam_vit_b_01ec64.pth"
    # vit_h(default)、vit_l、vit_b
    model_type = "vit_b"
    # 模型实例化
    sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
    sam.to(device="cuda")
    predictor = SamPredictor(sam)
    image = cv2.imread(r"C:\\workspace\\pycharm_workspace\\pytorch\\src\\segment-anything\\notebooks\\images\\truck.jpg")
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    predictor.set_image(image)
    input_point = np.array([[500, 375]])
    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()
    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()

SamAutomaticMaskGenerator => 直接生成所有可能的mask
masks = mask_generator.generate(image)

代码如下:

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

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)
    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)))

if __name__ == '__main__':
    sam_checkpoint = "C:\\workspace\\pycharm_workspace\\pytorch\\src\\segment-anything\\sam_vit_b_01ec64.pth"
    model_type = "vit_b"
    device = "cuda"
    sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
    sam.to(device=device)
    mask_generator = SamAutomaticMaskGenerator(sam)
    image = cv2.imread('images/dog.jpg')
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    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()

【模型导出onnx】

提供了一个onnx转换的脚本:
jupyter nbconvert --to script onnx_model_example.ipynb
同样修改一下权重类型和文件即可:
checkpoint = "C:\workspace\pycharm_workspace\pytorch\src\segment-anything\sam_vit_b_01ec64.pth"
model_type = "vit_b"

会生成两个onnx文件,quantized是量化过后的权重:

模型参数:

【onnx部署】java

下面是进行 java-onnx 部署的代码,见另外一篇文章:

http://t.csdn.cn/A07aE


本文转载自: https://blog.csdn.net/qq_34448345/article/details/130339999
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