ValueError: could not broadcast input array from shape (224,224,3) into shape (224,224)

Question

I have a list say, temp_list with following properties :

len(temp_list) = 9260  
temp_list[0].shape = (224,224,3)  

Now, when I am conver

Answer 1

You can covert numpy.ndarray to object using astype(object)

This will work:

>>> a = [np.zeros((224,224,3)).astype(object), np.zeros((224,224,3)).astype(object), np.zeros((224,224,13)).astype(object)]

Answer 2

@aravk33 's answer is absolutely correct.

I was going through the same problem. I had a data set of 2450 images. I just could not figure out why I was facing this issue.

Check the dimensions of all the images in your training data.

Add the following snippet while appending your image into your list:

if image.shape==(1,512,512):
    trainx.append(image)

Answer 3

This method does not need to modify dtype or ravel your numpy array.

The core idea is: 1.initialize with one extra row. 2.change the list(which has one more row) to array 3.delete the extra row in the result array e.g.

>>> a = [np.zeros((10,224)), np.zeros((10,))]
>>> np.array(a)
# this will raise error,
ValueError: could not broadcast input array from shape (10,224) into shape (10)

# but below method works
>>> a = [np.zeros((11,224)), np.zeros((10,))]
>>> b = np.array(a)
>>> b[0] = np.delete(b[0],0,0)
>>> print(b.shape,b[0].shape,b[1].shape)
# print result:(2,) (10,224) (10,)

Indeed, it's not necessarily to add one more row, as long as you can escape from the gap stated in @aravk33 and @user707650 's answer and delete the extra item later, it will be fine.

Answer 4

Yea, Indeed @Evert answer is perfectly correct. In addition I'll like to add one more reason that could encounter such error.

>>> np.array([np.zeros((20,200)),np.zeros((20,200)),np.zeros((20,200))])

This will be perfectly fine, However, This leads to error:

>>> np.array([np.zeros((20,200)),np.zeros((20,200)),np.zeros((20,201))])

ValueError: could not broadcast input array from shape (20,200) into shape (20)

The numpy arry within the list, must also be the same size.

Answer 5

I was facing the same problem because some of the images are grey scale images in my data set, so i solve my problem by doing this

    from PIL import Image
    img = Image.open('my_image.jpg').convert('RGB')
    # a line from my program
    positive_images_array = np.array([np.array(Image.open(img).convert('RGB').resize((150, 150), Image.ANTIALIAS)) for img in images_in_yes_directory])

Answer 6

At least one item in your list is either not three dimensional, or its second or third dimension does not match the other elements. If only the first dimension does not match, the arrays are still matched, but as individual objects, no attempt is made to reconcile them into a new (four dimensional) array. Some examples are below:

That is, the offending element's shape != (?, 224, 3),
or ndim != 3 (with the ? being non-negative integer).
That is what is giving you the error.

You'll need to fix that, to be able to turn your list into a four (or three) dimensional array. Without context, it is impossible to say if you want to lose a dimension from the 3D items or add one to the 2D items (in the first case), or change the second or third dimension (in the second case).

---

Here's an example of the error:

>>> a = [np.zeros((224,224,3)), np.zeros((224,224,3)), np.zeros((224,224))]
>>> np.array(a)
ValueError: could not broadcast input array from shape (224,224,3) into shape (224,224)

or, different type of input, but the same error:

>>> a = [np.zeros((224,224,3)), np.zeros((224,224,3)), np.zeros((224,224,13))]
>>> np.array(a)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: could not broadcast input array from shape (224,224,3) into shape (224,224)

Alternatively, similar but with a different error message:

>>> a = [np.zeros((224,224,3)), np.zeros((224,224,3)), np.zeros((224,100,3))]
>>> np.array(a)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: could not broadcast input array from shape (224,224,3) into shape (224)

But the following will work, albeit with different results than (presumably) intended:

>>> a = [np.zeros((224,224,3)), np.zeros((224,224,3)), np.zeros((10,224,3))]
>>> np.array(a)
# long output omitted
>>> newa = np.array(a)
>>> newa.shape
3  # oops
>>> newa.dtype
dtype('O')
>>> newa[0].shape
(224, 224, 3)
>>> newa[1].shape
(224, 224, 3)
>>> newa[2].shape
(10, 224, 3)
>>>