程序提示ValueError: too many values to unpack (expected 2),修改try: points3D = pd.read_csv('point_1.txt', sep='\t', header=None, error_bad_lines=False, na_values='?') coordinates = pd.read_csv('image_3_2.txt', sep='\t', header=None, error_bad_lines=False, na_values='?') tree = KDTree(points3D.iloc[:, :2].values) with open("result.txt", 'w', encoding='utf-8') as f1: for i in range(coordinates.shape[0]): x_axis_coord, y_axis_coord = coordinates.iloc[i, :2].values query_point = [x_axis_coord, y_axis_coord] dist, index = tree.query(query_point) f1.write(str(points3D.iloc[index, 0]) + " " + str(points3D.iloc[index, 1]) + " " + str(points3D.iloc[index, 2]) + " " + '\n')
时间: 2024-03-29 10:41:10 浏览: 19
这个错误提示意味着 unpack 的值太多了,期望的是两个值,但实际上有更多的值。可能的原因是在 `coordinates.iloc[i, :2].values` 这一行代码中,返回了三个或更多的值。你可以在这一行代码前面加上 `print(coordinates.iloc[i, :2].values)` 来查看这个数组的值,然后检查是否有多余的值。如果有,你可以使用 `x_axis_coord, y_axis_coord, _ = coordinates.iloc[i, :3].values` 来跳过多余的值。如果你不需要这个值,你可以用任何变量名代替 `_`。
相关问题
程序运行提示ValueError: too many values to unpack (expected 2),修改程序df1 = pd.read_csv('image_3_2.txt', sep='\t', header=None, error_bad_lines=False, na_values='?') df2 = pd.read_csv('image_6.txt', sep='\t', header=None, error_bad_lines=False, na_values='?') tree = KDTree(df2.iloc[:, :2].values) k = 4 distances, indices = tree.query(df1.iloc[:, :2].values, k=k) values = df2.iloc[indices[:, 1:], 2].values mean_values = values.mean(axis=1) df1.iloc[:, 2] = mean_values df1.to_csv('4.txt', sep='\t', header=None, index=None)
这个修改后的程序看起来是正确的,但是如果你仍然遇到了 'ValueError: too many values to unpack (expected 2)' 的错误,那么你可以尝试检查一下你的文件格式是否正确,文件中是否存在不符合预期的行或列。你还可以尝试打印出 DataFrame 的 shape 属性来查看它们的维数是否正确。此外,你还可以尝试使用 try-except 语句来捕获异常,并在出现异常时打印出错误的具体信息,以便更好地调试。下面是一个使用 try-except 语句的示例:
```python
import pandas as pd
from scipy.spatial import KDTree
try:
df1 = pd.read_csv('image_3_2.txt', sep='\t', header=None, error_bad_lines=False, na_values='?')
df2 = pd.read_csv('image_6.txt', sep='\t', header=None, error_bad_lines=False, na_values='?')
tree = KDTree(df2.iloc[:, :2].values)
k = 4
distances, indices = tree.query(df1.iloc[:, :2].values, k=k)
values = df2.iloc[indices[:, 1:], 2].values
mean_values = values.mean(axis=1)
df1.iloc[:, 2] = mean_values
df1.to_csv('4.txt', sep='\t', header=None, index=None)
except Exception as e:
print("Error: ", e)
```
这个示例程序会在出现异常时打印出错误信息,以便你更好地调试和解决问题。
cv2.CHAIN_APPROX_SIMPLE) ValueError: too many values to unpack (expected 2)
出现"ValueError: too many values to unpack (expected 2)"的错误是因为在执行cv2.findContours()函数时,返回的结果与期望的不一致。根据引用和引用的信息,可以看出这个函数应该返回两个值,即轮廓和层次结构。然而,在代码中尝试解包结果时,期望得到2个值,但实际上返回的值数量超过了预期。
为了解决这个问题,可以根据引用的建议进行修改。在调用cv2.findContours()函数时,将其返回值解包为3个变量,即binary、contours和hierarchy。这样就能够正确地获得所需的值,避免出现"ValueError: too many values to unpack (expected 2)"的错误。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* [ValueError: too many values to unpack (expected 2)](https://blog.csdn.net/qq_41701723/article/details/129177910)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v92^chatsearchT0_1"}}] [.reference_item style="max-width: 50%"]
- *2* *3* [opencv : ValueError: too many values to unpack (expected 2)](https://blog.csdn.net/u011304078/article/details/100134073)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v92^chatsearchT0_1"}}] [.reference_item style="max-width: 50%"]
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