在numpy中附加3d数组

Question

I'm trying to find dominant colors in image and then treshold the most dominant one. However I'm having trouble with data types. My formula gives the most dominant color as:

color=[10,10,10] # type=numpy.ndarray ,uint8

But it gives assertion error when I try to convert it:

color=cv2.cvtColor(color, cv2.COLOR_BGR2HSV) #gives assertion error

What cv2.cvtColor wants as an input is that:

color_ideal=[[[ 10, 10, 10 ]]]  #type=numpy.ndarray, uint8

To obtain it, I managed to manipulate color as such:

color=np.uint8(np.atleast_3d(clr).astype(int).reshape(1,1,3))

This seems working, but know I cannot append multiple colors to numpy array.Somehow, after appending the dimension is reduced to 1. My code is:

    color=np.uint8([[[]]])

    for item in clt.cluster_centers_:
       color=np.append(color,(np.uint8(np.atleast_3d(item).astype(int).reshape(1,1,3))))
#returns: color=[10,10,10] somehow its dimension is down to 1

My questions are:

1-How to properly append color data without loosing its dimension?

2-Is there easier way to handle this? I'm suprised how difficult it is to manipulate custom color pixel.

The full code is here in case it helps:

<!-- language: lang-py -->


    import cv2
    import numpy as np
    from sklearn.cluster import KMeans 

    def find_kmean_colors(img,no_cluster=2):
        clt = KMeans(no_cluster).fit(img)

        return clt
    def initialize(img='people_frontal.jpg'):
        img=cv2.imread('people_frontal_close_body.jpg')
        img=cv2.bilateralFilter(img,9,75,75)
        return img


    img=initialize()

    img_hsv =cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

    img_list= img.reshape((img.shape[0] * img_hsv.shape[1], 3))

    clt=(find_kmean_colors(img_list,1))


    color=np.uint8([[[]]])

    for i in clt.cluster_centers_:
        color=np.append(color,(np.uint8(np.atleast_3d(i).astype(int).reshape(1,1,3))))

    #color=np.uint8(np.atleast_3d(clt.cluster_centers_).astype(int).reshape(1,1,3))

    up=cv2.cvtColor(color,cv2.COLOR_BGR2HSV)

Answer 1

Without the cv2 code, I'm guessing about shapes here. But looks like img is a (n,m,3) array. img_list is (m1,3) , and clt as list of m1 items, and clt.cluster_centers_ a list of m1 arrays of shape (3,) .

For test sake lets make a list of lists (it could just as well be a list of arrays):

ctrs=[[10,10,10], [3,5,3], [20,10,10], [0,0,0]]
color = np.array(ctrs,dtype=np.uint8)  # (4,3) array
color = color.reshape(len(ctrs),1,3)

Just wrap it in np.array , and reshape to 3d.

array([[[10, 10, 10]],
       [[ 3,  5,  3]],
       [[20, 10, 10]],
       [[ 0,  0,  0]]], dtype=uint8)

Or it could be reshaped to (1,4,3) or (2,2,3).

Or closer to what you are trying:

np.concatenate([np.array(i,np.uint8).reshape(1,1,3) for i in ctrs])

You don't want to use atleast_3d here since it reshapes a (N,) array to (1,N,1) (see its docs). np.concatenate joins on the 1st axis, where as np.array adds a 1st dimension and then joins.

You probably could get append to work, but it just does a step by step concatenate, which is slower. In general if you need to append, do it with lists, and then convert to an array at the end.

---

There are various ways of preserving or restoring dimensions after slicing. If color is 3d and you need the i'th row also as 3d:

color[[i]]
color[i].reshape(1,...)
color[i][np.newaxis,...]

Reshaping operations like this do not add significant time to the processing, so don't be afraid to use them.