wenmin-wu/cv-point-centered-fixed-patch-cropping

Overview

Once you have point annotations (from dot-annotation-blob-diff-extraction or similar), the cleanest way to build a classification training set is to crop a fixed-size square patch centered on each point. The one gotcha: numpy slicing silently returns smaller arrays when the slice runs off the image edge, so img[y-h:y+h, x-h:x+h] yields a (PATCH-k, PATCH-k, 3) patch near borders without throwing. Check thumb.shape == (PATCH, PATCH, 3) before appending — it doubles as a border filter that avoids fabricating padded context.

Quick Start

import cv2
import numpy as np

PATCH = 32
half = PATCH // 2

X, y = [], []
for fname in file_names:
    img = cv2.imread(train_dir + fname)
    for cls_idx, cls in enumerate(classes):
        for (cx, cy) in coords[cls][fname]:
            thumb = img[cy - half:cy + half, cx - half:cx + half, :]
            if thumb.shape == (PATCH, PATCH, 3):    # reject border clips
                X.append(thumb)
                y.append(cls_idx)

X = np.stack(X); y = np.array(y)

Workflow

1. Pick PATCH to match the typical object diameter (32 for small sea lions, 64-128 for mid-size targets)
2. For every annotated (x, y, class) point, slice img[y-half:y+half, x-half:x+half, :]
3. Validate thumb.shape == (PATCH, PATCH, 3) — the check filters out any point within half pixels of the border
4. Append valid thumbnails and labels into parallel lists
5. np.stack at the end into a contiguous training tensor

Key Decisions

• Shape equality check over padding: padding fabricates context that wasn't in the data; shape-check just drops the handful of border points, which is usually harmless.
• Patch size ≈ object diameter: larger patches mostly add background and slow training; smaller patches crop the object.
• Stack once at the end: appending numpy arrays inside the loop is O(N²); lists + single np.stack is O(N).
• Keep labels as integer class indices, not one-hot — Keras sparse_categorical_crossentropy eats them directly and saves memory on large N.

References

• Use keras to classify Sea Lions: 0.91 accuracy