彩色直方图 HSV 1D

Question

我试图从这样的图像创建一个直方图 1d

但我不知道该怎么做。有人可以帮帮我吗？

这是我的 HSV 直方图的简单代码：

from matplotlib import pyplot as plt
import cv2

image = cv2.imread('sample/sample4.jpg')

cv2.cvtColor(image, cv2.COLOR_RGB2HSV)

H, S, V = image[:,:,0],image[:,:,1],image[:,:,2]

plt.figure(figsize=(10,8))
plt.subplot(311)                             #plot in the first cell
plt.subplots_adjust(hspace=.5)
plt.title("Hue")
plt.hist(np.ndarray.flatten(H), bins=180)
plt.subplot(312)                             #plot in the second cell
plt.title("Saturation")
plt.hist(np.ndarray.flatten(S), bins=128)
plt.subplot(313)                             #plot in the third cell
plt.title("Luminosity Value")
plt.hist(np.ndarray.flatten(V), bins=128)
plt.show()

谢谢你的帮助

Answer 1

这是一种使用 Python/OpenCV/Scipy/Matplotpy 的方法，但使用 BGR 颜色。

• 读取输入
• 使用 kmeans 将颜色减少到 16 种颜色
• 获取唯一颜色数组和唯一颜色计数
• 将每个计数绘制为按颜色着色的条形
• 保存结果

输入：

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

# read image into range 0 to 1
img = cv2.imread('barn.jpg') / 255

# set number of colors
number = 16

# quantize to 16 colors using kmeans
h, w, c = img.shape
img2 = img.reshape(h*w, c)
kmeans_cluster = cluster.KMeans(n_clusters=number)
kmeans_cluster.fit(img2)
cluster_centers = kmeans_cluster.cluster_centers_
cluster_labels = kmeans_cluster.labels_

# need to scale back to range 0-255 and reshape
img3 = cluster_centers[cluster_labels].reshape(h, w, c)*255.0
img3 = img3.astype('uint8')

cv2.imshow('reduced colors',img3)
cv2.waitKey(0)
cv2.destroyAllWindows()

# reshape img to 1 column of 3 colors
# -1 means figure out how big it needs to be for that dimension
img4 = img3.reshape(-1,3)

# get the unique colors
colors, counts = np.unique(img4, return_counts=True, axis=0)
print(colors)
print("xxx")
print(counts)
unique = zip(colors,counts)

# function to convert from r,g,b to hex 
def encode_hex(color):
    b=color[0]
    g=color[1]
    r=color[2]
    hex = '#'+str(bytearray([r,g,b]).hex())
    print(hex)
    return hex

# plot each color
fig = plt.figure()
for i, uni in enumerate(unique):
    color = uni[0]
    count = uni[1]
    plt.bar(i, count, color=encode_hex(color))

# show and save plot
plt.show()
fig.savefig('barn_color_historgram.png')
plt.close(fig) 

生成的彩色直方图：

Answer 2

这是 Python/OpenCV 中按 HSV 值排序的替代版本。

输入：

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

# read image into range 0 to 1
img = cv2.imread('barn.jpg') / 255

# set number of colors
number = 16

# quantize to 16 colors using kmeans
h, w, c = img.shape
img2 = img.reshape(h*w, c)
kmeans_cluster = cluster.KMeans(n_clusters=number)
kmeans_cluster.fit(img2)
cluster_centers = kmeans_cluster.cluster_centers_
cluster_labels = kmeans_cluster.labels_

# need to scale back to range 0-255 and reshape
img3 = cluster_centers[cluster_labels].reshape(h, w, c)*255.0
img3 = img3.astype('uint8')

cv2.imshow('reduced colors',img3)
cv2.waitKey(0)
cv2.destroyAllWindows()

# reshape img to 1 column of 3 colors
# -1 means figure out how big it needs to be for that dimension
img4 = img3.reshape(-1,3)

# get the unique colors
colors, counts = np.unique(img4, return_counts=True, axis=0)

# compute HSV Value equals max(r,g,b)
values = []
for color in colors:
    b=color[0]
    g=color[1]
    r=color[2]
    v=max(b,g,r)
    values.append(v)

# zip colors, counts, values together
unique = zip(colors,counts,values)

# make list of color, count, value
ccv_list = []
for color, count, value in unique:
    ccv_list.append((color, count, value))
    
# function to define key as third element
def takeThird(elem):
    return elem[2]

# sort ccv_list by Value (brightness)
ccv_list.sort(key=takeThird)

# plot each color sorted by increasing Value (brightness)
# pyplot uses normalized r,g,b in range 0 to 1
fig = plt.figure()
length = len(ccv_list)
for i in range(length):
    item = ccv_list[i]
    color = item[0]
    b = color[0]/255
    g = color[1]/255
    r = color[2]/255
    count = item[1]
    plt.bar(i, count, color=((r,g,b)))

# show and save plot
plt.show()
fig.savefig('barn_color_histogram2.png')
plt.close(fig) 

结果：