scipy.ndimage.binary_dilation(input, structure=None, iterations=1, mask=None, output=None, border_value=0, origin=0, brute_force=False) 使用示例 fromscipyimportndimageimportnumpyasnpb=np.ones([3,3])c=np.ones(2)print('b = ',b)a=np.zeros((5,5))a[2,2]=1print("a = ",a) ndimag...
from scipy.ndimage import morphology def dilation_demo(a, structure=None): b = morphology.binary_dilation(a, structure) img = expand_image(a, 255) return expand_image(np.logical_xor(a,b), 150, out=img) pl.imread("scipy_morphology_demo.png")[:,:,0].astype(np.uint8) img1= expand_...
importscipy.ndimageasspndiimportnumpyasnp image = np.random.rand(100,100) filtered_image = spndi.gaussian_filter(image, sigma =1) 这里gaussian_filter函数的第二个参数sigma表示高斯滤波器的标准差,它可以平滑图像,去除噪声。 形态学操作: 例如,binary_erosion和binary_dilation函数用于二值图像的腐蚀和膨胀...
图像处理(Image Processing) fromscipyimportndimage# 创建一个二维数组x = np.zeros((200,200)) x[60:-60,60:-60] =1# 创建一个结构元素selem = ndimage.generate_binary_structure(2,2)# 进行膨胀操作x_dilated = ndimage.binary_dilation(x, structure=selem)importmatplotlib.pyplotasplt plt.imshow(x,...
[ 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0.]]) In [140]: ndimage.binary_dilation(a).astype(a.dtype) Out[140]: array([[ 0., 0., 0., 0., 0.], [ 0., 0., 1., 0., 0.], [ 0., 1., 1., 1., 0.], [ 0., 0., 1., 0., 0.], [ 0., 0...
#%fig=四连通和八连通的膨胀运算 from scipy.ndimageimportmorphology defdilation_demo(a,structure=None):b=morphology.binary_dilation(a,structure)img=expand_image(a,255)returnexpand_image(np.logical_xor(a,b),150,out=img)a=pl.imread("scipy_morphology_demo.png")[:,:,0].astype(np.uint8)img1...
In [140]: ndimage.binary_dilation(a).astype(a.dtype) Out[140]: array([[ 0., 0., 0., 0., 0.], [ 0., 0., 1., 0., 0.], [ 0., 1., 1., 1., 0.], [ 0., 0., 1., 0., 0.], [ 0., 0., 0., 0., 0.]]) ...
>>> ndimage.binary_dilation(a).astype(a.dtype)array([[0.,0.,0.,0.,0.], [0.,0.,1.,0.,0.], [0.,1.,1.,1.,0.], [0.,0.,1.,0.,0.], [0.,0.,0.,0.,0.]]) 对灰度值图像也有效: >>> np.random.seed(2) ...
, 0.]]) In [140]: ndimage.binary_dilation(a).astype(a.dtype) Out[140]: array([[ 0., 0., 0., 0., 0.], [ 0., 0., 1., 0., 0.], [ 0., 1., 1., 1., 0.], [ 0., 0., 1., 0., 0.], [ 0., 0., 0., 0., 0.]]) ...
#%fig=四连通和八连通的膨胀运算 from scipy.ndimage import morphology def dilation_demo(a, structure=None): b = morphology.binary_dilation(a, structure) img = expand_image(a, 255) return expand_image(np.logical_xor(a,b), 150, out=img) a = pl.imread("scipy_morphology_demo.png")[:,:...