Numpy：如何用向量化替换数组？

Question

I have an image saved as numpy array of shape [Height, Width, 3] and I want to replace every pixel with another value based on the color of pixel, so the final array will have a shape [Height, Weight] .

My solution with for loop works but it's pretty slow. How can I use Numpy vectorization to make it more efficient?

image = cv2.imread("myimage.png")

result = np.zeros(shape=(image.shape[0], image.shape[1],))
for h in range(0, result.shape[0]):
        for w in range(0, result.shape[1]):
            result[h, w] = get_new_value(image[h, w])

Here is get_new_value function:

def get_new_value(array: np.ndarray) -> int:
    mapping = {
        (0, 0, 0): 0,
        (0, 0, 255): 5,
        (0, 100, 200): 8,
        # ...
    }
    return mapping[tuple(array)]

Answer 1

you can use np.select() as shown below:

img=np.array(
[[[123 123 123]
  [130 130 130]]

 [[129 128 128]
  [162 162 162]]])

condlist = [img==[123,123,123], img==[130, 130, 130], img==[129, 129, 129], img==[162, 162, 162]]
choicelist = [0, 5, 8, 9]
img_replaced = np.select(condlist, choicelist)
final = img_replaced[:, :, 0]

print('img_replaced')
print(img_replaced)
print('final')
print(final)

condlist is your list of colour values and choicelist is the list of replacements.

np.select then returns three channels and you just need to take one channel from that to give the array 'final' which is the format you want I believe

output is:

img_replaced
[[[0 0 0]
  [5 5 5]]

 [[0 0 0]
  [9 9 9]]]
final
[[0 5]
 [0 9]]

so code specific to your example and shown colour mappings would be:

image = cv2.imread("myimage.png")

condlist = [image==[0, 0, 0], image==[0, 0, 255], image==[0, 100, 200]]
choicelist = [0, 5, 8]
img_replaced = np.select(condlist, choicelist)
result = img_replaced[:, :, 0]