cv-keypoint-aware-raster-augmentation

Overview

BEV rasters paired with trajectory targets (lists of (x, y) points) cannot be augmented with image-only transforms — a horizontal flip of the raster without flipping the trajectory produces a garbage training pair. Albumentations supports this natively via KeypointParams: pass the trajectory points as keypoints, and the library applies the exact same geometric transform to both the image and the points. This unlocks shift/scale/rotate/flip/cutout on motion-prediction data with zero custom code.

Quick Start

import albumentations as A
import numpy as np

tfms = A.Compose([
    A.HorizontalFlip(p=0.5),
    A.ShiftScaleRotate(shift_limit=0.05, scale_limit=0.1,
                       rotate_limit=15, p=0.7),
    A.CoarseDropout(max_holes=4, max_height=16, max_width=16, p=0.3),
], keypoint_params=A.KeypointParams(format='xy', remove_invisible=False))

def augment(sample):
    image = sample['image'].transpose(1, 2, 0)            # (C,H,W) -> (H,W,C)
    kps = sample['target_positions'].tolist()             # [(x,y), ...]
    out = tfms(image=image, keypoints=kps)
    sample['image'] = out['image'].transpose(2, 0, 1)
    sample['target_positions'] = np.array(out['keypoints'], dtype=np.float32)
    return sample

Workflow

1. Build an A.Compose pipeline with any mix of geometric and pixel transforms
2. Add keypoint_params=A.KeypointParams(format='xy', remove_invisible=False) — remove_invisible=False is critical so flipped points outside the image aren't silently dropped
3. In the dataset's __getitem__, convert the raster from (C,H,W) to (H,W,C) and pass it with keypoints=target_points
4. Albumentations returns a dict with both transformed tensors
5. Transpose back and overwrite the sample — done

Key Decisions

• remove_invisible=False: without this, horizontal flip drops negative-x points and desyncs lengths.
• Match raster pixel space: the keypoints must be in image pixel coordinates (post world-to-image transform), not world meters.
• Rotation limit stays small: BEV data encodes a canonical heading; rotating too far breaks the agent-forward assumption.
• vs. manual flip/rotate: hand-rolled augmentation drifts out of sync with the image transform whenever you add a new op. Keypoint-aware pipelines stay consistent by construction.

References

• Understanding the data + Catalyst/Kekas baseline