【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):
1)、使用cv2.inRange()函数过滤背景后图片如下:
2)、可见使用白色分量过滤背景后,出现车辆的轮廓,因此,能够计算白色区域的面积,最大的则为该物体颜色
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