解释如下代码：def appendimages(im1,im2): """ Return a new image that appends the two images side-by-side. """ # select the image with the fewest rows and fill in enough empty rows rows1 = im1.shape[0] rows2 = im2.shape[0] if rows1 < rows2: im1 = np.concatenate((im1,np.zeros((rows2-rows1,im1.shape[1]))), axis=0) elif rows1 > rows2: im2 = np.concatenate((im2,np.zeros((rows1-rows2,im2.shape[1]))), axis=0) # if none of these cases they are equal, no filling needed. return np.concatenate((im1,im2), axis=1)

这段代码是实现了一个appendimages函数，它的作用是将两张图片水平拼接成一张图片。输入为两个图像(im1和im2)，输出为拼接后的图像。

该函数首先获取两个图像的行数(rows1和rows2)，然后比较它们的行数。如果第一个图像的行数(rows1)小于第二个图像的行数(rows2)，则在第一个图像的底部添加足够的空行，使得两个图像的行数相等。反之，如果第一个图像的行数(rows1)大于第二个图像的行数(rows2)，则在第二个图像的底部添加足够的空行，使得两个图像的行数相等。如果两个图像的行数已经相等，则不需要添加额外的空行。

最后，函数将两个行数相等的图像在水平方向上进行连接，并返回拼接后的结果。该函数常用于可视化图像处理结果或特征匹配结果等场景中。

相关问题

Exception in thread "main" java.lang.NoSuchMethodError: com.google.common.base.Preconditions.checkArgument(ZLjava/lang/String;Ljava/lang/Object;)V at org.apache.hadoop.conf.Configuration.set(Configuration.java:1357) at org.apache.hadoop.conf.Configuration.set(Configuration.java:1338) at org.apache.spark.deploy.SparkHadoopUtil$.org$apache$spark$deploy$SparkHadoopUtil$$appendS3AndSparkHadoopConfigurations(SparkHadoopUtil.scala:464) at org.apache.spark.deploy.SparkHadoopUtil$.newConfiguration(SparkHadoopUtil.scala:436) at org.apache.spark.deploy.SparkSubmit$$anonfun$2.apply(SparkSubmit.scala:334) at org.apache.spark.deploy.SparkSubmit$$anonfun$2.apply(SparkSubmit.scala:334) at scala.Option.getOrElse(Option.scala:121) at org.apache.spark.deploy.SparkSubmit.prepareSubmitEnvironment(SparkSubmit.scala:334) at org.apache.spark.deploy.SparkSubmit.submit(SparkSubmit.scala:143) at org.apache.spark.deploy.SparkSubmit.doSubmit(SparkSubmit.scala:86) at org.apache.spark.deploy.SparkSubmit$$anon$2.doSubmit(SparkSubmit.scala:924) at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:933) at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)

这个错误提示表明你的Spark应用程序在运行时遇到了NoSuchMethodError异常，具体原因是你的应用程序依赖的com.google.guava库版本与Hadoop库版本不兼容。在你的代码中使用了Hadoop的Configuration类时，它引用了Google Guava库中的方法，但是这个方法在你当前使用的Guava版本中不存在，从而导致了运行时异常。

为了解决这个问题，你需要升级或降级你的Guava库版本，以便与你当前使用的Hadoop库版本兼容。如果你使用的是Maven或Gradle构建工具，可以在项目的pom.xml或build.gradle文件中修改Guava库的版本号；如果你手动管理依赖关系，可以从Maven中央仓库中下载适当版本的Guava库，并将其添加到你的classpath中。

另外，如果你使用的是Spark on YARN模式，你还需要确保你的所有节点上都安装了相同版本的Guava库和Hadoop库，以便避免类库版本不一致的问题。

希望这个回答能够帮助你解决问题！

We will write a procedure every-nth that accepts two arguments: num is an integer and list-of-sents is a list of a certain number of lists. every-nth selects the num-th elements from each list in list-of-sents and returns the list of selected elements. Assume that all lists in listof-sents have more than num elements.

Here's a possible implementation of the every-nth procedure in Python:

def every_nth(num, list_of_sents):
    result = []
    for sentence in list_of_sents:
        result.append(sentence[num-1])
    return result

The procedure takes the `num` argument and the `list_of_sents` argument. It initializes an empty list called `result` to store the selected elements. It then loops through each sentence in the `list_of_sents` and appends the `num`-th element (i.e., the element at index `num-1`) to the `result` list. Finally, it returns the `result` list.

Here's an example usage of the every-nth procedure:

list_of_sents = [['a', 'b', 'c'], ['d', 'e', 'f'], ['g', 'h', 'i']]
selected_elements = every_nth(2, list_of_sents)
print(selected_elements)  # Output: ['b', 'e', 'h']

In this example, we call the every-nth procedure with `num=2` and `list_of_sents=[['a', 'b', 'c'], ['d', 'e', 'f'], ['g', 'h', 'i']]`. The procedure selects every second element from each sub-list in `list_of_sents`, resulting in the list `['b', 'e', 'h']`.