python - 如何从图像中提取 RGB 并仅将 RG 绘制为图形？ R代表X，G代表Y

Question

我正在尝试从图像中提取 RGB 分量并使用 matplotlib 绘制 3D RGB 直方图。但我不知道我该怎么做。

这是我当前的代码:

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

img_file = 'Paw03.png'
img = cv2.imread(img_file, cv2.IMREAD_COLOR)           # rgb
#hsv_img = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)         # hsv

rows, cols, ch = img.shape

for x in range(rows):
    for y in range(cols):
        if (img[x, y, 1] == img[0, 255, 0]):
            break;
        else:
            print "Pixel:", x, y
            print  "R:", R 
            print  "G:", g
            print  "B:", b
            print "\n"

plt.plot(r, 'ro', b, 'b^')
plt.xlim([0, 255])
plt.xlabel('Pixel')
plt.ylabel('Quantity')
plt.title('Distribution of RGB in the image')
plt.show()

但它不起作用!

所以，我尝试了三个:

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

img_file = 'Paw03.png'
img = cv2.imread(img_file, cv2.IMREAD_COLOR)           # rgb
#hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)        # hsv

rows, cols, ch = img.shape

for x in range(rows):
    for y in range(cols):
        for z in range(ch)
            if (img[x, y, z] == img[0, 255, 0]):
                break;
            else:
                print "Pixel:", x, y
                print  "R:", R 
                print  "G:", g
                print  "B:", b
                print "\n"

plt.plot(r, 'ro', b, 'b^')
plt.xlim([0, 255])
plt.xlabel('Pixel')
plt.ylabel('Quantity')
plt.title('Distribution of RGB in the image')
plt.show()

它只适用于打印到 for 并且每个像素也保存三次，而对于 matplotlib 它不起作用。

谁能帮帮我？

Answer 1

以下代码片段显示了图像 RGB 颜色的 3D 散点图:

import numpy as np
import matplotlib.image as mpimg
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

img = mpimg.imread('Paw03.png')
pixels = img.shape[0]*img.shape[1]
channels = 3
data = np.reshape(img[:, :, :channels], (pixels, channels))

histo_rgb, _ = np.histogramdd(data, bins=256)
r, g, b = np.nonzero(histo_rgb)

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter3D(r, g, b)
ax.set_xlabel('Red')
ax.set_ylabel('Green')
ax.set_zlabel('Blue')
plt.title('RGB colors')
plt.show()

这是您运行上面的代码时得到的结果(结果显然取决于所使用的特定图像):

如果您的目标是在 3D 中可视化红色和绿色 channel 强度的 2D 直方图，那么您可能会发现此代码很有用:

import numpy as np
import matplotlib.image as mpimg
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

img = mpimg.imread('Paw03.png')
pixels = img.shape[0]*img.shape[1]
channels = 3
data = np.reshape(img[:, :, :channels], (pixels, channels))

histo_rgb, _ = np.histogramdd(data, bins=256)
histo_rg = np.sum(histo_rgb, 2)
levels = np.arange(256)

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
for g in levels:
    ax.bar(levels, histo_rg[:, g], zs=g, zdir='y', color='r')
ax.set_xlabel('Red')
ax.set_ylabel('Green')
ax.set_zlabel('Number of pixels')
plt.show()

这是相应的输出: