The difference between stach and concatenate in numpy

The difference between np.stack and np.concatenate in numpy

Description: imgs is a list type. It stores a numpy array, shape = (1,h,w)

1.1, np.stack: stacking

Function: Add a new dimension before the first dimension, that is, CHW --> BCHW

• Case 1: imgs size is 1

im = np.stack(imgs)		# (1,h,w) --> (1,1,h,w)

• Case 2: imgs size is 10

im = np.stack(imgs)		# (1,h,w) --> (10,1,h,w)

1.2, np.concatenate: stitching

Function: On the basis of the original dimension, multiple numpy in imgs are dimensionally spliced.

• Case 1: imgs size is 1, axis=0

# axis=0表示在第一个维度上进行拼接操作
im = np.concatenate(imgs,axis=0)
im.shape = (1,h,w)

• Case 2: imgs size is 10, axis=0

# axis=0表示在第一个维度上进行拼接操作
im = np.concatenate(imgs,axis=0)
im.shape = (10,h,w)

• Case 3 : imgs size is 10, axis=1

# axis=1表示在第二个维度上进行拼接操作
im = np.concatenate(imgs,axis=1)
im.shape = (1,h*10,w)