如何检查一个二维NumPy数组内部是否包含特定的值模式？

如何检查一个二维NumPy数组内部是否包含特定的值模式？

方法1

从这一方法导出

asolution

到

Implement Matlab'sim2col 'sliding' inpython

这是设计来

rearrange slidingblocks from a 2D array intocolumns

。因此，为了解决这里的问题，

field_array

可以将来自的那些滑块堆积为列，并与的列向量版本进行比较

match_array

。

这是重新排列/堆叠功能的正式定义-

def im2col(A,BLKSZ):    # Parameters    M,N = A.shape    col_extent = N - BLKSZ[1] + 1    row_extent = M - BLKSZ[0] + 1    # Get Starting block indices    start_idx = np.arange(BLKSZ[0])[:,None]*N + np.arange(BLKSZ[1])    # Get offsetted indices across the height and width of input array    offset_idx = np.arange(row_extent)[:,None]*N + np.arange(col_extent)    # Get all actual indices & index into input array for final output    return np.take (A,start_idx.ravel()[:,None] + offset_idx.ravel())

为了解决我们的问题，这是基于

im2col

-

# Get sliding blocks of shape same as match_array from field_array into columns# Then, compare them with a column vector version of match array.col_match = im2col(field_array,match_array.shape) == match_array.ravel()[:,None]# Shape of output array that has field_array compared against a sliding match_arrayout_shape = np.asarray(field_array.shape) - np.asarray(match_array.shape) + 1# Now, see if all elements in a column are onES and reshape to out_shape. # Finally, find the position of TRUE indicesR,C = np.where(col_match.all(0).reshape(out_shape))

问题中给定样本的输出为-

In [151]: R,COut[151]: (array([6]), array([3]))

方法＃2

鉴于opencv已经具有执行差异平方的模板匹配功能，您可以使用它并寻找零差异，这将是您的匹配位置。因此，如果您可以访问cv2（opencv模块），则实现应如下所示-

import cv2from cv2 import matchTemplate as cv2mM = cv2m(field_array.astype('uint8'),match_array.astype('uint8'),cv2.TM_SQDIFF)R,C = np.where(M==0)

给我们-

In [204]: R,COut[204]: (array([6]), array([3]))

---

标杆管理

本节将比较建议解决该问题的所有方法的运行时。本节中列出的各种方法均归功于其贡献者。

方法定义-

def seek_array(search_in, search_for, return_coords = False):    si_x, si_y = search_in.shape    sf_x, sf_y = search_for.shape    for y in xrange(si_y-sf_y+1):        for x in xrange(si_x-sf_x+1): if numpy.array_equal(search_for, search_in[x:x+sf_x, y:y+sf_y]):     return (x,y) if return_coords else True    return None if return_coords else Falsedef skimage_based(field_array,match_array):    windows = view_as_windows(field_array, match_array.shape)    return (windows == match_array).all(axis=(2,3)).nonzero()def im2col_based(field_array,match_array):       col_match = im2col(field_array,match_array.shape)==match_array.ravel()[:,None]    out_shape = np.asarray(field_array.shape) - np.asarray(match_array.shape) + 1      return np.where(col_match.all(0).reshape(out_shape))def cv2_based(field_array,match_array):    M = cv2m(field_array.astype('uint8'),match_array.astype('uint8'),cv2.TM_SQDIFF)    return np.where(M==0)

运行时测试-

案例1（问题中的样本数据）：

In [11]: field_arrayOut[11]: array([[ 24,  25,  26,  27,  28,  29,  30,  31,  23],       [ 33,  34,  35,  36,  37,  38,  39,  40,  32],       [-39, -38, -37, -36, -35, -34, -33, -32, -40],       [-30, -29, -28, -27, -26, -25, -24, -23, -31],       [-21, -20, -19, -18, -17, -16, -15, -14, -22],       [-12, -11, -10,  -9,  -8,  -7,  -6,  -5, -13],       [ -3,  -2,  -1,   0,   1,   2,   3,   4,  -4],       [  6,   7,   8,   4,   5,   6,   7,  13,   5],       [ 15,  16,  17,   8,   9,  10,  11,  22,  14]])In [12]: match_arrayOut[12]: array([[ 0,  1,  2,  3],       [ 4,  5,  6,  7],       [ 8,  9, 10, 11]])In [13]: %timeit seek_array(field_array, match_array, return_coords = False)1000 loops, best of 3: 465 µs per loopIn [14]: %timeit skimage_based(field_array,match_array)10000 loops, best of 3: 97.9 µs per loopIn [15]: %timeit im2col_based(field_array,match_array)10000 loops, best of 3: 74.3 µs per loopIn [16]: %timeit cv2_based(field_array,match_array)10000 loops, best of 3: 30 µs per loop

案例2（更大的随机数据）：

In [17]: field_array = np.random.randint(0,4,(256,256))In [18]: match_array = field_array[100:116,100:116].copy()In [19]: %timeit seek_array(field_array, match_array, return_coords = False)1 loops, best of 3: 400 ms per loopIn [20]: %timeit skimage_based(field_array,match_array)10 loops, best of 3: 54.3 ms per loopIn [21]: %timeit im2col_based(field_array,match_array)10 loops, best of 3: 125 ms per loopIn [22]: %timeit cv2_based(field_array,match_array)100 loops, best of 3: 4.08 ms per loop