spm.py

from PIL import Image
import numpy as np

def create_feather_mask(height, width, feather_size=4):
    """
    2D mask HxW that smoothly transitions from 1.0 in the interior
    to 0.0 at the edges over `feather_size` pixels.
    """
    mask = np.ones((height, width), dtype=np.float32)
    if feather_size <= 0:
        return mask
    ramp = np.linspace(0.0, 1.0, feather_size, dtype=np.float32)
    # Top / Bottom
    mask[:feather_size, :]  *= ramp[:, None]
    mask[-feather_size:, :] *= ramp[::-1, None]
    # Left / Right
    mask[:, :feather_size]  *= ramp[None, :]
    mask[:, -feather_size:] *= ramp[None, ::-1]
    return mask

def _to_divisible_by(img, N):
    """Crop so width and height are divisible by N (top-left anchored)."""
    w, h = img.size
    W = (w // N) * N
    H = (h // N) * N
    if W == 0 or H == 0:
        raise ValueError("N is larger than the image grid (image too small).")
    if W != w or H != h:
        img = img.crop((0, 0, W, H))
    return img, W, H

def _edgelogic(i, j, ph, pw, N, overlap):
    """
    Base (no-overlap) patch is [i*ph:(i+1)*ph, j*pw:(j+1)*pw].
    Extend with overlap, biasing inward.
    Uses 2*overlap for edges to keep patch areas roughly comparable.
    Returns (start_h, end_h, start_w, end_w) BEFORE clamping to image bounds.
    """
    start_h = i * ph
    start_w = j * pw
    end_h   = start_h + ph
    end_w   = start_w + pw

    if overlap <= 0:
        return start_h, end_h, start_w, end_w

    # Vertical
    if i == 0:
        end_h += 2 * overlap
    elif i == N - 1:
        start_h -= 2 * overlap
    else:
        start_h -= overlap
        end_h   += overlap

    # Horizontal
    if j == 0:
        end_w += 2 * overlap
    elif j == N - 1:
        start_w -= 2 * overlap
    else:
        start_w -= overlap
        end_w   += overlap

    return start_h, end_h, start_w, end_w

def spm_augment(
    image,
    num_patches=4,   # N for an N×N grid
    mix_prob=0.5,
    beta_a=2.0,
    beta_b=2.0,
    overlap_px=0,
    seed=None
):
    """
    SPM-style augmentation with optional overlap + feathered blending.

    When overlap_px <= 0:
      - Standard global shuffle over N×N patches;
      - Per-patch mixing with a single alpha ~ Beta(a,b) for the image.

    When overlap_px > 0:
      - Each base cell (N×N grid) expands by +/-overlap_px (2*overlap at borders),
        clipped to the image. Patches are mixed per location and alpha sampled per-patch
        for a bit more stochasticity (can be changed to per-image alpha by editing below).
      - Patches are blended into the canvas with a feather mask of size `overlap_px`.
    """
    # Normalize to PIL and ensure divisibility
    if isinstance(image, np.ndarray):
        img = Image.fromarray(image).convert("RGB")
    else:
        img = image.convert("RGB")

    N = int(num_patches)
    rng = np.random.default_rng(seed)

    img, W, H = _to_divisible_by(img, N)
    arr_u8 = np.array(img, dtype=np.uint8)
    ph = H // N
    pw = W // N

    # Clamp overlap to < half patch size
    if overlap_px is None:
        overlap_px = 0
    overlap_px = int(overlap_px)
    max_ov = max(0, min(ph, pw) // 2 - 1)
    ov = int(np.clip(overlap_px, 0, max_ov))

    if ov <= 0:
        # === Non-overlap path ===
        arr = arr_u8
        # Build patches (row-major)
        patches = []
        for i in range(N):
            for j in range(N):
                y0 = i * ph
                x0 = j * pw
                patches.append(arr[y0:y0+ph, x0:x0+pw])

        total = N * N
        perm = rng.permutation(total)

        # One alpha per image
        if beta_a > 0 and beta_b > 0:
            alpha = float(rng.beta(beta_a, beta_b))
        else:
            alpha = 1.0

        out = arr.copy()
        mask = rng.random(total) < float(mix_prob)
        idx = 0
        for i in range(N):
            for j in range(N):
                y0 = i * ph
                x0 = j * pw
                if mask[idx]:
                    src = patches[idx].astype(np.float32)
                    shf = patches[perm[idx]].astype(np.float32)
                    if 0.0 < alpha < 1.0:
                        mixed = alpha * shf + (1.0 - alpha) * src
                        out[y0:y0+ph, x0:x0+pw] = np.clip(mixed, 0, 255).astype(np.uint8)
                    else:
                        out[y0:y0+ph, x0:x0+pw] = patches[perm[idx]]
                else:
                    out[y0:y0+ph, x0:x0+pw] = patches[idx]
                idx += 1

        return Image.fromarray(out)

    # === Overlap path with feather blending ===
    arr = arr_u8.astype(np.float32)
    # Precompute feather mask for max size patch
    feather_full = create_feather_mask(ph + 2*ov, pw + 2*ov, feather_size=ov)

    patches = []
    coords = []
    for i in range(N):
        for j in range(N):
            sh, eh, sw, ew = _edgelogic(i, j, ph, pw, N, ov)
            # Clamp to image bounds
            sh = max(0, sh); sw = max(0, sw)
            eh = min(H, eh); ew = min(W, ew)
            patches.append(arr[sh:eh, sw:ew])
            coords.append((sh, eh, sw, ew))

    total = len(patches)
    perm = rng.permutation(total)

    # We'll sample alpha per-patch to echo your overlap snippet
    def sample_alpha():
        if beta_a > 0 and beta_b > 0:
            return float(rng.beta(beta_a, beta_b))
        return 1.0

    canvas = np.zeros_like(arr, dtype=np.float32)
    weight = np.zeros((H, W), dtype=np.float32)

    for k, (sh, eh, sw, ew) in enumerate(coords):
        if rng.random() >= float(mix_prob):
            # keep original content in that region
            src = patches[k]
            patch = src
        else:
            lam = sample_alpha()
            src = patches[k].astype(np.float32)
            shf = patches[int(perm[k])].astype(np.float32)
            patch = lam * shf + (1.0 - lam) * src

        ph_k, pw_k, _ = patch.shape
        # Slice feather mask down if needed (near borders)
        mask2d = feather_full[:ph_k, :pw_k]
        if arr.shape[2] == 1:
            mask3d = mask2d[..., None]
        else:
            mask3d = np.repeat(mask2d[..., None], arr.shape[2], axis=2)

        # Accumulate
        canvas[sh:eh, sw:ew] += patch * mask3d
        weight[sh:eh, sw:ew] += mask2d

    # Normalize
    weight = np.clip(weight, 1e-8, None)
    out = (canvas / weight[..., None])
    out = np.clip(out, 0, 255).astype(np.uint8)
    return Image.fromarray(out)