用Python实现文献J.-H. Syu, S.-J. Wang, and L.-C. Wang, “Hierarchical image segmentation based on iterative contraction and merging,” IEEE Transactions on Image Processing, vol. 26, no. 5, pp. 2246–2260, May 2017.的迭代收缩与合并算法
时间: 2024-01-08 08:02:55 浏览: 33
迭代收缩与合并算法是一种经典的图像分割算法,下面是用Python实现这篇论文中提出的算法的示例代码:
```python
import numpy as np
import cv2
def ICM(image, max_iter=10):
"""
迭代收缩与合并算法实现函数
:param image: 输入图像
:param max_iter: 最大迭代次数
:return: 分割结果
"""
h, w = image.shape[:2]
labels = np.arange(h*w).reshape(h, w) # 初始化标签矩阵
num_labels = h*w # 当前标签数量
iter_count = 0 # 迭代次数
while iter_count < max_iter and num_labels > 1:
print(f"Iteration {iter_count}")
iter_count += 1
for i in range(h):
for j in range(w):
if j < w-1 and labels[i][j] != labels[i][j+1]:
# 计算相邻像素间的权值
diff = np.abs(image[i][j] - image[i][j+1])
weight = np.exp(-diff)
# 合并连通域
l1, l2 = labels[i][j], labels[i][j+1]
if l1 < l2:
labels[labels == l2] = l1
else:
labels[labels == l1] = l2
if i < h-1 and j < w-1 and labels[i][j] != labels[i+1][j+1]:
# 计算相邻像素间的权值
diff = np.abs(image[i][j] - image[i+1][j+1])
weight = np.exp(-diff)
# 合并连通域
l1, l2 = labels[i][j], labels[i+1][j+1]
if l1 < l2:
labels[labels == l2] = l1
else:
labels[labels == l1] = l2
if i < h-1 and labels[i][j] != labels[i+1][j]:
# 计算相邻像素间的权值
diff = np.abs(image[i][j] - image[i+1][j])
weight = np.exp(-diff)
# 合并连通域
l1, l2 = labels[i][j], labels[i+1][j]
if l1 < l2:
labels[labels == l2] = l1
else:
labels[labels == l1] = l2
# 更新标签数量
num_labels = len(np.unique(labels))
return labels
# 测试代码
image = cv2.imread("test.jpg", cv2.IMREAD_GRAYSCALE)
labels = ICM(image)
cv2.imshow("image", image)
cv2.imshow("labels", labels.astype(np.uint8)*255//np.max(labels))
cv2.waitKey(0)
cv2.destroyAllWindows()
```
需要注意的是,为了方便显示,上述代码将标签矩阵转换为了灰度图像进行显示,因此在使用时需要根据实际需求进行修改。