可以建议使用np.unique和np.bincount来获得最主要的颜色的两种方法。此外，在链接页面中，它谈到bincount作为更快的替代方案，所以这可能是要走的路。

方法#1

def unique_count_app(a):
    colors, count = np.unique(a.reshape(-1,a.shape[-1]), axis=0, return_counts=True)
    return colors[count.argmax()]

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方法#2

def bincount_app(a):
    a2D = a.reshape(-1,a.shape[-1])
    col_range = (256, 256, 256) # generically : a2D.max(0)+1
    a1D = np.ravel_multi_index(a2D.T, col_range)
    return np.unravel_index(np.bincount(a1D).argmax(), col_range)

型
在[0,9)密集范围内对1000 x 1000彩色图像进行验证和计时，以获得可重现的结果-

In [28]: np.random.seed(0)
    ...: a = np.random.randint(0,9,(1000,1000,3))
    ...: 
    ...: print unique_count_app(a)
    ...: print bincount_app(a)
[4 7 2]
(4, 7, 2)

In [29]: %timeit unique_count_app(a)
1 loop, best of 3: 820 ms per loop

In [30]: %timeit bincount_app(a)
100 loops, best of 3: 11.7 ms per loop

型

进一步提高

利用multi-core和numexpr模块进一步提升大数据处理能力-

import numexpr as ne

def bincount_numexpr_app(a):
    a2D = a.reshape(-1,a.shape[-1])
    col_range = (256, 256, 256) # generically : a2D.max(0)+1
    eval_params = {'a0':a2D[:,0],'a1':a2D[:,1],'a2':a2D[:,2],
                   's0':col_range[0],'s1':col_range[1]}
    a1D = ne.evaluate('a0*s0*s1+a1*s0+a2',eval_params)
    return np.unravel_index(np.bincount(a1D).argmax(), col_range)

型
时间-

In [90]: np.random.seed(0)
    ...: a = np.random.randint(0,9,(1000,1000,3))

In [91]: %timeit unique_count_app(a)
    ...: %timeit bincount_app(a)
    ...: %timeit bincount_numexpr_app(a)
1 loop, best of 3: 843 ms per loop
100 loops, best of 3: 12 ms per loop
100 loops, best of 3: 8.94 ms per loop

型

@Divakar给出了一个很好的答案。但是如果你想将自己的代码移植到OpenCV，那么：

img = cv2.imread('image.jpg',cv2.IMREAD_UNCHANGED)

    data = np.reshape(img, (-1,3))
    print(data.shape)
    data = np.float32(data)

    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
    flags = cv2.KMEANS_RANDOM_CENTERS
    compactness,labels,centers = cv2.kmeans(data,1,None,criteria,10,flags)

    print('Dominant color is: bgr({})'.format(centers[0].astype(np.int32)))

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您的图像的结果：
主色为：bgr（[41 31 23]）
时间：0.10798478126525879秒

cv2.calcHist()的等效代码是替换：

(hist, _) = np.histogram(clt.labels_, bins=num_labels)

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与

dmin, dmax, _, _ = cv2.minMaxLoc(clt.labels_)

if np.issubdtype(data.dtype, 'float'): dmax += np.finfo(data.dtype).eps
else: dmax += 1

hist = cv2.calcHist([clt.labels_], [0], None, [num_labels], [dmin, dmax]).flatten()

型
注意cv2.calcHist只接受uint8和float32作为元素类型。

更新

看起来opencv和numpy的分箱方式是不同的，因为如果分箱的数量没有Map到值的范围，直方图就会不同：

import numpy as np
from matplotlib import pyplot as plt
import cv2

#data = np.random.normal(128, 1, (100, 100)).astype('float32')
data = np.random.randint(0, 256, (100, 100), 'uint8')
BINS = 20

np_hist, _ = np.histogram(data, bins=BINS)

dmin, dmax, _, _ = cv2.minMaxLoc(data)
if np.issubdtype(data.dtype, 'float'): dmax += np.finfo(data.dtype).eps
else: dmax += 1

cv_hist = cv2.calcHist([data], [0], None, [BINS], [dmin, dmax]).flatten()

plt.plot(np_hist, '-', label='numpy')
plt.plot(cv_hist, '-', label='opencv')
plt.gcf().set_size_inches(15, 7)
plt.legend()
plt.show()

型

@Divakar的答案的改进版本。RGBA图像的单独解决方案。RGBA图像的完全透明像素不会被计算在内。

import numpy as np

def most_common_color_RGB(image: np.ndarray):
    """input image ndarray shape should be RGB shape, for example: (512, 512, 3)"""
    a2D = image.reshape(-1, image.shape[-1])

    col_range = (256, 256, 256)  # generically : a2D.max(0)+1
    a1D = np.ravel_multi_index(a2D.T, col_range)
    return np.unravel_index(np.bincount(a1D).argmax(), col_range)

def most_common_color_RGBA(image_RGBA: np.ndarray):
    """input image ndarray shape should be RGBA shape, for example: (512, 512, 4)"""
    RGB_pixels = image_RGBA.reshape(-1, 4)
    # remove transparent pixels
    just_non_alpha = RGB_pixels[RGB_pixels[:, 3] != 0]
    if just_non_alpha.shape[0] == 0:
        return False
    # delete alpha channel
    just_non_alpha = np.delete(just_non_alpha, 3, axis=1)
    col_range = (256, 256, 256)  # generically : a2D.max(0)+1
    a1D = np.ravel_multi_index(just_non_alpha.T, col_range)
    return np.unravel_index(np.bincount(a1D).argmax(), col_range)

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