CODEWITHSUNDEEP
pythonarraysimagenumpyscikit-learn
Boris Kalinin
Boris Kalinin

Reputation: 134

Reshaping numpy array of images results in extra dimension

I have 440 images with the same size 924 x 640 and three channels. I load them via

image_data = []
for filename in iglob(os.path.join(store, '*.jpg')):
    image_data.append(plt.imread(filename))

Then I make a numpy ndarray from this list:

image_np_orig = np.array(image_data)

This array has a shape (440,) and it consists of elements with shape of (924, 640, 3). I want to make some t-SNE transformations on this array of images, so I want to reshape the array to make it's shape look like (440, 1):

image_np = image_np_orig.reshape(image_np_orig.shape[0], -1)

Expectation / Reality

I expect to see an array image_np of shape (440, 1) where each element of the first dimension (axis=0) is an array of shape (924, 640, 3). However I get an array image_np of shape (440, 1), where each element of the first dimension is an array of shape (1,) and in these arrays each element of their respective first dimensions is of shape (924, 640, 3).

What I've tried

I've tried

image_np = image_np_orig[:, np.newaxis]

with the same results.

I`ve also tried

image_np = np.stack(image_np_orig)

which lead to image_np with the shape of (440, 924, 640, 3) and then I got the mistake during the t-SNE transform:

from sklearn.manifold import TSNE
tsne = TSNE(n_components=2, init='pca')
X_tsne = tsne.fit_transform(image_np)

returns ValueError: Found array with dim 4. Estimator expected <=2.

Probably relevant

It may be relevant that image_np_orig has dtype object and image_np_orig[0] has dtype uint8. If this is relevant then how can I reshape arrays of different types?

Upvotes: 3

Views: 3352

Answers (2)

fountainhead
fountainhead

Reputation: 3722

From what I understand, you have an array of shape (440, 1, 924, 640, 3), but you actually need (440, 924, 640, 3)

Try:

image_np = image_np_orig.squeeze()

This will squeeze out the unnwanted dimension.

Upvotes: 2

Quang Hoang
Quang Hoang

Reputation: 150735

I'm not sure why the first approach doesn't work for you. But since image_np = np.stack(image_np_orig) returns the 4D data, you can go from there:

image_np = np.stack(image_np_orig).reshape(len(image_np_orig), -1)

Upvotes: 0

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