update stitch algorithm and ver to 0.3.1

Readme.md

@@ -25,7 +25,7 @@ pip install orisys-{version}-{python-version}-{python-version}-{os}_{machine}.wh
 ```
 如
 ```bash
-pip install orisys-0.3.0-cp310-cp310-win_amd64.whl
+pip install orisys-0.3.1-cp310-cp310-win_amd64.whl
 ```
 
 3. **安装 CuPy（根据您的 CUDA 版本选择）：**

build_nuitka.py

@@ -21,11 +21,11 @@ def install_nuitka():
 def get_version() -> str:
     init_file = Path("src/orisys/__init__.py")
     if not init_file.exists():
-        return "0.3.0"
+        return "0.3.1"
     for line in init_file.read_text(encoding="utf-8").splitlines():
         if line.startswith("__version__"):
             return line.split("=", 1)[1].strip().strip('"').strip("'")
-    return "0.3.0"
+    return "0.3.1"
 
 
 def find_compiled_extension(output_dir: Path) -> Path:

doc/get_started.md

@@ -1,5 +1,5 @@
 叠动科技软件使用手册  
-版本： 0.3.0  
+版本： 0.3.1  
 更新日期： 2026年3月6日
 
 # 简介
@@ -57,9 +57,9 @@ orisys SDK 以 .whl 文件形式提供，您需要根据操作系统、Python
 ## 下载正确的 .whl 文件
 从官方渠道获取 .whl 文件，文件名格式通常为：
 orisys-{版本}-{Python版本}-{Python版本}-{操作系统}_{硬件架构}.whl
-例如：orisys-0.3.0-cp310-cp310-win_amd64.whl 表示：
+例如：orisys-0.3.1-cp310-cp310-win_amd64.whl 表示：
 
-版本：0.3.0
+版本：0.3.1
 Python 版本：3.10（cp310）
 操作系统：Windows（win）
 硬件架构：64位（amd64）
@@ -71,7 +71,7 @@ Python 版本：3.10（cp310）
 
 ``` bash
 # 请将文件名替换为您实际下载的文件名
-pip install orisys-0.3.0-cp310-cp310-win_amd64.whl
+pip install orisys-0.3.1-cp310-cp310-win_amd64.whl
 ```
 
 ## 安装 CuPy（可选）
@@ -108,7 +108,7 @@ python
 import orisys
 print(orisys.__version__)
 ```
-如果输出版本号（例如 0.3.0），则说明安装成功。
+如果输出版本号（例如 0.3.1），则说明安装成功。
 
 # 使用方法
 ## 运行示例程序

setup.py

@@ -9,7 +9,7 @@ if configs_dir.exists():
 
 setup(
     name="orisys",
-    version="0.3.0",
+    version="0.3.1",
     description="Orisys - Optical Tactile Sensor SDK",
     author="Your Name",
     package_dir={"": "src"},

src/orisys/Stitcher.py

@@ -30,10 +30,30 @@ class Stitcher:
         self.row_tol = config_stitcher["row_tol"]
         self.box_size = config_stitcher["box_size"]
         self._blend_coef_cache = {}
+        self._feature_stitch_cache = {
+            "ref_idx": None,
+            "moving_indices": None,
+            "tile_shapes": None,
+            "transforms": None,
+            "canvas": {},
+        }
+        self._feature_stitch_frame_idx = 0
+        self._feature_stitch_print_every = 30
+        self._feature_stitch_pad = 5
+        self._feature_stitch_match_brightness = False
 
         if self.verbose:
             print(f"Stitcher initialized with config: {self.config_stitcher}")
 
+    def configure_feature_stitch(self, *, match_brightness=None, pad=None, print_every=None):
+        """Update runtime options for feature-based stitching."""
+        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))
+ 
     def _get_blend_coef(self, ov_pos, dim, ndim):
         key = (ov_pos, dim, ndim)
         coef = self._blend_coef_cache.get(key)
@@ -48,6 +68,15 @@ class Stitcher:
         return blend_coef
 
     def stitch_rt_optimized(self, change_sv=False, s_mean=28., v_mean=180., blend=True):
+        feature_stitched = self.stitch_feature_optimized(
+            blend=blend,
+            match_brightness=self._feature_stitch_match_brightness,
+            pad=self._feature_stitch_pad,
+            print_every=self._feature_stitch_print_every,
+        )
+        if feature_stitched is not None:
+            return feature_stitched
+
         tile_list = self.tile_list
         stitch_calibration = self.stitch_calibration
         ncol = self.ncol
@@ -101,6 +130,63 @@ class Stitcher:
         
         return img_stitched_height
 
+    def stitch_feature_optimized(self, blend=True, match_brightness=False, pad=5, print_every=30):
+        """
+        Stitch the current tile grid by estimating tile-to-reference transforms once and
+        reusing them across frames. Falls back to overlap stitching when feature matching
+        cannot build a valid layout.
+        """
+        plan = self._get_feature_stitch_plan()
+        if plan is None:
+            return None
+
+        ref_idx, t_ref, moving_indices, moving_tiles = plan
+        self._feature_stitch_frame_idx += 1
+        frame_idx = self._feature_stitch_frame_idx
+        tile_shapes = tuple(tile.shape[:2] for tile in self.tile_list[: self.nrow * self.ncol])
+        cache = self._feature_stitch_cache
+        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"] = {}
+
+        H_use_list = cache["transforms"]
+        if H_use_list is None:
+            do_verbose_first = self.verbose and frame_idx % print_every == 0
+            H_use_list = []
+            for j, tile in enumerate(moving_tiles):
+                H_use_j, _ = self._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 self._render_feature_tiles(
+            t_ref,
+            moving_tiles,
+            H_use_list,
+            pad=pad,
+            frame_idx=frame_idx,
+            print_every=print_every,
+            verbose=self.verbose,
+            match_brightness=match_brightness,
+            canvas_cache=cache["canvas"],
+            blend=blend,
+        )
+
     def __find_ov(self, img1, img2, ov_pos, dim=1):
         
         _dim = len(img1.shape)
@@ -125,6 +211,418 @@ class Stitcher:
         
         return ov_pos, ov_1, ov_2
 
+    def _get_feature_stitch_plan(self):
+        if not self.isstitch:
+            return None
+
+        total_tiles = self.nrow * self.ncol
+        if total_tiles <= 1 or len(self.tile_list) < total_tiles:
+            return None
+
+        # Use the center tile as the reference frame for a stable translation-only canvas.
+        ref_idx = (self.nrow // 2) * self.ncol + (self.ncol // 2)
+        if ref_idx >= total_tiles:
+            return None
+
+        moving_indices = tuple(i for i in range(total_tiles) if i != ref_idx)
+        moving_tiles = [self.tile_list[i] for i in moving_indices]
+        return ref_idx, self.tile_list[ref_idx], moving_indices, moving_tiles
+
+    def _tile_to_bgr(self, img):
+        if img.ndim == 2:
+            return cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+        return img
+
+    def _tile_to_gray(self, bgr):
+        if bgr.ndim == 2:
+            return bgr
+        return cv2.cvtColor(bgr, cv2.COLOR_BGR2GRAY)
+
+    def _ensure_u8_gray(self, 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(self, 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(
+        self,
+        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 tile frame.
+        """
+        t0_bgr = self._tile_to_bgr(t0)
+        t1_bgr = self._tile_to_bgr(t1)
+        g0 = self._ensure_u8_gray(self._tile_to_gray(t0_bgr))
+        g1 = self._ensure_u8_gray(self._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 = self._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 _translation_from_H_to_ref(self, H):
+        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])
+
+    def _tile_weight_map(self, h, w, margin=16):
+        """
+        Linear ramp weight that is 0 at tile edges and 1.0 at margin pixels inward.
+        Used for feathered blending at tile overlaps.
+        """
+        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 _blend_tile_inplace(self, blend, wsum, cov, src, weight, y0, x0):
+        """Copy non-overlap tiles directly; blend only when this tile touches existing content."""
+        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
+        w_patch = weight[sy0 : sy0 + sh, sx0 : sx0 + sw]
+        blend_patch = blend[y0d:y1d, x0d:x1d]
+        wsum_patch = wsum[y0d:y1d, x0d:x1d]
+        cov_patch = cov[y0d:y1d, x0d:x1d]
+        src_patch = src[sy0 : sy0 + sh, sx0 : sx0 + sw]
+
+        if not np.any(cov_patch):
+            blend_patch[:] = src_patch
+            wsum_patch[:] = w_patch
+        else:
+            total = wsum_patch + w_patch
+            alpha = w_patch / np.maximum(total, 1e-6)
+            src_patch_f = src_patch.astype(np.float32, copy=False)
+            blend_patch += (src_patch_f - blend_patch) * alpha[:, :, None]
+            wsum_patch[:] = total
+
+        cov_patch[:] = 1
+
+    def _mean_gray_bgr(self, bgr):
+        return float(cv2.cvtColor(bgr, cv2.COLOR_BGR2GRAY).mean())
+
+    def _match_bgr_brightness(self, tile_bgr, ref_mean_gray):
+        delta = int(round(ref_mean_gray - self._mean_gray_bgr(tile_bgr)))
+        if delta == 0:
+            return tile_bgr
+        return np.clip(tile_bgr.astype(np.int16) + delta, 0, 255).astype(np.uint8)
+
+    def _largest_fully_covered_rect(self, cov):
+        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(self, cov):
+        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 = self._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 _geom_cache_key(self, 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 _render_feature_tiles(
+        self,
+        t_ref_bgr,
+        tiles_bgr,
+        H_use_list,
+        *,
+        pad=5,
+        frame_idx=0,
+        print_every=30,
+        verbose=False,
+        match_brightness=False,
+        canvas_cache=None,
+        blend=True,
+    ):
+        """
+        Composite multiple tiles into one canvas using translation only, where each tile
+        origin maps into the reference tile coordinates.
+        """
+        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
+
+        t_ref_bgr = self._tile_to_bgr(t_ref_bgr)
+        h0c, w0c = t_ref_bgr.shape[:2]
+        if match_brightness:
+            ref_mean_gray = self._mean_gray_bgr(t_ref_bgr)
+            tiles_bgr_c = [self._match_bgr_brightness(self._tile_to_bgr(tb), ref_mean_gray) for tb in tiles_bgr]
+        else:
+            tiles_bgr_c = [self._tile_to_bgr(tb) for tb in tiles_bgr]
+        t_after_brightness = cv2.getTickCount() / cv2.getTickFrequency() if do_profile else 0.0
+
+        geom_key = self._geom_cache_key(t_ref_bgr, tiles_bgr_c, pad)
+        geom_cache_hit = canvas_cache.get("geom_key") == geom_key
+        if not geom_cache_hit:
+            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 = self._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 = self._translation_from_H_to_ref(H_use)
+                pastes.append((int(np.floor(ty - y_min_f)), int(np.floor(tx - x_min_f))))
+
+            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["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)
+            canvas_cache["weight_maps"] = {}
+        else:
+            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"]
+        t_after_geom = cv2.getTickCount() / cv2.getTickFrequency() if do_profile else 0.0
+
+        blend_buf = canvas_cache["blend_buf"]
+        wsum = canvas_cache["wsum_buf"]
+        cov = canvas_cache["cov_buf"]
+        weight_maps = canvas_cache["weight_maps"]
+        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 if blend else 0
+        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] = self._tile_weight_map(th, tw, blend_margin)
+            self._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] = self._tile_weight_map(h0c, w0c, blend_margin)
+        self._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 = self._compute_final_crop_rect(cov)
+            canvas_cache["crop_rect"] = crop_rect
+        if crop_rect is not None:
+            y0, y1, x0, x1 = crop_rect
+            stitched = np.clip(blend_buf[y0:y1, x0:x1], 0, 255).astype(np.uint8)
+        else:
+            stitched = np.clip(blend_buf, 0, 255).astype(np.uint8)
+        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"
+            )
+
+        return stitched
+
     def find_stitch_boxes(self,img_src,show = False):
 
         img_corr = self.__correct_edge_bloom(img_src)
@@ -622,3 +1120,5 @@ class Stitcher:
                 continue
             filtered.append((x,y,ww,hh))
         return filtered
+
+    

src/orisys/__init__.py

@@ -5,7 +5,7 @@ A Python library for processing and analyzing optical tactile sensor data,
 including image stitching, optical flow tracking, and deformation decomposition.
 """
 
-__version__ = "0.3.0"
+__version__ = "0.3.1"
 __author__ = "Orisys"
 
 from .sensor import Sensor

src/orisys/util.py

@@ -44,7 +44,8 @@ def draw_arrows(frame, optical_flow, fps=True, threshold=5, grid_spacing=40, arr
         if mag[i] < threshold:
             cv2.circle(image, pt1, radius=1, color=below_threshold_color, thickness=-1)
         else:
-            thickness = max(1, int(2 * mag[i] / mag.max())) if mag.max() > 0 else 2
+            # thickness = max(1, int(2 * mag[i] / mag.max())) if mag.max() > 0 else 2
+            thickness = 1
             cv2.arrowedLine(image, pt1, pt2, color, thickness=thickness, tipLength=0.2)
     return image
 

test_stitch.py

@@ -6,428 +6,14 @@
 2. 循环获取图像并计算形变和触碰点
 3. 读取各类传感器数据
 4. 可视化显示
-
-
 """
 import os
-os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0" 
+import argparse
+
 import cv2
 import orisys
-import numpy as np
-import argparse
-import time
 
-
+os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0"
-def _tile_to_bgr(img):
-    if img.ndim == 2:
-        return cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
-    return img
-
-
-def _tile_to_gray_bgr(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 _translation_from_H_to_ref(H):
-    """
-    Use only the translation part of H (tile -> reference): where tile origin (0,0) lands in ref coords.
-    Ignores rotation/scale/shear for fast compositing (slice paste).
-    """
-    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])
-
-
-def _paste_bgr(dst, src, y0, x0):
-    """Paste src into dst at top-left (y0, x0), clipping to dst bounds."""
-    h, w = src.shape[:2]
-    H, W = dst.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 = y0d - y0
-    sx0 = x0d - x0
-    sh = y1d - y0d
-    sw = x1d - x0d
-    dst[y0d:y1d, x0d:x1d] = src[sy0 : sy0 + sh, sx0 : sx0 + sw]
-
-
-def _accumulate_tile_bgr(acc, wsum, src, y0, x0):
-    """
-    Add tile BGR into float accumulators for mean blending where tiles overlap.
-    acc: (H,W,3) float32, wsum: (H,W) float32 counts per pixel.
-    """
-    h, w = src.shape[:2]
-    H, W = acc.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 = y0d - y0
-    sx0 = x0d - x0
-    sh = y1d - y0d
-    sw = x1d - x0d
-    patch = src[sy0 : sy0 + sh, sx0 : sx0 + sw].astype(np.float32, copy=False)
-    acc[y0d:y1d, x0d:x1d] += patch
-    wsum[y0d:y1d, x0d:x1d] += 1.0
-
-
-def _finalize_mean_blend(acc, wsum):
-    """Average accumulated colors; pixels with no coverage stay 0."""
-    out_h, out_w = wsum.shape
-    stitched = np.zeros((out_h, out_w, 3), dtype=np.uint8)
-    valid = wsum > 1e-6
-    stitched[valid] = np.clip(acc[valid] / wsum[valid, None], 0.0, 255.0).astype(np.uint8)
-    return stitched
-
-
-def _h_list_signature(H_use_list):
-    """Stable signature to detect when cached canvas geometry must be rebuilt."""
-    parts = []
-    for H in H_use_list:
-        if H is None:
-            parts.append("None")
-        else:
-            parts.append(np.asarray(H, dtype=np.float64).tobytes())
-    return tuple(parts)
-
-
-# Tuning (if stitch position is wrong or unstable):
-# 1) ORB: nfeatures, fastThreshold, edgeThreshold in ORB_create.
-# 2) Lowe ratio: lower => stricter matches; higher => more matches, more outliers.
-# 3) ransac_reproj_thresh: affine/homography RANSAC inlier threshold (pixels).
-# 4) Temporal: smooth_alpha, min_inliers, max_mean_reproj_err.
-def estimate_bfmatcher_stitch_transform(
-    t0,
-    t1,
-    *,
-    H_prev=None,
-    smooth_alpha=0.2,
-    min_inliers=30,
-    max_mean_reproj_err=8.0,
-    ransac_reproj_thresh=5.0,
-    ratio=0.9,
-    orb_nfeatures=5000,
-    verbose=True,
-):
-    """
-    Estimate 3x3 transform H_use mapping t1 coordinates -> t0 coordinates (t0 is reference).
-
-    Parameters
-    ----------
-    t0, t1 : ndarray
-        Tile images (BGR or grayscale).
-    H_prev : ndarray or None
-        Previous frame's H_use for temporal smoothing; pass None on first frame.
-
-    Returns
-    -------
-    H_use : ndarray (3,3) or None
-        Normalized homography/affine-as-homography. None if not enough features or no valid model.
-    H_prev_out : ndarray or None
-        Value to pass as H_prev on the next call (same as H_use when successful, else unchanged H_prev).
-    info : dict
-        Keys may include: ok, method, inliers, mean_err, good_matches, k0, k1, t0_bgr, t1_bgr (for viz).
-    """
-    t0_bgr = _tile_to_bgr(t0)
-    t1_bgr = _tile_to_bgr(t1)
-    g0 = _ensure_u8_gray(_tile_to_gray_bgr(t0_bgr))
-    g1 = _ensure_u8_gray(_tile_to_gray_bgr(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, H_prev, info
-
-    bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=False)
-    matches = bf.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)
-    info["good"] = good
-    if verbose:
-        print(f"BFMatcher good matches: {len(good)}")
-
-    if len(good) < 4:
-        return None, H_prev, 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, inliers_aff = 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)
-        inlier_mask = inliers_aff
-    else:
-        method = "homography"
-        H, inlier_mask = cv2.findHomography(pts1, pts0, cv2.RANSAC, ransac_reproj_thresh)
-
-    inliers = int(inlier_mask.sum()) if inlier_mask is not None else 0
-    info["method"] = method
-    info["inliers"] = inliers
-    if verbose:
-        print(f"Estimated {method} inliers: {inliers}")
-
-    if H is None:
-        info["transform_none"] = True
-        return None, H_prev, 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
-
-    H_use = H
-    if H_prev is not None:
-        if inliers < min_inliers or mean_err > max_mean_reproj_err:
-            H_use = H_prev
-        else:
-            H_use = smooth_alpha * H + (1.0 - smooth_alpha) * H_prev
-            if abs(H_use[2, 2]) > 1e-12:
-                H_use = H_use / H_use[2, 2]
-
-    info["ok"] = True
-    return H_use, H_use.copy(), info
-
-
-def render_stitched_preview(
-    t0_bgr,
-    t1_bgr,
-    H_use,
-    *,
-    pad=None,
-    frame_idx=0,
-    print_every=30,
-    verbose=True,
-):
-    """
-    Warp t1 with H_use (t1->t0), paste t0 on a fixed padded canvas. Returns stitched BGR image.
-    """
-    if H_use is None:
-        return None
-    h0c, w0c = t0_bgr.shape[:2]
-    if pad is None:
-        pad = max(50, int(0.1 * w0c))
-    out_w = w0c + 2 * pad
-    out_h = h0c + 2 * pad
-    T_pad = np.array([[1, 0, pad], [0, 1, pad], [0, 0, 1]], dtype=np.float64)
-    H_total = T_pad @ H_use
-
-    if verbose and frame_idx % print_every == 0:
-        h1c, w1c = t1_bgr.shape[:2]
-        corners1 = np.float32([[0, 0], [w1c, 0], [w1c, h1c], [0, h1c]]).reshape(-1, 1, 2)
-        corners1_warped = cv2.perspectiveTransform(corners1, H_total).reshape(-1, 2)
-        corners0_out = np.float32(
-            [
-                [pad, pad],
-                [pad + w0c, pad],
-                [pad + w0c, pad + h0c],
-                [pad, pad + h0c],
-            ]
-        )
-        print("=== Stitch transform debug ===")
-        print("H_use (t1 -> t0):\n", H_use)
-        print("H_total (output canvas):\n", H_total)
-        print("t1 corners warped into output (x,y):", corners1_warped)
-        print("t0 corners in output (x,y):", corners0_out)
-
-    stitched = cv2.warpPerspective(t1_bgr, H_total, (out_w, out_h), flags=cv2.INTER_LINEAR)
-    stitched[pad : pad + h0c, pad : pad + w0c] = t0_bgr
-    return stitched
-
-
-def _geom_cache_key(t_ref_bgr, tiles_bgr_c, H_use_list, pad):
-    """Invalidate canvas layout when ref/tile sizes, transforms, or pad change."""
-    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, _h_list_signature(H_use_list))
-
-
-def render_multi_tiles_preview(
-    t_ref_bgr,
-    tiles_bgr,
-    H_use_list,
-    *,
-    pad=60,
-    frame_idx=0,
-    print_every=30,
-    verbose=False,
-    canvas_cache=None,
-):
-    """
-    Composite multiple tiles into one canvas using translation only (where each tile's
-    origin maps in ref coords). No per-tile warpPerspective.
-
-    - t_ref_bgr: reference tile; output is in a shifted ref-aligned frame.
-    - each H_use maps tile_i -> reference; only the translation of origin (0,0) is used.
-    - tiles_bgr and H_use_list must have the same length.
-    - canvas_cache: optional dict, updated in place to reuse out_w/out_h and paste origins
-      when geometry is unchanged.
-    Overlapping regions use mean blending (equal weight per contributing tile).
-    """
-    if H_use_list is None:
-        return None
-
-    if canvas_cache is None:
-        canvas_cache = {}
-
-    t_ref_bgr = _tile_to_bgr(t_ref_bgr)
-    h0c, w0c = t_ref_bgr.shape[:2]
-    tiles_bgr_c = [_tile_to_bgr(tb) for tb in tiles_bgr]
-
-    geom_key = _geom_cache_key(t_ref_bgr, tiles_bgr_c, H_use_list, pad)
-
-    if canvas_cache.get("geom_key") != geom_key:
-        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(0.0 - y_min_f))
-        ref_x0 = int(np.floor(0.0 - x_min_f))
-
-        pastes = []
-        for tile_bgr, H_use in zip(tiles_bgr_c, 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))))
-
-        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
-    else:
-        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"]
-
-    acc = np.zeros((out_h, out_w, 3), dtype=np.float32)
-    wsum = np.zeros((out_h, out_w), dtype=np.float32)
-    _accumulate_tile_bgr(acc, wsum, t_ref_bgr, ref_y0, ref_x0)
-    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
-        _accumulate_tile_bgr(acc, wsum, tile_bgr, y0, x0)
-    stitched = _finalize_mean_blend(acc, wsum)
-    
-    # Crop away any uncovered regions (which correspond to black padding at borders).
-    # Use `wsum` for robustness even if a tile's actual content is black.
-    valid = wsum > 1e-6
-    if np.any(valid):
-        ys, xs = np.where(valid)
-        y0c, y1c = int(ys.min()), int(ys.max()) + 1
-        x0c, x1c = int(xs.min()), int(xs.max()) + 1
-        stitched = stitched[y0c:y1c, x0c:x1c]
-
-    stitched = cv2.GaussianBlur(stitched, (5, 5), 0)
-
-
-    if verbose and frame_idx % print_every == 0:
-        xmf = canvas_cache.get("x_min_f", 0.0)
-        ymf = canvas_cache.get("y_min_f", 0.0)
-        print(
-            f"render_multi_tiles_preview: canvas out_w={out_w}, out_h={out_h}, "
-            f"x_min={xmf:.2f}, y_min={ymf:.2f}"
-        )
-
-    return stitched
 
 
 def main():
@@ -436,41 +22,28 @@ def main():
     parser.add_argument("--config", "-c", type=str, default="./config/ddjx01.json", help="Configuration file path")
     parser.add_argument("--cal", "-cal", type=str, default="./config/ddjx01.npy", help="Calibration file path")
     parser.add_argument("--verbose", "-verbose", type=bool, default=True, help="Print detailed information")
+    parser.add_argument(
+        "--match-brightness",
+        dest="match_brightness",
+        action="store_true",
+        help="Enable per-tile brightness matching",
+    )
+    parser.set_defaults(match_brightness=False)
     args = parser.parse_args()
 
+    try:
+        vid_src = int(args.video)
+    except ValueError:
+        vid_src = args.video
+
     # 步骤1：创建传感器对象 (必要步骤)，
     # config_name为配置文件名（内置配置文件名）或路径（自定义配置文件路径）
     # cal_path为校准文件路径, 每一个设备需要创建/使用一个校准文件
-
+    sensor = orisys.Sensor(vid_src, config_name=args.config, cal_path=args.cal, verbose=args.verbose)
-    # 方式1: 从视频文件读取
+    sensor.stitcher.configure_feature_stitch(match_brightness=args.match_brightness)
-    try:
-        input = int(args.video)
-        is_camera = True
-        sensor = orisys.Sensor(input, config_name=args.config, cal_path= args.cal, verbose=args.verbose)
-    except:
-        is_camera = False
-        sensor = orisys.Sensor(args.video, config_name=args.config, cal_path= args.cal, verbose=args.verbose)
 
     print("\n按 'q' 退出程序，按 'r' 重置传感器追踪器")
 
-    # For stabilizing frame-to-frame stitching (reduce oscillation).
-    # Stores the last good transform (mapping t1 -> t0) and exponentially smooths it.
-    smooth_alpha = 0.2  # 0..1; higher => less smoothing, more jitter
-    min_inliers = 30    # if inliers are too low, keep previous transform
-    max_mean_reproj_err = 8.0  # if reprojection error is too high, keep previous transform
-    frame_idx = 0
-    print_every = 30  # reduce console spam; set to 1 for per-frame output
-
-    # Stitch a 3x3 (9 tiles) block from the row-major `tile_list`.
-    # For your 3x5 layout (nrow=3, ncol=5), this stitches the "middle 3x3"
-    # using the center tile of that 3x3 as the reference frame.
-    H_prev_tiles = {}  # key: tile_list index, value: H_prev
-    # Cache BFMatcher-estimated transforms from the first frame.
-    H_use_list_cached = None
-    moving_indices_cached = None
-    crop_pad = 5  # tighter crop in stitched canvas
-    canvas_cache = {}
-
     while True:
         # 步骤2：信息提取
         # 步骤 2.1: 获取并拼接图像 (必要步骤)
@@ -478,118 +51,13 @@ def main():
         if img is None:
             print("Failed to get image (end of stream or capture error). Exiting.")
             break
-        # boxes = sensor.stitcher.boxes
-        # draw boxes for checking
-        # for box in boxes:
-        #     x0, y0, x1, y1 = box
-        #     cv2.rectangle(sensor.frame, (x0, y0), (x1, y1), (0, 255, 0), 2)
-        # cv2.imshow("boxes", sensor.frame)
-        # cv2.waitKey(0)
 
-        tiles = sensor.stitcher.tile_list
+        if img is not None:
-        frame_idx += 1
+            cv2.imshow("OpenCV Stitch (feature-based)", img)
 
-        ncol = getattr(sensor.stitcher, "ncol", 0) or 0
+        # 步骤 2.2: 计算形变场 (必要步骤) — sensor.compute_deformation(...)
-        nrow = getattr(sensor.stitcher, "nrow", 0) or 0
-
-        grid_r, grid_c = 3, 3
-        needed = grid_r * grid_c
-        if len(tiles) < needed or ncol < grid_c or nrow < grid_r:
-            print(f"tile_list too small for 9-tile stitch: len={len(tiles)} ncol={ncol} nrow={nrow} needed={needed}")
-            cv2.waitKey(1)
-            continue
-
-        # Center the 3x3 block in the full (nrow x ncol) layout.
-        # Use the center tile of that 3x3 as reference frame.
-        start_r = (nrow - grid_r) // 2
-        start_c = (ncol - grid_c) // 2
-
-        idx_ref = (start_r + 1) * ncol + (start_c + 1)
-        if idx_ref < 0 or idx_ref >= len(tiles):
-            print("Computed reference tile index out of range:", idx_ref)
-            cv2.waitKey(1)
-            continue
-
-        t_ref = tiles[idx_ref]
-
-        # Stitcher: warp each moving tile into the reference tile coordinate system.
-        moving_tiles = []
-        moving_indices = []
-        for r in range(grid_r):
-            for c in range(grid_c):
-                idx = (start_r + r) * ncol + (start_c + c)
-                if idx == idx_ref:
-                    continue
-                if idx < 0 or idx >= len(tiles):
-                    continue
-                moving_tiles.append(tiles[idx])
-                moving_indices.append(idx)
-
-        # Only run BFMatcher transform estimation on the first frame.
-        if H_use_list_cached is None or moving_indices_cached != moving_indices:
-            H_use_list = []
-            do_verbose_first = (frame_idx % print_every == 0)
-            for j, (tile, tile_idx) in enumerate(zip(moving_tiles, moving_indices)):
-                H_prev_j = H_prev_tiles.get(tile_idx)
-                H_use_j, H_prev_j_out, info_j = estimate_bfmatcher_stitch_transform(
-                    t_ref,
-                    tile,
-                    H_prev=H_prev_j,
-                    smooth_alpha=smooth_alpha,
-                    min_inliers=min_inliers,
-                    max_mean_reproj_err=max_mean_reproj_err,
-                    verbose=(do_verbose_first and j == 0),
-                )
-                H_prev_tiles[tile_idx] = H_prev_j_out
-                H_use_list.append(H_use_j)
-
-                if (
-                    j == 0
-                    and do_verbose_first
-                    and info_j.get("good") is not None
-                    and len(info_j["good"]) >= 4
-                ):
-                    draw = cv2.drawMatches(
-                        info_j["t0_bgr"],
-                        info_j["k0"],
-                        info_j["t1_bgr"],
-                        info_j["k1"],
-                        info_j["good"],
-                        None,
-                        matchColor=(0, 255, 0),
-                        singlePointColor=(255, 0, 0),
-                        flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS,
-                    )
-                    cv2.imshow("BFMatcher Matches (t_ref vs t_moving0)", draw)
-
-            H_use_list_cached = H_use_list
-            moving_indices_cached = list(moving_indices)
-        else:
-            H_use_list = H_use_list_cached
-
-        start_time = time.perf_counter()
-        stitched = render_multi_tiles_preview(
-            t_ref,
-            moving_tiles,
-            H_use_list,
-            pad=crop_pad,
-            frame_idx=frame_idx,
-            print_every=print_every,
-            verbose=False,
-            canvas_cache=canvas_cache,
-        )
-        end_time = time.perf_counter()
-        print(f"render_multi_tiles_preview: {end_time - start_time} seconds")
-        
-        if stitched is not None:
-            cv2.imshow("OpenCV Stitch (9 tiles, middle-ref stabilized)", stitched)
-        # cv2.imshow("tiles_1_2 (two rows, 2nd shifted)", canvas)
-        # cv2.waitKey(0)
-        # 步骤 2.2: 计算形变场 (必要步骤)
-        # is_touched = sensor.compute_deformation(check_motion=True, threshold=6)
         # 步骤 2.3: 计算触碰点
-       
+        # 步骤 3: 键盘控制
-        #步骤 3.5: 键盘控制
         key = cv2.waitKey(1) & 0xFF
         if key == ord("q"):
             print("\n退出程序...")
@@ -605,5 +73,5 @@ def main():
     sensor.disconnect()  # 释放视频源和相关资源
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()