我正在开发一个项目,该项目的起点是识别某些斑点的颜色,为此,我正在绘制具有这些图像RGB颜色的3D图形。这样,我就确定了这些斑点的一些醒目的颜色,如下所示。
颜色是解释的主观性和主观性的问题。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color space 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()
答案 0 :(得分:4)
使用OpenCV for Python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,要使用此功能,图片必须为RGB或BGR。
react-native-firebase
结果是这样的:
希望对您有帮助!
答案 1 :(得分:0)
静态地图:
gif图:
我更喜欢使用HSV查找特定的颜色范围,例如:
Choosing the correct upper and lower HSV boundaries for color detection with`cv::inRange` (OpenCV)
How to define a threshold value to detect only green colour objects in an image :Opencv
How to detect two different colors using `cv2.inRange` in Python-OpenCV?
what are recommended color spaces for detecting orange color in open cv?