关于宾图,我有兴趣有效地计算图像中的个人形体。 同样,作为邻里的问题,人们如何适应在广场形象中体现的各种形态?
Pseudocode是最受欢迎的。
我可以回答后半部分,但如果上半部分将“沙皮”定义为某种颜色的毗连性皮,我就这样做了。 保持整个图像的阵列,显示哪些雕像是“黄的”。 (A bool[,] do the trick, in C#-speak.)
然后开始对每张钢材进行循环处理。 如果已经看到,它就会ski。
否则,如果看不到它,它就带有“非沙皮”的颜色,就将其看到并移走。
否则,它就没有见到,而且是一种形式。 从这里看,四条绕行路线中的每一条都有间谍。 如果你看上见,就会停下来,回头(这防止了一场无限的循环发生)。 如果六氯苯是非蓝色,就立即标出并停止。 否则,六氯苯是一种形状色。 在你正在积累的“血清定义”数据上加起来。
一旦这一进程完成,你就发现了一种形态。 将其存放在其他地方,并继续搜寻。 现在应该看到这种形形形形色色的螺纹,不再由算法来考虑。
一旦形成状物色素堆积了,这种形状分解法必然会发生逆转,大形可能过度流入。 或许应当利用一种以蒸.为基础的阻塞办法来进行整个形状的搜索。
(如果你认为直径相邻的餐具足以将两种形状连接成一种,只是搜查所有8个相邻的餐厅,而不只是4个横向和纵向相邻的餐厅)。)
“沙皮”的可能概念是相关组成部分。 在该案中,一项经限定的算法将给予你希望:。 如果你也想到X和Y级,可以扩大工会定义,以追踪这些地方。
改变图像数据时,你将采用所有图象和尺寸(形状的中继器)进行每一形状。
如果你的形象在体内形成,那么博览会将既作为个体体处理。
如果需要密码寄送我邮件,将乐于分享。
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