区域生长法的编程实现——程序员数字图像处理第一步.docx

数字图像处理的书数不胜数。相关的方法，从直方图、卷积到小波、机器学习方面的方法也举不胜举。OpenCV库给我们提供了一整套图像存储、显示方法外，也集成了很多很多的图像、视频处理算法以及机器学习算法，以函数的形式提供给我们使用。然而在初学阶段，我们往往过分依赖于OpenCV中的那些库函数。虽说OpenCV集成了很多高效算法，然而，毕竟不是无所不包。OpenCV中基础的算法有时会缺失，有时用起来不是很得心应手。区域生长算法就是一例。区域生长法，思路很简单：将一个图像中的一点作为种子，此种子会沿8邻域/4邻域进行扩展，扩展具有一定限制（即差值不能过大，当然你可以引入梯度这个概念，甚至二维偏导数），求出种子生长的最终结果。把示例程序的几张结果发过来，何谓区域生长法，大家一看便知。区域生长法对于较为均衡的背景中提取目标物而言，效果较为理想。与阈值法、边缘检测法孰优孰劣，俺笔力及精力有限，不进行比较了。单从工程角度，区域生长法是统计闭合图形个数的基础。这里自己写了一下这个程序的演示代码，只负责将背景同生长不上去的区域分开：cppview plaincopy1. #defineINIT02. #defineSEED-13. #defineCONT-24. #defineINVAL-35. 6. intcolor_templ5*6*37. =8. 0,0,0,40,40,40,80,80,80,120,120,120,160,160,160,200,200,200,9. 76,0,13,113,52,0,127,91,0,188,114,0,203,140,68,236,211,190,10. 0,0,121,93,0,158,91,20,237,255,61,255,170,110,240,200,161,245,11. 38,88,0,77,118,46,66,130,98,112,182,46,118,197,124,174,221,178,12. 19,57,96,10,98,163,0,160,171,0,242,255,59,245,255,139,247,25513. ;14. 15. classg_point16. 17. public:18. g_point();19. g_point(intx_i,inty_i);20. g_point(intx_i,inty_i,intlbl_i);21. public:22. intx;23. inty;24. intlbl;/标签25. ;26. 27. g_point:g_point(intx_i,inty_i)28. 29. x=x_i;30. y=y_i;31. lbl=SEED;32. 33. 34. g_point:g_point()35. 36. x=0;37. y=0;38. lbl=INIT;39. 40. 41. g_point:g_point(intx_i,inty_i,intlbl_i)42. 43. x=x_i;44. y=y_i;45. lbl=lbl_i;46. 47. 48. voidregionGrowth(IplImage*img,int*p_mat,intx_g,inty_g,intthreshold_g)49. 50. /常用变量：51. intx;52. inty;53. intgradient=0;54. 55. 56. /设置前次边缘点57. std:listcont_pts_pre;58. std:list*pcont_pts_pre=&cont_pts_pre;59. 60. 61. /设置边缘点62. std:listcont_pts;63. std:list*pcont_pts=&cont_pts;64. 65. 66. /种子点+边缘点=下次的种子点67. /上次的边缘点=前次边缘点（用于下次减少点数)68. cont_pts_pre.push_back(classg_point(x_g,y_g,CONT);69. p_maty_g*img-width+x_g=SEED;70. 71. std:list:iteratoriter_cont;72. std:list:iteratoriter_prt;73. std:list:iteratoriter_swap;74. 75. 76. 77. 78. while(!cont_pts_pre.empty()79. 80. 81. 82. 83. /一轮生长84. iter_cont=cont_pts_pre.begin();85. while(iter_cont!=cont_pts_pre.end()86. 87. x=(*iter_cont).x;88. y=(*iter_cont).y;89. /if(!(x-10|y-1width+(x-1)=INIT)92. /93. /gradient=(char*)(img-imageData+y*img-widthStep)x-94. /(char*)(img-imageData+(y-1)*img-widthStep)x-1;95. /if(abs(gradient)width+(x-1)=SEED;99. /100. /else/不满足阈值要求101. /102. /p_mat(y-1)*img-width+(x-1)=INVAL;103. /104. /105. /106. if(!(x-1width+(x-1)=INIT)109. 110. gradient=(char*)(img-imageData+y*img-widthStep)x-111. (char*)(img-imageData+(y)*img-widthStep)x-1;112. if(abs(gradient)width+(x-1)=SEED;116. 117. else/不满足阈值要求118. 119. p_mat(y)*img-width+(x-1)=INVAL;120. 121. 122. 123. 124. /if(!(x-1=img-height)/#3125. /126. /if(p_mat(y+1)*img-width+(x-1)=INIT)127. /128. /gradient=(char*)(img-imageData+y*img-widthStep)x-129. /(char*)(img-imageData+(y+1)*img-widthStep)x-1;130. /if(abs(gradient)width+(x-1)=SEED;134. /135. /else/不满足阈值要求136. /137. /p_mat(y+1)*img-width+(x-1)=INVAL;138. /139. /140. /141. /142. if(!(y+1=img-height)/#4143. 144. if(p_mat(y+1)*img-width+(x)=INIT)145. 146. gradient=(char*)(img-imageData+y*img-widthStep)x-147. (char*)(img-imageData+(y+1)*img-widthStep)x;148. if(abs(gradient)width+(x)=SEED;152. 153. else/不满足阈值要求154. 155. p_mat(y+1)*img-width+(x)=INVAL;156. 157. 158. 159. /if(!(x+1=img-width|y+1=img-height)/#5160. /161. /if(p_mat(y+1)*img-width+(x+1)=INIT)162. /163. /gradient=(char*)(img-imageData+y*img-widthStep)x-164. /(char*)(img-imageData+(y+1)*img-widthStep)x+1;165. /if(abs(gradient)width+(x+1)=SEED;169. /170. /else/不满足阈值要求171. /172. /p_mat(y+1)*img-width+(x+1)=INVAL;173. /174. /175. /176. /177. if(!(x+1=img-width)/#6178. 179. if(p_mat(y)*img-width+(x+1)=INIT)180. 181. gradient=(char*)(img-imageData+y*img-widthStep)x-182. (char*)(img-imageData+(y)*img-widthStep)x+1;183. if(abs(gradient)width+(x+1)=SEED;187. 188. else/不满足阈值要求189. 190. p_mat(y)*img-width+(x+1)=INVAL;191. 192. 193. 194. 195. /if(!(x+1=img-width|y-1width+(x+1)=INIT)198. /199. /gradient=(char*)(img-imageData+y*img-widthStep)x-200. /(char*)(img-imageData+(y-1)*img-widthStep)x+1;201. /if(abs(gradient)width+(x+1)=SEED;205. /206. /else/不满足阈值要求207. /208. /p_mat(y-1)*img-width+(x+1)=INVAL;209. /210. /211. /212. /213. if(!(y-1width+(x)=INIT)216. 217. gradient=(char*)(img-imageData+y*img-widthStep)x-218. (char*)(img-imageData+(y-1)*img-widthStep)x;219. if(abs(gradient)width+(x)=SEED;223. 224. else/不满足阈值要求225. 226. p_mat(y-1)*img-width+(x)=INVAL;227. 228. 229. 230. 231. iter_cont+;232. 233. 234. /将cont_pts中的点赋给cont_pts_pre235. cont_pts_pre.clear();236. iter_swap=cont_pts.begin();237. while(iter_swap!=cont_pts.end()238. 239. cont_pts_pre.push_back(*iter_swap);240. iter_swap+;241. 242. cont_pts.clear();243. 244. 245. 246. /结束while247. 248. 249. 250. 251. cont_pts_pre.clear();252. cont_pts.clear();253. 254. /deletepseed_pts;255. /deletepcont_pts_pre;256. /deletepcont_pts;257. 258. 259. 260. voiddisplay_mat(int*p_mat,intwidth,intheight)261. 262. IplImage*disp;263. disp=cvCreateImage(cvSize(width,height),8,3);264. cvZero(disp);265. 266. for(inth=0;hheight;h+)267. 268. for(intw=0;wimageData+h*disp-widthStep)w*3+0=color_templ0*18+0*3+0;273. (UCHAR*)(disp-imageData+h*disp-widthStep)w*3+1=color_templ0*18+0*3+1;274. (UCHAR*)(disp-imageData+h*disp-widthStep)w*3+2=color_templ0*18+0*3+2;275. 276. elseif(p_math*width+w=INIT)277. 278. (UCHAR*)(disp-imageData+h*disp-widthStep)w*3+0=color_templ0*18+2*3+0;279. (UCHAR*)(disp-imageData+h*disp-widthStep)w*3+1=color_templ0*18+2*3+1;280. (UCHAR*)(disp-imageData+h*disp-widthStep)w*3+2=color_templ0*18+2*3+2;281. 282. elseif(p_math*width+w=INVAL)283. 284. (UCHAR*)(disp-imageData+h*disp-widthStep)w*3+0=color_templ0*18+4*3+0;285. (UCHAR*)(disp-imageData+h*disp-widthStep)w*3+1=color_templ0*18+4*3+1;286. (UCHAR*)(disp-imageData+h*disp-widthStep)w*3+2=color_templ0*18+4*3+2;287. 288. 289. 290. cvNamedWindow(display);291. cvShowImage(display,disp);292. cvWaitKey(0);293. 294. cvDestroyWindow(display);295. cvReleaseImage(&disp);296. 297. 298. 299. int_tmain(intargc,_TCHAR*argv)300. 301. IplImage*img;302. img=cvLoadImage(1.bmp,CV_LOAD_IMAGE_ANYCOLOR);303. 304. 305. /建立需要使用的单通道8位深图像306. IplImage*img_gray;307. img_gray=cvCreateImage(cvGetSize(img),img-depth,1);308. cvSplit(img,NULL,img_gray,NULL,NULL);309. 310. cvNamedWindow(originalimage,-1);311. cvShowImage(originalimage,img_gray);312. cvWaitKey(0);313. 314. /建立所需的与图像同样宽高的整型数矩阵并赋初值315. int*pres_mat;316. pres_mat=newintimg_gray-width*img_gray-height;317. for(inti=0;iwidth*img_gray-height;i+)318. 319. pres_mati=INIT;320. 321. 322. /开始进行区域生长323. regionGrowth(img_gray,pres_mat,1,1,50);324. 325. display_mat(pres_mat,img_gray-width,img_gray-height);326. 327. return0;328. regionGrowth函数详解参数：img：需要进行区域生长的图像，8位单通道的IplImagep_map：整型数矩阵指针，宽高与图像相同，用于储存图像生长结果矩阵已经被全部赋值为INIT(#define INIT 0)x_g, y_g区域生长初始种子点threshold_g区域生长邻域梯