NumPy数组的阈值像素索引

要仅检测红色通道，您可以执行以下操作-

np.argwhere(image[:,:,0] > threshold)

解释 ：

1. 与进行比较red-channel，threshold以得到与输入图像形状相同的布尔数组，但没有第三个轴（颜色通道）。
2. 使用np.argwhere获得成功匹配的索引。

对于要查看是否有任何通道超出某个阈值的情况，请使用.any(-1)（满足最后一个轴/颜色通道条件的任何元素）。

np.argwhere((image > threshold).any(-1))

样品运行

输入图片：

In [76]: image
Out[76]: 
array([[[118,  94, 109],
        [ 36, 122,   6],
        [ 85,  91,  58],
        [ 30,   2,  23]],

       [[ 32,  47,  50],
        [  1, 105, 141],
        [ 91, 120,  58],
        [129, 127, 111]]], dtype=uint8)

In [77]: threshold
Out[77]: 100

情况1：仅红色通道

In [69]: np.argwhere(image[:,:,0] > threshold)
Out[69]: 
array([[0, 0],
       [1, 3]])

In [70]: image[0,0]
Out[70]: array([118,  94, 109], dtype=uint8)

In [71]: image[1,3]
Out[71]: array([129, 127, 111], dtype=uint8)

案例2：任意渠道

In [72]: np.argwhere((image > threshold).any(-1))
Out[72]: 
array([[0, 0],
       [0, 1],
       [1, 1],
       [1, 2],
       [1, 3]])

In [73]: image[0,1]
Out[73]: array([ 36, 122,   6], dtype=uint8)

In [74]: image[1,1]
Out[74]: array([  1, 105, 141], dtype=uint8)

In [75]: image[1,2]
Out[75]: array([ 91, 120,  58], dtype=uint8)

替代np.anyin的更快np.einsum

np.einsum可能会被骗去执行np.any工作，事实证明它快了一点。

因此，boolean_arr.any(-1)将等同于np.einsum('ijk->ij',boolean_arr)。

以下是跨各种数据大小的关联运行时-

In [105]: image = np.random.randint(0,255,(30,30,3)).astype('uint8')
     ...: %timeit np.argwhere((image > threshold).any(-1))
     ...: %timeit np.argwhere(np.einsum('ijk->ij',image>threshold))
     ...: out1 = np.argwhere((image > threshold).any(-1))
     ...: out2 = np.argwhere(np.einsum('ijk->ij',image>threshold))
     ...: print np.allclose(out1,out2)
     ...: 
10000 loops, best of 3: 79.2 µs per loop
10000 loops, best of 3: 56.5 µs per loop
True

In [106]: image = np.random.randint(0,255,(300,300,3)).astype('uint8')
     ...: %timeit np.argwhere((image > threshold).any(-1))
     ...: %timeit np.argwhere(np.einsum('ijk->ij',image>threshold))
     ...: out1 = np.argwhere((image > threshold).any(-1))
     ...: out2 = np.argwhere(np.einsum('ijk->ij',image>threshold))
     ...: print np.allclose(out1,out2)
     ...: 
100 loops, best of 3: 5.47 ms per loop
100 loops, best of 3: 3.69 ms per loop
True

In [107]: image = np.random.randint(0,255,(3000,3000,3)).astype('uint8')
     ...: %timeit np.argwhere((image > threshold).any(-1))
     ...: %timeit np.argwhere(np.einsum('ijk->ij',image>threshold))
     ...: out1 = np.argwhere((image > threshold).any(-1))
     ...: out2 = np.argwhere(np.einsum('ijk->ij',image>threshold))
     ...: print np.allclose(out1,out2)
     ...: 
1 loops, best of 3: 833 ms per loop
1 loops, best of 3: 640 ms per loop
True