也许你可以尝试使用连通分量来分析每个模式的结构。像这样：

from skimage import measure, filters
import numpy as np

# segments by connected components
segmentation = measure.label(closing)

# finds areas of each connected component, omits background
areas = np.bincount(segmentation.ravel())[1:]
# finds threhsold to remove small components
t = filters.threshold_otsu(areas)
# finds connected component indexes, which areas greater than the threshold
indexes = np.where(areas > t)[0] + 1
# filters out small components
dominant_patterns = np.isin(segmentation, indexes) 

# this is applicable if the image well centered
# forehead center is approximately positioned at image center
# flip image by columns
dominant_patterns_flipped = np.flip(dominant_patterns, axis=1)
# finds intersection with flipped image
omega_like = dominant_patterns * dominant_patterns_flipped

注意：如果图像居中，即前额位于图像的中心，则适用此方法，并且假设存在垂直对称。

这将给你以下图像：

现在我们可以按行和列对图像进行分析，以计算每行和每列的像素出现次数，使用以下函数：

import numpy as np

def row_col_profiles(image):
    """
    Returns pixels occurances per row and column
    """
    rows, cols = np.indices((image.shape))
    
    row_lengths = np.bincount(rows.ravel())
    number_of_pixels_on_each_row = np.bincount(rows.ravel(), image.ravel())
    row_profile = number_of_pixels_on_each_row / row_lengths

    col_lengths = np.bincount(cols.ravel())
    number_of_pixels_on_each_col = np.bincount(cols.ravel(), image.ravel())
    col_profile = number_of_pixels_on_each_col / col_lengths

    return row_profile, col_profile

row_profile, col_profile = row_col_profiles(omega_like)

您可以绘制如下轮廓图：

import matplotlib.pyplot as plt

fig, axes = plt.subplots(1,2, figsize=(10, 5))
axes[0].plot(row_profile)
axes[0].set_title("Horizontal Line Profile")
axes[0].set_xlabel("Row Index")
axes[0].set_ylabel("Bright Pixel Occurance")
axes[0].grid()
axes[1].plot(col_profile)
axes[1].set_title("Vertical Line Profile")
axes[1].set_xlabel("Column Index")
axes[1].set_ylabel("Bright Pixel Occurance")
axes[1].grid()

你会得到类似这样的结果：

为了检查我们是否有类似欧米茄的模式，我们可以从该配置文件中获取一些阈值，例如 0.2，并且检查至少我们在垂直配置文件中是否有 2 个相对相同水平的峰值（我使用了最大值的 -10％）。

is_omega_like = row_profile.max()>=0.2 and col_profile.max()>=0.2 and len(np.where(col_profile>col_profile.max()*0.9)[0])>=2

您还可以尝试测量一些属性，并在连通分量上找到一些合理的阈值。请查看 文档 .