一張圖片上有上百種顏色，如何在一張圖上篩選出小於五種的基本色，或者在一張圖上進行顏色劃分歸類？

01-21

為了做品牌，我們需要從符合品牌調性的圖片中提取品牌色，但一張圖片的顏色有上百種我們怎麼把這些顏色歸類劃分提取呢？

如果是編程實現的話，可以對像素進行 K-Means 聚類，找出一幅圖像中最主要的 K 種顏色。

下面的代碼和結果來自：

How-To: OpenCV and Python K-Means Color Clustering

作者是：Adrian Rosebrock

使用 K-Means 對圖片進行顏色聚類的結果（K的值需要用戶指定，需要提取多少種顏色K就設為多少）：

（圖片來自PyImageSearch）

（圖片來自PyImageSearch）Python 代碼（使用了 OpenCV 庫）：

# USAGE # python color_kmeans.py --image images/jp.png --clusters 3


# Author: Adrian Rosebrock

# Website: www.pyimagesearch.com
# import the necessary packages

from sklearn.cluster import KMeans

import matplotlib.pyplot as plt

import argparse

import utils

import cv2
# construct the argument parser and parse the arguments

ap = argparse.ArgumentParser()

ap.add_argument("-i", "--image", required = True, help = "Path to the image")

ap.add_argument("-c", "--clusters", required = True, type = int,

    help = "# of clusters")

args = vars(ap.parse_args())
# load the image and convert it from BGR to RGB so that

# we can dispaly it with matplotlib

image = cv2.imread(args["image"])

image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# show our image

plt.figure()

plt.axis("off")

plt.imshow(image)
# reshape the image to be a list of pixels

image = image.reshape((image.shape[0] * image.shape[1], 3))
# cluster the pixel intensities

clt = KMeans(n_clusters = args["clusters"])

clt.fit(image)
# build a histogram of clusters and then create a figure

# representing the number of pixels labeled to each color

hist = utils.centroid_histogram(clt)

bar = utils.plot_colors(hist, clt.cluster_centers_)

# show our color bart plt.figure() plt.axis("off") plt.imshow(bar) plt.show()

上面代碼用使用的「utils.py」：

# import the necessary packages import numpy as np import cv2


def centroid_histogram(clt):

    # grab the number of different clusters and create a histogram

    # based on the number of pixels assigned to each cluster

    numLabels = np.arange(0, len(np.unique(clt.labels_)) + 1)

    (hist, _) = np.histogram(clt.labels_, bins = numLabels)
    # normalize the histogram, such that it sums to one

    hist = hist.astype("float")

    hist /= hist.sum()
    # return the histogram

    return hist
def plot_colors(hist, centroids):

    # initialize the bar chart representing the relative frequency

    # of each of the colors

    bar = np.zeros((50, 300, 3), dtype = "uint8")

    startX = 0
    # loop over the percentage of each cluster and the color of

    # each cluster

    for (percent, color) in zip(hist, centroids):

        # plot the relative percentage of each cluster

        endX = startX + (percent * 300)

        cv2.rectangle(bar, (int(startX), 0), (int(endX), 50),

            color.astype("uint8").tolist(), -1)

        startX = endX