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

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 converting into numpy array,

x = np.array(temp_list)  

I am getting the error :

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

Can someone help me here?

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)]

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.

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.

@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)

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])