refractor stitcher class into module

src/orisys/stitch/__init__.py

@@ -0,0 +1,33 @@
+"""Compound-eye stitching package (ops, backends, feature/box/tile modules)."""
+
+from .api import Stitcher
+from .backends import available_blend_backends, get_blend_backend
+from .config import BLEND_MODES, StitchStatus, resolve_blend_mode, validate_box_grid
+from .ops import (
+    build_blend_coef,
+    compute_final_crop_rect,
+    copy_into_tile_buffer,
+    coverage_ratio,
+    largest_fully_covered_rect,
+    match_bgr_brightness,
+    paste_tile_clipped,
+    tile_weight_map,
+)
+
+__all__ = [
+    "Stitcher",
+    "StitchStatus",
+    "BLEND_MODES",
+    "resolve_blend_mode",
+    "validate_box_grid",
+    "get_blend_backend",
+    "available_blend_backends",
+    "tile_weight_map",
+    "paste_tile_clipped",
+    "copy_into_tile_buffer",
+    "build_blend_coef",
+    "compute_final_crop_rect",
+    "largest_fully_covered_rect",
+    "match_bgr_brightness",
+    "coverage_ratio",
+]

src/orisys/stitch/api.py

@@ -0,0 +1,324 @@
+"""Public Stitcher façade composing tiles, boxes, feature stitch, and blend backends."""
+
+from __future__ import annotations
+
+import time
+from typing import Optional
+
+import numpy as np
+
+from .backends import get_blend_backend
+from .config import (
+    BLEND_MODES,
+    StitchStatus,
+    numba_blend_available,
+    resolve_blend_mode,
+)
+from . import boxes as boxes_mod
+from . import feature as feature_mod
+from .ops import build_blend_coef
+from . import tiles as tiles_mod
+
+
+class Stitcher:
+    """Compound-eye stitcher (feature/ORB path + feathered blend backends)."""
+
+    def __init__(self, isstitch=False, config_stitcher=None, verbose=False):
+        self.isstitch = isstitch
+        self.config_stitcher = config_stitcher
+        self.verbose = verbose
+
+        self.min_area_frac = config_stitcher["min_area_frac"]
+        self.max_area_frac = config_stitcher["max_area_frac"]
+        self.max_aspect_ratio = config_stitcher["max_aspect_ratio"]
+
+        self.stitch_valid = False
+        self.boxes = self.units = None
+        self.index = config_stitcher["index"]
+        self.stitch_calibration = self.tile_list = []
+        self.ncol = config_stitcher["n"][0]
+        self.nrow = config_stitcher["n"][1]
+        self.nominal_ov_pos = config_stitcher["nominal_ov_pos"]
+        self.margin = config_stitcher["margin"]
+        self.bound = self.nominal_ov_pos - self.margin
+        self.row_tol = config_stitcher["row_tol"]
+        self.box_size = config_stitcher["box_size"]
+        # Physical tile count for box detection (often 15); grid n is the active stitch layout.
+        self.n_boxes_target = int(config_stitcher.get("n_boxes_target", 15))
+
+        self._blend_coef_cache = {}
+        self._feature_stitch_cache = {
+            "ref_idx": None,
+            "moving_indices": None,
+            "tile_shapes": None,
+            "transforms": None,
+            "canvas": {},
+        }
+        self._feature_frame_idx = {"idx": 0}
+        self._feature_stitch_print_every = 30
+        self._feature_stitch_pad = 5
+        self._feature_stitch_match_brightness = False
+        self._blend_mode, fell_back = resolve_blend_mode(config_stitcher)
+        self._blend_backend_name = "numpy" if self._blend_mode == "none" else self._blend_mode
+        self._numba_failed = False
+        self._blend_backend = get_blend_backend(
+            self._blend_backend_name,
+            on_numba_failure=self._mark_numba_failed,
+        )
+        self._last_coverage = 0.0
+        self._last_ms = 0.0
+
+        if fell_back and self.isstitch:
+            print(
+                "Warning: stitcher blend=numba requested but numba is "
+                "unavailable; using numpy blend path."
+            )
+        elif self.isstitch and self.verbose:
+            print(f"Stitch blend: {self._blend_mode}")
+
+        if self.verbose:
+            print(f"Stitcher initialized with config: {self.config_stitcher}")
+
+    @property
+    def blend_mode(self):
+        """Configured blend mode: ``none``, ``numpy``, or ``numba``."""
+        return self._blend_mode
+
+    @property
+    def status(self) -> StitchStatus:
+        return StitchStatus(
+            blend_mode=self._blend_mode,
+            backend_name=getattr(self._blend_backend, "name", self._blend_backend_name),
+            numba_failed=self._numba_failed,
+            coverage=float(self._last_coverage),
+            last_ms=float(self._last_ms),
+            tile_count=len(self.tile_list) if self.tile_list is not None else 0,
+            stitch_valid=bool(self.stitch_valid),
+            box_count=len(self.boxes) if self.boxes is not None else 0,
+            expected_boxes=int(self.n_boxes_target),
+        )
+
+    def configure_feature_stitch(
+        self,
+        *,
+        match_brightness=None,
+        pad=None,
+        print_every=None,
+        blend=None,
+        blend_backend=None,
+    ):
+        if match_brightness is not None:
+            self._feature_stitch_match_brightness = bool(match_brightness)
+        if pad is not None:
+            self._feature_stitch_pad = max(0, int(pad))
+        if print_every is not None:
+            self._feature_stitch_print_every = max(1, int(print_every))
+        mode = blend if blend is not None else blend_backend
+        if mode is not None:
+            mode = str(mode).strip().lower()
+            if mode not in BLEND_MODES:
+                raise ValueError(
+                    f"Unknown blend={mode!r}; expected one of {sorted(BLEND_MODES)}"
+                )
+            if mode == "numba" and (not numba_blend_available() or self._numba_failed):
+                print("Warning: numba unavailable; using numpy blend.")
+                mode = "numpy"
+            self._blend_mode = mode
+            self._blend_backend_name = "numpy" if mode == "none" else mode
+            if mode == "numba":
+                self._numba_failed = False
+            self._blend_backend = get_blend_backend(
+                self._blend_backend_name,
+                on_numba_failure=self._mark_numba_failed,
+            )
+
+    def _resolve_call_blend(self, blend):
+        if blend is None:
+            mode = self._blend_mode
+        elif isinstance(blend, bool):
+            if blend:
+                mode = self._blend_mode if self._blend_mode != "none" else "numpy"
+            else:
+                return False, self._blend_backend_name
+        else:
+            mode = str(blend).strip().lower()
+            if mode not in BLEND_MODES:
+                raise ValueError(
+                    f"Unknown blend={mode!r}; expected one of {sorted(BLEND_MODES)}"
+                )
+            if mode == "numba" and not numba_blend_available():
+                mode = "numpy"
+
+        if mode == "numba" and self._numba_failed:
+            mode = "numpy"
+
+        if mode == "none":
+            return False, self._blend_backend_name
+        return True, mode
+
+    def _mark_numba_failed(self, exc):
+        if self._numba_failed:
+            return
+        self._numba_failed = True
+        self._blend_backend_name = "numpy"
+        if self._blend_mode == "numba":
+            self._blend_mode = "numpy"
+        self._blend_backend = get_blend_backend("numpy")
+        print(
+            f"Warning: numba blend failed ({type(exc).__name__}: {exc}); "
+            f"falling back to numpy."
+        )
+
+    def _get_blend_coef(self, ov_pos, dim, ndim):
+        key = (ov_pos, dim, ndim)
+        coef = self._blend_coef_cache.get(key)
+        if coef is not None:
+            return coef
+        blend_coef = build_blend_coef(ov_pos, dim, ndim)
+        self._blend_coef_cache[key] = blend_coef
+        return blend_coef
+
+    def _ensure_blend_backend(self, backend_name: str):
+        name = "numpy" if backend_name == "none" else backend_name
+        if self._numba_failed and name == "numba":
+            name = "numpy"
+        current = getattr(self._blend_backend, "name", None)
+        if current != name:
+            self._blend_backend = get_blend_backend(
+                name,
+                on_numba_failure=self._mark_numba_failed,
+            )
+        self._blend_backend_name = name
+
+    def stitch_rt_optimized(self, change_sv=False, s_mean=28.0, v_mean=180.0, blend=None):
+        """Stitch the current tile list via the feature/ORB path.
+
+        Returns ``None`` when a valid feature layout cannot be built.
+        """
+        use_feather, blend_backend = self._resolve_call_blend(blend)
+        prev_backend = self._blend_backend_name
+        self._ensure_blend_backend(blend_backend)
+        t0 = time.perf_counter()
+        try:
+            feature_stitched = self.stitch_feature_optimized(
+                blend=use_feather,
+                match_brightness=self._feature_stitch_match_brightness,
+                pad=self._feature_stitch_pad,
+                print_every=self._feature_stitch_print_every,
+            )
+        finally:
+            if self._numba_failed:
+                self._ensure_blend_backend("numpy")
+            else:
+                self._ensure_blend_backend(prev_backend)
+        self._last_ms = (time.perf_counter() - t0) * 1000.0
+        canvas = self._feature_stitch_cache.get("canvas") or {}
+        self._last_coverage = float(canvas.get("last_coverage", self._last_coverage))
+        if feature_stitched is not None:
+            return feature_stitched
+        return None
+
+    def stitch_feature_optimized(self, blend=True, match_brightness=False, pad=5, print_every=30):
+        return feature_mod.stitch_feature_optimized(
+            isstitch=self.isstitch,
+            index=self.index,
+            nrow=self.nrow,
+            ncol=self.ncol,
+            tile_list=self.tile_list,
+            cache=self._feature_stitch_cache,
+            blend_backend=self._blend_backend,
+            blend=blend,
+            match_brightness=match_brightness,
+            pad=pad,
+            print_every=print_every,
+            verbose=self.verbose,
+            frame_idx_holder=self._feature_frame_idx,
+        )
+
+    def find_stitch_boxes(self, img_src, show=False):
+        n_boxes, boxes, grid_ok = boxes_mod.find_stitch_boxes(
+            img_src,
+            box_size=self.box_size,
+            min_area_frac=self.min_area_frac,
+            max_area_frac=self.max_area_frac,
+            max_aspect_ratio=self.max_aspect_ratio,
+            row_tol=self.row_tol,
+            nrow=self.nrow,
+            ncol=self.ncol,
+            show=show,
+            n_boxes_target=self.n_boxes_target,
+        )
+        self.boxes = boxes
+        # Detection target is physical unit count; active stitch grid is nrow*ncol via index.
+        self.stitch_valid = bool(self.isstitch and n_boxes == self.n_boxes_target)
+        if self.isstitch and n_boxes != self.n_boxes_target:
+            print(
+                f"Warning: stitch box count {n_boxes} != expected "
+                f"n_boxes_target={self.n_boxes_target}; stitch_valid=False."
+            )
+        elif self.isstitch and not grid_ok and self.verbose:
+            # Soft note: active index length may be nrow*ncol while more boxes exist.
+            print(
+                f"Note: detected {n_boxes} boxes; active stitch grid is "
+                f"{self.nrow}x{self.ncol} via index ({len(tiles_mod.normalize_index(self.index))} tiles)."
+            )
+        return n_boxes, self.boxes
+
+    def get_stitch_unit(self, img, flip_x=True, flip_y=True, index=np.arange(15)):
+        active_index = index if self.units is None else self.index
+        if isinstance(active_index, int):
+            selected_index = {active_index}
+        else:
+            selected_index = set(int(i) for i in active_index)
+        self.units = tiles_mod.extract_units_from_boxes(
+            img,
+            self.boxes,
+            selected_index=selected_index,
+            units=self.units,
+            flip_x=flip_x,
+            flip_y=flip_y,
+        )
+        return True
+
+    def extract_tiles(self):
+        if not self.isstitch:
+            tile_idx = 7
+            self.index = [tile_idx] if isinstance(tile_idx, int) else tile_idx
+            self.ncol = 1
+            self.nrow = 1
+
+        tile_list = tiles_mod.get_tile_by_index(self.units, self.index)
+        self.tile_list = tile_list
+        return tile_list
+
+    def draw_boxes(self, img, thickness=2):
+        return boxes_mod.draw_boxes(img, self.boxes, thickness=thickness)
+
+    # Compatibility aliases used by older call sites / tests.
+    def _copy_into_tile_buffer(self, src_view, dst, flip_x=True, flip_y=True):
+        from .ops import copy_into_tile_buffer
+
+        copy_into_tile_buffer(src_view, dst, flip_x=flip_x, flip_y=flip_y)
+
+    def _tile_weight_map(self, h, w, margin=16):
+        from .ops import tile_weight_map
+
+        return tile_weight_map(h, w, margin)
+
+    def _paste_tile_clipped(self, canvas, tile, y0, x0, cov=None):
+        from .ops import paste_tile_clipped
+
+        paste_tile_clipped(canvas, tile, y0, x0, cov=cov)
+
+    def _blend_tile_inplace(self, blend, wsum, cov, src, weight, y0, x0, scratch=None):
+        self._blend_backend.blend_tile_inplace(blend, wsum, cov, src, weight, y0, x0)
+
+    def _compute_final_crop_rect(self, cov):
+        from .ops import compute_final_crop_rect
+
+        return compute_final_crop_rect(cov)
+
+    def _largest_fully_covered_rect(self, cov):
+        from .ops import largest_fully_covered_rect
+
+        return largest_fully_covered_rect(cov)

src/orisys/stitch/backends/__init__.py

@@ -0,0 +1,47 @@
+"""Blend backend registry (numpy / numba; future: torch / cupy)."""
+
+from __future__ import annotations
+
+from typing import Callable, Optional
+
+from .base import BlendBackend
+from .numba_backend import NumbaBlendBackend
+from .numpy_backend import NumpyBlendBackend
+from ..config import numba_blend_available
+
+_BACKENDS = {
+    "numpy": NumpyBlendBackend,
+    "numba": NumbaBlendBackend,
+}
+
+
+def get_blend_backend(
+    name: str,
+    *,
+    on_numba_failure: Optional[Callable[[BaseException], None]] = None,
+) -> BlendBackend:
+    """Return a blend backend instance by name.
+
+    Unknown names raise ``ValueError``. ``numba`` falls back to construction
+    even if JIT is missing; the backend itself falls back to numpy on failure.
+    """
+    key = str(name).strip().lower()
+    if key == "none":
+        # Feather off uses paste path; keep a numpy backend available if forced.
+        key = "numpy"
+    if key not in _BACKENDS:
+        raise ValueError(
+            f"Unknown blend backend {name!r}; expected one of "
+            f"{sorted(set(_BACKENDS) | {'none'})} "
+            f"(future backends e.g. 'torch' / 'cupy' are not registered yet)."
+        )
+    if key == "numba":
+        return NumbaBlendBackend(on_failure=on_numba_failure)
+    return NumpyBlendBackend()
+
+
+def available_blend_backends() -> list[str]:
+    names = ["none", "numpy"]
+    if numba_blend_available():
+        names.append("numba")
+    return names

src/orisys/stitch/backends/base.py

@@ -0,0 +1,29 @@
+"""BlendBackend protocol for CPU (and future GPU) stitch compositors."""
+
+from __future__ import annotations
+
+from typing import Protocol, runtime_checkable
+
+
+@runtime_checkable
+class BlendBackend(Protocol):
+    """Array-in / in-place composite kernel.
+
+    Implementations must accept contiguous float32/uint8 NumPy arrays today.
+    Future Torch/CuPy backends can adapt tensors to this contract or extend it.
+    """
+
+    name: str
+
+    def blend_tile_inplace(
+        self,
+        blend,
+        wsum,
+        cov,
+        src,
+        weight,
+        y0: int,
+        x0: int,
+    ) -> None:
+        """Composite ``src`` weighted by ``weight`` onto canvas buffers at (y0, x0)."""
+        ...

src/orisys/stitch/backends/numba_backend.py

@@ -0,0 +1,53 @@
+"""Numba blend backend wrapping ``orisys_blend.kernel``."""
+
+from __future__ import annotations
+
+import numpy as np
+
+from .numpy_backend import NumpyBlendBackend, blend_kernel_numpy
+
+try:
+    from orisys_blend.kernel import _NUMBA_AVAILABLE, blend_kernel as _blend_kernel_numba
+except Exception:
+    _NUMBA_AVAILABLE = False
+    _blend_kernel_numba = None
+
+if _blend_kernel_numba is None:
+    _NUMBA_AVAILABLE = False
+
+
+class NumbaBlendBackend:
+    name = "numba"
+
+    def __init__(self, *, on_failure=None):
+        self._on_failure = on_failure
+        self._fallback = NumpyBlendBackend()
+        self.failed = False
+
+    def blend_tile_inplace(self, blend, wsum, cov, src, weight, y0, x0) -> None:
+        h, w = src.shape[:2]
+        H, W = blend.shape[:2]
+        y0d, x0d = max(0, y0), max(0, x0)
+        y1d, x1d = min(H, y0 + h), min(W, x0 + w)
+        if y0d >= y1d or x0d >= x1d:
+            return
+
+        sy0, sx0 = y0d - y0, x0d - x0
+        sh, sw = y1d - y0d, x1d - x0d
+        src_f = src[sy0 : sy0 + sh, sx0 : sx0 + sw].astype(np.float32, copy=False)
+        args = (
+            blend[y0d:y1d, x0d:x1d],
+            wsum[y0d:y1d, x0d:x1d],
+            cov[y0d:y1d, x0d:x1d],
+            src_f,
+            weight[sy0 : sy0 + sh, sx0 : sx0 + sw],
+        )
+        if not self.failed and _NUMBA_AVAILABLE and _blend_kernel_numba is not None:
+            try:
+                _blend_kernel_numba(*args)
+                return
+            except Exception as exc:
+                self.failed = True
+                if self._on_failure is not None:
+                    self._on_failure(exc)
+        blend_kernel_numpy(*args)

src/orisys/stitch/backends/numpy_backend.py

@@ -0,0 +1,61 @@
+"""NumPy blend backend (no JIT)."""
+
+from __future__ import annotations
+
+import numpy as np
+
+
+def blend_kernel_numpy(blend_patch, wsum_patch, cov_patch, src_patch, w_patch):
+    """Incremental weighted-average composite (pure NumPy, no JIT).
+
+    Uses contiguous arithmetic instead of boolean fancy indexing. Callers must
+    zero ``blend`` / ``wsum`` on uncovered pixels (as the stitcher reset does);
+    then the covered lerp also paints first-touch pixels correctly when ``w > 0``.
+    """
+    w = w_patch if w_patch.dtype == np.float32 else np.asarray(w_patch, dtype=np.float32)
+    src = src_patch if src_patch.dtype == np.float32 else np.asarray(src_patch, dtype=np.float32)
+
+    uncovered = cov_patch == 0
+    if uncovered.all():
+        np.copyto(blend_patch, src)
+        np.copyto(wsum_patch, w)
+        cov_patch.fill(1)
+        return
+
+    ws = wsum_patch + w
+    a = w / np.maximum(ws, np.float32(1e-6))
+    for c in range(src.shape[2]):
+        bc = blend_patch[..., c]
+        bc += (src[..., c] - bc) * a
+    np.copyto(wsum_patch, ws)
+
+    if uncovered.any():
+        need_fix = uncovered & (w < np.float32(1e-6))
+        if need_fix.any():
+            blend_patch[need_fix] = src[need_fix]
+            wsum_patch[need_fix] = w[need_fix]
+
+    cov_patch.fill(1)
+
+
+class NumpyBlendBackend:
+    name = "numpy"
+
+    def blend_tile_inplace(self, blend, wsum, cov, src, weight, y0, x0) -> None:
+        h, w = src.shape[:2]
+        H, W = blend.shape[:2]
+        y0d, x0d = max(0, y0), max(0, x0)
+        y1d, x1d = min(H, y0 + h), min(W, x0 + w)
+        if y0d >= y1d or x0d >= x1d:
+            return
+
+        sy0, sx0 = y0d - y0, x0d - x0
+        sh, sw = y1d - y0d, x1d - x0d
+        src_f = src[sy0 : sy0 + sh, sx0 : sx0 + sw].astype(np.float32, copy=False)
+        blend_kernel_numpy(
+            blend[y0d:y1d, x0d:x1d],
+            wsum[y0d:y1d, x0d:x1d],
+            cov[y0d:y1d, x0d:x1d],
+            src_f,
+            weight[sy0 : sy0 + sh, sx0 : sx0 + sw],
+        )

src/orisys/stitch/boxes.py

@@ -0,0 +1,217 @@
+"""Box discovery and visualization for compound-eye calibration."""
+
+from __future__ import annotations
+
+import os
+from typing import List, Optional, Sequence, Tuple
+
+import cv2
+import numpy as np
+
+
+def correct_edge_bloom(img, method: str = "divide", kernel_size=None, clip: bool = True):
+    """Illumination correction to reduce edge bloom (box detection only)."""
+    if img is None:
+        raise ValueError("Input image is None.")
+
+    h, w = img.shape[:2]
+    if kernel_size is None:
+        k = max(31, min(h, w) // 2)
+        if k % 2 == 0:
+            k += 1
+        kernel_size = k
+    img_f = img.astype(np.float32)
+    bg = cv2.GaussianBlur(img_f, (kernel_size, kernel_size), 0)
+    if method == "subtract":
+        mean_bg = bg.mean(axis=(0, 1), keepdims=True)
+        corr = img_f - (bg - mean_bg)
+    else:
+        eps = 1e-6
+        mean_bg = bg.mean(axis=(0, 1), keepdims=True) + eps
+        gain = mean_bg * 0.5
+        corr = (img_f / (bg + eps)) * gain
+    if clip:
+        corr = np.clip(corr, 0, 255).astype(np.uint8)
+    else:
+        corr = corr.astype(np.uint8)
+    return corr
+
+
+def filter_boxes_by_size(
+    boxes: Sequence[Tuple[int, int, int, int]],
+    img_shape,
+    *,
+    min_area_frac: float,
+    max_area_frac: float,
+    max_aspect_ratio: float,
+) -> List[Tuple[int, int, int, int]]:
+    h, w = img_shape
+    img_area = h * w
+    min_area = img_area * float(min_area_frac)
+    max_area = img_area * float(max_area_frac)
+    filtered = []
+    for (x, y, ww, hh) in boxes:
+        area = ww * hh
+        if area < min_area or area > max_area or (abs(ww - hh) > max_aspect_ratio):
+            continue
+        filtered.append((x, y, ww, hh))
+    return filtered
+
+
+def detect_nonblack_regions(
+    img,
+    *,
+    box_size: Sequence[int],
+    min_area_frac: float,
+    max_area_frac: float,
+    max_aspect_ratio: float,
+    morph_k: int = 3,
+    n_boxes_target: int = 15,
+    bk_thresh_step: int = 5,
+    bk_thresh_max: int = 150,
+    show: bool = False,
+    row_tol: float = 0.4,
+    save_preview_path: str = "./config/compound_eye.png",
+) -> Tuple[int, List[Tuple[int, int, int, int]]]:
+    """Detect tile boxes; returns ``(n_boxes, boxes as x0,y0,x1,y1)``."""
+    if img is None:
+        raise ValueError("Input image is None.")
+
+    h, w = img.shape[:2]
+    n_boxes = 0
+    bk_thresh = 0
+    boxes: List[Tuple[int, int, int, int]] = []
+
+    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+    v = hsv[:, :, 2]
+    while n_boxes != n_boxes_target and bk_thresh <= bk_thresh_max:
+        _, v_mask = cv2.threshold(v, bk_thresh, 255, cv2.THRESH_BINARY)
+        mask = v_mask.copy()
+        edge = 20
+        if w > edge * 2:
+            mask[:, :edge] = 0
+            mask[:, -edge:] = 0
+
+        k = max(1, morph_k)
+        kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (k, k))
+        mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel, iterations=1)
+        mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel, iterations=2)
+        contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+        boxes = []
+        for c in contours:
+            x, y, ww, hh = cv2.boundingRect(c)
+            boxes.append((x, y, ww, hh))
+
+        boxes = filter_boxes_by_size(
+            boxes,
+            (h, w),
+            min_area_frac=min_area_frac,
+            max_area_frac=max_area_frac,
+            max_aspect_ratio=max_aspect_ratio,
+        )
+        n_boxes = len(boxes)
+        bk_thresh += bk_thresh_step
+
+    print(
+        "Detected {} candidate boxes with bk_thresh={}".format(
+            n_boxes, bk_thresh - bk_thresh_step
+        )
+    )
+    if n_boxes == 0:
+        return 0, []
+
+    target_w = int(box_size[0])
+    target_h = int(box_size[1])
+    cropped_boxes = []
+    for (x, y, ww, hh) in boxes:
+        cx = x + ww // 2
+        cy = y + hh // 2
+        nx0 = int(cx - target_w // 2)
+        ny0 = int(cy - target_h // 2)
+        nx0 = max(0, min(nx0, w - target_w))
+        ny0 = max(0, min(ny0, h - target_h))
+        cropped_boxes.append((nx0, ny0, nx0 + target_w, ny0 + target_h))
+
+    if cropped_boxes:
+        heights = [b[3] - b[1] for b in cropped_boxes]
+        median_height = np.median(heights) if heights else target_h
+        row_tolerance = max(1, int(median_height * row_tol))
+
+        def get_row_key(box):
+            y = box[1]
+            quantized_y = (y // row_tolerance) * row_tolerance
+            return (quantized_y, box[0])
+
+        cropped_boxes = sorted(cropped_boxes, key=get_row_key)
+    else:
+        cropped_boxes = sorted(cropped_boxes, key=lambda b: (b[1], b[0]))
+
+    img_with_boxes = draw_boxes(img, cropped_boxes)
+    if show:
+        cv2.imshow("Detected Boxes", img_with_boxes)
+        cv2.waitKey(0)
+
+    if save_preview_path:
+        parent = os.path.dirname(save_preview_path)
+        if parent:
+            os.makedirs(parent, exist_ok=True)
+        cv2.imwrite(save_preview_path, img_with_boxes)
+
+    return len(cropped_boxes), cropped_boxes
+
+
+def draw_boxes(img, boxes, thickness: int = 2):
+    if boxes is None or len(boxes) == 0:
+        print("No boxes to draw")
+        return img
+
+    img_draw = img.copy()
+    color = (0, 255, 0)
+    for i, (x0, y0, x1, y1) in enumerate(boxes):
+        cv2.rectangle(img_draw, (x0, y0), (x1, y1), color, thickness)
+        text_size = cv2.getTextSize(str(i), cv2.FONT_HERSHEY_SIMPLEX, 0.6, 2)[0]
+        text_pos = (x0 + 5, y0 + text_size[1] + 5)
+        cv2.rectangle(
+            img_draw,
+            (text_pos[0] - 2, text_pos[1] - text_size[1] - 2),
+            (text_pos[0] + text_size[0] + 2, text_pos[1] + 2),
+            (0, 0, 0),
+            -1,
+        )
+        cv2.putText(img_draw, str(i), text_pos, cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
+    return img_draw
+
+
+def find_stitch_boxes(
+    img_src,
+    *,
+    box_size,
+    min_area_frac,
+    max_area_frac,
+    max_aspect_ratio,
+    row_tol,
+    nrow: int,
+    ncol: int,
+    show: bool = False,
+    n_boxes_target: Optional[int] = None,
+):
+    """Run bloom correction + detection; return ``(n_boxes, boxes, grid_ok)``.
+
+    ``n_boxes_target`` is the physical tile count to detect (default 15).
+    ``grid_ok`` is True when that count matches ``n_boxes_target``.
+    """
+    img_corr = correct_edge_bloom(img_src)
+    target = int(n_boxes_target) if n_boxes_target is not None else 15
+    n_boxes, boxes = detect_nonblack_regions(
+        img_corr,
+        box_size=box_size,
+        min_area_frac=min_area_frac,
+        max_area_frac=max_area_frac,
+        max_aspect_ratio=max_aspect_ratio,
+        n_boxes_target=target,
+        show=show,
+        row_tol=row_tol,
+    )
+    grid_ok = n_boxes == target
+    return n_boxes, boxes, grid_ok

src/orisys/stitch/config.py

@@ -0,0 +1,76 @@
+"""Stitch configuration, blend-mode resolution, and status."""
+
+from __future__ import annotations
+
+import os
+from dataclasses import dataclass
+from typing import Any, Mapping, Optional
+
+# JSON ``stitcher.blend`` / ``blend_list``: none = fast paste; numpy/numba = feathered.
+BLEND_MODES = frozenset({"none", "numpy", "numba"})
+DEFAULT_BLEND_MODE = "none"
+
+
+def numba_blend_available() -> bool:
+    try:
+        from orisys_blend.kernel import _NUMBA_AVAILABLE, blend_kernel
+    except Exception:
+        return False
+    return bool(_NUMBA_AVAILABLE and blend_kernel is not None)
+
+
+def resolve_blend_mode(config_stitcher: Optional[Mapping[str, Any]] = None):
+    """Resolve stitcher blend mode from env, then JSON ``blend``.
+
+    Returns ``(mode, fell_back_from_numba)`` where mode is one of
+    ``none`` / ``numpy`` / ``numba``.
+
+    Priority: ``ORISYS_STITCH_BLEND``, ``ORISYS_STITCH_BLEND_BACKEND``,
+    then ``config_stitcher["blend"]`` / legacy ``blend_backend``, then ``none``.
+    """
+    requested = None
+    for key in ("ORISYS_STITCH_BLEND", "ORISYS_STITCH_BLEND_BACKEND"):
+        env = os.environ.get(key, "").strip().lower()
+        if env in BLEND_MODES:
+            requested = env
+            break
+
+    if requested is None and isinstance(config_stitcher, dict):
+        for key in ("blend", "blend_backend"):
+            cfg = str(config_stitcher.get(key, "")).strip().lower()
+            if cfg in BLEND_MODES:
+                requested = cfg
+                break
+
+    if requested is None:
+        requested = DEFAULT_BLEND_MODE
+
+    if requested == "numba" and not numba_blend_available():
+        return "numpy", True
+    return requested, False
+
+
+def expected_box_count(nrow: int, ncol: int) -> int:
+    return int(nrow) * int(ncol)
+
+
+def validate_box_grid(boxes, nrow: int, ncol: int) -> bool:
+    """True when detected boxes match the configured grid size."""
+    if boxes is None:
+        return False
+    return len(boxes) == expected_box_count(nrow, ncol)
+
+
+@dataclass
+class StitchStatus:
+    """Runtime health snapshot for compound-eye stitching."""
+
+    blend_mode: str = DEFAULT_BLEND_MODE
+    backend_name: str = "numpy"
+    numba_failed: bool = False
+    coverage: float = 0.0
+    last_ms: float = 0.0
+    tile_count: int = 0
+    stitch_valid: bool = False
+    box_count: int = 0
+    expected_boxes: int = 0

src/orisys/stitch/feature.py

@@ -0,0 +1,599 @@
+"""Feature-based (ORB/BFMatcher) stitch plan and render orchestration."""
+
+from __future__ import annotations
+
+from typing import Any, Dict, Optional, Sequence
+
+import cv2
+import numpy as np
+
+from .backends.base import BlendBackend
+from .ops import (
+    compute_final_crop_rect,
+    coverage_ratio,
+    match_bgr_brightness,
+    mean_gray_bgr,
+    paste_tile_clipped,
+    tile_weight_map,
+    translation_from_H_to_ref,
+)
+from .tiles import normalize_index
+
+
+def tile_to_bgr(img):
+    if img.ndim == 2:
+        return cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+    return img
+
+
+def tile_to_gray(bgr):
+    if bgr.ndim == 2:
+        return bgr
+    return cv2.cvtColor(bgr, cv2.COLOR_BGR2GRAY)
+
+
+def ensure_u8_gray(gray):
+    if gray.dtype == np.uint8:
+        return gray
+    return cv2.normalize(gray, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8)
+
+
+def preprocess_gray_for_orb(g0, g1):
+    g0 = cv2.GaussianBlur(g0, (5, 5), 0)
+    g1 = cv2.GaussianBlur(g1, (5, 5), 0)
+    g0 = cv2.equalizeHist(g0)
+    g1 = cv2.equalizeHist(g1)
+    return g0, g1
+
+
+def estimate_bfmatcher_stitch_transform(
+    t0,
+    t1,
+    *,
+    ransac_reproj_thresh=5.0,
+    ratio=0.9,
+    orb_nfeatures=5000,
+    verbose=True,
+):
+    """Estimate a 3x3 transform mapping tile coordinates into the reference frame."""
+    t0_bgr = tile_to_bgr(t0)
+    t1_bgr = tile_to_bgr(t1)
+    g0 = ensure_u8_gray(tile_to_gray(t0_bgr))
+    g1 = ensure_u8_gray(tile_to_gray(t1_bgr))
+
+    if verbose:
+        print(
+            f"BF stitcher debug: t0 dtype={t0.dtype}, t1 dtype={t1.dtype}, "
+            f"g0 dtype={g0.dtype}, g0 range=({np.min(g0)}, {np.max(g0)}), "
+            f"g1 dtype={g1.dtype}, g1 range=({np.min(g1)}, {np.max(g1)})"
+        )
+
+    # g0, g1 = preprocess_gray_for_orb(g0, g1)
+    orb = cv2.ORB_create(
+        nfeatures=orb_nfeatures,
+        scaleFactor=1.2,
+        nlevels=8,
+        edgeThreshold=15,
+        fastThreshold=5,
+    )
+    k0, d0 = orb.detectAndCompute(g0, None)
+    k1, d1 = orb.detectAndCompute(g1, None)
+
+    info = {
+        "ok": False,
+        "method": None,
+        "inliers": 0,
+        "mean_err": None,
+        "good_matches": 0,
+        "k0": k0,
+        "k1": k1,
+        "t0_bgr": t0_bgr,
+        "t1_bgr": t1_bgr,
+    }
+    if d0 is None or d1 is None or len(k0) < 4 or len(k1) < 4:
+        if verbose:
+            print(
+                "OpenCV BFMatcher stitcher: not enough features.",
+                f"k0={0 if k0 is None else len(k0)}",
+                f"k1={0 if k1 is None else len(k1)}",
+                f"d0_is_none={d0 is None}",
+                f"d1_is_none={d1 is None}",
+            )
+        return None, info
+
+    matches = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=False).knnMatch(d0, d1, k=2)
+    good = []
+    for m_n in matches:
+        if len(m_n) != 2:
+            continue
+        m, n = m_n
+        if m.distance < ratio * n.distance:
+            good.append(m)
+
+    info["good_matches"] = len(good)
+    if verbose:
+        print(f"BFMatcher good matches: {len(good)}")
+    if len(good) < 4:
+        return None, info
+
+    pts0 = np.float32([k0[m.queryIdx].pt for m in good]).reshape(-1, 1, 2)
+    pts1 = np.float32([k1[m.trainIdx].pt for m in good]).reshape(-1, 1, 2)
+    method = "affine_partial2d"
+    M, inlier_mask = cv2.estimateAffinePartial2D(
+        pts1,
+        pts0,
+        method=cv2.RANSAC,
+        ransacReprojThreshold=ransac_reproj_thresh,
+    )
+    if M is not None:
+        H = np.vstack([M, [0, 0, 1]]).astype(np.float64)
+    else:
+        method = "homography"
+        H, inlier_mask = cv2.findHomography(pts1, pts0, cv2.RANSAC, ransac_reproj_thresh)
+
+    info["method"] = method
+    info["inliers"] = int(inlier_mask.sum()) if inlier_mask is not None else 0
+    if verbose:
+        print(f"Estimated {method} inliers: {info['inliers']}")
+    if H is None:
+        return None, info
+
+    if abs(H[2, 2]) > 1e-12:
+        H = H / H[2, 2]
+
+    mean_err = 1e9
+    if inlier_mask is not None:
+        inlier_bool = inlier_mask.ravel().astype(bool)
+        if np.any(inlier_bool):
+            projected = cv2.perspectiveTransform(pts1[inlier_bool], H)
+            err = np.linalg.norm(
+                projected.reshape(-1, 2) - pts0[inlier_bool].reshape(-1, 2),
+                axis=1,
+            )
+            mean_err = float(err.mean()) if err.size else 1e9
+    info["mean_err"] = mean_err
+    info["ok"] = True
+    return H, info
+
+
+def get_feature_stitch_plan(isstitch, index, nrow, ncol, tile_list):
+    if not isstitch:
+        return None
+
+    configured_indices = normalize_index(index)
+    total_tiles = len(configured_indices)
+    expected_tiles = nrow * ncol
+    if total_tiles <= 1 or total_tiles != expected_tiles or len(tile_list) < total_tiles:
+        return None
+
+    ref_pos = (nrow // 2) * ncol + (ncol // 2)
+    if ref_pos >= total_tiles:
+        return None
+
+    selected_positions = tuple(range(total_tiles))
+    ref_idx = configured_indices[ref_pos]
+    moving_positions = tuple(pos for pos in selected_positions if pos != ref_pos)
+    moving_indices = tuple(configured_indices[pos] for pos in moving_positions)
+    moving_tiles = [tile_list[pos] for pos in moving_positions]
+    return ref_idx, tile_list[ref_pos], moving_indices, moving_tiles, selected_positions
+
+
+def _geom_cache_key(t_ref_bgr, tiles_bgr_c, pad):
+    ref_h, ref_w = t_ref_bgr.shape[:2]
+    shapes = tuple(tb.shape[:2] for tb in tiles_bgr_c)
+    return ref_h, ref_w, shapes, pad
+
+
+def ensure_feature_geom_cache(
+    canvas_cache,
+    t_ref_bgr,
+    tiles_bgr_c,
+    H_use_list,
+    pad,
+    *,
+    allocate_blend_bufs=True,
+):
+    h0c, w0c = t_ref_bgr.shape[:2]
+    geom_key = _geom_cache_key(t_ref_bgr, tiles_bgr_c, pad)
+    geom_cache_hit = canvas_cache.get("geom_key") == geom_key
+    if geom_cache_hit:
+        if allocate_blend_bufs and (
+            "blend_buf" not in canvas_cache
+            or "wsum_buf" not in canvas_cache
+            or "cov_buf" not in canvas_cache
+        ):
+            out_h = canvas_cache["out_h"]
+            out_w = canvas_cache["out_w"]
+            canvas_cache["blend_buf"] = np.zeros((out_h, out_w, 3), dtype=np.float32)
+            canvas_cache["wsum_buf"] = np.zeros((out_h, out_w), dtype=np.float32)
+            canvas_cache["cov_buf"] = np.zeros((out_h, out_w), dtype=np.uint8)
+        return True
+
+    ref_corners = np.array([[0, 0], [w0c, 0], [w0c, h0c], [0, h0c]], dtype=np.float64)
+    pts = [ref_corners]
+    for tile_bgr, H_use in zip(tiles_bgr_c, H_use_list):
+        if H_use is None:
+            continue
+        hi, wi = tile_bgr.shape[:2]
+        tx, ty = translation_from_H_to_ref(H_use)
+        pts.append(
+            np.array(
+                [[tx, ty], [tx + wi, ty], [tx + wi, ty + hi], [tx, ty + hi]],
+                dtype=np.float64,
+            )
+        )
+
+    all_xy = np.concatenate(pts, axis=0)
+    x_min_f = float(all_xy[:, 0].min()) - pad
+    y_min_f = float(all_xy[:, 1].min()) - pad
+    x_max_f = float(all_xy[:, 0].max()) + pad
+    y_max_f = float(all_xy[:, 1].max()) + pad
+    out_w = max(1, int(np.ceil(x_max_f - x_min_f)))
+    out_h = max(1, int(np.ceil(y_max_f - y_min_f)))
+    ref_y0 = int(np.floor(-y_min_f))
+    ref_x0 = int(np.floor(-x_min_f))
+
+    pastes = []
+    for H_use in H_use_list:
+        if H_use is None:
+            pastes.append(None)
+            continue
+        tx, ty = translation_from_H_to_ref(H_use)
+        pastes.append((int(np.floor(ty - y_min_f)), int(np.floor(tx - x_min_f))))
+
+    _dr_y0 = max(0, ref_y0)
+    _dr_x0 = max(0, ref_x0)
+    _dr_y1 = min(out_h, ref_y0 + h0c)
+    _dr_x1 = min(out_w, ref_x0 + w0c)
+    for _tb, _pa in zip(tiles_bgr_c, pastes):
+        if _pa is None:
+            continue
+        _py, _px = _pa
+        _th, _tw = _tb.shape[:2]
+        _dr_y0 = min(_dr_y0, max(0, _py))
+        _dr_x0 = min(_dr_x0, max(0, _px))
+        _dr_y1 = max(_dr_y1, min(out_h, _py + _th))
+        _dr_x1 = max(_dr_x1, min(out_w, _px + _tw))
+
+    canvas_cache["geom_key"] = geom_key
+    canvas_cache["out_w"] = out_w
+    canvas_cache["out_h"] = out_h
+    canvas_cache["ref_y0"] = ref_y0
+    canvas_cache["ref_x0"] = ref_x0
+    canvas_cache["pastes"] = pastes
+    canvas_cache["x_min_f"] = x_min_f
+    canvas_cache["y_min_f"] = y_min_f
+    canvas_cache["crop_rect"] = None
+    canvas_cache["dirty_rect"] = (_dr_y0, _dr_y1, _dr_x0, _dr_x1)
+    canvas_cache["weight_maps"] = {}
+    canvas_cache["uint8_buf"] = np.zeros((out_h, out_w, 3), dtype=np.uint8)
+    if allocate_blend_bufs:
+        canvas_cache["blend_buf"] = np.zeros((out_h, out_w, 3), dtype=np.float32)
+        canvas_cache["wsum_buf"] = np.zeros((out_h, out_w), dtype=np.float32)
+        canvas_cache["cov_buf"] = np.zeros((out_h, out_w), dtype=np.uint8)
+    else:
+        canvas_cache["cov_buf"] = np.zeros((out_h, out_w), dtype=np.uint8)
+        canvas_cache.pop("blend_buf", None)
+        canvas_cache.pop("wsum_buf", None)
+    return False
+
+
+def paste_feature_tiles_fast(
+    t_ref_bgr,
+    tiles_bgr_c,
+    H_use_list,
+    canvas_cache,
+    *,
+    geom_cache_hit,
+    frame_idx=0,
+    print_every=30,
+    verbose=False,
+    t_profile0=0.0,
+    t_after_brightness=0.0,
+    t_after_geom=0.0,
+):
+    do_profile = verbose and frame_idx % print_every == 0
+    out_w = canvas_cache["out_w"]
+    out_h = canvas_cache["out_h"]
+    ref_y0 = canvas_cache["ref_y0"]
+    ref_x0 = canvas_cache["ref_x0"]
+    pastes = canvas_cache["pastes"]
+
+    canvas = canvas_cache.get("uint8_buf")
+    if canvas is None or canvas.shape[0] != out_h or canvas.shape[1] != out_w:
+        canvas = np.zeros((out_h, out_w, 3), dtype=np.uint8)
+        canvas_cache["uint8_buf"] = canvas
+
+    dirty = canvas_cache.get("dirty_rect")
+    if geom_cache_hit and dirty is not None:
+        dy0, dy1, dx0, dx1 = dirty
+        canvas[dy0:dy1, dx0:dx1] = 0
+    else:
+        canvas.fill(0)
+    t_after_reset = cv2.getTickCount() / cv2.getTickFrequency() if do_profile else 0.0
+
+    need_crop = canvas_cache.get("crop_rect") is None
+    cov = canvas_cache.get("cov_buf") if need_crop else None
+    if need_crop:
+        if cov is None or cov.shape != (out_h, out_w):
+            cov = np.zeros((out_h, out_w), dtype=np.uint8)
+            canvas_cache["cov_buf"] = cov
+        else:
+            if dirty is not None:
+                dy0, dy1, dx0, dx1 = dirty
+                cov[dy0:dy1, dx0:dx1] = 0
+            else:
+                cov.fill(0)
+
+    pasted_tiles = 0
+    for tile_bgr, H_use, paste in zip(tiles_bgr_c, H_use_list, pastes):
+        if H_use is None or paste is None:
+            continue
+        y0, x0 = paste
+        paste_tile_clipped(canvas, tile_bgr, y0, x0, cov=cov)
+        pasted_tiles += 1
+    paste_tile_clipped(canvas, t_ref_bgr, ref_y0, ref_x0, cov=cov)
+    t_after_paste = cv2.getTickCount() / cv2.getTickFrequency() if do_profile else 0.0
+
+    crop_rect = canvas_cache.get("crop_rect")
+    if crop_rect is None and cov is not None:
+        crop_rect = compute_final_crop_rect(cov)
+        canvas_cache["crop_rect"] = crop_rect
+    if crop_rect is not None:
+        y0, y1, x0, x1 = crop_rect
+        roi = canvas[y0:y1, x0:x1]
+    else:
+        roi = canvas
+
+    rh, rw = roi.shape[:2]
+    stitch_buf = canvas_cache.get("stitch_buf")
+    if stitch_buf is None or stitch_buf.shape[0] != rh or stitch_buf.shape[1] != rw:
+        stitch_buf = np.empty((rh, rw, 3), dtype=np.uint8)
+        canvas_cache["stitch_buf"] = stitch_buf
+    np.copyto(stitch_buf, roi)
+    t_after_finalize = cv2.getTickCount() / cv2.getTickFrequency() if do_profile else 0.0
+
+    if do_profile:
+        xmf = canvas_cache.get("x_min_f", 0.0)
+        ymf = canvas_cache.get("y_min_f", 0.0)
+        print(
+            f"stitch_feature_optimized: canvas out_w={out_w}, out_h={out_h}, "
+            f"x_min={xmf:.2f}, y_min={ymf:.2f} (fast_paste)"
+        )
+        print(
+            "stitch_feature_optimized steps: "
+            f"brightness={t_after_brightness - t_profile0:.6f}s, "
+            f"geom={t_after_geom - t_after_brightness:.6f}s ({'hit' if geom_cache_hit else 'miss'}), "
+            f"reset={t_after_reset - t_after_geom:.6f}s, "
+            f"paste={t_after_paste - t_after_reset:.6f}s ({pasted_tiles + 1} tiles incl ref), "
+            f"finalize={t_after_finalize - t_after_paste:.6f}s, "
+            f"total={t_after_finalize - t_profile0:.6f}s"
+        )
+    canvas_cache["last_coverage"] = coverage_ratio(cov) if cov is not None else canvas_cache.get("last_coverage", 0.0)
+    return stitch_buf
+
+
+def render_feature_tiles(
+    t_ref_bgr,
+    tiles_bgr,
+    H_use_list,
+    *,
+    blend_backend: BlendBackend,
+    pad=5,
+    frame_idx=0,
+    print_every=30,
+    verbose=False,
+    match_brightness=False,
+    canvas_cache=None,
+    blend=True,
+):
+    if H_use_list is None:
+        return None
+
+    if canvas_cache is None:
+        canvas_cache = {}
+    do_profile = verbose and frame_idx % print_every == 0
+    t_profile0 = cv2.getTickCount() / cv2.getTickFrequency() if do_profile else 0.0
+
+    if t_ref_bgr.ndim == 2:
+        t_ref_bgr = cv2.cvtColor(t_ref_bgr, cv2.COLOR_GRAY2BGR)
+    h0c, w0c = t_ref_bgr.shape[:2]
+    if match_brightness:
+        ref_mean_gray = mean_gray_bgr(t_ref_bgr)
+        tiles_bgr_c = [
+            match_bgr_brightness(
+                tb if tb.ndim == 3 else cv2.cvtColor(tb, cv2.COLOR_GRAY2BGR),
+                ref_mean_gray,
+            )
+            for tb in tiles_bgr
+        ]
+    elif any(tb.ndim == 2 for tb in tiles_bgr):
+        tiles_bgr_c = [tb if tb.ndim == 3 else cv2.cvtColor(tb, cv2.COLOR_GRAY2BGR) for tb in tiles_bgr]
+    else:
+        tiles_bgr_c = tiles_bgr
+    t_after_brightness = cv2.getTickCount() / cv2.getTickFrequency() if do_profile else 0.0
+
+    geom_cache_hit = ensure_feature_geom_cache(
+        canvas_cache,
+        t_ref_bgr,
+        tiles_bgr_c,
+        H_use_list,
+        pad,
+        allocate_blend_bufs=bool(blend),
+    )
+    t_after_geom = cv2.getTickCount() / cv2.getTickFrequency() if do_profile else 0.0
+
+    if not blend:
+        return paste_feature_tiles_fast(
+            t_ref_bgr,
+            tiles_bgr_c,
+            H_use_list,
+            canvas_cache,
+            geom_cache_hit=geom_cache_hit,
+            frame_idx=frame_idx,
+            print_every=print_every,
+            verbose=verbose,
+            t_profile0=t_profile0,
+            t_after_brightness=t_after_brightness,
+            t_after_geom=t_after_geom,
+        )
+
+    out_w = canvas_cache["out_w"]
+    out_h = canvas_cache["out_h"]
+    ref_y0 = canvas_cache["ref_y0"]
+    ref_x0 = canvas_cache["ref_x0"]
+    pastes = canvas_cache["pastes"]
+
+    blend_buf = canvas_cache["blend_buf"]
+    wsum = canvas_cache["wsum_buf"]
+    cov = canvas_cache["cov_buf"]
+    weight_maps = canvas_cache["weight_maps"]
+    if geom_cache_hit:
+        _dr = canvas_cache.get("dirty_rect")
+        if _dr is not None:
+            _dy0, _dy1, _dx0, _dx1 = _dr
+            blend_buf[_dy0:_dy1, _dx0:_dx1] = 0
+            wsum[_dy0:_dy1, _dx0:_dx1] = 0
+            cov[_dy0:_dy1, _dx0:_dx1] = 0
+        else:
+            blend_buf.fill(0)
+            wsum.fill(0)
+            cov.fill(0)
+    t_after_reset = cv2.getTickCount() / cv2.getTickFrequency() if do_profile else 0.0
+
+    blend_margin = 16
+    blended_tiles = 0
+    for tile_bgr, H_use, paste in zip(tiles_bgr_c, H_use_list, pastes):
+        if H_use is None or paste is None:
+            continue
+        y0, x0 = paste
+        th, tw = tile_bgr.shape[:2]
+        wkey = (th, tw, blend_margin)
+        if wkey not in weight_maps:
+            weight_maps[wkey] = tile_weight_map(th, tw, blend_margin)
+        blend_backend.blend_tile_inplace(blend_buf, wsum, cov, tile_bgr, weight_maps[wkey], y0, x0)
+        blended_tiles += 1
+
+    ref_wkey = (h0c, w0c, blend_margin)
+    if ref_wkey not in weight_maps:
+        weight_maps[ref_wkey] = tile_weight_map(h0c, w0c, blend_margin)
+    blend_backend.blend_tile_inplace(
+        blend_buf, wsum, cov, t_ref_bgr, weight_maps[ref_wkey], ref_y0, ref_x0
+    )
+    t_after_blend = cv2.getTickCount() / cv2.getTickFrequency() if do_profile else 0.0
+
+    crop_rect = canvas_cache.get("crop_rect")
+    if crop_rect is None:
+        crop_rect = compute_final_crop_rect(cov)
+        canvas_cache["crop_rect"] = crop_rect
+    if crop_rect is not None:
+        y0, y1, x0, x1 = crop_rect
+        roi = blend_buf[y0:y1, x0:x1]
+    else:
+        roi = blend_buf
+    rh, rw = roi.shape[:2]
+    stitch_buf = canvas_cache.get("stitch_buf")
+    if stitch_buf is None or stitch_buf.shape[0] != rh or stitch_buf.shape[1] != rw:
+        stitch_buf = np.empty((rh, rw, 3), dtype=np.uint8)
+        canvas_cache["stitch_buf"] = stitch_buf
+    cv2.convertScaleAbs(roi, dst=stitch_buf)
+    t_after_finalize = cv2.getTickCount() / cv2.getTickFrequency() if do_profile else 0.0
+
+    if do_profile:
+        xmf = canvas_cache.get("x_min_f", 0.0)
+        ymf = canvas_cache.get("y_min_f", 0.0)
+        print(
+            f"stitch_feature_optimized: canvas out_w={out_w}, out_h={out_h}, "
+            f"x_min={xmf:.2f}, y_min={ymf:.2f}"
+        )
+        print(
+            "stitch_feature_optimized steps: "
+            f"brightness={t_after_brightness - t_profile0:.6f}s, "
+            f"geom={t_after_geom - t_after_brightness:.6f}s ({'hit' if geom_cache_hit else 'miss'}), "
+            f"reset={t_after_reset - t_after_geom:.6f}s, "
+            f"blend={t_after_blend - t_after_reset:.6f}s ({blended_tiles + 1} tiles incl ref), "
+            f"finalize={t_after_finalize - t_after_blend:.6f}s, "
+            f"total={t_after_finalize - t_profile0:.6f}s"
+        )
+
+    canvas_cache["last_coverage"] = coverage_ratio(cov)
+    canvas_cache["last_ms"] = (
+        (t_after_finalize - t_profile0) * 1000.0 if do_profile else canvas_cache.get("last_ms", 0.0)
+    )
+    return stitch_buf
+
+
+def stitch_feature_optimized(
+    *,
+    isstitch: bool,
+    index,
+    nrow: int,
+    ncol: int,
+    tile_list: Sequence,
+    cache: Dict[str, Any],
+    blend_backend: BlendBackend,
+    blend: bool = True,
+    match_brightness: bool = False,
+    pad: int = 5,
+    print_every: int = 30,
+    verbose: bool = False,
+    frame_idx_holder: Optional[Dict[str, int]] = None,
+):
+    """Estimate transforms once and composite the current tile grid."""
+    plan = get_feature_stitch_plan(isstitch, index, nrow, ncol, tile_list)
+    if plan is None:
+        return None
+
+    ref_idx, t_ref, moving_indices, moving_tiles, selected_positions = plan
+    if frame_idx_holder is None:
+        frame_idx_holder = {"idx": 0}
+    frame_idx_holder["idx"] = int(frame_idx_holder.get("idx", 0)) + 1
+    frame_idx = frame_idx_holder["idx"]
+    tile_shapes = tuple(tile_list[pos].shape[:2] for pos in selected_positions)
+    cache_changed = (
+        cache["ref_idx"] != ref_idx
+        or cache["moving_indices"] != moving_indices
+        or cache["tile_shapes"] != tile_shapes
+    )
+
+    if cache_changed:
+        cache["ref_idx"] = ref_idx
+        cache["moving_indices"] = moving_indices
+        cache["tile_shapes"] = tile_shapes
+        cache["transforms"] = None
+        cache["canvas"] = {}
+        if verbose:
+            print(
+                "Feature stitch tile selection: "
+                f"ref_idx={ref_idx}, moving_indices={list(moving_indices)}"
+            )
+
+    H_use_list = cache["transforms"]
+    if H_use_list is None:
+        do_verbose_first = verbose and frame_idx % print_every == 0
+        H_use_list = []
+        for j, tile in enumerate(moving_tiles):
+            H_use_j, _ = estimate_bfmatcher_stitch_transform(
+                t_ref,
+                tile,
+                verbose=(do_verbose_first and j == 0),
+            )
+            H_use_list.append(H_use_j)
+        cache["transforms"] = H_use_list
+
+    if not any(H is not None for H in H_use_list):
+        return None
+
+    return render_feature_tiles(
+        t_ref,
+        moving_tiles,
+        H_use_list,
+        blend_backend=blend_backend,
+        pad=pad,
+        frame_idx=frame_idx,
+        print_every=print_every,
+        verbose=verbose,
+        match_brightness=match_brightness,
+        canvas_cache=cache["canvas"],
+        blend=blend,
+    )

src/orisys/stitch/ops.py

@@ -0,0 +1,136 @@
+"""Pure array compute helpers for stitching (backend-agnostic).
+
+These functions take/return NumPy arrays and must not depend on ``Stitcher``
+instance state so future Torch/CuPy backends can reuse the same call shapes.
+"""
+
+from __future__ import annotations
+
+from typing import Optional, Tuple
+
+import cv2
+import numpy as np
+
+
+def tile_weight_map(h: int, w: int, margin: int = 16) -> np.ndarray:
+    """Linear ramp weight: 0 at edges, 1.0 at ``margin`` pixels inward."""
+    wy = np.ones(h, dtype=np.float32)
+    wx = np.ones(w, dtype=np.float32)
+    m = min(margin, h // 2, w // 2)
+    if m > 0:
+        ramp = np.linspace(0.0, 1.0, m + 1, dtype=np.float32)[1:]
+        wy[:m] = ramp
+        wy[-m:] = ramp[::-1]
+        wx[:m] = ramp
+        wx[-m:] = ramp[::-1]
+    return wy[:, None] * wx[None, :]
+
+
+def paste_tile_clipped(canvas, tile, y0: int, x0: int, cov=None) -> None:
+    """Paste tile into canvas with clipping; optionally mark coverage."""
+    th, tw = tile.shape[:2]
+    H, W = canvas.shape[:2]
+    y0d, x0d = max(0, y0), max(0, x0)
+    y1d, x1d = min(H, y0 + th), min(W, x0 + tw)
+    if y0d >= y1d or x0d >= x1d:
+        return
+    sy0, sx0 = y0d - y0, x0d - x0
+    sh, sw = y1d - y0d, x1d - x0d
+    np.copyto(canvas[y0d:y1d, x0d:x1d], tile[sy0 : sy0 + sh, sx0 : sx0 + sw])
+    if cov is not None:
+        cov[y0d:y1d, x0d:x1d] = 1
+
+
+def copy_into_tile_buffer(src_view, dst, flip_x: bool = True, flip_y: bool = True) -> None:
+    """Flip/copy ``src_view`` into preallocated ``dst``."""
+    if flip_x and flip_y:
+        cv2.flip(src_view, -1, dst)
+    elif flip_x:
+        cv2.flip(src_view, 1, dst)
+    elif flip_y:
+        cv2.flip(src_view, 0, dst)
+    else:
+        np.copyto(dst, src_view)
+
+
+def build_blend_coef(ov_pos: int, dim: int, ndim: int) -> np.ndarray:
+    """1D/2D blend ramp for legacy overlap stitching."""
+    blend_coef = np.linspace(1.0, 0.0, ov_pos, dtype=np.float32)
+    if dim == 1:
+        return blend_coef.reshape(1, ov_pos, 1) if ndim == 3 else blend_coef.reshape(1, ov_pos)
+    return blend_coef.reshape(ov_pos, 1, 1) if ndim == 3 else blend_coef.reshape(ov_pos, 1)
+
+
+def mean_gray_bgr(bgr) -> float:
+    ch = cv2.mean(bgr)
+    return (ch[0] + ch[1] + ch[2]) / 3.0
+
+
+def match_bgr_brightness(tile_bgr, ref_mean_gray: float):
+    delta = int(round(ref_mean_gray - mean_gray_bgr(tile_bgr)))
+    if delta == 0:
+        return tile_bgr
+    lut = np.clip(np.arange(256, dtype=np.int16) + delta, 0, 255).astype(np.uint8)
+    return cv2.LUT(tile_bgr, lut)
+
+
+def largest_fully_covered_rect(cov) -> Optional[Tuple[int, int, int, int]]:
+    if cov.size == 0:
+        return None
+
+    mask = cov > 0
+    if not np.any(mask):
+        return None
+
+    h, w = mask.shape
+    heights = np.zeros(w, dtype=np.int32)
+    best_area = 0
+    best_rect = None
+    for y in range(h):
+        heights = np.where(mask[y], heights + 1, 0)
+        stack = []
+        for x in range(w + 1):
+            cur_h = heights[x] if x < w else 0
+            start = x
+            while stack and stack[-1][1] > cur_h:
+                start0, h0 = stack.pop()
+                area = h0 * (x - start0)
+                if area > best_area:
+                    best_area = area
+                    best_rect = (y - h0 + 1, y + 1, start0, x)
+                start = start0
+            if not stack or stack[-1][1] < cur_h:
+                stack.append((start, cur_h))
+    return best_rect
+
+
+def compute_final_crop_rect(cov) -> Optional[Tuple[int, int, int, int]]:
+    valid = cov > 0
+    if not np.any(valid):
+        return None
+
+    ys, xs = np.where(valid)
+    by0, by1 = int(ys.min()), int(ys.max()) + 1
+    bx0, bx1 = int(xs.min()), int(xs.max()) + 1
+    rect2 = largest_fully_covered_rect(cov[by0:by1, bx0:bx1])
+    if rect2 is None:
+        return by0, by1, bx0, bx1
+
+    y0, y1, x0, x1 = rect2
+    return by0 + y0, by0 + y1, bx0 + x0, bx0 + x1
+
+
+def coverage_ratio(cov) -> float:
+    if cov is None or cov.size == 0:
+        return 0.0
+    return float(np.mean(cov > 0))
+
+
+def translation_from_H_to_ref(H) -> Tuple[float, float]:
+    if H is None:
+        return 0.0, 0.0
+    H = np.asarray(H, dtype=np.float64)
+    if abs(H[2, 2]) > 1e-12:
+        H = H / H[2, 2]
+    pt = H @ np.array([[0.0], [0.0], [1.0]], dtype=np.float64)
+    return float(pt[0, 0]), float(pt[1, 0])

src/orisys/stitch/tiles.py

@@ -0,0 +1,86 @@
+"""Tile extraction and indexing for compound-eye frames."""
+
+from __future__ import annotations
+
+from typing import Iterable, List, Optional, Sequence, Tuple
+
+import numpy as np
+
+from .ops import copy_into_tile_buffer
+
+
+def extract_units_from_boxes(
+    img,
+    boxes: Sequence[Tuple[int, int, int, int]],
+    *,
+    selected_index: Iterable[int],
+    units: Optional[list] = None,
+    flip_x: bool = True,
+    flip_y: bool = True,
+) -> list:
+    """Crop/flip tiles from ``boxes`` into ``units`` buffers (reused when possible)."""
+    h, w = img.shape[:2]
+    selected = set(selected_index)
+    flipped_units = [] if units is None else units
+
+    for i, (x0, y0, x1, y1) in enumerate(boxes):
+        if i not in selected:
+            continue
+
+        x0 = max(0, int(x0))
+        y0 = max(0, int(y0))
+        x1 = min(w, int(x1))
+        y1 = min(h, int(y1))
+        if x1 <= x0 or y1 <= y0:
+            continue
+
+        src_view = img[y0:y1, x0:x1]
+        if units is None:
+            tile = np.empty_like(src_view)
+            copy_into_tile_buffer(src_view, tile, flip_x=flip_x, flip_y=flip_y)
+            flipped_units.append((x0, y0, tile))
+        else:
+            _, _, tile = flipped_units[i]
+            if tile.shape != src_view.shape or tile.dtype != src_view.dtype:
+                tile = np.empty_like(src_view)
+            copy_into_tile_buffer(src_view, tile, flip_x=flip_x, flip_y=flip_y)
+            flipped_units[i] = (x0, y0, tile)
+
+    return flipped_units
+
+
+def get_tile_by_index(units: Sequence, idx) -> List:
+    if isinstance(idx, int):
+        idx = [idx]
+    if len(idx) > len(units):
+        raise ValueError("Index exceeds number of tiles.")
+    tile_list = []
+    for i in range(len(idx)):
+        _xi, _yi, tile = units[idx[i]]
+        tile_list.append(tile)
+    return tile_list
+
+
+def get_tile_index_by_position(units: Sequence, x, y, tol: int = 20) -> Optional[int]:
+    i = 0
+    for xi, yi, _tile in units:
+        if abs(int(x) - int(xi)) <= tol and abs(int(y) - int(yi)) <= tol:
+            return i
+        i += 1
+    return None
+
+
+def get_unit_pos(units: Sequence, idx) -> List[Tuple[int, int]]:
+    if isinstance(idx, int):
+        idx = [idx]
+    if len(idx) > len(units):
+        raise ValueError("Index exceeds number of vision units.")
+    return [(units[i][0], units[i][1]) for i in idx]
+
+
+def normalize_index(index) -> Tuple[int, ...]:
+    if isinstance(index, int):
+        return (index,)
+    if isinstance(index, np.ndarray):
+        return tuple(int(i) for i in index.tolist())
+    return tuple(int(i) for i in index)