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

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)]

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])

@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)

In my case problem was in my data set, Basically i was need to pre process on my data before further processing, because in my data set images are in random formats like RGB and grayscale, So dimensions mismatch. I simply follow Mudasir Habib's answer.

from PIL import Image
img = Image.open('my_image.jpg').convert('RGB')

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.

SOLVED - I got same arror : X_test = np.array(X_test, ) ValueError: could not broadcast input array from shape (50,50,3) into shape (50,50)

printed every images shape and got like this:

~

1708 : (50, 50, 3)

1709 : (50, 50)

1710 : (50, 50)

1711 : (50, 50, 3)

1712 : (50, 50, 3)

1713 : (50, 50, 3)

~

which means Mixed 1D and 3D datas after reading 2 different image folders and shuffling them

img: first one is Grayscale and second one is Color image

Added cv2.IMREAD_GRAYSCALE and problem is solved

Summary: in image data which I wanted to convert into np array contained different dimensional images

-> checked image data

-> found out that there are 1D, and 3D images

-> made 3D images Grayscale(1D)

-> problem is solved

I totally agree with @mudassir's Answer. If you have agumented your dataset, then its highly likely that you get this error. As in most of the augumentation, it automatically applies grayscale effect which is actually two-dimensional whereas the original pictures (RGB) are three-dimensional. I myself was using roboflow's dataset that was already augumented and had the similar issue. I then removed the "graysclaing step" and still it gave the error. However, one i removed grayscale, hue and saturation, it worked like a charm. I would suggest you try that too.