这是我使用的矢量化实现NumPy http://www.numpy.org/:
在我的测试中,我使用了这五张图像:
相关部分:
import numpy as np
import scipy.ndimage
# Load five images:
ims = [scipy.ndimage.imread(str(i + 1) + '.png', flatten=True) for i in range(5)]
# Stack the reshaped images (rows) vertically:
ims = np.vstack([im.reshape(1,im.shape[0] * im.shape[1]) for im in ims])
# Compute the median column by column and reshape to the original shape:
median = np.median(ims, axis=0).reshape(100, 100)
完整的脚本:
import numpy as np
import scipy.ndimage
import matplotlib.pyplot as plt
ims = [scipy.ndimage.imread(str(i + 1) + '.png', flatten=True) for i in range(5)]
print ims[0].shape # (100, 100)
ims = np.vstack([im.reshape(1,im.shape[0] * im.shape[1]) for im in ims])
print ims.shape # (5, 10000)
median = np.median(ims, axis=0).reshape(100, 100)
fig = plt.figure(figsize=(100./109., 100./109.), dpi=109, frameon=False)
ax = fig.add_axes([0, 0, 1, 1])
ax.axis('off')
plt.imshow(median, cmap='Greys_r')
plt.show()
中位数(numpy.median
)五张图像的结果如下所示:
有趣的部分:平均值(numpy.mean
)结果如下所示:
好吧,科学遇见艺术。 :-)