1300 lines
59 KiB
Python
1300 lines
59 KiB
Python
"""
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||
批量配准 .bip 文件到参考 .tif 文件
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问题：当图像中大部分是水体时，匹配过多出现在掩膜边缘，同时过滤时将本来就少的陆地匹配点也过滤掉了
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||
"""
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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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import csv
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from datetime import datetime
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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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from vismatch.viz import plot_matches, plot_keypoints
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import logging
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try:
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from skimage.transform import PiecewiseAffineTransform, PolynomialTransform
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SKIMAGE_AVAILABLE = True
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except ImportError:
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SKIMAGE_AVAILABLE = False
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logging.warning("scikit-image 不可用，将跳过 piecewise_affine 和 polynomial 变换")
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try:
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from matplotlib.path import Path as MplPath
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from scipy.spatial import ConvexHull
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MATPLOTLIB_SCIPY_AVAILABLE = True
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except ImportError:
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MATPLOTLIB_SCIPY_AVAILABLE = False
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MplPath = None
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logging.warning("matplotlib 或 scipy 不可用，piecewise_affine 将退化为矩形内判断")
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try:
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import SimpleITK as sitk
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SITK_AVAILABLE = True
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except ImportError:
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SITK_AVAILABLE = False
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logging.warning("SimpleITK 不可用，将使用仿射变换作为替代")
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try:
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import pirt
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PIRT_AVAILABLE = True
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except ImportError:
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PIRT_AVAILABLE = False
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logging.warning("PIRT 不可用，将使用 SimpleITK TPS 作为替代")
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try:
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from scipy.interpolate import Rbf
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SCIPY_AVAILABLE = True
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except ImportError:
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SCIPY_AVAILABLE = False
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logging.warning("scipy 不可用，将跳过 TPS 变换")
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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\guidingsahn\result.tif" # 参考 tif 文件路径
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BIP_DIR = Path(r"E:\is2\guidingsahn") # .bip 文件所在文件夹
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OUT_DIR = Path(r"E:\is2\guidingsahn\output") # 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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# 变换方法选择（按优先级尝试）
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TRANSFORM_METHODS = ["similarity", "affine", "homography"]
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# 可选: "similarity", "affine", "homography", "piecewise_affine", "polynomial", "polynomial_order3", "tps"
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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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MASK_PAD_PX = 100 # 匹配掩膜扩张像素（仅用于匹配阶段）
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# 质量控制阈值
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MIN_INLIERS = 10
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MIN_INLIER_RATIO = 0.01
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# 掩膜边缘羽化与过滤
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FEATHER_PX = 20 # 掩膜羽化宽度(像素，先在全分辨率/ROI分辨率上做)
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EDGE_BAND_PX = 30 # 剔除距离掩膜边界小于此像素的匹配点(在小图上按比例缩放)
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# 纹理过滤
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MIN_GRAD_QUANTILE = 0.20 # 梯度幅值的分位阈值(0~1)，低于该阈值的点视为低纹理，剔除
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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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# 创建统计输出目录和文件
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STATS_DIR = OUT_DIR / "stats"
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STATS_DIR.mkdir(parents=True, exist_ok=True)
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STATS_CSV = STATS_DIR / "registration_stats.csv"
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# ---------- 工具函数 ----------
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def init_stats_csv(csv_path: Path):
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"""初始化统计CSV文件"""
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if not csv_path.exists():
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with open(csv_path, 'w', newline='', encoding='utf-8') as f:
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writer = csv.writer(f)
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writer.writerow([
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'timestamp', 'filename', 'num_inliers', 'num_matches', 'inlier_ratio',
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'selected_method', 'median_error', 'p95_error', 'success'
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])
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def log_registration_stats(csv_path: Path, filename: str, num_inliers: int, num_matches: int,
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inlier_ratio: float, selected_method: str, median_error: float,
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p95_error: float, success: bool):
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"""记录配准统计信息到CSV"""
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timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
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with open(csv_path, 'a', newline='', encoding='utf-8') as f:
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writer = csv.writer(f)
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writer.writerow([
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timestamp, filename, num_inliers, num_matches, f"{inlier_ratio:.4f}",
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selected_method, f"{median_error:.4f}", f"{p95_error:.4f}", success
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])
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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 estimate_transform(method, k0, k1):
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||
"""统一的变换估计函数，支持多种变换类型"""
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||
if method == "translation":
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# 简单平移：用内点的平均位移
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if len(k0) == 0:
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return None, None
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dx = np.mean(k1[:, 0] - k0[:, 0])
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dy = np.mean(k1[:, 1] - k0[:, 1])
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A = np.array([[1, 0, dx], [0, 1, dy]], dtype=np.float32)
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return "A", A
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elif method == "euclidean":
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# 欧式变换（旋转+平移），约束等比缩放=1
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A, _ = cv2.estimateAffinePartial2D(k0, k1, method=cv2.RANSAC, ransacReprojThreshold=3.0)
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return "A", A
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elif method == "similarity":
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# 相似变换（旋转+等比缩放+平移）
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A, _ = cv2.estimateAffinePartial2D(k0, k1, method=cv2.RANSAC, ransacReprojThreshold=3.0)
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return "A", A
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elif method == "affine":
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# 全仿射变换（旋转+非等比缩放+剪切+平移）
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A, _ = cv2.estimateAffine2D(k0, k1, method=cv2.RANSAC, ransacReprojThreshold=3.0)
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return "A", A
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elif method == "homography":
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# 投影变换（8DOF，透视）
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H, _ = cv2.findHomography(k0, k1, method=cv2.USAC_MAGSAC, ransacReprojThreshold=3.0)
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return "H", H
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elif method == "piecewise_affine":
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# 分片仿射变换
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||
if not SKIMAGE_AVAILABLE:
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return None, None
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try:
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tform = PiecewiseAffineTransform()
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||
tform.estimate(k0, k1)
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||
return "piecewise", tform
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except Exception:
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||
return None, None
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||
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elif method == "polynomial":
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# 多项式变换（2阶）
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||
if not SKIMAGE_AVAILABLE:
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return None, None
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try:
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tform = PolynomialTransform()
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tform.estimate(k0, k1, order=2)
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return "polynomial", tform
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||
except Exception:
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||
return None, None
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||
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else:
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raise ValueError(f"未知变换方法: {method}")
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||
def evaluate_transform_quality(transform_type, transform, k0, k1):
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"""评估变换质量（重投影误差）"""
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if transform is None or len(k0) == 0:
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return np.inf, np.inf
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||
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if transform_type == "A":
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# 仿射变换重投影误差
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A = transform
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ones = np.ones((k0.shape[0], 1), dtype=np.float32)
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pred = (A @ np.hstack([k0, ones]).T).T
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e = np.sqrt(((pred - k1) ** 2).sum(axis=1))
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elif transform_type == "H":
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# 单应变换重投影误差
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H = transform
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ones = np.ones((k0.shape[0], 1), dtype=np.float32)
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src_h = np.hstack([k0, ones]).T
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warped = H @ src_h
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warped /= (warped[2:3, :] + 1e-6)
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pred = warped[:2, :].T
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e = np.sqrt(((pred - k1) ** 2).sum(axis=1))
|
||
|
||
elif transform_type in ["piecewise", "polynomial"]:
|
||
# scikit-image 变换重投影误差
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pred = transform(k0)
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e = np.sqrt(((pred - k1) ** 2).sum(axis=1))
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||
|
||
else:
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||
return np.inf, np.inf
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||
|
||
return float(np.median(e)), float(np.percentile(e, 95))
|
||
|
||
def _norm01_hw(x: np.ndarray) -> np.ndarray:
|
||
"""对单波段(H,W)做简单百分位归一化到[0,1]，增强跨传感器强度配准稳定性"""
|
||
x = x.astype(np.float32, copy=False)
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||
p2 = float(np.percentile(x, 2))
|
||
p98 = float(np.percentile(x, 98))
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||
y = (x - p2) / (p98 - p2 + 1e-6)
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||
return np.clip(y, 0.0, 1.0)
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||
|
||
def _np_to_sitk_float_image(arr_hw: np.ndarray, origin_xy=(0.0, 0.0)):
|
||
"""
|
||
numpy(H,W)->SimpleITK Image。
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||
物理坐标约定为“像素坐标系”：spacing=1, direction=I，origin=(x0,y0)。
|
||
"""
|
||
img = sitk.GetImageFromArray(arr_hw.astype(np.float32, copy=False))
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||
img.SetSpacing((1.0, 1.0))
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||
img.SetOrigin((float(origin_xy[0]), float(origin_xy[1])))
|
||
img.SetDirection((1.0, 0.0, 0.0, 1.0))
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||
return img
|
||
|
||
def _compute_bbox_from_k1(k1_global: np.ndarray, ref_w: int, ref_h: int, pad: int = 10):
|
||
"""用目标侧匹配点(k1_global)计算裁剪窗口(min_x,min_y,w,h)，并裁到参考影像范围内"""
|
||
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)
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||
min_y = max(0, min_y)
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||
max_x = min(ref_w, max_x)
|
||
max_y = min(ref_h, max_y)
|
||
|
||
bbox_w = max_x - min_x
|
||
bbox_h = max_y - min_y
|
||
return min_x, min_y, bbox_w, bbox_h
|
||
|
||
def _downscale_mask_hw(mask_hw: np.ndarray, target_h: int, target_w: int) -> np.ndarray:
|
||
"""将(H,W)二值掩膜缩放到目标尺寸，保持最近邻"""
|
||
m = cv2.resize(mask_hw.astype(np.uint8), (target_w, target_h), interpolation=cv2.INTER_NEAREST)
|
||
return m > 0
|
||
|
||
def _soft_alpha_from_mask(mask_hw: np.ndarray, feather_px: int) -> np.ndarray:
|
||
"""
|
||
二值掩膜 -> 软掩膜 alpha∈[0,1]，边缘处按距离线性上升，避免硬边缘。
|
||
mask_hw: bool/uint8 (H,W) True/1表示有效
|
||
"""
|
||
if mask_hw is None:
|
||
return None
|
||
m = (mask_hw.astype(np.uint8) > 0).astype(np.uint8) * 255
|
||
# 距离变换仅对前景内部有效，计算到边界的距离
|
||
dist = cv2.distanceTransform(m, distanceType=cv2.DIST_L2, maskSize=3)
|
||
if feather_px <= 0:
|
||
alpha = (dist > 0).astype(np.float32)
|
||
else:
|
||
alpha = np.clip(dist / float(feather_px), 0.0, 1.0).astype(np.float32)
|
||
return alpha # (H,W) float32
|
||
|
||
def _distance_keep_mask(mask_hw: np.ndarray, min_dist_px: int) -> np.ndarray:
|
||
"""
|
||
生成"远离边界"的保留掩膜：仅保留距离边界>=min_dist_px的像素。
|
||
"""
|
||
if mask_hw is None:
|
||
return None
|
||
m = (mask_hw.astype(np.uint8) > 0).astype(np.uint8) * 255
|
||
dist = cv2.distanceTransform(m, distanceType=cv2.DIST_L2, maskSize=3)
|
||
keep = dist >= float(max(1, min_dist_px))
|
||
return keep
|
||
|
||
def _grad_mask_from_chw(img_chw: np.ndarray, quantile: float) -> np.ndarray:
|
||
"""
|
||
根据梯度幅值生成纹理掩膜（H,W）True=纹理足够。
|
||
使用与匹配同尺寸的CHW图像。
|
||
"""
|
||
# 转灰度
|
||
g = img_chw.mean(axis=0).astype(np.float32) # (H,W)
|
||
gx = cv2.Sobel(g, cv2.CV_32F, 1, 0, ksize=3)
|
||
gy = cv2.Sobel(g, cv2.CV_32F, 0, 1, ksize=3)
|
||
mag = np.sqrt(gx*gx + gy*gy)
|
||
thr = float(np.quantile(mag, quantile)) if mag.size > 0 else 0.0
|
||
return mag >= thr # (H,W) bool
|
||
|
||
def _filter_matches_by_masks(result: dict, src_mask_small: np.ndarray, ref_mask_small: np.ndarray) -> dict:
|
||
"""将匹配与内点严格限制在掩膜内"""
|
||
if src_mask_small is None or ref_mask_small is None:
|
||
return result
|
||
|
||
def keep_in_mask(kpts: np.ndarray, mask_hw: np.ndarray) -> np.ndarray:
|
||
if kpts is None or len(kpts) == 0:
|
||
return np.zeros((0,), dtype=bool)
|
||
kpts = np.asarray(kpts)
|
||
xs = np.clip(np.rint(kpts[:, 0]).astype(int), 0, mask_hw.shape[1] - 1)
|
||
ys = np.clip(np.rint(kpts[:, 1]).astype(int), 0, mask_hw.shape[0] - 1)
|
||
return mask_hw[ys, xs]
|
||
|
||
# 过滤 matched_kpts
|
||
if "matched_kpts0" in result and "matched_kpts1" in result:
|
||
mk0 = np.asarray(result["matched_kpts0"])
|
||
mk1 = np.asarray(result["matched_kpts1"])
|
||
if len(mk0) == len(mk1) and len(mk0) > 0:
|
||
keep_m = keep_in_mask(mk0, src_mask_small) & keep_in_mask(mk1, ref_mask_small)
|
||
result["matched_kpts0"] = mk0[keep_m]
|
||
result["matched_kpts1"] = mk1[keep_m]
|
||
|
||
# 过滤 inlier_kpts
|
||
if "inlier_kpts0" in result and "inlier_kpts1" in result and result["inlier_kpts0"] is not None:
|
||
ik0 = np.asarray(result["inlier_kpts0"])
|
||
ik1 = np.asarray(result["inlier_kpts1"])
|
||
if len(ik0) == len(ik1) and len(ik0) > 0:
|
||
keep_i = keep_in_mask(ik0, src_mask_small) & keep_in_mask(ik1, ref_mask_small)
|
||
result["inlier_kpts0"] = ik0[keep_i]
|
||
result["inlier_kpts1"] = ik1[keep_i]
|
||
result["num_inliers"] = int(len(result["inlier_kpts0"]))
|
||
|
||
return result
|
||
|
||
def process_bip_to_tif(bip_path: Path, ref_dataset, matcher, out_dir: Path, stats_csv: 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}")
|
||
|
||
# 初始化统计变量
|
||
num_inliers = 0
|
||
num_matches = 0
|
||
inlier_ratio = 0.0
|
||
selected_method = "none"
|
||
median_error = float('inf')
|
||
p95_error = float('inf')
|
||
success = False
|
||
|
||
# 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"源图有效掩膜"的包围盒推参考ROI（比整图bounds更贴近有效重叠）
|
||
try:
|
||
src_mask = (src.read_masks(1) > 0) # True=有效
|
||
rows_any = np.any(src_mask, axis=1)
|
||
cols_any = np.any(src_mask, axis=0)
|
||
if rows_any.any() and cols_any.any():
|
||
rmin = int(rows_any.argmax())
|
||
rmax = int(src.height - 1 - rows_any[::-1].argmax())
|
||
cmin = int(cols_any.argmax())
|
||
cmax = int(src.width - 1 - cols_any[::-1].argmax())
|
||
valid_win_src = rasterio.windows.Window(cmin, rmin, cmax - cmin + 1, rmax - rmin + 1)
|
||
valid_bounds_src = rasterio.windows.bounds(valid_win_src, transform=src.transform)
|
||
b = transform_bounds(src_crs, ref_crs, *valid_bounds_src, densify_pts=21)
|
||
else:
|
||
# 掩膜无效时回退到整图bounds
|
||
b = transform_bounds(src_crs, ref_crs, *src.bounds, densify_pts=21)
|
||
except Exception:
|
||
src_mask = None # 后续可选源图掩膜时用到
|
||
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
|
||
|
||
# 将源图有效掩膜重投影到参考ROI，并适度膨胀后作为匹配掩膜
|
||
try:
|
||
if src_mask is None:
|
||
src_mask = (src.read_masks(1) > 0)
|
||
ref_roi_transform = ref_dataset.window_transform(win)
|
||
roi_h, roi_w = int(win.height), int(win.width)
|
||
dst_mask = np.zeros((roi_h, roi_w), dtype=np.uint8)
|
||
|
||
reproject(
|
||
source=src_mask.astype(np.uint8),
|
||
destination=dst_mask,
|
||
src_transform=src.transform,
|
||
src_crs=src_crs,
|
||
dst_transform=ref_roi_transform,
|
||
dst_crs=ref_crs,
|
||
resampling=Resampling.nearest
|
||
)
|
||
|
||
if MASK_PAD_PX > 0:
|
||
k = max(1, MASK_PAD_PX * 2 + 1) # odd kernel size
|
||
k = min(k, 99) # 防止核过大导致性能问题，可按需调整/删除
|
||
kernel = np.ones((k, k), np.uint8)
|
||
dst_mask = cv2.dilate(dst_mask, kernel, iterations=1)
|
||
except Exception:
|
||
# 掩膜获取/重投影失败则不使用掩膜
|
||
dst_mask = None
|
||
|
||
# 转换为匹配所需的格式
|
||
src_img = _to_3ch_float01(src_arr)
|
||
ref_img = _to_3ch_float01(ref_arr)
|
||
|
||
# 软掩膜：避免在边界产生硬高对比边
|
||
try:
|
||
alpha_src = _soft_alpha_from_mask(src_mask, FEATHER_PX) if src_mask is not None else None
|
||
except Exception:
|
||
alpha_src = None
|
||
try:
|
||
alpha_ref = _soft_alpha_from_mask(dst_mask, FEATHER_PX) if dst_mask is not None else None
|
||
except Exception:
|
||
alpha_ref = None
|
||
|
||
if alpha_src is not None:
|
||
alpha_src3 = np.repeat(alpha_src[None, ...], 3, axis=0).astype(src_img.dtype)
|
||
src_img = src_img * alpha_src3
|
||
|
||
if alpha_ref is not None:
|
||
alpha_ref3 = np.repeat(alpha_ref[None, ...], 3, axis=0).astype(ref_img.dtype)
|
||
ref_img = ref_img * alpha_ref3
|
||
|
||
# 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)
|
||
|
||
# 与小图同尺寸的掩膜
|
||
src_mask_small = _downscale_mask_hw(src_mask, src_small.shape[1], src_small.shape[2]) if 'src_mask' in locals() and src_mask is not None else None
|
||
ref_mask_small = _downscale_mask_hw(dst_mask, ref_small.shape[1], ref_small.shape[2]) if 'dst_mask' in locals() and dst_mask is not None else None
|
||
|
||
# 剔除掩膜边缘带（小图尺度的最小距离）
|
||
def _scale_px(px_full: int, full_wh, small_wh) -> int:
|
||
# 用平均缩放；也可以分别对H/W计算后取最小
|
||
sy = small_wh[0] / max(1, full_wh[0])
|
||
sx = small_wh[1] / max(1, full_wh[1])
|
||
s = 0.5 * (sx + sy)
|
||
return max(1, int(round(px_full * s)))
|
||
|
||
edge_band_src_small = _scale_px(EDGE_BAND_PX, (src_img.shape[1], src_img.shape[2]), (src_small.shape[1], src_small.shape[2]))
|
||
edge_band_ref_small = _scale_px(EDGE_BAND_PX, (ref_img.shape[1], ref_img.shape[2]), (ref_small.shape[1], ref_small.shape[2]))
|
||
|
||
keep_src_edge = _distance_keep_mask(src_mask_small, edge_band_src_small) if src_mask_small is not None else None
|
||
keep_ref_edge = _distance_keep_mask(ref_mask_small, edge_band_ref_small) if ref_mask_small is not None else None
|
||
|
||
# 纹理掩膜
|
||
keep_src_tex = _grad_mask_from_chw(src_small, MIN_GRAD_QUANTILE)
|
||
keep_ref_tex = _grad_mask_from_chw(ref_small, MIN_GRAD_QUANTILE)
|
||
|
||
# 组合最终保留掩膜（边缘+纹理），二者都要满足
|
||
def _combine_keep(m_edge, m_tex):
|
||
if m_edge is None:
|
||
return m_tex
|
||
return (m_edge & m_tex)
|
||
|
||
keep_src_final = _combine_keep(keep_src_edge, keep_src_tex)
|
||
keep_ref_final = _combine_keep(keep_ref_edge, keep_ref_tex)
|
||
|
||
# 将匹配与内点严格限制在最终掩膜内
|
||
def _filter_by_bool_masks(res, m_src, m_ref):
|
||
if m_src is None or m_ref is None:
|
||
return res
|
||
|
||
def keep_in(mask_hw, pts):
|
||
if pts is None or len(pts) == 0:
|
||
return np.zeros((0,), dtype=bool)
|
||
xs = np.clip(np.rint(pts[:, 0]).astype(int), 0, mask_hw.shape[1] - 1)
|
||
ys = np.clip(np.rint(pts[:, 1]).astype(int), 0, mask_hw.shape[0] - 1)
|
||
return mask_hw[ys, xs]
|
||
|
||
# matched
|
||
if "matched_kpts0" in res and "matched_kpts1" in res:
|
||
mk0 = np.asarray(res["matched_kpts0"]); mk1 = np.asarray(res["matched_kpts1"])
|
||
if len(mk0) == len(mk1) and len(mk0) > 0:
|
||
keep_m = keep_in(m_src, mk0) & keep_in(m_ref, mk1)
|
||
res["matched_kpts0"] = mk0[keep_m]
|
||
res["matched_kpts1"] = mk1[keep_m]
|
||
|
||
# inliers
|
||
if "inlier_kpts0" in res and "inlier_kpts1" in res and res["inlier_kpts0"] is not None:
|
||
ik0 = np.asarray(res["inlier_kpts0"]); ik1 = np.asarray(res["inlier_kpts1"])
|
||
if len(ik0) == len(ik1) and len(ik0) > 0:
|
||
keep_i = keep_in(m_src, ik0) & keep_in(m_ref, ik1)
|
||
res["inlier_kpts0"] = ik0[keep_i]
|
||
res["inlier_kpts1"] = ik1[keep_i]
|
||
res["num_inliers"] = int(len(res["inlier_kpts0"]))
|
||
return res
|
||
|
||
result = _filter_by_bool_masks(result, keep_src_final, keep_ref_final)
|
||
|
||
# 统计（以过滤后的结果为准）
|
||
num_inl = int(result.get("num_inliers", len(result.get("inlier_kpts0", []))))
|
||
num_m = len(result.get("matched_kpts0", []))
|
||
ratio = (num_inl / num_m) if num_m else 0.0
|
||
|
||
# 更新统计变量
|
||
num_inliers = num_inl
|
||
num_matches = num_m
|
||
inlier_ratio = ratio
|
||
|
||
logger.info(f"匹配结果: 内点={num_inl}, 匹配点={num_m}, 内点比例={ratio:.2f}")
|
||
|
||
# 保存匹配可视化图像（使用与匹配同尺寸的图像，保持CHW格式）
|
||
viz_dir = out_dir / "visualizations"
|
||
viz_dir.mkdir(exist_ok=True)
|
||
|
||
matches_path = viz_dir / f"{bip_path.stem}_matches.png"
|
||
plot_matches(src_small, ref_small, result, save_path=str(matches_path))
|
||
logger.info(f"匹配可视化已保存: {matches_path}")
|
||
|
||
# 关键点可视化（源图像）
|
||
kpts_src_path = viz_dir / f"{bip_path.stem}_keypoints_src.png"
|
||
plot_keypoints(
|
||
src_small,
|
||
{"all_kpts0": result["all_kpts0"], "all_desc0": result["all_desc0"]},
|
||
save_path=str(kpts_src_path)
|
||
)
|
||
logger.info(f"源图像关键点可视化已保存: {kpts_src_path}")
|
||
|
||
# 关键点可视化（参考图像）
|
||
kpts_ref_path = viz_dir / f"{bip_path.stem}_keypoints_ref.png"
|
||
plot_keypoints(
|
||
ref_small,
|
||
{"all_kpts0": result["all_kpts1"], "all_desc0": result["all_desc1"]},
|
||
save_path=str(kpts_ref_path)
|
||
)
|
||
logger.info(f"参考图像关键点可视化已保存: {kpts_ref_path}")
|
||
|
||
if num_inl < MIN_INLIERS or ratio < MIN_INLIER_RATIO:
|
||
logger.warning(f"匹配质量不足: {bip_path.name}")
|
||
# 记录失败的统计信息
|
||
log_registration_stats(stats_csv, bip_path.name, num_inliers, num_matches,
|
||
inlier_ratio, "failed_quality_check", median_error, p95_error, False)
|
||
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_median_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_median_error = med_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}")
|
||
# 记录失败的统计信息
|
||
log_registration_stats(stats_csv, bip_path.name, num_inliers, num_matches,
|
||
inlier_ratio, "failed_transform", median_error, p95_error, False)
|
||
return False
|
||
|
||
# 更新统计变量
|
||
selected_method = best_method
|
||
median_error = best_median_error
|
||
p95_error = best_error
|
||
|
||
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}")
|
||
success = True
|
||
log_registration_stats(stats_csv, bip_path.name, num_inliers, num_matches,
|
||
inlier_ratio, selected_method, median_error, p95_error, success)
|
||
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}")
|
||
success = True
|
||
log_registration_stats(stats_csv, bip_path.name, num_inliers, num_matches,
|
||
inlier_ratio, selected_method, median_error, p95_error, success)
|
||
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", "polynomial_order3"]:
|
||
# 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 in ["polynomial", "polynomial_order3"]:
|
||
# 对于多项式，估计逆变换
|
||
order = 2 if best_transform_type == "polynomial" else 3
|
||
t_inv = PolynomialTransform()
|
||
t_inv.estimate(k1_global, k0_full, order=order) # 顺序：目标->源
|
||
|
||
# 目标侧点集的内点判定（用于限制外推）
|
||
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],[min_x + bbox_w, min_y],
|
||
[min_x + bbox_w, min_y + bbox_h],[min_x, min_y + bbox_h]], dtype=float)
|
||
hull_path = MplPath(rect)
|
||
|
||
def point_inside(xy):
|
||
return hull_path.contains_points(xy)
|
||
else:
|
||
def point_inside(xy):
|
||
return ((xy[:,0] >= min_x) & (xy[:,0] <= min_x + bbox_w) &
|
||
(xy[:,1] >= min_y) & (xy[:,1] <= min_y + bbox_h))
|
||
|
||
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
|
||
inside = point_inside(xy)
|
||
xy_src = np.full_like(xy, fill_value=-1.0)
|
||
if np.any(inside):
|
||
xy_src[inside] = t_inv(xy[inside]) # -> (x_src, y_src) in full-src
|
||
# 确保坐标在源图像范围内
|
||
xy_src[:, 0] = np.clip(xy_src[:, 0], 0, src.height - 1)
|
||
xy_src[:, 1] = np.clip(xy_src[:, 1], 0, src.width - 1)
|
||
return np.column_stack([xy_src[:, 1], xy_src[:, 0]]) # -> (row_src, col_src)
|
||
elif best_transform_type == "piecewise": # 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:
|
||
# 使用当前裁剪窗口的边界创建矩形
|
||
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}")
|
||
success = True
|
||
log_registration_stats(stats_csv, bip_path.name, num_inliers, num_matches,
|
||
inlier_ratio, selected_method, median_error, p95_error, success)
|
||
return True
|
||
|
||
except Exception as e:
|
||
logger.warning(f"{best_transform_type}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 ----
|
||
transform = best_transform
|
||
try:
|
||
min_x, min_y, bbox_w, bbox_h = _compute_bbox_from_k1(
|
||
k1_global, ref_dataset.width, ref_dataset.height, pad=10
|
||
)
|
||
if bbox_w <= 0 or bbox_h <= 0:
|
||
logger.warning(f"tps变换最小外接矩形无效: {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)
|
||
|
||
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], [min_x + bbox_w, min_y],
|
||
[min_x + bbox_w, min_y + bbox_h], [min_x, min_y + bbox_h]],
|
||
dtype=float
|
||
)
|
||
hull_path = MplPath(rect)
|
||
|
||
def point_inside(xy):
|
||
return hull_path.contains_points(xy)
|
||
else:
|
||
def point_inside(xy):
|
||
return (
|
||
(xy[:, 0] >= min_x) & (xy[:, 0] <= min_x + bbox_w) &
|
||
(xy[:, 1] >= min_y) & (xy[:, 1] <= min_y + bbox_h)
|
||
)
|
||
|
||
off_x, off_y = min_x, min_y
|
||
tps_inv = transform["inv"] # ref -> src
|
||
|
||
def inv_map_rc(coords):
|
||
rc = np.asarray(coords, dtype=np.float64)
|
||
xy_ref = np.column_stack([rc[:, 1] + off_x, rc[:, 0] + off_y]) # full-ref (x, y)
|
||
inside = point_inside(xy_ref)
|
||
xy_src = np.full_like(xy_ref, fill_value=-1.0, dtype=np.float64)
|
||
if np.any(inside):
|
||
# 使用RBF插值计算逆映射
|
||
xy_src[inside, 0] = tps_inv["rbf_x"](xy_ref[inside, 0], xy_ref[inside, 1])
|
||
xy_src[inside, 1] = tps_inv["rbf_y"](xy_ref[inside, 0], xy_ref[inside, 1])
|
||
return np.column_stack([xy_src[:, 1], xy_src[:, 0]]) # (row_src, col_src)
|
||
|
||
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
|
||
)
|
||
|
||
# 优先用 skimage.warp；缺失时用 SimpleITK Resample 兜底
|
||
if SKIMAGE_AVAILABLE:
|
||
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,
|
||
output_shape=(bbox_h, bbox_w),
|
||
mode='constant',
|
||
cval=dst_nodata,
|
||
preserve_range=True
|
||
).astype(np.float32)
|
||
|
||
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)
|
||
else:
|
||
# OpenCV remap 版本（无需 skimage/SimpleITK）
|
||
with rasterio.open(out_path, "w", **out_profile) as out_ds:
|
||
# 创建映射网格
|
||
y_coords, x_coords = np.mgrid[0:bbox_h, 0:bbox_w]
|
||
coords = np.column_stack([y_coords.ravel(), x_coords.ravel()])
|
||
|
||
# 计算逆映射
|
||
mapped_coords = inv_map_rc(coords)
|
||
map_y = mapped_coords[:, 0].reshape(bbox_h, bbox_w).astype(np.float32)
|
||
map_x = mapped_coords[:, 1].reshape(bbox_h, bbox_w).astype(np.float32)
|
||
|
||
for b in range(1, src.count + 1):
|
||
src_band = src.read(b).astype(np.float32)
|
||
|
||
# 使用OpenCV的remap进行重采样
|
||
dst_band = cv2.remap(
|
||
src_band, map_x, map_y,
|
||
interpolation=cv2.INTER_LINEAR,
|
||
borderMode=cv2.BORDER_CONSTANT,
|
||
borderValue=dst_nodata
|
||
)
|
||
|
||
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(TPS): {bip_path.name} -> {out_path.name}")
|
||
return True
|
||
|
||
except Exception as e:
|
||
logger.warning(f"tps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}")
|
||
success = True
|
||
log_registration_stats(stats_csv, bip_path.name, num_inliers, num_matches,
|
||
inlier_ratio, "affine_fallback", median_error, p95_error, success)
|
||
return True
|
||
|
||
except Exception as e:
|
||
logger.error(f"处理失败 {bip_path.name}: {str(e)}")
|
||
# 记录失败的统计信息
|
||
try:
|
||
log_registration_stats(stats_csv, bip_path.name, num_inliers, num_matches,
|
||
inlier_ratio, "exception", median_error, p95_error, False)
|
||
except:
|
||
pass # 避免统计记录失败影响主要错误处理
|
||
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
|
||
|
||
# 初始化统计CSV文件
|
||
init_stats_csv(STATS_CSV)
|
||
logger.info(f"统计信息将保存到: {STATS_CSV}")
|
||
|
||
# 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, STATS_CSV):
|
||
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()
|