I have a function color_image_padding that takes an RGB image and adds one layer of zeros padding to the borders. The image has dimensions (Width, Height, 3), with 3 representing the 3 color channels.
My code is:
import numpy as np
def color_image_padding(image: np.ndarray) -> np.ndarray:
return np.pad(image, pad_width=1)
I'm seeing this error:
"operands could not be broadcast together with shapes (4,4,3) (4,4,5)"
It's probably the color channels that are causing this error. Doesn't np.pad split the image into 3 matrices and add the zero padding accordingly?
Thanks in advance for your assistance!
EDIT
See comments below... It turns out that the generalized function image_padding() was throwing an error message because some greyscale images (i.e. 2D Numpy matrices) were passed in. Here's a minimal example:
bar = np.ones((1, 3))
bar.ndim
2
def image_padding(image: np.ndarray, amt: int) -> np.ndarray:
return np.pad(image, pad_width=((amt, amt), (amt, amt), (0, 0)))
image_padding(bar, 2)
Full Traceback:
ValueError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_8116/4065018867.py in <module>
----> 1 img(bar, 3)
~\AppData\Local\Temp/ipykernel_8116/1455868751.py in img(image, amt)
1 def img(image, amt):
----> 2 return np.pad(image, pad_width=((amt, amt), (amt, amt), (0, 0)))
<__array_function__ internals> in pad(*args, **kwargs)
~\anaconda3\lib\site-packages\numpy\lib\arraypad.py in pad(array, pad_width, mode, **kwargs)
741
742 # Broadcast to shape (array.ndim, 2)
--> 743 pad_width = _as_pairs(pad_width, array.ndim, as_index=True)
744
745 if callable(mode):
~\anaconda3\lib\site-packages\numpy\lib\arraypad.py in _as_pairs(x, ndim, as_index)
516 # Converting the array with `tolist` seems to improve performance
517 # when iterating and indexing the result (see usage in `pad`)
--> 518 return np.broadcast_to(x, (ndim, 2)).tolist()
519
520
<__array_function__ internals> in broadcast_to(*args, **kwargs)
~\anaconda3\lib\site-packages\numpy\lib\stride_tricks.py in broadcast_to(array, shape, subok)
409 [1, 2, 3]])
410 """
--> 411 return _broadcast_to(array, shape, subok=subok, readonly=True)
412
413
~\anaconda3\lib\site-packages\numpy\lib\stride_tricks.py in _broadcast_to(array, shape, subok, readonly)
346 'negative')
347 extras = []
--> 348 it = np.nditer(
349 (array,), flags=['multi_index', 'refs_ok', 'zerosize_ok'] extras,
350 op_flags=['readonly'], itershape=shape, order='C')
ValueError: operands could not be broadcast together with remapped shapes [original->remapped]: (3,2) and requested shape (2,2)
Testing whether the image is greyscale or color resolves the issue:
def image_padding(image: np.ndarray, amt: int) -> np.ndarray:
if image.ndim == 2:
return np.pad(image, pad_width=(amt, amt))
elif img.ndim == 3:
return np.pad(image, pad_width=((amt, amt), (amt, amt), (0, 0)))
CodePudding user response：
So this reproduces your error - using the three term pad_width on a 2d array:
ok with 3d:
In [194]: x = np.ones((5,5,3),int)
In [196]: amt_padding=1;np.pad(x, pad_width=((amt_padding, amt_padding), (amt_padding, amt_padding), (0, 0))).shape
Out[196]: (7, 7, 3)
but if the array is 2d:
In [197]: amt_padding=1;np.pad(x[:,:,0], pad_width=((amt_padding, amt_padding), (amt_padding, amt_padding), (0, 0)))
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Input In [197], in <cell line: 1>()
----> 1 amt_padding=1;np.pad(x[:,:,0], pad_width=((amt_padding, amt_padding), (amt_padding, amt_padding), (0, 0)))
File <__array_function__ internals>:5, in pad(*args, **kwargs)
File ~\anaconda3\lib\site-packages\numpy\lib\arraypad.py:743, in pad(array, pad_width, mode, **kwargs)
740 raise TypeError('`pad_width` must be of integral type.')
742 # Broadcast to shape (array.ndim, 2)
--> 743 pad_width = _as_pairs(pad_width, array.ndim, as_index=True)
745 if callable(mode):
746 # Old behavior: Use user-supplied function with np.apply_along_axis
747 function = mode
File ~\anaconda3\lib\site-packages\numpy\lib\arraypad.py:518, in _as_pairs(x, ndim, as_index)
514 raise ValueError("index can't contain negative values")
516 # Converting the array with `tolist` seems to improve performance
517 # when iterating and indexing the result (see usage in `pad`)
--> 518 return np.broadcast_to(x, (ndim, 2)).tolist()
File <__array_function__ internals>:5, in broadcast_to(*args, **kwargs)
File ~\anaconda3\lib\site-packages\numpy\lib\stride_tricks.py:411, in broadcast_to(array, shape, subok)
366 @array_function_dispatch(_broadcast_to_dispatcher, module='numpy')
367 def broadcast_to(array, shape, subok=False):
368 """Broadcast an array to a new shape.
369
370 Parameters
(...)
409 [1, 2, 3]])
410 """
--> 411 return _broadcast_to(array, shape, subok=subok, readonly=True)
File ~\anaconda3\lib\site-packages\numpy\lib\stride_tricks.py:348, in _broadcast_to(array, shape, subok, readonly)
345 raise ValueError('all elements of broadcast shape must be non-'
346 'negative')
347 extras = []
--> 348 it = np.nditer(
349 (array,), flags=['multi_index', 'refs_ok', 'zerosize_ok'] extras,
350 op_flags=['readonly'], itershape=shape, order='C')
351 with it:
352 # never really has writebackifcopy semantics
353 broadcast = it.itviews[0]
ValueError: operands could not be broadcast together with remapped shapes [original->remapped]: (3,2) and requested shape (2,2)
It's passing the task to np.nditer (via broadcast_to), which is raising the error. That would account for why I've never seen it before. I've explored nditer some, but it's not something I regularly use or recommend to others.
The _as_pairs expands widths like
In [206]: np.lib.arraypad._as_pairs(1,3, as_index=True)
Out[206]: ((1, 1), (1, 1), (1, 1))
In [207]: np.lib.arraypad._as_pairs(((1,),(2,),(3,)),3, as_index=True)
Out[207]: [[1, 1], [2, 2], [3, 3]]