万物分割SAM使用教程
文章目录
- 安装
- 使用
-
- 全图分割
- 点
- 框
- 完整代码
原理篇
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()