""" 批量配准 .bip 文件到参考 .tif 文件 直接进行配准 """ 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 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\yaopu\result.tif" # 参考 tif 文件路径 BIP_DIR = Path(r"E:\is2\yaopu") # .bip 文件所在文件夹 OUT_DIR = Path(r"E:\is2\yaopu\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" # 使用密集匹配模型的稠密流直接进行配准 # 匹配参数 MATCH_MAX_SIDE = 1200 # 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 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 # ==== 稠密流直接重采样（无需后续显式变换估计） ==== # 1) 取稠密流（优先 ref->src）。不同模型的键名可能不同，这里做兼容 flow_small = None for k in ["flow_ref2src", "flow21", "flow_1_0", "flow10", "flow"]: if k in result: flow_small = result[k] break if flow_small is None: # 回退：优先 DIS 光流（更快/稳），若不可用再用 Farneback (ref -> src) ref_small_rgb = np.transpose(ref_small, (1, 2, 0)) # (H,W,3) src_small_rgb = np.transpose(src_small, (1, 2, 0)) ref_small_gray = cv2.cvtColor((ref_small_rgb * 255).astype(np.uint8), cv2.COLOR_RGB2GRAY) src_small_gray = cv2.cvtColor((src_small_rgb * 255).astype(np.uint8), cv2.COLOR_RGB2GRAY) flow_small = None try: dis = cv2.DISOpticalFlow_create(cv2.DISOPTICAL_FLOW_PRESET_MEDIUM) flow_small = dis.calc(ref_small_gray, src_small_gray, None).astype(np.float32) except Exception: pass if flow_small is None: # 典型参数：可按影像特性微调（窗口、迭代次数等） flow_small = cv2.calcOpticalFlowFarneback( ref_small_gray, src_small_gray, None, 0.5, 3, 25, 3, 5, 1.2, 0 ).astype(np.float32) # flow_small 期望形状 (h_s, w_s, 2)，分量为 (dx, dy): 参考像素到源像素的位移 flow_small = np.asarray(flow_small, dtype=np.float32) if flow_small.ndim != 3 or flow_small.shape[2] != 2: logger.warning(f"稠密流形状异常: {flow_small.shape}") return False # 2) 将小图的流放大到 ROI 全分辨率，并按比例放大位移 roi_h, roi_w = ref_img.shape[1], ref_img.shape[2] # 注意 ref_img 是 ROI 子图 scale_x = roi_w / flow_small.shape[1] scale_y = roi_h / flow_small.shape[0] flow_full = cv2.resize(flow_small, (roi_w, roi_h), interpolation=cv2.INTER_LINEAR) flow_full[..., 0] *= scale_x # dx flow_full[..., 1] *= scale_y # dy # 3) 生成 remap 所需的源坐标图（map_x, map_y），在"参考ROI坐标系"内工作 yy, xx = np.meshgrid(np.arange(roi_h, dtype=np.float32), np.arange(roi_w, dtype=np.float32), indexing="ij") map_x = xx + flow_full[..., 0] # 到源图的 x（列） map_y = yy + flow_full[..., 1] # 到源图的 y（行） # 4) 根据有效映射范围求最小外接矩形（仅统计落在源图范围内的像素） valid = (map_x >= 0) & (map_x <= (src.width - 1)) & (map_y >= 0) & (map_y <= (src.height - 1)) if not np.any(valid): logger.warning(f"稠密流无有效映射: {bip_path.name}") return False ys, xs = np.where(valid) pad = 0 min_y = max(int(ys.min()) - pad, 0) max_y = min(int(ys.max()) + 1 + pad, roi_h) min_x = max(int(xs.min()) - pad, 0) max_x = min(int(xs.max()) + 1 + pad, roi_w) crop_h = max_y - min_y crop_w = max_x - min_x if crop_h <= 0 or crop_w <= 0: logger.warning(f"最小外接矩形无效: {bip_path.name}") return False # 只对外接矩形区域做重采样，减少内存 map_x_crop = map_x[min_y:max_y, min_x:max_x].astype(np.float32) map_y_crop = map_y[min_y:max_y, min_x:max_x].astype(np.float32) # 5) 计算输出的地理变换：参考ROI窗口 + 外接矩形子窗口 # 先得到 ROI 的 transform，再叠加子窗口偏移 roi_transform = ref_dataset.window_transform(win) crop_window_global = rasterio.windows.Window( win.col_off + min_x, win.row_off + min_y, crop_w, crop_h ) out_transform = ref_dataset.window_transform(crop_window_global) # 6) 写出 ENVI/BIP（按最小外接矩形） 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=crop_h, width=crop_w, count=src.count, transform=out_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) # 反向映射采样：输出像素在参考ROI坐标，去源图(map_y,map_x)取值 warped = cv2.remap( src_band, map_x_crop, map_y_crop, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT, borderValue=float(dst_nodata) ).astype(np.float32) # 转回目标 dtype，保持 nodata if np.issubdtype(np.dtype(out_profile["dtype"]), np.integer): mask = (warped == dst_nodata) if src_nodata is not None else None info = np.iinfo(out_profile["dtype"]) warped = np.clip(warped, info.min, info.max).astype(out_profile["dtype"]) if mask is not None: warped[mask] = dst_nodata else: warped = warped.astype(out_profile["dtype"]) out_ds.write(warped, b) logger.info(f"成功配准(DenseFlow): {bip_path.name} -> {out_path.name}") return True # ---- 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()