宜配屋

【更新】主要提供两种方案：

方案一：（参考网上代码，感觉实用性不是很强）使用PIL截取图像，然后将RGB转为HSV进行判断，统计判断颜色，最后输出RGB值

方案二：使用opencv库函数进行处理。（效果不错）

1、将图片颜色转为hsv，
2、使用cv2.inRange()函数进行背景颜色过滤
3、将过滤后的颜色进行二值化处理
4、进行形态学腐蚀膨胀，cv2.dilate()
5、统计白色区域面积

详解：方案一：

转载出处：www.jb51.net/article/62526.htm

项目实际需要，对识别出来的车车需要标记颜色，因此采用方案如下：

1、通过import PIL.ImageGrab as ImageGrab 将识别出来的汽车矩形框裁剪出来

img_color=image.crop((left,right,top,bottom))

2、将裁剪出来的image进行颜色图像识别

RGB和hsv中间的转换关系，网上很多，我也没有具体去研究如何转换的，能用就行

附上测试，封装成函数方法：

import colorsys
import PIL.Image as Image
 
def get_dominant_color(image):
  max_score = 0.0001
  dominant_color = None
  for count,(r,g,b) in image.getcolors(image.size[0]*image.size[1]):
    # 转为HSV标准
    saturation = colorsys.rgb_to_hsv(r/255.0, g/255.0, b/255.0)[1]
    y = min(abs(r*2104+g*4130+b*802+4096+131072)>>13,235)
    y = (y-16.0)/(235-16)
 
    #忽略高亮色
    if y > 0.9:
      continue
    score = (saturation+0.1)*count
    if score > max_score:
      max_score = score
      dominant_color = (r,g,b)
  return dominant_color
 
 
if __name__ == '__main__':
  image = Image.open('test.jpg')
  image = image.convert('RGB')
  print(get_dominant_color(image))

测试图

结果

在这个网上查询RGB数值对应的颜色

http://tools.jb51.net/static/colorpicker/index.html

方案二：opencv计算机视觉库函数处理

1、定义HSV颜色字典，参考网上HSV颜色分类

代码如下：

import numpy as np
import collections
 
#定义字典存放颜色分量上下限
#例如：{颜色: [min分量, max分量]}
#{'red': [array([160, 43, 46]), array([179, 255, 255])]}
 
def getColorList():
  dict = collections.defaultdict(list)
 
  # 黑色
  lower_black = np.array([0, 0, 0])
  upper_black = np.array([180, 255, 46])
  color_list = []
  color_list.append(lower_black)
  color_list.append(upper_black)
  dict['black'] = color_list
 
  # #灰色
  # lower_gray = np.array([0, 0, 46])
  # upper_gray = np.array([180, 43, 220])
  # color_list = []
  # color_list.append(lower_gray)
  # color_list.append(upper_gray)
  # dict['gray']=color_list
 
  # 白色
  lower_white = np.array([0, 0, 221])
  upper_white = np.array([180, 30, 255])
  color_list = []
  color_list.append(lower_white)
  color_list.append(upper_white)
  dict['white'] = color_list
 
  #红色
  lower_red = np.array([156, 43, 46])
  upper_red = np.array([180, 255, 255])
  color_list = []
  color_list.append(lower_red)
  color_list.append(upper_red)
  dict['red']=color_list
 
  # 红色2
  lower_red = np.array([0, 43, 46])
  upper_red = np.array([10, 255, 255])
  color_list = []
  color_list.append(lower_red)
  color_list.append(upper_red)
  dict['red2'] = color_list
 
  #橙色
  lower_orange = np.array([11, 43, 46])
  upper_orange = np.array([25, 255, 255])
  color_list = []
  color_list.append(lower_orange)
  color_list.append(upper_orange)
  dict['orange'] = color_list
 
  #黄色
  lower_yellow = np.array([26, 43, 46])
  upper_yellow = np.array([34, 255, 255])
  color_list = []
  color_list.append(lower_yellow)
  color_list.append(upper_yellow)
  dict['yellow'] = color_list
 
  #绿色
  lower_green = np.array([35, 43, 46])
  upper_green = np.array([77, 255, 255])
  color_list = []
  color_list.append(lower_green)
  color_list.append(upper_green)
  dict['green'] = color_list
 
  #青色
  lower_cyan = np.array([78, 43, 46])
  upper_cyan = np.array([99, 255, 255])
  color_list = []
  color_list.append(lower_cyan)
  color_list.append(upper_cyan)
  dict['cyan'] = color_list
 
  #蓝色
  lower_blue = np.array([100, 43, 46])
  upper_blue = np.array([124, 255, 255])
  color_list = []
  color_list.append(lower_blue)
  color_list.append(upper_blue)
  dict['blue'] = color_list
 
  # 紫色
  lower_purple = np.array([125, 43, 46])
  upper_purple = np.array([155, 255, 255])
  color_list = []
  color_list.append(lower_purple)
  color_list.append(upper_purple)
  dict['purple'] = color_list
 
  return dict
 
 
if __name__ == '__main__':
  color_dict = getColorList()
  print(color_dict)
 
  num = len(color_dict)
  print('num=',num)
 
  for d in color_dict:
    print('key=',d)
    print('value=',color_dict[d][1])

2、颜色识别

import cv2
import numpy as np
import colorList
 
filename='car04.jpg'
 
#处理图片
def get_color(frame):
  print('go in get_color')
  hsv = cv2.cvtColor(frame,cv2.COLOR_BGR2HSV)
  maxsum = -100
  color = None
  color_dict = colorList.getColorList()
  for d in color_dict:
    mask = cv2.inRange(hsv,color_dict[d][0],color_dict[d][1])
    cv2.imwrite(d+'.jpg',mask)
    binary = cv2.threshold(mask, 127, 255, cv2.THRESH_BINARY)[1]
    binary = cv2.dilate(binary,None,iterations=2)
    img, cnts, hiera = cv2.findContours(binary.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
    sum = 0
    for c in cnts:
      sum+=cv2.contourArea(c)
    if sum > maxsum :
      maxsum = sum
      color = d
 
  return color
 
 
if __name__ == '__main__':
  frame = cv2.imread(filename)
  print(get_color(frame))

3、结果

原始图像（网上找的测试图）：