Python3识别判断图片主要颜色并和颜色库进行对比的方法

article/2025/8/26 19:40:11

【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：http://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 Imagedef get_dominant_color(image):max_score = 0.0001dominant_color = Nonefor 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:continuescore = (saturation+0.1)*countif score > max_score:max_score = scoredominant_color = (r,g,b)return dominant_colorif __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://www.sioe.cn/yingyong/yanse-rgb-16/

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2lower_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_listreturn dictif __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 colorListfilename='car04.jpg'#处理图片
def get_color(frame):print('go in get_color')hsv = cv2.cvtColor(frame,cv2.COLOR_BGR2HSV)maxsum = -100color = Nonecolor_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 = 0for c in cnts:sum+=cv2.contourArea(c)if sum > maxsum :maxsum = sumcolor = dreturn colorif __name__ == '__main__':frame = cv2.imread(filename)print(get_color(frame))

3、结果

原始图像（网上找的测试图）：