360 lines
16 KiB
Python
360 lines
16 KiB
Python
"""
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批量配准 .bip 文件到参考 .tif 文件
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仿射变换修改
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"""
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from pathlib import Path
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import numpy as np
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import cv2
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import rasterio
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from rasterio.windows import from_bounds
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from rasterio.warp import transform_bounds, reproject, Resampling
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from affine import Affine
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from vismatch import get_matcher
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import logging
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from osgeo import gdal
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# 设置日志
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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logger = logging.getLogger(__name__)
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# ---------- 配置 ----------
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# 请根据实际情况修改这些路径
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REF_TIF = r"E:\is2\jiashixian\result.tif" # 参考 tif 文件路径
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BIP_DIR = Path(r"E:\is2\jiashixian\Geoout\1") # .bip 文件所在文件夹
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OUT_DIR = Path(r"E:\is2\jiashixian\matchanything-roma") # 输出文件夹
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# 匹配算法选择
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MATCHER_NAME = "matchanything-roma" # 可选: xfeat-star, loftr, roma, superpoint-lightglue, sift-lightglue 等
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DEVICE = "cuda" # 或 "cpu"
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# 匹配参数
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MATCH_MAX_SIDE = 1200 # 匹配时最大边长（像素）
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ROI_PAD_PX = 500 # 粗定位窗口的padding（参考tif像素）
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# 质量控制阈值
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MIN_INLIERS = 30 # 最少内点数
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MIN_INLIER_RATIO = 0.15 # 最少内点比例
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# 创建输出目录
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OUT_DIR.mkdir(parents=True, exist_ok=True)
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# ---------- 工具函数 ----------
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def _to_3ch_float01(arr_chw: np.ndarray) -> np.ndarray:
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"""将任意通道数的数组转换为 (3,H,W) float32 in [0,1]"""
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arr = arr_chw.astype(np.float32)
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if arr.shape[0] == 1:
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# 单波段复制为3通道
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arr = np.repeat(arr, 3, axis=0)
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elif arr.shape[0] >= 3:
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# 取前3波段
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arr = arr[:3]
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else:
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raise ValueError(f"不支持的通道数: {arr.shape[0]}")
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# 百分位数拉伸，增强跨传感器匹配稳定性
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p2 = np.percentile(arr, 2)
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p98 = np.percentile(arr, 98)
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arr = (arr - p2) / (p98 - p2 + 1e-6)
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arr = np.clip(arr, 0.0, 1.0)
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return arr
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def _downscale_chw(arr_chw: np.ndarray, max_side: int) -> np.ndarray:
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"""等比缩放 (C,H,W) 到 max(H,W) <= max_side"""
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c, h, w = arr_chw.shape
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s = min(1.0, max_side / max(h, w))
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if s >= 1.0:
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return arr_chw
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new_w = int(round(w * s))
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new_h = int(round(h * s))
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# 用opencv缩放(逐通道)
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out = np.stack([cv2.resize(arr_chw[i], (new_w, new_h), interpolation=cv2.INTER_AREA) for i in range(c)], axis=0)
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return out
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def _expand_window(win, pad, max_w, max_h):
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"""扩展窗口并确保边界有效"""
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col_off = int(max(0, win.col_off - pad))
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row_off = int(max(0, win.row_off - pad))
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col_end = int(min(max_w, win.col_off + win.width + pad))
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row_end = int(min(max_h, win.row_off + win.height + pad))
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return rasterio.windows.Window(col_off, row_off, col_end - col_off, row_end - row_off)
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def process_bip_to_tif(bip_path: Path, ref_dataset, matcher, out_dir: Path):
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"""处理单个 .bip 文件到参考 .tif 的配准"""
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try:
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with rasterio.open(bip_path) as src:
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logger.info(f"处理文件: {bip_path.name}")
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# 检查CRS
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if src.crs is None:
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logger.warning(f"源文件 {bip_path.name} 缺少CRS信息，尝试使用参考文件的CRS")
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src_crs = ref_dataset.crs
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else:
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src_crs = src.crs
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ref_crs = ref_dataset.crs
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if ref_crs is None:
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raise RuntimeError(f"参考文件缺少CRS信息")
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# 1) 用地理信息把 src.bounds 转到 ref CRS，再裁 ref ROI
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b = transform_bounds(src_crs, ref_crs, *src.bounds, densify_pts=21)
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win0 = from_bounds(*b, transform=ref_dataset.transform)
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win = _expand_window(win0, ROI_PAD_PX, ref_dataset.width, ref_dataset.height)
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if win.width <= 0 or win.height <= 0:
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logger.warning(f"无重叠区域: {bip_path.name}")
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return False
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# 2) 读取数据
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# 读取所有波段，如果是多波段的话
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src_arr = src.read() # (bands, H, W)
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if src_arr.ndim == 2: # 单波段
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src_arr = src_arr[None, ...] # 增加波段维度
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# 读取参考文件的ROI
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ref_arr = ref_dataset.read(window=win) # (bands, h, w)
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if ref_arr.ndim == 2: # 单波段
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ref_arr = ref_arr[None, ...] # 增加波段维度
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# 转换为匹配所需的格式
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src_img = _to_3ch_float01(src_arr)
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ref_img = _to_3ch_float01(ref_arr)
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# 3) 匹配用降采样版本，提速 + 增稳
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src_small = _downscale_chw(src_img, MATCH_MAX_SIDE)
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ref_small = _downscale_chw(ref_img, MATCH_MAX_SIDE)
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logger.info(f"匹配尺寸: src {src_small.shape[1:]} -> ref {ref_small.shape[1:]}")
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# 4) 精配准（img0=src, img1=ref_roi）
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result = matcher(src_small, ref_small)
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num_inl = int(result["num_inliers"])
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num_m = len(result["matched_kpts0"])
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ratio = (num_inl / num_m) if num_m else 0.0
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logger.info(f"匹配结果: 内点={num_inl}, 匹配点={num_m}, 内点比例={ratio:.2f}")
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if num_inl < MIN_INLIERS or ratio < MIN_INLIER_RATIO:
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logger.warning(f"匹配质量不足: {bip_path.name}")
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return False
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# 5) 用内点估计仿射变换（相似/全仿射二选一）
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k0 = result["inlier_kpts0"].astype(np.float32)
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k1 = result["inlier_kpts1"].astype(np.float32)
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# 相似（旋转+等比缩放+平移，4DOF）
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A_partial, _ = cv2.estimateAffinePartial2D(k0, k1, method=cv2.RANSAC, ransacReprojThreshold=3.0)
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# 全仿射（旋转+非等比缩放+剪切+平移，6DOF）
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A_full, _ = cv2.estimateAffine2D(k0, k1, method=cv2.RANSAC, ransacReprojThreshold=3.0)
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def _reproj_err(A2x3: np.ndarray, pts0: np.ndarray, pts1: np.ndarray):
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if A2x3 is None:
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return np.inf, np.inf
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ones = np.ones((pts0.shape[0], 1), dtype=np.float32)
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src_h = np.hstack([pts0, ones]) # (N,3)
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pred = (A2x3 @ src_h.T).T # (N,2)
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e = np.sqrt(((pred - pts1) ** 2).sum(axis=1))
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return float(np.median(e)), float(np.percentile(e, 95))
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med_p, p95_p = _reproj_err(A_partial, k0, k1)
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med_f, p95_f = _reproj_err(A_full, k0, k1)
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if (p95_f < p95_p) or (abs(p95_f - p95_p) < 0.5 and med_f < med_p):
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A = A_full
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model_type = "affine_full"
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else:
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A = A_partial
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model_type = "affine_partial"
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if A is None:
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logger.warning(f"仿射估计失败: {bip_path.name}")
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return False
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logger.info(f"选用模型: {model_type}, partial(p50={med_p:.2f},p95={p95_p:.2f}), full(p50={med_f:.2f},p95={p95_f:.2f})")
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# 6) 基于内点构建 GCP，并使用 GDAL TPS（薄板样条）实现非刚性（B样条近似效果）配准
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# 注：GDAL 提供 TPS（薄板样条），在遥感配准中与B样条同属光滑非刚性方法，工程上更常用
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# 这里我们直接用 TPS 来实现你所需的“B样条变换”效果
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# 将 small 尺寸的内点坐标映射回 full 分辨率
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s0x = src_img.shape[2] / src_small.shape[2]
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s0y = src_img.shape[1] / src_small.shape[1]
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s1x = ref_img.shape[2] / ref_small.shape[2]
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s1y = ref_img.shape[1] / ref_small.shape[1]
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S0_inv = np.array([[s0x, 0, 0], [0, s0y, 0], [0, 0, 1]], dtype=np.float64) # small -> full (src)
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S1_inv = np.array([[s1x, 0, 0], [0, s1y, 0], [0, 0, 1]], dtype=np.float64) # small -> full (ref ROI)
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ones = np.ones((k0.shape[0], 1), dtype=np.float32)
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k0_full = (S0_inv @ np.hstack([k0, ones]).T).T[:, :2] # 源：full 源像素
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k1_roi_full = (S1_inv @ np.hstack([k1, ones]).T).T[:, :2]
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# 加上 ROI 偏移，得到参考影像全局像素坐标
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k1_global = k1_roi_full + np.array([win.col_off, win.row_off], dtype=np.float32)
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if k0_full.shape[0] < 8:
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logger.warning(f"TPS 需要更多控制点（>=8），当前 {k0_full.shape[0]}，回退到仿射输出。")
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else:
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# 生成 GCP：目标为参考地图坐标（由全局像素 + ref_transform 转换）
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ref_transform = ref_dataset.transform
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def px_to_map(xp, yp):
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X = ref_transform.a * xp + ref_transform.b * yp + ref_transform.c
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Y = ref_transform.d * xp + ref_transform.e * yp + ref_transform.f
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return X, Y
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gcps = []
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for (sx, sy), (rx, ry) in zip(k0_full, k1_global):
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mx, my = px_to_map(rx, ry)
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gcps.append(gdal.GCP(mx, my, 0.0, float(sx), float(sy)))
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# 计算参考影像的输出边界与分辨率
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minx = ref_transform.c
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maxy = ref_transform.f
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maxx = minx + ref_transform.a * ref_dataset.width + ref_transform.b * ref_dataset.height
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miny = maxy + ref_transform.d * ref_dataset.width + ref_transform.e * ref_dataset.height
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src_nodata = src.nodata
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dst_nodata = src_nodata if src_nodata is not None else 0
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# 输出路径（ENVI+BIP）
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out_path = out_dir / f"{bip_path.stem}_registered.bip"
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# 使用 GDAL Warp 执行 TPS 变换到参考网格
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# 注意：需要系统已安装 GDAL Python 绑定
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try:
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warp_opts = gdal.WarpOptions(
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format="ENVI",
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outputBounds=(minx, miny, maxx, maxy),
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xRes=abs(ref_transform.a),
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yRes=abs(ref_transform.e),
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dstSRS=ref_crs.to_wkt() if hasattr(ref_crs, "to_wkt") else str(ref_crs),
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tps=True, # 启用薄板样条（非刚性）
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resampleAlg="bilinear",
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srcNodata=src_nodata,
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dstNodata=dst_nodata,
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multithread=True,
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creationOptions=["INTERLEAVE=BIP"],
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gcps=gcps,
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)
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# 直接从源文件路径进行Warp
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warp_ds = gdal.Warp(
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destNameOrDestDS=str(out_path),
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srcDSOrSrcDSTab=str(bip_path),
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options=warp_opts,
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)
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if warp_ds is None:
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logger.warning("GDAL Warp(TPS) 失败，回退到仿射输出。")
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else:
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warp_ds = None # 关闭文件
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logger.info(f"成功配准(TPS): {bip_path.name} -> {out_path.name}")
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return True
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except Exception as e:
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logger.warning(f"GDAL Warp(TPS) 异常，回退到仿射输出: {e}")
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# ---- 回退：使用仿射（你前面的流程），保证最小可用结果 ----
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# 构造 full_src -> full_ref_roi 的仿射并回写到地图坐标
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# 仍沿用上面已估计的 A（partial/full 二选一）
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s0x = src_img.shape[2] / src_small.shape[2]
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s0y = src_img.shape[1] / src_small.shape[1]
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s1x = ref_img.shape[2] / ref_small.shape[2]
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s1y = ref_img.shape[1] / ref_small.shape[1]
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S0 = np.array([[1/s0x, 0, 0], [0, 1/s0y, 0], [0, 0, 1]], dtype=np.float64)
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S1_inv = np.array([[s1x, 0, 0], [0, s1y, 0], [0, 0, 1]], dtype=np.float64)
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A3 = np.eye(3, dtype=np.float64); A3[:2, :] = A
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M_full = S1_inv @ A3 @ S0
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T_off = np.array([[1, 0, win.col_off], [0, 1, win.row_off], [0, 0, 1]], dtype=np.float64)
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ref_transform = ref_dataset.transform
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Rt = np.array([[ref_transform.a, ref_transform.b, ref_transform.c],
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[ref_transform.d, ref_transform.e, ref_transform.f],
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[0, 0, 1]], dtype=np.float64)
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src_pixel_to_map_corrected = Rt @ T_off @ M_full
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corrected_affine = Affine(
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src_pixel_to_map_corrected[0, 0], src_pixel_to_map_corrected[0, 1], src_pixel_to_map_corrected[0, 2],
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src_pixel_to_map_corrected[1, 0], src_pixel_to_map_corrected[1, 1], src_pixel_to_map_corrected[1, 2],
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)
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out_path = out_dir / f"{bip_path.stem}_registered.bip"
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src_nodata = src.nodata
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dst_nodata = src_nodata if src_nodata is not None else 0
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out_profile = ref_dataset.profile.copy()
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out_profile.update(
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driver="ENVI",
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dtype=src.dtypes[0],
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height=ref_dataset.height,
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width=ref_dataset.width,
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count=src.count,
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transform=ref_transform,
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crs=ref_crs,
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interleave="bip",
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compress=None,
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nodata=dst_nodata
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)
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with rasterio.open(out_path, "w", **out_profile) as out_ds:
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for b in range(1, src.count + 1):
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src_band = src.read(b).astype(np.float32)
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dst_band = np.zeros((ref_dataset.height, ref_dataset.width), dtype=np.float32)
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reproject(
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source=src_band,
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destination=dst_band,
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src_transform=corrected_affine,
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src_crs=ref_crs,
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dst_transform=ref_transform,
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dst_crs=ref_crs,
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src_nodata=src_nodata,
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dst_nodata=dst_nodata,
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resampling=Resampling.bilinear,
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)
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if np.issubdtype(np.dtype(out_profile["dtype"]), np.integer):
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mask = (dst_band == dst_nodata) if src_nodata is not None else None
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info = np.iinfo(out_profile["dtype"])
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dst_band = np.clip(dst_band, info.min, info.max).astype(out_profile["dtype"])
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if mask is not None:
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dst_band[mask] = dst_nodata
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else:
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dst_band = dst_band.astype(out_profile["dtype"])
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out_ds.write(dst_band, b)
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logger.info(f"成功配准(仿射回退): {bip_path.name} -> {out_path.name}")
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return True
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except Exception as e:
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logger.error(f"处理失败 {bip_path.name}: {str(e)}")
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return False
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# ---------- 主逻辑 ----------
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def main():
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logger.info("开始批量配准处理...")
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# 检查输入文件是否存在
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if not Path(REF_TIF).exists():
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logger.error(f"参考文件不存在: {REF_TIF}")
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return
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if not BIP_DIR.exists():
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logger.error(f"BIP文件夹不存在: {BIP_DIR}")
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return
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# 初始化匹配器
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logger.info(f"初始化匹配器: {MATCHER_NAME} on {DEVICE}")
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matcher = get_matcher(MATCHER_NAME, device=DEVICE)
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# 打开参考文件
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with rasterio.open(REF_TIF) as ref:
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logger.info(f"参考文件信息: {ref.width}x{ref.height}, CRS: {ref.crs}")
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# 查找所有 .bip 文件
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bip_files = list(BIP_DIR.glob("*.bip"))
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logger.info(f"找到 {len(bip_files)} 个 .bip 文件")
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success_count = 0
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for bip_path in bip_files:
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if process_bip_to_tif(bip_path, ref, matcher, OUT_DIR):
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success_count += 1
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logger.info(f"处理完成: {success_count}/{len(bip_files)} 个文件成功配准")
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if __name__ == "__main__":
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main()
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