使用最近邻缩放绘制或调整绘制的量化图像

Question

按照K means clustering 的这个示例，我想重新创建相同的 - 只是我非常希望最终图像仅包含量化颜色（+ 白色背景）。实际上，彩条被平滑在一起以创建混合颜色的像素线。

虽然它们看起来非常相似，但图像（上半部分）是我从 CV2 中得到的，它总共包含 38 种颜色。 下图只有10种颜色，就是我要的。

让我们用 6 倍放大倍率看一点：

我试过了：

# OpenCV and Python K-Means Color Clustering
# build a histogram of clusters and then create a figure
# representing the number of pixels labeled to each color
hist = colour_utils.centroid_histogram(clt)
bar = colour_utils.plot_colors(hist, clt.cluster_centers_)
bar = cv2.resize(bar, (460, 345), 0, 0, interpolation = cv2.INTER_NEAREST)

但是，resize 似乎没有调整大小的效果或更改缩放类型。我也不知道是什么控制了初始图像大小。 困惑。

有什么想法吗？

Answer 1

我建议您使用cv2.imshow 显示图像，而不是使用matplotlib。

cv2.imshow 默认显示图像“像素到像素”，而matplotlib.pyplot 将图像尺寸与轴的大小相匹配。

bar_bgr = cv2.cvtColor(bar, cv2.COLOR_RGB2BGR)  # Convert RGB to BGR
cv2.imshow('bar', bar_bgr)
cv2.waitKey()
cv2.destroyAllWindows()

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如果您想使用matplotlib，请查看：Display image with a zoom = 1 with Matplotlib imshow() (how to?)。

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用于测试的代码：

# import the necessary packages
import numpy as np
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import argparse
#import utils
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
    
    # return the bar chart
    return bar


# load the image and convert it from BGR to RGB so that
# we can dispaly it with matplotlib
image = cv2.imread('chelsea.png')
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 = 5)
clt.fit(image)


# 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("off")
#plt.imshow(bar)
#plt.show()

bar = cv2.resize(bar, (460, 345), 0, 0, interpolation = cv2.INTER_NEAREST)

bar_bgr = cv2.cvtColor(bar, cv2.COLOR_RGB2BGR)  # Convert RGB to BGR
cv2.imshow('bar', bar_bgr)
cv2.waitKey()
cv2.destroyAllWindows()