4 源码说明
import sys import cv2 import math import numpy as np import matplotlib.pyplot as plt size = (320,240) range_rgb = {'red': (0, 0, 255), 'blue': (255, 0, 0), 'green': (0, 255, 0)} __target_color = ('red', 'green', 'blue') #LAB颜色空间红、蓝、绿的颜色范围，即在(0, 160, 135) ~(255, 255, 255)之间认为是红色，以此类推 lab_data_max = {'red': (255, 255, 255), 'black': ( 89, 255, 255), 'blue': ( 255, 254, 90), 'green': ( 255, 120, 180), 'white': ( 255, 255, 255)} lab_data_min = {'red': (0, 160, 135), 'black': ( 0, 0, 0), 'blue': ( 0, 120, 0), 'green': ( 0, 0, 100), 'white': ( 193, 0, 0)} #在轮廓列表中获取面积最大的轮廓，返回面积最大的轮廓和该轮廓的面积 def GetAreaMaxContour(contours): coutousAreaMax = 0 coutoursMax = None for c in contours: coutoursAreaTmp = math.fabs(cv2.contourArea(c)) if coutoursAreaTmp > coutousAreaMax : coutoursAreaMax = coutoursAreaTmp coutoursMax = c return coutoursAreaMax , coutoursMax #坐标的映射，根据轮廓尺寸等比例映射 def map( x , in_min , in_max , out_min , out_max ): return (x-in_min)*(out_max-out_min)/(in_max-in_min) + out_min #主函数 if __name__=="__main__": img = cv2.imread("test10.1.jpg") img_h,img_w = img.shape[:2] #获取图片的尺寸 img_red = img img_blue = img img_green=img frm_resize = cv2.resize(img , size , interpolation=cv2.INTER_NEAREST) #为简化处理，加快处理速度，将图片缩小 frm_gb = cv2.GaussianBlur( frm_resize,(3,3),3) #高斯滤波 frm_lab = cv2.cvtColor(frm_gb , cv2.COLOR_BGR2LAB) #为便于确定颜色，转成LAB颜色空间 Coutour_Max = None Coutour_Max_Area = 0 for i in lab_data_max: if i in __target_color: frm_mask=cv2.inRange( frm_lab , (lab_data_min[i][0],lab_data_min[i][1],lab_data_min[i][2]), (lab_data_max[i][0],lab_data_max[i][1],lab_data_max[i][2]))#范围内颜色转成黑白 opened = cv2.morphologyEx( frm_mask, cv2.MORPH_OPEN ,np.ones((3,3),np.uint8)) closed = cv2.morphologyEx( opened, cv2.MORPH_CLOSE ,np.ones((3,3),np.uint8)) #开合操作，去除非连续点 contours= cv2.findContours( closed, cv2.RETR_EXTERNAL ,cv2.CHAIN_APPROX_NONE)[-2] #找轮廓 Coutour_TmpMax_Area , Coutour_TmpMax=GetAreaMaxContour(contours) #寻找面积最大的轮廓 if Coutour_TmpMax is not None: if Coutour_TmpMax_Area > Coutour_Max_Area : Coutour_Max_Area = Coutour_TmpMax_Area Coutour_Max = Coutour_TmpMax Coutour_Max_Color = i if i == "red" : img_red = closed if i == "blue" : img_blue = closed if i == "green" : img_green = closed #展示最终的效果 (center_x,center_y),radius = cv2.minEnclosingCircle(Coutour_Max) center_x = int(map( center_x , 0 ,size[0], 0 ,img_w)) center_y = int(map( center_y , 0 ,size[1], 0 , img_h)) radius = int(map( radius , 0 ,size[0] , 0 ,img_w)) cv2.circle( img , (int(center_x),int(center_y)) , int( radius) , range_rgb[Coutour_Max_Color], 2) cv2.putText(img, "Color: " + Coutour_Max_Color, (10, img.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.65, range_rgb[Coutour_Max_Color], 2) plt.figure(figsize=(200,100),dpi=6) plt.subplot(221),plt.imshow(img),plt.title("org") plt.subplot(222),plt.imshow(img_red),plt.title("red") plt.subplot(223),plt.imshow(img_blue),plt.title("blue") plt.subplot(224),plt.imshow(img_green),plt.title("green") plt.show() cv2.waitKey(0) cv2.destoryAllWindows()
5 运行结果展示
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