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| # coding=utf-8
# 导入一些python包
from scipy.spatial import distance as dist
from collections import OrderedDict
import numpy as np
import cv2
# 创建一个颜色标签类
class ColorLabeler:
def __init__(self):
# 初始化一个颜色词典
colors = OrderedDict({
"red": (255, 0, 0),
"green": (0, 255, 0),
"blue": (0, 0, 255)})
# 为LAB图像分配空间
self.lab = np.zeros((len(colors), 1, 3), dtype="uint8")
self.colorNames = []
# 循环 遍历颜色词典
for (i, (name, rgb)) in enumerate(colors.items()):
# 进行参数更新
self.lab[i] = rgb
self.colorNames.append(name)
# 进行颜色空间的变换
self.lab = cv2.cvtColor(self.lab, cv2.COLOR_RGB2LAB)
def label(self, image, c):
# 根据轮廓构造一个mask，然后计算mask区域的平均值
mask = np.zeros(image.shape[:2], dtype="uint8")
cv2.drawContours(mask, [c], -1, 255, -1)
mask = cv2.erode(mask, None, iterations=2)
mean = cv2.mean(image, mask=mask)[:3]
# 初始化最小距离
minDist = (np.inf, None)
# 遍历已知的LAB颜色值
for (i, row) in enumerate(self.lab):
# 计算当前l*a*b*颜色值与图像平均值之间的距离
d = dist.euclidean(row[0], mean)
# 如果当前的距离小于最小的距离，则进行变量更新
if d < minDist[0]:
minDist = (d, i)
# 返回最小距离对应的颜色值
return self.colorNames[minDist[1]]
|