在文章图像主题色提取简单实现一文中，对颜色空间八等分，即r/g/b三个方向从128处切分，获取八个小的立方体，对每个立方体像素坐标值求平均数，得到八种颜色，效果还算理想，但限制也很明显。比如颜色数目固定，如果图片颜色单一，可能最后八个小立方体中出现没有值的情况。
解决方案
针对上一个实现，每次切分前对颜色空间进行排序，选择rank最前的进行切分，每次切分的位置也加入像素数量因素，其中：
rank = pixelSum * (v)^vBalance
pixelSum ：像素数量，v：体积，vBalance：体积影响系数（非负）很显然体积影响系数越大，提取出的颜色分布范围越大。切分点的位置与之类似，不再赘述。
代码实现
上次是js实现的，这次由于初学python，就用python来实现吧
from PIL import Image
from queue import PriorityQueue
import re
import os
import urllib.request
import time
import copy
''' 提取图片主色调 '''
def colorExt(imageName, colorNum, vBalance, padding):
# 读取图片，并对图片进行缩放
match = re.match('^(.*)(/|\\\\)([^/\\\\]+)(\.[^./\\\\]+)$', imageName)
path = match.group(1) + "/"
name = match.group(3)
suffix = match.group(4)
image = Image.open(imageName)
size = image.size
sizeX = 1920
sizeY = int(sizeX * size[1] / size[0])
totalSize = sizeX * sizeY
image.thumbnail((sizeX, sizeY))
print((sizeX, sizeY))
imageColors = image.getcolors(sizeX * sizeY)
# 计算颜色范围
rMax = imageColors[0][1][0]
gMax = imageColors[0][1][1]
bMax = imageColors[0][1][2]
rMin = imageColors[0][1][0]
gMin = imageColors[0][1][1]
bMin = imageColors[0][1][2]
for _, (r, g, b) in imageColors:
if r > rMax:
rMax = r
elif r < rMin:
rMin = r
if g > gMax:
gMax = g
elif g < gMin:
gMin = g
if b > bMax:
bMax = b
elif b < bMin:
bMin = b
colorMin = [rMin, gMin, bMin]
colorMax = [rMax, gMax, bMax]
print(colorMin, colorMax)
# colorRange ((rMin, rMax),(gMin, gMax), (bMin, bMax))颜色范围
# pixelSum 像素总数
# pixelSet 像素集合
class ColorBox:
def __init__(self, colorRange, pixelSum, pixelSet):
self.colorRange = colorRange
self.pixelSum = pixelSum
self.pixelSet = pixelSet
self.v = (colorRange[0][1] - colorRange[0][0]) * (colorRange[1][1] - colorRange[1][0]) * (colorRange[2][1] - colorRange[2][0]) >> 16
self.rank = (-1) * pixelSum * (self.v)**vBalance
def __lt__(self, other):
return self.rank < other.rank
def getCutSide(colorRange): # 获取切割边:{r:0, g:1, b:2}
vSize = [0] * 3
for i in range(3):
vSize[i] = colorRange[i][1] - colorRange[i][0]
return vSize.index(max(vSize))
def cutRange(colorRange, cutSide, cutValue): # 将颜色范围一切为二
ret0 = copy.deepcopy(colorRange)
ret1 = copy.deepcopy(colorRange)
ret0[cutSide][1] = cutValue
ret1[cutSide][0] = cutValue
return (ret0, ret1)
def colorCut(colorBox): # 颜色切分
cutValue = 0
colorRange = colorBox.colorRange
cutSide = getCutSide(colorRange) # 分割边
pixelCount = 0 # 当前像素累加数
sourceList = colorBox.pixelSet # 像素集合
pixelSum = colorBox.pixelSum
cutPoint = 0 # 切分点
sourceList.sort(key=lambda x: x[1][cutSide])
for pixelPoint in sourceList: # pixelPoint:(count, (r, g, b))
pixelCount += pixelPoint[0]
cutPoint += 1
if pixelCount*((pixelPoint[1][cutSide] - colorRange[cutSide][0]))**vBalance > (pixelSum - pixelCount)*((colorRange[cutSide][1] - pixelPoint[1][cutSide]))**vBalance: # 达到一半
cutValue = pixelPoint[1][cutSide]
break
if cutPoint == len(sourceList): # 到最后一个才触发，丢掉最后一个元素
newRange = cutRange(colorRange, cutSide, sourceList[cutPoint - 2][1][cutSide])
box0 = ColorBox(newRange[0], pixelCount - sourceList[cutPoint - 1][0], sourceList[:(cutPoint-1)])
return [box0]
else:
newRange = cutRange(colorRange, cutSide, cutValue)
box0 = ColorBox(newRange[0], pixelCount, sourceList[0:cutPoint])
box1 = ColorBox(newRange[1], colorBox.pixelSum -
pixelCount, sourceList[cutPoint:])
return [box0, box1]
def doCut(queue): # 递归切分
if queue.qsize() < colorNum:
box = queue.get()[1] # 获取rank第一的box
c = colorCut(box)
for vbox in c:
# print("Rank:" + str(vbox.rank))
queue.put((vbox.rank, vbox))
return doCut(queue)
else:
return queue
def sumColor(colorList): # 颜色求和
sumList = [0] * 4
for count, (r, g, b) in colorList:
sumList[0] += count
sumList[1] += r * count
sumList[2] += g * count
sumList[3] += b * count
if sumList[0] != 0:
sumList[1] //= sumList[0]
sumList[2] //= sumList[0]
sumList[3] //= sumList[0]
return sumList
def getMainColor(queue, number): # 根据box计算主色调
colorList = []
for i in range(number):
box = queue.get()[1]
colorList.append(sumColor(box.pixelSet))
return colorList
# 初始化
initRange = [[rMin, rMax], [gMin, gMax], [bMin, bMax]]
initBox = ColorBox(initRange, totalSize, imageColors)
initQueue = PriorityQueue()
initQueue.put((initBox.rank, initBox))
resQueue = doCut(initQueue)
mainColor = getMainColor(resQueue, colorNum)
mainColor.sort(key=lambda x: x[0],reverse=True)
print(mainColor)
# 生成图片
region = image.crop((0, 0, sizeX, sizeY))
x = int(sizeX * 1.2)
y = sizeY
yEach = y // colorNum
new = Image.new('RGB', (x, y), (255, 255, 255))
new.paste(region, (0, 0))
for i in range(0, colorNum):
for n in range(i * yEach + padding, (i + 1) * yEach - padding):
for m in range(sizeX + padding, x - padding):
new.putpixel(
(m, n), (mainColor[i][1], mainColor[i][2], mainColor[i][3]))
new.save(path + 'color/' + name + '-' + str(vBalance) + '-color' + suffix)
# 读取文件夹
def listPath(path): # 传入存储的list
path_list = []
for file in os.listdir(path):
file_path = os.path.join(path, file)
if os.path.isfile(file_path):
path_list.append(file_path)
return path_list
pathList = listPath("E:\python\壁纸")
for path in pathList:
colorExt(path,8,8,5)
# for vBalance in range(0,20,2):
# colorExt("E:/python/b.jpg", 8, vBalance, 5)效果展示
上图依旧是提取8中颜色，颜色影响系数依次是0, 2, 4 … 14, 16，不难看出，本图片中体积影响系数到6以后结果就没有多大差别。