""" 批量配准 .bip 文件到参考 .tif 文件 使用 SimpleITK 实现 B 样条变换 """ from pathlib import Path import numpy as np import cv2 import rasterio from rasterio.windows import from_bounds from rasterio.warp import transform_bounds, reproject, Resampling from affine import Affine from vismatch import get_matcher import logging try: from skimage.transform import PiecewiseAffineTransform, PolynomialTransform SKIMAGE_AVAILABLE = True except ImportError: SKIMAGE_AVAILABLE = False logging.warning("scikit-image 不可用，将跳过 piecewise_affine 和 polynomial 变换") try: from matplotlib.path import Path as MplPath from scipy.spatial import ConvexHull MATPLOTLIB_SCIPY_AVAILABLE = True except ImportError: MATPLOTLIB_SCIPY_AVAILABLE = False MplPath = None logging.warning("matplotlib 或 scipy 不可用，piecewise_affine 将退化为矩形内判断") try: import SimpleITK as sitk SITK_AVAILABLE = True except ImportError: SITK_AVAILABLE = False logging.warning("SimpleITK 不可用，将使用仿射变换作为替代") try: import pirt PIRT_AVAILABLE = True except ImportError: PIRT_AVAILABLE = False logging.warning("PIRT 不可用，将使用 SimpleITK TPS 作为替代") # 设置日志 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') logger = logging.getLogger(__name__) # ---------- 配置 ---------- # 请根据实际情况修改这些路径 REF_TIF = r"E:\is2\guidingsahn\result.tif" # 参考 tif 文件路径 BIP_DIR = Path(r"E:\is2\guidingsahn") # .bip 文件所在文件夹 OUT_DIR = Path(r"E:\is2\guidingsahn\output") # 输出文件夹 # 匹配算法选择 MATCHER_NAME = "matchanything-roma" # 可选: xfeat-star, loftr, roma, superpoint-lightglue, sift-lightglue 等 DEVICE = "cuda" # 或 "cpu" # 变换方法选择（按优先级尝试） TRANSFORM_METHODS = ["homography"] # 可选: "similarity", "affine", "homography", "piecewise_affine", "polynomial", "tps" # 匹配参数 MATCH_MAX_SIDE = 1500 # 匹配时最大边长（像素） ROI_PAD_PX = 500 # 粗定位窗口的padding（参考tif像素） # 质量控制阈值 MIN_INLIERS = 10 # 最少内点数 MIN_INLIER_RATIO = 0.01 # 最少内点比例 # 创建输出目录 OUT_DIR.mkdir(parents=True, exist_ok=True) # ---------- 工具函数 ---------- def _to_3ch_float01(arr_chw: np.ndarray) -> np.ndarray: """将任意通道数的数组转换为 (3,H,W) float32 in [0,1]""" arr = arr_chw.astype(np.float32) if arr.shape[0] == 1: # 单波段复制为3通道 arr = np.repeat(arr, 3, axis=0) elif arr.shape[0] >= 3: # 取前3波段 arr = arr[:3] else: raise ValueError(f"不支持的通道数: {arr.shape[0]}") # 百分位数拉伸，增强跨传感器匹配稳定性 p2 = np.percentile(arr, 2) p98 = np.percentile(arr, 98) arr = (arr - p2) / (p98 - p2 + 1e-6) arr = np.clip(arr, 0.0, 1.0) return arr def _downscale_chw(arr_chw: np.ndarray, max_side: int) -> np.ndarray: """等比缩放 (C,H,W) 到 max(H,W) <= max_side""" c, h, w = arr_chw.shape s = min(1.0, max_side / max(h, w)) if s >= 1.0: return arr_chw new_w = int(round(w * s)) new_h = int(round(h * s)) # 用opencv缩放(逐通道) out = np.stack([cv2.resize(arr_chw[i], (new_w, new_h), interpolation=cv2.INTER_AREA) for i in range(c)], axis=0) return out def _expand_window(win, pad, max_w, max_h): """扩展窗口并确保边界有效""" col_off = int(max(0, win.col_off - pad)) row_off = int(max(0, win.row_off - pad)) col_end = int(min(max_w, win.col_off + win.width + pad)) row_end = int(min(max_h, win.row_off + win.height + pad)) return rasterio.windows.Window(col_off, row_off, col_end - col_off, row_end - row_off) def estimate_transform(method, k0, k1): """统一的变换估计函数，支持多种变换类型""" if method == "translation": # 简单平移：用内点的平均位移 if len(k0) == 0: return None, None dx = np.mean(k1[:, 0] - k0[:, 0]) dy = np.mean(k1[:, 1] - k0[:, 1]) A = np.array([[1, 0, dx], [0, 1, dy]], dtype=np.float32) return "A", A elif method == "euclidean": # 欧式变换（旋转+平移），约束等比缩放=1 A, _ = cv2.estimateAffinePartial2D(k0, k1, method=cv2.RANSAC, ransacReprojThreshold=3.0) return "A", A elif method == "similarity": # 相似变换（旋转+等比缩放+平移） A, _ = cv2.estimateAffinePartial2D(k0, k1, method=cv2.RANSAC, ransacReprojThreshold=3.0) return "A", A elif method == "affine": # 全仿射变换（旋转+非等比缩放+剪切+平移） A, _ = cv2.estimateAffine2D(k0, k1, method=cv2.RANSAC, ransacReprojThreshold=3.0) return "A", A elif method == "homography": # 投影变换（8DOF，透视） H, _ = cv2.findHomography(k0, k1, method=cv2.USAC_MAGSAC, ransacReprojThreshold=3.0) return "H", H elif method == "piecewise_affine": # 分片仿射变换 if not SKIMAGE_AVAILABLE: return None, None try: tform = PiecewiseAffineTransform() tform.estimate(k0, k1) return "piecewise", tform except Exception: return None, None elif method == "polynomial": # 多项式变换（2阶） if not SKIMAGE_AVAILABLE: return None, None try: tform = PolynomialTransform() tform.estimate(k0, k1, order=2) return "polynomial", tform except Exception: return None, None elif method == "tps": # 薄板样条变换（如果SimpleITK可用） SITK_TPS = SITK_AVAILABLE and hasattr(sitk, "ThinPlateSplineKernelTransform") if not SITK_TPS: return None, None try: tps_transform = sitk.ThinPlateSplineKernelTransform() tps_transform.SetKernelTypeToThinPlateSpline() fixed_landmarks = sitk.vectorDPoint() moving_landmarks = sitk.vectorDPoint() for (rx, ry), (sx, sy) in zip(k1, k0): fixed_landmarks.push_back([float(rx), float(ry)]) moving_landmarks.push_back([float(sx), float(sy)]) tps_transform.SetFixedLandmarks(fixed_landmarks) tps_transform.SetMovingLandmarks(moving_landmarks) return "tps", tps_transform except Exception: return None, None else: raise ValueError(f"未知变换方法: {method}") def evaluate_transform_quality(transform_type, transform, k0, k1): """评估变换质量（重投影误差）""" if transform is None or len(k0) == 0: return np.inf, np.inf if transform_type == "A": # 仿射变换重投影误差 A = transform ones = np.ones((k0.shape[0], 1), dtype=np.float32) pred = (A @ np.hstack([k0, ones]).T).T e = np.sqrt(((pred - k1) ** 2).sum(axis=1)) elif transform_type == "H": # 单应变换重投影误差 H = transform ones = np.ones((k0.shape[0], 1), dtype=np.float32) src_h = np.hstack([k0, ones]).T warped = H @ src_h warped /= (warped[2:3, :] + 1e-6) pred = warped[:2, :].T e = np.sqrt(((pred - k1) ** 2).sum(axis=1)) elif transform_type in ["piecewise", "polynomial"]: # scikit-image 变换重投影误差 pred = transform(k0) e = np.sqrt(((pred - k1) ** 2).sum(axis=1)) elif transform_type == "tps": # TPS 变换重投影误差（SimpleITK） pred = [] for pt in k0: transformed_pt = transform.TransformPoint([float(pt[0]), float(pt[1])]) pred.append([transformed_pt[0], transformed_pt[1]]) pred = np.array(pred) e = np.sqrt(((pred - k1) ** 2).sum(axis=1)) else: return np.inf, np.inf return float(np.median(e)), float(np.percentile(e, 95)) def process_bip_to_tif(bip_path: Path, ref_dataset, matcher, out_dir: Path): """处理单个 .bip 文件到参考 .tif 的配准""" try: with rasterio.open(bip_path) as src: logger.info(f"处理文件: {bip_path.name}") # 检查CRS if src.crs is None: logger.warning(f"源文件 {bip_path.name} 缺少CRS信息，尝试使用参考文件的CRS") src_crs = ref_dataset.crs else: src_crs = src.crs ref_crs = ref_dataset.crs if ref_crs is None: raise RuntimeError(f"参考文件缺少CRS信息") # 1) 用地理信息把 src.bounds 转到 ref CRS，再裁 ref ROI b = transform_bounds(src_crs, ref_crs, *src.bounds, densify_pts=21) win0 = from_bounds(*b, transform=ref_dataset.transform) win = _expand_window(win0, ROI_PAD_PX, ref_dataset.width, ref_dataset.height) if win.width <= 0 or win.height <= 0: logger.warning(f"无重叠区域: {bip_path.name}") return False # 2) 读取数据 # 读取所有波段，如果是多波段的话 src_arr = src.read() # (bands, H, W) if src_arr.ndim == 2: # 单波段 src_arr = src_arr[None, ...] # 增加波段维度 # 读取参考文件的ROI ref_arr = ref_dataset.read(window=win) # (bands, h, w) if ref_arr.ndim == 2: # 单波段 ref_arr = ref_arr[None, ...] # 增加波段维度 # 转换为匹配所需的格式 src_img = _to_3ch_float01(src_arr) ref_img = _to_3ch_float01(ref_arr) # 3) 匹配用降采样版本，提速 + 增稳 src_small = _downscale_chw(src_img, MATCH_MAX_SIDE) ref_small = _downscale_chw(ref_img, MATCH_MAX_SIDE) logger.info(f"匹配尺寸: src {src_small.shape[1:]} -> ref {ref_small.shape[1:]}") # 4) 精配准（img0=src, img1=ref_roi） result = matcher(src_small, ref_small) num_inl = int(result["num_inliers"]) num_m = len(result["matched_kpts0"]) ratio = (num_inl / num_m) if num_m else 0.0 logger.info(f"匹配结果: 内点={num_inl}, 匹配点={num_m}, 内点比例={ratio:.2f}") if num_inl < MIN_INLIERS or ratio < MIN_INLIER_RATIO: logger.warning(f"匹配质量不足: {bip_path.name}") return False # 5) 用内点估计多种变换并自动选择最优 # 先计算全分辨率坐标 k0_small = result["inlier_kpts0"].astype(np.float32) k1_small = result["inlier_kpts1"].astype(np.float32) s0x = src_img.shape[2] / src_small.shape[2] s0y = src_img.shape[1] / src_small.shape[1] s1x = ref_img.shape[2] / ref_small.shape[2] s1y = ref_img.shape[1] / ref_small.shape[1] S0_inv = np.array([[s0x, 0, 0],[0, s0y, 0],[0, 0, 1]], dtype=np.float32) # small -> full (src) S1_inv = np.array([[s1x, 0, 0],[0, s1y, 0],[0, 0, 1]], dtype=np.float32) # small -> full (ref ROI) ones = np.ones((k0_small.shape[0], 1), dtype=np.float32) k0_full = (S0_inv @ np.hstack([k0_small, ones]).T).T[:, :2] # 全分辨率源像素 k1_roi_full = (S1_inv @ np.hstack([k1_small, ones]).T).T[:, :2] # ROI内参考像素 k1_global = k1_roi_full + np.array([win.col_off, win.row_off], dtype=np.float32) # 全局参考像素 # 用全分辨率坐标进行所有模型的估计和评估 best_transform = None best_transform_type = None best_error = np.inf best_method = None for method in TRANSFORM_METHODS: transform_type, transform = estimate_transform(method, k0_full, k1_global) if transform is None: continue med_err, p95_err = evaluate_transform_quality(transform_type, transform, k0_full, k1_global) # 选择重投影误差最小的变换 if p95_err < best_error: best_transform = transform best_transform_type = transform_type best_error = p95_err best_method = method logger.debug(f"方法 {method}: p50={med_err:.2f}, p95={p95_err:.2f}") if best_transform is None: logger.warning(f"所有变换方法都失败: {bip_path.name}") return False logger.info(f"选用变换: {best_method} ({best_transform_type}), 误差 p95={best_error:.2f}") # 6) 根据变换类型进行相应的配准处理 if best_transform_type == "A": # 仿射变换：A 已是 src_full_pixel -> ref_full_pixel，直接构造像素->地图仿射 A = best_transform # 2x3, src_full_pixel -> ref_full_pixel A3 = np.eye(3, dtype=np.float64) A3[:2, :] = A # src_pixel -> map ref_transform = ref_dataset.transform Rt = np.array([[ref_transform.a, ref_transform.b, ref_transform.c], [ref_transform.d, ref_transform.e, ref_transform.f], [0, 0, 1]], dtype=np.float64) M_map = Rt @ A3 corrected_affine = Affine(M_map[0,0], M_map[0,1], M_map[0,2], M_map[1,0], M_map[1,1], M_map[1,2]) # 用 M_map 求最小外接矩形（先到 map，再到 ref 像素） Rt_inv = np.linalg.inv(Rt) src_h, src_w = src.height, src.width corners = np.array([[0,0],[src_w,0],[src_w,src_h],[0,src_h]], dtype=np.float64) corn_h = np.hstack([corners, np.ones((4,1))]).T map_corners = (M_map @ corn_h).T[:, :2] pix_corners = (Rt_inv @ np.hstack([map_corners, np.ones((4,1))]).T).T[:, :2] min_x = int(np.floor(pix_corners[:,0].min())) - 10 max_x = int(np.ceil (pix_corners[:,0].max())) + 10 min_y = int(np.floor(pix_corners[:,1].min())) - 10 max_y = int(np.ceil (pix_corners[:,1].max())) + 10 min_x = max(0, min_x); min_y = max(0, min_y) max_x = min(ref_dataset.width, max_x) max_y = min(ref_dataset.height, max_y) bbox_w = max_x - min_x bbox_h = max_y - min_y if bbox_w <= 0 or bbox_h <= 0: logger.warning(f"最小外接矩形无效: {bip_path.name}") return False bbox_window = rasterio.windows.Window(min_x, min_y, bbox_w, bbox_h) bbox_transform = ref_dataset.window_transform(bbox_window) out_path = out_dir / f"{bip_path.stem}_registered.bip" src_nodata = src.nodata dst_nodata = src_nodata if src_nodata is not None else 0 out_profile = ref_dataset.profile.copy() out_profile.update( driver="ENVI", dtype=src.dtypes[0], height=bbox_h, width=bbox_w, count=src.count, transform=bbox_transform, crs=ref_crs, interleave="bip", compress=None, nodata=dst_nodata ) # 重采样到最小外接矩形 with rasterio.open(out_path, "w", **out_profile) as out_ds: for b in range(1, src.count + 1): src_band = src.read(b).astype(np.float32) dst_band = np.zeros((bbox_h, bbox_w), dtype=np.float32) reproject( source=src_band, destination=dst_band, src_transform=corrected_affine, src_crs=ref_crs, dst_transform=bbox_transform, dst_crs=ref_crs, src_nodata=src_nodata, dst_nodata=dst_nodata, resampling=Resampling.bilinear, ) if np.issubdtype(np.dtype(out_profile["dtype"]), np.integer): mask = (dst_band == dst_nodata) if src_nodata is not None else None info = np.iinfo(out_profile["dtype"]) dst_band = np.clip(dst_band, info.min, info.max).astype(out_profile["dtype"]) if mask is not None: dst_band[mask] = dst_nodata else: dst_band = dst_band.astype(out_profile["dtype"]) out_ds.write(dst_band, b) logger.info(f"成功配准(Affine): {bip_path.name} -> {out_path.name}") return True # ---- 非仿射变换处理 ---- elif best_transform_type == "H": # 单应变换：H 已是 src_full_pixel -> ref_full_pixel H_full = best_transform # 3x3 try: # 用 H_full 映射源四角 -> 参考像素，求最小外接矩形 src_h, src_w = src.height, src.width corners = np.array([[0,0],[src_w,0],[src_w,src_h],[0,src_h]], dtype=np.float32) corn_h = np.hstack([corners, np.ones((4,1), dtype=np.float32)]).T dst_h = (H_full @ corn_h) dst = (dst_h[:2] / (dst_h[2:]+1e-6)).T min_x = int(np.floor(dst[:,0].min())) - 10 max_x = int(np.ceil (dst[:,0].max())) + 10 min_y = int(np.floor(dst[:,1].min())) - 10 max_y = int(np.ceil (dst[:,1].max())) + 10 min_x = max(0, min_x); min_y = max(0, min_y) max_x = min(ref_dataset.width, max_x) max_y = min(ref_dataset.height, max_y) bbox_w = max_x - min_x bbox_h = max_y - min_y if bbox_w <= 0 or bbox_h <= 0: logger.warning(f"单应变换最小外接矩形无效: {bip_path.name}") return False # 创建输出窗口 bbox_window = rasterio.windows.Window(min_x, min_y, bbox_w, bbox_h) bbox_transform = ref_dataset.window_transform(bbox_window) # 子窗口坐标的单应矩阵（输出坐标系是子窗口像素） T_off = np.array([[1,0,min_x],[0,1,min_y],[0,0,1]], dtype=np.float64) H_sub = np.linalg.inv(T_off) @ H_full out_path = out_dir / f"{bip_path.stem}_registered.bip" src_nodata = src.nodata dst_nodata = src_nodata if src_nodata is not None else 0 out_profile = ref_dataset.profile.copy() out_profile.update( driver="ENVI", dtype=src.dtypes[0], height=bbox_h, width=bbox_w, count=src.count, transform=bbox_transform, crs=ref_crs, interleave="bip", compress=None, nodata=dst_nodata ) # 使用 OpenCV 进行单应变换重采样 with rasterio.open(out_path, "w", **out_profile) as out_ds: for b in range(1, src.count + 1): src_band = src.read(b).astype(np.float32) dst_band = np.full((bbox_h, bbox_w), dst_nodata, dtype=np.float32) # 使用 OpenCV warpPerspective（子窗口坐标） dst_band = cv2.warpPerspective( src_band, H_sub, (bbox_w, bbox_h), flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT, borderValue=dst_nodata ) # 转回目标 dtype if np.issubdtype(np.dtype(out_profile["dtype"]), np.integer): mask = (dst_band == dst_nodata) if src_nodata is not None else None info = np.iinfo(out_profile["dtype"]) dst_band = np.clip(dst_band, info.min, info.max).astype(out_profile["dtype"]) if mask is not None: dst_band[mask] = dst_nodata else: dst_band = dst_band.astype(out_profile["dtype"]) out_ds.write(dst_band, b) logger.info(f"成功配准(Homography): {bip_path.name} -> {out_path.name}") return True except Exception as e: logger.warning(f"单应变换异常: {e}") # 继续到仿射回退 elif best_transform_type in ["piecewise", "polynomial"]: # 分片仿射或多项式变换：使用 scikit-image transform = best_transform # 已用 k0_full/k1_global 估计 try: # 用目标侧匹配点（k1_global）决定外接矩形（更稳） pad = 10 min_x = int(np.floor(k1_global[:, 0].min())) - pad max_x = int(np.ceil (k1_global[:, 0].max())) + pad min_y = int(np.floor(k1_global[:, 1].min())) - pad max_y = int(np.ceil (k1_global[:, 1].max())) + pad min_x = max(0, min_x) min_y = max(0, min_y) max_x = min(ref_dataset.width, max_x) max_y = min(ref_dataset.height, max_y) bbox_w = max_x - min_x bbox_h = max_y - min_y if bbox_w <= 0 or bbox_h <= 0: logger.warning(f"{best_transform_type}变换最小外接矩形无效: {bip_path.name}") return False # 创建输出窗口 bbox_window = rasterio.windows.Window(min_x, min_y, bbox_w, bbox_h) bbox_transform = ref_dataset.window_transform(bbox_window) out_path = out_dir / f"{bip_path.stem}_registered.bip" src_nodata = src.nodata dst_nodata = src_nodata if src_nodata is not None else 0 out_profile = ref_dataset.profile.copy() out_profile.update( driver="ENVI", dtype=src.dtypes[0], height=bbox_h, width=bbox_w, count=src.count, transform=bbox_transform, crs=ref_crs, interleave="bip", compress=None, nodata=dst_nodata ) # 定义带偏移的逆映射函数 off_x, off_y = min_x, min_y if best_transform_type == "polynomial": # 对于多项式，估计逆变换 t_inv = PolynomialTransform() t_inv.estimate(k1_global, k0_full, order=2) # 顺序：目标->源 def inv_map_rc(coords): # coords: (N,2) in (row, col) rc = np.asarray(coords) xy = np.column_stack([rc[:, 1] + off_x, rc[:, 0] + off_y]) # -> (x, y) in full-ref xy_src = t_inv(xy) # -> (x_src, y_src) in full-src return np.column_stack([xy_src[:, 1], xy_src[:, 0]]) # -> (row_src, col_src) else: # piecewise_affine # 目标侧点集的内点判定 if MATPLOTLIB_SCIPY_AVAILABLE: try: hull = ConvexHull(k1_global) hull_path = MplPath(k1_global[hull.vertices]) except Exception: rect = np.array([[min_x, min_y],[max_x, min_y],[max_x, max_y],[min_x, max_y]], dtype=float) hull_path = MplPath(rect) def point_inside(xy): return hull_path.contains_points(xy) else: # 退化为矩形内判断 def point_inside(xy): return (xy[:,0] >= min_x) & (xy[:,0] <= max_x) & (xy[:,1] >= min_y) & (xy[:,1] <= max_y) def inv_map_rc(coords): rc = np.asarray(coords) xy = np.column_stack([rc[:, 1] + off_x, rc[:, 0] + off_y]) # (x,y) in full-ref inside = point_inside(xy) xy_src = np.full_like(xy, fill_value=-1.0) if np.any(inside): xy_src[inside] = transform.inverse(xy[inside]) # -> full-src (x_src, y_src) return np.column_stack([xy_src[:, 1], xy_src[:, 0]]) # -> (row_src, col_src) # 使用 scikit-image 进行变换重采样 from skimage.transform import warp with rasterio.open(out_path, "w", **out_profile) as out_ds: for b in range(1, src.count + 1): src_band = src.read(b).astype(np.float32) dst_band = warp( src_band, inverse_map=inv_map_rc, # 带偏移和轴序修正的逆映射 output_shape=(bbox_h, bbox_w), mode='constant', cval=dst_nodata, preserve_range=True ).astype(np.float32) # 转回目标 dtype if np.issubdtype(np.dtype(out_profile["dtype"]), np.integer): mask = (dst_band == dst_nodata) if src_nodata is not None else None info = np.iinfo(out_profile["dtype"]) dst_band = np.clip(dst_band, info.min, info.max).astype(out_profile["dtype"]) if mask is not None: dst_band[mask] = dst_nodata else: dst_band = dst_band.astype(out_profile["dtype"]) out_ds.write(dst_band, b) logger.info(f"成功配准({best_transform_type}): {bip_path.name} -> {out_path.name}") return True except Exception as e: logger.warning(f"{best_transform_type}变换异常: {e}") # 继续到仿射回退 elif best_transform_type == "tps": # B样条变换：优先使用 PIRT，如果不可用则使用 SimpleITK TPS try: if PIRT_AVAILABLE: # 使用 PIRT 实现 B样条弹性变换 logger.info("使用 PIRT B样条变换") # 读取用于配准的单波段并归一化 ref_roi_data = ref_dataset.read(1, window=win).astype(np.float32) src_band_data = src.read(1).astype(np.float32) from skimage.exposure import rescale_intensity ref_roi_reg = rescale_intensity(ref_roi_data, in_range='image', out_range=(0.0, 1.0)) src_reg = rescale_intensity(src_band_data, in_range='image', out_range=(0.0, 1.0)) # 构建 PIRT 注册器 reg = pirt.Registration(fixed=ref_roi_reg, moving=src_reg) # 设置 B样条变换 bspline = pirt.transform.BSplineTransform(grid_spacing=(96, 96)) # 可调节控制点间距 reg.set_transformation(bspline) # 设置相似度度量（NCC 或 MI） reg.set_similarity(pirt.metrics.NCC()) # 多分辨率金字塔 reg.set_pyramid([4, 2, 1]) # 优化器设置 reg.set_optimizer(pirt.optimizers.LBFGS(max_iter=200), smooth=1.0) # 执行注册 reg.run() # 获取前向映射（参考网格到源的位移场） phi = reg.get_forward_mapping() # (H, W, 2) 位移场 # 应用到所有波段 out_path = out_dir / f"{bip_path.stem}_registered.bip" src_nodata = src.nodata dst_nodata = src_nodata if src_nodata is not None else 0 out_profile = ref_dataset.profile.copy() out_profile.update( driver="ENVI", dtype=src.dtypes[0], height=ref_roi_data.shape[0], width=ref_roi_data.shape[1], count=src.count, transform=ref_dataset.window_transform(win), crs=ref_crs, interleave="bip", compress=None, nodata=dst_nodata ) with rasterio.open(out_path, "w", **out_profile) as out_ds: for b in range(1, src.count + 1): src_band = src.read(b).astype(np.float32) # 使用 PIRT 的 warp 函数应用位移场 warped = pirt.warp(src_band, phi, mode='constant', cval=float(dst_nodata)) band_data = warped.astype(out_profile["dtype"]) out_ds.write(band_data, b) logger.info(f"成功配准(B样条-PIRT): {bip_path.name} -> {out_path.name}") return True elif SITK_AVAILABLE and hasattr(sitk, "LandmarkBasedTransformInitializer"): # 回退到 SimpleITK TPS logger.info("PIRT 不可用，使用 SimpleITK TPS") # 1) 统一坐标系：用"全图像素"作为物理坐标（spacing=1, origin=(0,0), direction=I） fixed_pts = [(float(x1), float(y1)) for (x1,y1) in k1_global] # 参考(输出)侧 moving_pts = [(float(x0), float(y0)) for (x0,y0) in k0_full] # 源(输入)侧 # 2) 构造 TPS(参考→源) 用于 Resample（输出点 -> 输入点） tps_ref2src = sitk.LandmarkBasedTransformInitializer( sitk.Transform(2, sitk.sitkThinPlateSplineKernelTransform), fixed_pts, # fixed = 参考 moving_pts # moving = 源 ) # 3) 构造 TPS(源→参考) 仅用于外接矩形估计（源顶点投到参考） tps_src2ref = sitk.LandmarkBasedTransformInitializer( sitk.Transform(2, sitk.sitkThinPlateSplineKernelTransform), moving_pts, # fixed = 源 fixed_pts # moving = 参考 ) # 4) 用 tps_src2ref 变换源四角，求参考全图上的外接矩形，并与参考范围求交 src_h, src_w = src.height, src.width src_corners = np.array([[0,0],[src_w,0],[src_w,src_h],[0,src_h]], dtype=np.float32) dst_corners = np.array([tps_src2ref.TransformPoint((float(x),float(y))) for x,y in src_corners], dtype=np.float32) min_x = max(0, int(np.floor(dst_corners[:,0].min())) - 10) max_x = min(ref_dataset.width, int(np.ceil (dst_corners[:,0].max())) + 10) min_y = max(0, int(np.floor(dst_corners[:,1].min())) - 10) max_y = min(ref_dataset.height, int(np.ceil (dst_corners[:,1].max())) + 10) bbox_w, bbox_h = max_x-min_x, max_y-min_y if bbox_w <= 0 or bbox_h <= 0: logger.warning(f"TPS最小外接矩形无效: {bip_path.name}") return False bbox_window = rasterio.windows.Window(min_x, min_y, bbox_w, bbox_h) bbox_transform = ref_dataset.window_transform(bbox_window) # 5) 参考(输出)影像定义：spacing=1，origin=(min_x,min_y)，direction=I ref_img = sitk.Image([bbox_w, bbox_h], sitk.sitkFloat32) ref_img.SetSpacing((1.0, 1.0)) ref_img.SetOrigin((float(min_x), float(min_y))) ref_img.SetDirection((1.0,0.0,0.0,1.0)) # 6) 源影像：用 rasterio 读为 numpy，再转 SITK（spacing=1, origin=0, direction=I） src_band = src.read(1).astype(np.float32) src_img = sitk.GetImageFromArray(src_band) # 7) 重采样：设置参考图像 + 变换=参考→源（tps_ref2src） res = sitk.ResampleImageFilter() res.SetReferenceImage(ref_img) res.SetTransform(tps_ref2src) res.SetInterpolator(sitk.sitkLinear) if src.nodata is not None: res.SetDefaultPixelValue(float(src.nodata)) # 8) 写 ENVI/BIP：对所有波段逐一 TPS 重采样 out_path = out_dir / f"{bip_path.stem}_registered.bip" src_nodata = src.nodata dst_nodata = src_nodata if src_nodata is not None else 0 out_profile = ref_dataset.profile.copy() out_profile.update( driver="ENVI", dtype=src.dtypes[0], height=bbox_h, width=bbox_w, count=src.count, transform=bbox_transform, crs=ref_crs, interleave="bip", compress=None, nodata=dst_nodata ) with rasterio.open(out_path, "w", **out_profile) as out_ds: for b in range(1, src.count + 1): src_band = src.read(b).astype(np.float32) src_img_band = sitk.GetImageFromArray(src_band) warped = res.Execute(src_img_band) band_data = sitk.GetArrayFromImage(warped).astype(out_profile["dtype"]) out_ds.write(band_data, b) logger.info(f"成功配准(TPS-SimpleITK): {bip_path.name} -> {out_path.name}") return True else: logger.warning("PIRT 和 SimpleITK TPS 都不可用，回退到仿射") # 继续到仿射回退 except Exception as e: logger.warning(f"B样条变换异常: {e}") # 继续到仿射回退 # ---- 回退：使用仿射变换，保证最小可用结果 ---- # 重新估计仿射变换作为fallback A_fallback, _ = cv2.estimateAffine2D(k0_full, k1_global, method=cv2.RANSAC, ransacReprojThreshold=3.0) if A_fallback is None: logger.warning(f"仿射回退也失败: {bip_path.name}") return False # 构造 full_src -> full_ref_roi 的仿射并回写到地图坐标 s0x = src_img.shape[2] / src_small.shape[2] s0y = src_img.shape[1] / src_small.shape[1] s1x = ref_img.shape[2] / ref_small.shape[2] s1y = ref_img.shape[1] / ref_small.shape[1] S0 = np.array([[1/s0x, 0, 0], [0, 1/s0y, 0], [0, 0, 1]], dtype=np.float64) S1_inv = np.array([[s1x, 0, 0], [0, s1y, 0], [0, 0, 1]], dtype=np.float64) A3 = np.eye(3, dtype=np.float64); A3[:2, :] = A_fallback M_full = S1_inv @ A3 @ S0 T_off = np.array([[1, 0, win.col_off], [0, 1, win.row_off], [0, 0, 1]], dtype=np.float64) ref_transform = ref_dataset.transform Rt = np.array([[ref_transform.a, ref_transform.b, ref_transform.c], [ref_transform.d, ref_transform.e, ref_transform.f], [0, 0, 1]], dtype=np.float64) src_pixel_to_map_corrected = Rt @ T_off @ M_full corrected_affine = Affine( src_pixel_to_map_corrected[0, 0], src_pixel_to_map_corrected[0, 1], src_pixel_to_map_corrected[0, 2], src_pixel_to_map_corrected[1, 0], src_pixel_to_map_corrected[1, 1], src_pixel_to_map_corrected[1, 2], ) # 计算源 BIP 四角经过仿射变换后的最小外接矩形 # 将 rasterio.Affine 转为 3x3 像素->地图矩阵 M_map = np.array([ [corrected_affine.a, corrected_affine.b, corrected_affine.c], [corrected_affine.d, corrected_affine.e, corrected_affine.f], [0.0, 0.0, 1.0] ], dtype=np.float64) # 参考底图的 像素->地图 矩阵及其逆 ref_transform = ref_dataset.transform Rt = np.array([ [ref_transform.a, ref_transform.b, ref_transform.c], [ref_transform.d, ref_transform.e, ref_transform.f], [0.0, 0.0, 1.0] ], dtype=np.float64) Rt_inv = np.linalg.inv(Rt) # 源影像四角（源像素坐标） src_h, src_w = src.height, src.width src_corners = np.array([[0,0],[src_w,0],[src_w,src_h],[0,src_h]], dtype=np.float64) corners_h = np.hstack([src_corners, np.ones((4,1))]).T # (3,4) # 源像素 -> 地图坐标 map_corners = (M_map @ corners_h).T[:, :2] # 地图坐标 -> 参考像素坐标 pix_corners_h = (Rt_inv @ np.hstack([map_corners, np.ones((4,1))]).T).T # (4,3) pix_corners = pix_corners_h[:, :2] # 最小外接矩形（像素） min_x = int(np.floor(pix_corners[:,0].min())) - 10 max_x = int(np.ceil( pix_corners[:,0].max())) + 10 min_y = int(np.floor(pix_corners[:,1].min())) - 10 max_y = int(np.ceil( pix_corners[:,1].max())) + 10 # 边界裁剪 min_x = max(0, min_x); min_y = max(0, min_y) max_x = min(ref_dataset.width, max_x) max_y = min(ref_dataset.height, max_y) bbox_w = max_x - min_x bbox_h = max_y - min_y # 如果外接矩形太小，跳过 if bbox_w <= 0 or bbox_h <= 0: logger.warning(f"最小外接矩形无效: {bip_path.name}") return False # 创建裁剪窗口和变换 bbox_window = rasterio.windows.Window(min_x, min_y, bbox_w, bbox_h) bbox_transform = ref_dataset.window_transform(bbox_window) out_path = out_dir / f"{bip_path.stem}_registered.bip" src_nodata = src.nodata dst_nodata = src_nodata if src_nodata is not None else 0 # 更新输出 profile 使用最小外接矩形 out_profile = ref_dataset.profile.copy() out_profile.update( driver="ENVI", dtype=src.dtypes[0], height=bbox_h, width=bbox_w, count=src.count, transform=bbox_transform, # 使用最小外接矩形的变换 crs=ref_crs, interleave="bip", compress=None, nodata=dst_nodata ) # 重采样到最小外接矩形 with rasterio.open(out_path, "w", **out_profile) as out_ds: for b in range(1, src.count + 1): src_band = src.read(b).astype(np.float32) dst_band = np.zeros((bbox_h, bbox_w), dtype=np.float32) reproject( source=src_band, destination=dst_band, src_transform=corrected_affine, src_crs=ref_crs, dst_transform=bbox_transform, dst_crs=ref_crs, src_nodata=src_nodata, dst_nodata=dst_nodata, resampling=Resampling.bilinear, ) # 转回目标 dtype if np.issubdtype(np.dtype(out_profile["dtype"]), np.integer): mask = (dst_band == dst_nodata) if src_nodata is not None else None info = np.iinfo(out_profile["dtype"]) dst_band = np.clip(dst_band, info.min, info.max).astype(out_profile["dtype"]) if mask is not None: dst_band[mask] = dst_nodata else: dst_band = dst_band.astype(out_profile["dtype"]) out_ds.write(dst_band, b) logger.info(f"成功配准(仿射回退): {bip_path.name} -> {out_path.name}") return True except Exception as e: logger.error(f"处理失败 {bip_path.name}: {str(e)}") return False # ---------- 主逻辑 ---------- def main(): logger.info("开始批量配准处理...") # 检查输入文件是否存在 if not Path(REF_TIF).exists(): logger.error(f"参考文件不存在: {REF_TIF}") return if not BIP_DIR.exists(): logger.error(f"BIP文件夹不存在: {BIP_DIR}") return # 初始化匹配器 logger.info(f"初始化匹配器: {MATCHER_NAME} on {DEVICE}") matcher = get_matcher(MATCHER_NAME, device=DEVICE) # 打开参考文件 with rasterio.open(REF_TIF) as ref: logger.info(f"参考文件信息: {ref.width}x{ref.height}, CRS: {ref.crs}") # 查找所有 .bip 文件 bip_files = list(BIP_DIR.glob("*.bip")) logger.info(f"找到 {len(bip_files)} 个 .bip 文件") success_count = 0 for bip_path in bip_files: if process_bip_to_tif(bip_path, ref, matcher, OUT_DIR): success_count += 1 logger.info(f"处理完成: {success_count}/{len(bip_files)} 个文件成功配准") if __name__ == "__main__": main()