ValueError: could not broadcast input array from shape (110,110,3) into shape (110,110)

Question

I'm building a neural network and I'm trying to load colored images into the network but I keep getting a reshaping error. I resized all of the images to the smallest dimensions (in this case 110 x 110) but when I try to convert the X (an unflattened 3d list of the pixels of each image) to a numpy array to be called xTrain with this line of code:

xTrain = np.array(X[:trainNum])

i get this error: "ValueError: could not broadcast input array from shape (110,110,3) into shape (110,110)"

does anyone know why it keeps doing that? i assume it's because of my data because my partner copied the same exact code with his own images and the conversion to a numpy array was successful but mine isn't. for reference the list titled X is in this format:

[array([[[137, 151, 199],
    [ 93, 114, 166],
    [116, 121, 164],
    ...,
    [124, 124, 175],
    [160, 162, 193],
    [154, 157, 177]],

   [[ 81,  94, 153],
    [106, 123, 184],
    [119, 124, 180],...

how do I fix this?

Answer 1

Most likely, your X list contains a mixture of grayscale and RGB images.

img_rgb = np.zeros((110, 110, 3))
img_gry = np.zeros((110, 110))

X_good = [img_rgb, img_rgb, img_rgb]
np.array(X_good[:])
# OK

X_bad = [img_rgb, img_gry, img_rgb]
np.array(X_bad[:])
# ValueError: could not broadcast input array from shape (110,110,3) into shape (110,110)

You can convert the grayscale image(s) in X to RGB:

def make_rgb(img):
    if len(img.shape) == 3:
        return img
    img3 = np.empty(img.shape + (3,))
    img3[:, :, :] = img[:, :, np.newaxis]
    return img3

X_repaired = [make_rgb(im) for im in X_bad]

np.array(X_repaired[:])
# No problem