CODEWITHSUNDEEP
python-3.xopencvpython
Anurag H
Anurag H

Reputation: 1029

Applying CV2 Grayscale to a Numpy 4D array

While I know how to convert a single color image (32,32,3) to grayscale using CV2:

img = cv2.cvtColor( img, cv2.COLOR_RGB2GRAY )

I need to convert a batch of 60,000 images in a 4D numpy array (60000,32,32,3), how can I achieve that?

Upvotes: 1

Views: 1300

Answers (2)

Aray Karjauv
Aray Karjauv

Reputation: 2945

One more option using numpy:

grayscale_imgs = np.dot(img_stack, [0.299 , 0.587, 0.114])
grayscale_imgs.shape # => (60000, 32, 32)

More about the weighted sum can be found here

Upvotes: 1

Andriy Makukha
Andriy Makukha

Reputation: 8324

Let's say your 4D array of images is called img_stack with shape (60000,32,32,3).

You could do:

gray_stack = np.empty_like(img_stack[...,0])
for i in range(img_stack.shape[0]):
    gray_stack[i] = cv2.cvtColor(img_stack[i], cv2.COLOR_RGB2GRAY)

Resulting shape is (60000,32,32).

Or you could do:

gray_stack = np.empty_like(img_stack[...,:1])
for i in range(img_stack.shape[0]):
    gray_stack[i,:,:,0] = cv2.cvtColor(img_stack[i], cv2.COLOR_RGB2GRAY)

Resulting shape is (60000,32,32,1).

Bonus Tensorflow solution:

gray_stack = tf.image.rgb_to_grayscale(img_stack, name=None)

Resulting shape will be (60000,32,32,1).

The above OpenCV solutions might actually perform faster.

Upvotes: 2

Related Questions