如何使用opencv创建LAB颜色图表？

Question

我正在开发一个项目，以识别某些斑点的颜色为起点，为此，我正在绘制带有这些图像RGB颜色的3D图形。由此，我确定了这些斑点的一些醒目的颜色，如下所示。

颜色是理解的主观性和解释性的问题。此步骤的目的是进行识别，以便您可以找到颜色的图案而不会造成差异。因此，我一直在互联网上搜索，为此，建议使用颜色空间L * a * b *。

有了这个，有人可以帮我获得带有LAB颜色的图表，还是指出另一种更好地对这些斑点的颜色进行分类的方法？

用于绘制3D图形的代码

import numpy as np
import mpl_toolkits.mplot3d.axes3d as p3
import matplotlib.pyplot as plt
import colorsys
from PIL import Image

# (1) Import the file to be analyzed!
img_file = Image.open("IMD405.png")
img = img_file.load()

# (2) Get image width & height in pixels
[xs, ys] = img_file.size
max_intensity = 100
hues = {}

# (3) Examine each pixel in the image file
for x in xrange(0, xs):
  for y in xrange(0, ys):
    # (4)  Get the RGB color of the pixel
    [r, g, b] = img[x, y]

# (5)  Normalize pixel color values
r /= 255.0
g /= 255.0
b /= 255.0

# (6)  Convert RGB color to HSV
[h, s, v] = colorsys.rgb_to_hsv(r, g, b)

# (7)  Marginalize s; count how many pixels have matching (h, v)
if h not in hues:
  hues[h] = {}
if v not in hues[h]:
  hues[h][v] = 1
else:
  if hues[h][v] < max_intensity:
    hues[h][v] += 1

# (8)   Decompose the hues object into a set of one dimensional arrays we can use with matplotlib
h_ = []
v_ = []
i = []
colours = []

for h in hues:
  for v in hues[h]:
    h_.append(h)
    v_.append(v)
    i.append(hues[h][v])
    [r, g, b] = colorsys.hsv_to_rgb(h, 1, v)
    colours.append([r, g, b])

# (9)   Plot the graph!
fig = plt.figure()
ax = p3.Axes3D(fig)
ax.scatter(h_, v_, i, s=5, c=colours, lw=0)

ax.set_xlabel('Hue')
ax.set_ylabel('Value')
ax.set_zlabel('Intensity')
fig.add_axes(ax)
plt.savefig('plot-IMD405.png')
plt.show()

Answer 1

使用OpenCV for Python确实非常简单。在这里，我创建了一个绘制样本图像的函数。请注意，对于此功能，图像必须是RGB或BGR。

import cv2
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

image_BGR = np.uint8(np.random.rand(50,50,3) * 255)
#this image above is just an example. To load a real image use the line below
#image_BGR = cv2.imread('path/to/image')

def toLAB(image, input_type = 'BGR'):
  conversion = cv2.COLOR_BGR2LAB if input_type == 'BGR' else cv2.COLOR_RGB2LAB
  image_LAB = cv2.cvtColor(image, conversion)

  y,x,z = image_LAB.shape
  LAB_flat = np.reshape(image_LAB, [y*x,z])

  colors = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) if input_type == 'BGR' else image
  colors = np.reshape(colors, [y*x,z])/255.

  fig = plt.figure()
  ax = fig.add_subplot(111, projection='3d')
  ax.scatter(xs=LAB_flat[:,2], ys=LAB_flat[:,1], zs=LAB_flat[:,0], s=10,  c=colors, lw=0)
  ax.set_xlabel('A')
  ax.set_ylabel('B')
  ax.set_zlabel('L')

  plt.show()

  return image_LAB 

lab_image = toLAB(image_BGR)

结果是这样的：

希望对您有所帮助！