reliability_scores = [match.distance for match in good_matches] # 根据可靠性得分对点对进行排序 sorted_indices = np.argsort(reliability_scores) sorted_matched_points_left = matched_points_left[sorted_indices] sorted_matched_points_right = matched_points_right[sorted_indices] # 获取最可靠的两个点 reliable_points_left = sorted_matched_points_left[:2] reliable_points_right = sorted_matched_points_right[:2]
时间: 2023-06-20 17:10:24 浏览: 72
这段代码的作用是根据SIFT算法匹配出的特征点之间的可靠性得分对它们进行排序,然后取出得分最高的前两个点,作为左右图像中的可靠匹配点。具体来说:
- `good_matches`是经过筛选后的SIFT算法匹配出的可靠点对
- `reliability_scores = [match.distance for match in good_matches]`将每个点对的可靠性得分存储到一个列表中
- `sorted_indices = np.argsort(reliability_scores)` 对可靠性得分进行排序,并将排序后的索引存储到`sorted_indices`中
- `sorted_matched_points_left = matched_points_left[sorted_indices]` 和 `sorted_matched_points_right = matched_points_right[sorted_indices]`分别将左右图像中对应的点按照可靠性得分进行排序
- `reliable_points_left = sorted_matched_points_left[:2]` 和 `reliable_points_right = sorted_matched_points_right[:2]`分别取出左右图像中可靠匹配点中得分最高的前两个点
这样就得到了左右图像中最可靠的两个匹配点,可以用它们来进行后续的三角测量和深度估计等任务。
相关问题
np.argsort(reliability_scores)
### 回答1:
这个代码是用来对一个数组进行排序并返回排序后的索引值,其中参数reliability_scores是要排序的数组。具体来说,np.argsort()函数会返回排序后的索引值数组,其中每个元素是原数组中对应位置的元素在排序后的索引值。例如,如果reliability_scores=[0.2, 0.5, 0.8, 0.1, 0.3],则np.argsort(reliability_scores)的返回值为[3, 0, 4, 1, 2],表示排序后的数组为[0.1, 0.2, 0.3, 0.5, 0.8],其中0.1的索引为3,0.2的索引为0,以此类推。
### 回答2:
`np.argsort(reliability_scores)`是一个numpy函数,用于返回一个数组或列表的排序索引。排序索引是指按照原始数组中元素的大小顺序排列的位置。
具体说来,`np.argsort(reliability_scores)`会返回一个由排序后的索引组成的数组。这个数组将原始数组`reliability_scores`中的元素按照从小到大的顺序排序。可以通过使用这些排序索引,可以方便地对原始数组进行排序或提取特定的元素。
举个例子来说,假设`reliability_scores`是一个包含信用可靠性分数的数组,数组的索引表示个体的身份。`np.argsort(reliability_scores)`将返回一个排序后的索引数组,根据分数从低到高的顺序排列。这样可以通过索引数组找到对应的个体身份。例如,`np.argsort(reliability_scores)[0]`将表示最低分的个体的索引,`np.argsort(reliability_scores)[-1]`将表示最高分的个体的索引。
总而言之,`np.argsort(reliability_scores)`用于确定一个数组中元素的排序顺序,并返回排序后的索引数组。这样可以方便地对数组进行排序、提取或者参考。
### 回答3:
np.argsort(reliability_scores)是一个numpy函数,用于对给定数组reliability_scores进行排序,并返回排序后的索引。
例如,假设reliability_scores为[0.8, 0.5, 0.9, 0.6],那么np.argsort(reliability_scores)的结果将是[1, 3, 0, 2]。这意味着原数组中的第一个元素0.8在排序后位于索引为1的位置,第二个元素0.5在排序后位于索引为3的位置,依此类推。
通过np.argsort(reliability_scores)可以获得原数组reliability_scores的排序结果对应的索引值,然后可以根据这些索引值对原数组进行重排序或提取相关的元素。
this is incompatible with sql_mode=only_full_group_by ; bad SQL grammar
Hi there! It seems like you're encountering an error related to the SQL mode "only_full_group_by". This mode is a stricter setting that requires all columns in the SELECT statement to be either aggregated or included in the GROUP BY clause.
If you're getting an error message stating that your query is incompatible with "sql_mode=only_full_group_by" or experiencing a "bad SQL grammar" issue, it means that your query violates this condition.
To resolve this error, you have a few options:
1. Adjust your SQL query: Make sure that all non-aggregated columns in the SELECT statement are also included in the GROUP BY clause. This ensures that your query conforms to the "only_full_group_by" mode.
2. Disable the "only_full_group_by" mode: If you have the necessary privileges, you can modify the SQL mode settings to disable "only_full_group_by". However, keep in mind that this might have implications on the correctness and reliability of your query results.
3. Use an aggregate function for non-grouped columns: Another way to address this error is to use an appropriate aggregate function, such as MAX(), MIN(), SUM(), etc., for the non-aggregated columns in your SELECT statement.
Please provide more details about your specific SQL query or error message if you need further assistance.
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