-215 opencv assertion_error ai_generated true

cv2.error: OpenCV(4.9.0) /tmp/opencv-4.9.0/modules/core/src/kmeans.cpp:245: error: (-215:Assertion failed) N >= K in function 'cv::kmeans'

ID: opencv/kmeans-n-less-than-k

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95%Fix Rate

88%Confidence

1Evidence

2024-01-10First Seen

Version Compatibility

Version	Status	Introduced	Deprecated	Notes
4.8.0	active	—	—	—
4.9.0	active	—	—	—
4.10.0	active	—	—	—
5.0.0	active	—	—	—

The number of data points (N) provided to k-means is less than the requested number of clusters (K), making clustering impossible.

generic

提供给 k-means 的数据点数量 (N) 少于请求的聚类数量 (K)，导致无法进行聚类。

95% success Reduce K to be less than or equal to N: `k = min(k, len(data))` before calling kmeans.
```
Reduce K to be less than or equal to N: `k = min(k, len(data))` before calling kmeans.
```
85% success Collect more data points to ensure N >= K, e.g., by increasing the sample size in data acquisition.
```
Collect more data points to ensure N >= K, e.g., by increasing the sample size in data acquisition.
```
90% success Add a check before kmeans: `if len(samples) < k: raise ValueError('Not enough samples for k-means')`.
```
Add a check before kmeans: `if len(samples) < k: raise ValueError('Not enough samples for k-means')`.
```

Reduce K to be less than or equal to N: `k = min(k, len(data))` before calling kmeans.

Collect more data points to ensure N >= K, e.g., by increasing the sample size in data acquisition.

Add a check before kmeans: `if len(samples) < k: raise ValueError('Not enough samples for k-means')`.

Common approaches that don't work:

80% fail
Duplicating points creates artificial clusters and does not solve the fundamental issue of insufficient unique data.
95% fail
More iterations do not help when N < K; the algorithm cannot proceed with fewer points than clusters.
90% fail
The assertion fails before any distance computation; the error is in the input validation.