# 分析图像中的主色时删除背景色（python，OpenCV）Removing background color(s) from when analysing dominant colors in an image (python, OpenCV)

``````import cv2
import numpy as np
from sklearn import metrics
import matplotlib.pyplot as plt

# Load the image
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Resize it for efficiency (color ratios are the same)
h, w, _ = image.shape
w_new = int(100 * w / max(w, h) )
h_new = int(100 * h / max(w, h) )

image = cv2.resize(image, (w_new, h_new));

# Step 1: Clustering colours with K-Means
from sklearn.cluster import KMeans

# Reshape the image to be a list of pixels
image_array = image.reshape((image.shape[0] * image.shape[1], 3))

# Clusters the pixels
clt = KMeans(n_clusters = 7)
clt.fit(image_array)

# By Adrian Rosebrock
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

# Finds how many pixels are in each cluster
hist = centroid_histogram(clt)

# Sort the clusters according to how many pixel they have
zipped = list(zip(hist, clt.cluster_centers_))
zipped.sort(reverse=True, key=lambda x : x[0])
hist, clt.cluster_centers = zip(*zipped)

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

# return the bar chart
return bar

# build a histogram of clusters and then create a figure
# representing the number of pixels labeled to each color
hist = centroid_histogram(clt)
bar = plot_colors(hist, clt.cluster_centers_)

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

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