""" 批量配准 .bip 文件到参考 .tif 文件 使用地理信息进行粗定位，然后用图像匹配进行精配准 """ 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 # 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\jiashixian\result.tif" # 参考 tif 文件路径 BIP_DIR = Path(r"E:\is2\jiashixian\Geoout\1") # .bip 文件所在文件夹 OUT_DIR = Path(r"E:\is2\jiashixian\matchanything-roma") # 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" # 匹配参数 MATCH_MAX_SIDE = 1200 # 匹配时最大边长（像素） ROI_PAD_PX = 300 # 粗定位窗口的padding（参考tif像素） # 质量控制阈值 MIN_INLIERS = 30 # 最少内点数 MIN_INLIER_RATIO = 0.15 # 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 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) 用内点估计仿射变换 k0 = result["inlier_kpts0"].astype(np.float32) k1 = result["inlier_kpts1"].astype(np.float32) A, _ = cv2.estimateAffinePartial2D(k0, k1, method=cv2.RANSAC, ransacReprojThreshold=3.0) if A is None: logger.warning(f"仿射估计失败: {bip_path.name}") return False # 6) 把"src_small->ref_small"的仿射映射回"src_full->ref_full_roi" # 缩放系数：full/small 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) # full -> small S1_inv = np.array([[s1x, 0, 0], [0, s1y, 0], [0, 0, 1]], dtype=np.float64) # small -> full(roi) A3 = np.eye(3, dtype=np.float64) A3[:2, :] = A # small src -> small ref_roi # full_src -> full_ref_roi（用 S0，而不是 S0_inv） M_full = S1_inv @ A3 @ S0 # 7) 目标输出：重采样到 ref 全图网格 # 需要"src像素 -> 地图坐标"的修正 transform T_off = np.array([[1, 0, win.col_off], [0, 1, win.row_off], [0, 0, 1]], dtype=np.float64) # ref_transform 转 3x3 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 # 转回 Affine 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], ) # 8) 重采样输出到 ref 网格（多波段，BIP 输出） out_path = out_dir / f"{bip_path.stem}_registered.bip" # 获取 NoData 值 src_nodata = src.nodata # 可能是 None、0、65535 等 dst_nodata = src_nodata if src_nodata is not None else 0 # 以参考影像的网格/坐标为目标，波段数和数据类型继承源影像 out_profile = ref_dataset.profile.copy() out_profile.update( driver="ENVI", dtype=src.dtypes[0], # 尽量保持与源影像一致的数据类型 height=ref_dataset.height, width=ref_dataset.width, count=src.count, # 多波段 transform=ref_transform, crs=ref_crs, interleave="bip", # 指定 BIP compress=None, nodata=dst_nodata # 设置 NoData 值 ) with rasterio.open(out_path, "w", **out_profile) as out_ds: for b in range(1, src.count + 1): # 逐波段重投影（float32 计算更稳） src_band = src.read(b).astype(np.float32) dst_band = np.zeros((ref_dataset.height, ref_dataset.width), dtype=np.float32) reproject( source=src_band, destination=dst_band, src_transform=corrected_affine, # 由原逻辑推导的 src像素->ref像素 的仿射 src_crs=ref_crs, dst_transform=ref_transform, dst_crs=ref_crs, src_nodata=src_nodata, # 新增 NoData 处理 dst_nodata=dst_nodata, # 新增 NoData 处理 resampling=Resampling.bilinear, ) # 调试统计信息 logger.info(f"波段 {b}: min={float(np.nanmin(dst_band)):.2f}, max={float(np.nanmax(dst_band)):.2f}, " f"mean={float(np.nanmean(dst_band)):.2f}, nodata占比={(dst_band==dst_nodata).mean():.2%}") # 转回目标 dtype（若为整型，先裁剪再转型） if np.issubdtype(np.dtype(out_profile["dtype"]), np.integer): if src_nodata is not None: # 保持 nodata 不被拉伸 mask = (dst_band == dst_nodata) info = np.iinfo(out_profile["dtype"]) dst_band = np.clip(dst_band, info.min, info.max) dst_band = dst_band.astype(out_profile["dtype"]) if src_nodata 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: {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()