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- import ruptures as rpt
- import numpy as np
- import pandas as pd
- from sklearn.preprocessing import StandardScaler
- from typing import List, Tuple
- def find_2d_data_bkps(X: List[Tuple[int, int]]) -> List[int]:
- X_clean = [point if point is not None else (np.nan, np.nan) for point in X]
- X = np.array(X_clean, dtype=float)
- X = pd.DataFrame(X).interpolate("linear").bfill().ffill().to_numpy()
- X_std = StandardScaler().fit_transform(X)
- algo = rpt.KernelCPD(kernel="rbf", jump=1).fit(X_std)
- bkps = algo.predict(pen=10)
- return bkps[:-1]
- def get_interval_average_bbox(
- bboxes: List[Tuple[int, int, int, int] | None], bkps: List[int]
- ) -> List[Tuple[int, int, int, int]]:
- average_bboxes = []
- for left, right in zip(bkps[:-1], bkps[1:]):
- bboxes_interval = bboxes[left:right]
- valid_bboxes = [bbox for bbox in bboxes_interval if bbox is not None]
- if len(valid_bboxes) > 0:
- average_bbox = np.mean(valid_bboxes, axis=0)
- average_bboxes.append(tuple(map(int, average_bbox)))
- else:
- average_bboxes.append(None)
- return average_bboxes
- def find_idxs_interval(idxs: List[int], bkps: List[int]) -> List[int]:
- def _find_idx_interval(_idx: int) -> int:
- left = 0
- right = len(bkps) - 2
- while left <= right:
- mid = (left + right) // 2
- if bkps[mid] <= _idx < bkps[mid + 1]:
- return mid
- elif _idx < bkps[mid]:
- right = mid - 1
- else:
- left = mid + 1
- return min(max(left, 0), len(bkps) - 2)
- intervals = []
- for idx in idxs:
- interval_idx = _find_idx_interval(idx)
- intervals.append(interval_idx)
- return intervals
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