# cv2.error: OpenCV(4.9.0) /tmp/opencv-4.9.0/modules/core/src/kmeans.cpp:245: error: (-215:断言失败) N >= K 在函数 'kmeans' 中

- **ID:** `opencv/kmeans-clustering-empty-labels`
- **领域:** opencv
- **类别:** assertion_error
- **错误码:** `-215`
- **验证级别:** ai_generated
- **修复率:** 95%

## 根因

k-means 聚类中数据点数量 (N) 小于请求的簇数量 (K)，导致断言失败。

## 版本兼容性

| 版本 | 状态 | 引入 | 弃用 |
|------|------|------|------|
| 4.8.0 | active | — | — |
| 4.9.0 | active | — | — |
| 4.10.0 | active | — | — |

## 解决方案

1. ```
   Ensure K is less than or equal to the number of data points. Add a check: `if len(data) < K: K = len(data)` before calling kmeans.
   ```
2. ```
   Use a smaller K value appropriate for the dataset: `K = min(K, len(data))`
   ```
3. ```
   Collect more data points or use a different clustering algorithm (e.g., DBSCAN) that doesn't require specifying K.
   ```

## 无效尝试

- **** — Using K=1 might avoid the assertion but is not meaningful for clustering; it's a workaround but not a fix. (40% 失败率)
- **** — Randomly duplicating points to increase N distorts the data distribution and produces incorrect clusters. (80% 失败率)
- **** — Transposing the data matrix doesn't change the number of samples; it only changes feature dimensions. (70% 失败率)