1509 lines
66 KiB
Python
1509 lines
66 KiB
Python
"""
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批量配准 .bip 文件到参考 .tif 文件
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使用 实现非刚性配准
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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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import logging
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import threading
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import queue
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from dataclasses import dataclass
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import tkinter as tk
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from tkinter import ttk, filedialog, messagebox
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import sys
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import os
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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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@dataclass
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class Config:
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"""配置参数类"""
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ref_tif: str
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bip_dir: str
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out_dir: str
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matcher_name: str
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device: str
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transform_methods: list
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match_max_side: int
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roi_pad_px: int
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min_inliers: int
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min_inlier_ratio: float
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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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# 默认配置，请根据实际情况修改这些路径
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DEFAULT_REF_TIF = r"E:\is2\yaopu\result.tif" # 参考 tif 文件路径
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DEFAULT_BIP_DIR = r"E:\is2\yaopu" # .bip 文件所在文件夹
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DEFAULT_OUT_DIR = r"E:\is2\yaopu\output" # 输出文件夹
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# 默认匹配算法选择
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DEFAULT_MATCHER_NAME = "matchanything-roma" # 可选: xfeat-star, loftr, roma, superpoint-lightglue, sift-lightglue 等
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DEFAULT_DEVICE = "cuda" # 或 "cpu"
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# 默认变换方法选择（按优先级尝试）
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DEFAULT_TRANSFORM_METHODS = ["homography", "affine", "piecewise_affine"]
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# 可选: "similarity", "affine", "homography", "piecewise_affine", "polynomial"
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# 默认匹配参数
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DEFAULT_MATCH_MAX_SIDE = 1200 # 匹配时最大边长（像素）
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DEFAULT_ROI_PAD_PX = 500 # 粗定位窗口的padding（参考tif像素）
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# 默认质量控制阈值
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DEFAULT_MIN_INLIERS = 10 # 最少内点数
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DEFAULT_MIN_INLIER_RATIO = 0.01 # 最少内点比例
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# 向后兼容的全局变量（用于命令行模式）
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REF_TIF = DEFAULT_REF_TIF
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BIP_DIR = Path(DEFAULT_BIP_DIR)
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OUT_DIR = Path(DEFAULT_OUT_DIR)
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MATCHER_NAME = DEFAULT_MATCHER_NAME
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DEVICE = DEFAULT_DEVICE
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TRANSFORM_METHODS = DEFAULT_TRANSFORM_METHODS
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MATCH_MAX_SIDE = DEFAULT_MATCH_MAX_SIDE
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ROI_PAD_PX = DEFAULT_ROI_PAD_PX
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MIN_INLIERS = DEFAULT_MIN_INLIERS
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MIN_INLIER_RATIO = DEFAULT_MIN_INLIER_RATIO
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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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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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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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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))
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||
|
||
elif transform_type in ["piecewise", "polynomial"]:
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||
# 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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||
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else:
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return np.inf, np.inf
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||
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return float(np.median(e)), float(np.percentile(e, 95))
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def _norm01_hw(x: np.ndarray) -> np.ndarray:
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"""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))
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||
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)):
|
||
"""
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||
numpy(H,W)->SimpleITK Image。
|
||
物理坐标约定为“像素坐标系”：spacing=1, direction=I，origin=(x0,y0)。
|
||
"""
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||
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])))
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||
img.SetDirection((1.0, 0.0, 0.0, 1.0))
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||
return img
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||
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||
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"""
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||
min_x = int(np.floor(k1_global[:, 0].min())) - pad
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max_x = int(np.ceil (k1_global[:, 0].max())) + pad
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min_y = int(np.floor(k1_global[:, 1].min())) - pad
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||
max_y = int(np.ceil (k1_global[:, 1].max())) + pad
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||
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||
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)
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||
max_y = min(ref_h, max_y)
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||
|
||
bbox_w = max_x - min_x
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||
bbox_h = max_y - min_y
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||
return min_x, min_y, bbox_w, bbox_h
|
||
|
||
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
|
||
|
||
# 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}")
|
||
|
||
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 run_batch(config: Config, on_progress=None, on_log=None, stop_event=None):
|
||
"""批量配准处理函数
|
||
|
||
Args:
|
||
config: 配置参数
|
||
on_progress: 进度回调函数 (current_idx, total, filename)
|
||
on_log: 日志回调函数 (message)
|
||
stop_event: 停止事件，用于取消处理
|
||
"""
|
||
def log(message):
|
||
if on_log:
|
||
on_log(message)
|
||
logger.info(message)
|
||
|
||
log("开始批量配准处理...")
|
||
|
||
# 检查输入文件是否存在
|
||
if not Path(config.ref_tif).exists():
|
||
log(f"参考文件不存在: {config.ref_tif}")
|
||
return
|
||
|
||
if not Path(config.bip_dir).exists():
|
||
log(f"BIP文件夹不存在: {config.bip_dir}")
|
||
return
|
||
|
||
# 创建输出目录
|
||
out_dir = Path(config.out_dir)
|
||
out_dir.mkdir(parents=True, exist_ok=True)
|
||
|
||
# 创建统计输出目录和文件
|
||
stats_dir = out_dir / "stats"
|
||
stats_dir.mkdir(parents=True, exist_ok=True)
|
||
stats_csv = stats_dir / "registration_stats.csv"
|
||
|
||
# 初始化统计CSV文件
|
||
init_stats_csv(stats_csv)
|
||
log(f"统计信息将保存到: {stats_csv}")
|
||
|
||
# 初始化匹配器
|
||
log(f"初始化匹配器: {config.matcher_name} on {config.device}")
|
||
matcher = get_matcher(config.matcher_name, device=config.device)
|
||
|
||
# 打开参考文件
|
||
with rasterio.open(config.ref_tif) as ref:
|
||
log(f"参考文件信息: {ref.width}x{ref.height}, CRS: {ref.crs}")
|
||
|
||
# 查找所有 .bip 文件
|
||
bip_dir = Path(config.bip_dir)
|
||
bip_files = list(bip_dir.glob("*.bip"))
|
||
log(f"找到 {len(bip_files)} 个 .bip 文件")
|
||
|
||
success_count = 0
|
||
for i, bip_path in enumerate(bip_files):
|
||
if stop_event and stop_event.is_set():
|
||
log("处理被用户取消")
|
||
break
|
||
|
||
if on_progress:
|
||
on_progress(i, len(bip_files), bip_path.name)
|
||
|
||
if process_bip_to_tif(bip_path, ref, matcher, out_dir, stats_csv):
|
||
success_count += 1
|
||
|
||
if on_progress:
|
||
on_progress(len(bip_files), len(bip_files), "完成")
|
||
|
||
log(f"处理完成: {success_count}/{len(bip_files)} 个文件成功配准")
|
||
|
||
def main():
|
||
"""命令行入口"""
|
||
# 使用默认配置运行
|
||
config = Config(
|
||
ref_tif=REF_TIF,
|
||
bip_dir=BIP_DIR,
|
||
out_dir=OUT_DIR,
|
||
matcher_name=MATCHER_NAME,
|
||
device=DEVICE,
|
||
transform_methods=TRANSFORM_METHODS,
|
||
match_max_side=MATCH_MAX_SIDE,
|
||
roi_pad_px=ROI_PAD_PX,
|
||
min_inliers=MIN_INLIERS,
|
||
min_inlier_ratio=MIN_INLIER_RATIO
|
||
)
|
||
run_batch(config)
|
||
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")
|
||
|
||
# ---------- GUI 相关 ----------
|
||
class QueueHandler(logging.Handler):
|
||
"""自定义日志处理器，将日志发送到队列"""
|
||
def __init__(self, log_queue):
|
||
super().__init__()
|
||
self.log_queue = log_queue
|
||
|
||
def emit(self, record):
|
||
self.log_queue.put(self.format(record))
|
||
|
||
class RegistrationGUI:
|
||
def __init__(self, root):
|
||
self.root = root
|
||
self.root.title("遥感影像批量配准工具")
|
||
self.root.geometry("1000x800")
|
||
|
||
# 日志队列和停止事件
|
||
self.log_queue = queue.Queue()
|
||
self.stop_event = threading.Event()
|
||
self.processing_thread = None
|
||
|
||
# 设置日志处理器
|
||
queue_handler = QueueHandler(self.log_queue)
|
||
queue_handler.setFormatter(logging.Formatter('%(asctime)s - %(levelname)s - %(message)s'))
|
||
logger.addHandler(queue_handler)
|
||
logger.setLevel(logging.INFO)
|
||
|
||
# 创建GUI组件
|
||
self.create_widgets()
|
||
|
||
# 定期检查日志队列
|
||
self.check_log_queue()
|
||
|
||
def create_widgets(self):
|
||
"""创建GUI组件"""
|
||
# 主框架
|
||
main_frame = ttk.Frame(self.root, padding="10")
|
||
main_frame.grid(row=0, column=0, sticky=(tk.W, tk.E, tk.N, tk.S))
|
||
|
||
# 配置展开面板
|
||
config_frame = ttk.LabelFrame(main_frame, text="配置参数", padding="5")
|
||
config_frame.grid(row=0, column=0, columnspan=2, sticky=(tk.W, tk.E), pady=(0, 10))
|
||
|
||
# 输入文件选择
|
||
ttk.Label(config_frame, text="参考TIF文件:").grid(row=0, column=0, sticky=tk.W, padx=(0, 5))
|
||
self.ref_tif_var = tk.StringVar(value=DEFAULT_REF_TIF)
|
||
ttk.Entry(config_frame, textvariable=self.ref_tif_var, width=50).grid(row=0, column=1, sticky=(tk.W, tk.E), padx=(0, 5))
|
||
ttk.Button(config_frame, text="选择文件", command=self.select_ref_tif).grid(row=0, column=2)
|
||
|
||
ttk.Label(config_frame, text="BIP文件夹:").grid(row=1, column=0, sticky=tk.W, padx=(0, 5))
|
||
self.bip_dir_var = tk.StringVar(value=DEFAULT_BIP_DIR)
|
||
ttk.Entry(config_frame, textvariable=self.bip_dir_var, width=50).grid(row=1, column=1, sticky=(tk.W, tk.E), padx=(0, 5))
|
||
ttk.Button(config_frame, text="选择文件夹", command=self.select_bip_dir).grid(row=1, column=2)
|
||
|
||
ttk.Label(config_frame, text="输出文件夹:").grid(row=2, column=0, sticky=tk.W, padx=(0, 5))
|
||
self.out_dir_var = tk.StringVar(value=DEFAULT_OUT_DIR)
|
||
ttk.Entry(config_frame, textvariable=self.out_dir_var, width=50).grid(row=2, column=1, sticky=(tk.W, tk.E), padx=(0, 5))
|
||
ttk.Button(config_frame, text="选择文件夹", command=self.select_out_dir).grid(row=2, column=2)
|
||
|
||
# 匹配器选择
|
||
ttk.Label(config_frame, text="匹配算法:").grid(row=3, column=0, sticky=tk.W, padx=(0, 5), pady=(10, 0))
|
||
self.matcher_var = tk.StringVar(value=DEFAULT_MATCHER_NAME)
|
||
matcher_combo = ttk.Combobox(config_frame, textvariable=self.matcher_var, width=47)
|
||
matcher_combo['values'] = [
|
||
"liftfeat", "loftr", "eloftr", "se2loftr", "xoftr", "aspanformer",
|
||
"matchanything-eloftr", "matchanything-roma", "matchformer",
|
||
"sift-lightglue", "superpoint-lightglue", "disk-lightglue",
|
||
"aliked-lightglue", "doghardnet-lightglue", "roma", "romav2",
|
||
"tiny-roma", "dedode", "steerers", "affine-steerers",
|
||
"dedode-kornia", "sift-nn", "orb-nn", "patch2pix", "superglue",
|
||
"r2d2", "d2net", "duster", "master", "doghardnet-nn", "xfeat",
|
||
"xfeat-star", "xfeat-lightglue", "dedode-lightglue", "gim-dkm",
|
||
"gim-lightglue", "omniglue", "xfeat-subpx", "xfeat-lightglue-subpx",
|
||
"dedode-subpx", "superpoint-lightglue-subpx", "aliked-lightglue-subpx",
|
||
"sift-sphereglue", "superpoint-sphereglue", "minima", "minima-roma",
|
||
"minima-roma-tiny", "minima-superpoint-lightglue", "minima-loftr",
|
||
"minima-xoftr", "edm", "lisrd-aliked", "lisrd-superpoint", "lisrd",
|
||
"lisrd-sift", "ripe", "topicfm", "topicfm-plus", "silk", "zippypoint",
|
||
"xfeat-steerers-perm", "xfeat-steerers-learned", "xfeat-star-steerers-perm",
|
||
"xfeat-star-steerers-learned"
|
||
]
|
||
matcher_combo.grid(row=3, column=1, columnspan=2, sticky=(tk.W, tk.E), pady=(10, 0))
|
||
|
||
# 设备选择
|
||
ttk.Label(config_frame, text="设备:").grid(row=4, column=0, sticky=tk.W, padx=(0, 5))
|
||
self.device_var = tk.StringVar(value=DEFAULT_DEVICE)
|
||
device_frame = ttk.Frame(config_frame)
|
||
device_frame.grid(row=4, column=1, columnspan=2, sticky=(tk.W, tk.E))
|
||
ttk.Radiobutton(device_frame, text="CUDA", variable=self.device_var, value="cuda").pack(side=tk.LEFT)
|
||
ttk.Radiobutton(device_frame, text="CPU", variable=self.device_var, value="cpu").pack(side=tk.LEFT)
|
||
|
||
# 变换方法选择
|
||
ttk.Label(config_frame, text="变换方法 (按优先级):").grid(row=5, column=0, sticky=tk.W, padx=(0, 5), pady=(10, 0))
|
||
|
||
transform_frame = ttk.Frame(config_frame)
|
||
transform_frame.grid(row=5, column=1, columnspan=2, sticky=(tk.W, tk.E), pady=(10, 0))
|
||
|
||
# 变换方法列表
|
||
self.transform_listbox = tk.Listbox(transform_frame, selectmode=tk.MULTIPLE, height=5, exportselection=False)
|
||
transform_methods = ["similarity", "affine", "homography", "piecewise_affine", "polynomial"]
|
||
for method in transform_methods:
|
||
self.transform_listbox.insert(tk.END, method)
|
||
if method in DEFAULT_TRANSFORM_METHODS:
|
||
self.transform_listbox.selection_set(transform_methods.index(method))
|
||
|
||
scrollbar = ttk.Scrollbar(transform_frame, orient=tk.VERTICAL, command=self.transform_listbox.yview)
|
||
self.transform_listbox.configure(yscrollcommand=scrollbar.set)
|
||
|
||
self.transform_listbox.pack(side=tk.LEFT, fill=tk.BOTH, expand=True)
|
||
scrollbar.pack(side=tk.RIGHT, fill=tk.Y)
|
||
|
||
# 移动按钮
|
||
button_frame = ttk.Frame(transform_frame)
|
||
button_frame.pack(side=tk.RIGHT, padx=(5, 0))
|
||
ttk.Button(button_frame, text="↑ 上移", command=self.move_up).pack(fill=tk.X, pady=(0, 2))
|
||
ttk.Button(button_frame, text="↓ 下移", command=self.move_down).pack(fill=tk.X)
|
||
|
||
# 参数设置
|
||
param_frame = ttk.LabelFrame(config_frame, text="参数设置", padding="5")
|
||
param_frame.grid(row=6, column=0, columnspan=3, sticky=(tk.W, tk.E), pady=(10, 0))
|
||
|
||
ttk.Label(param_frame, text="匹配最大边长:").grid(row=0, column=0, sticky=tk.W, padx=(0, 5))
|
||
self.match_max_side_var = tk.IntVar(value=DEFAULT_MATCH_MAX_SIDE)
|
||
ttk.Entry(param_frame, textvariable=self.match_max_side_var, width=10).grid(row=0, column=1, sticky=tk.W)
|
||
|
||
ttk.Label(param_frame, text="ROI填充像素:").grid(row=0, column=2, sticky=tk.W, padx=(10, 5))
|
||
self.roi_pad_px_var = tk.IntVar(value=DEFAULT_ROI_PAD_PX)
|
||
ttk.Entry(param_frame, textvariable=self.roi_pad_px_var, width=10).grid(row=0, column=3, sticky=tk.W)
|
||
|
||
ttk.Label(param_frame, text="最少内点数:").grid(row=1, column=0, sticky=tk.W, padx=(0, 5), pady=(5, 0))
|
||
self.min_inliers_var = tk.IntVar(value=DEFAULT_MIN_INLIERS)
|
||
ttk.Entry(param_frame, textvariable=self.min_inliers_var, width=10).grid(row=1, column=1, sticky=tk.W, pady=(5, 0))
|
||
|
||
ttk.Label(param_frame, text="最少内点比例:").grid(row=1, column=2, sticky=tk.W, padx=(10, 5), pady=(5, 0))
|
||
self.min_inlier_ratio_var = tk.DoubleVar(value=DEFAULT_MIN_INLIER_RATIO)
|
||
ttk.Entry(param_frame, textvariable=self.min_inlier_ratio_var, width=10).grid(row=1, column=3, sticky=tk.W, pady=(5, 0))
|
||
|
||
# 控制按钮
|
||
control_frame = ttk.Frame(main_frame)
|
||
control_frame.grid(row=1, column=0, columnspan=2, sticky=(tk.W, tk.E), pady=(10, 0))
|
||
|
||
self.start_btn = ttk.Button(control_frame, text="开始处理", command=self.start_processing)
|
||
self.start_btn.pack(side=tk.LEFT, padx=(0, 10))
|
||
|
||
self.stop_btn = ttk.Button(control_frame, text="停止处理", command=self.stop_processing, state=tk.DISABLED)
|
||
self.stop_btn.pack(side=tk.LEFT)
|
||
|
||
# 进度条
|
||
progress_frame = ttk.LabelFrame(main_frame, text="处理进度", padding="5")
|
||
progress_frame.grid(row=2, column=0, columnspan=2, sticky=(tk.W, tk.E), pady=(10, 0))
|
||
|
||
self.progress_var = tk.DoubleVar()
|
||
self.progress_bar = ttk.Progressbar(progress_frame, variable=self.progress_var, maximum=100)
|
||
self.progress_bar.pack(fill=tk.X, pady=(0, 5))
|
||
|
||
self.progress_label = ttk.Label(progress_frame, text="准备就绪")
|
||
self.progress_label.pack(anchor=tk.W)
|
||
|
||
# 日志窗口
|
||
log_frame = ttk.LabelFrame(main_frame, text="处理日志", padding="5")
|
||
log_frame.grid(row=3, column=0, columnspan=2, sticky=(tk.W, tk.E, tk.N, tk.S), pady=(10, 0))
|
||
|
||
# 日志文本框和滚动条
|
||
log_text_frame = ttk.Frame(log_frame)
|
||
log_text_frame.pack(fill=tk.BOTH, expand=True)
|
||
|
||
self.log_text = tk.Text(log_text_frame, height=15, wrap=tk.WORD)
|
||
scrollbar = ttk.Scrollbar(log_text_frame, orient=tk.VERTICAL, command=self.log_text.yview)
|
||
self.log_text.configure(yscrollcommand=scrollbar.set)
|
||
|
||
self.log_text.pack(side=tk.LEFT, fill=tk.BOTH, expand=True)
|
||
scrollbar.pack(side=tk.RIGHT, fill=tk.Y)
|
||
|
||
# 日志控制按钮
|
||
log_btn_frame = ttk.Frame(log_frame)
|
||
log_btn_frame.pack(fill=tk.X, pady=(5, 0))
|
||
|
||
ttk.Button(log_btn_frame, text="清空日志", command=self.clear_log).pack(side=tk.LEFT, padx=(0, 5))
|
||
ttk.Button(log_btn_frame, text="保存日志", command=self.save_log).pack(side=tk.LEFT)
|
||
|
||
# 配置网格权重
|
||
self.root.columnconfigure(0, weight=1)
|
||
self.root.rowconfigure(0, weight=1)
|
||
main_frame.columnconfigure(1, weight=1)
|
||
main_frame.rowconfigure(3, weight=1)
|
||
|
||
def select_ref_tif(self):
|
||
"""选择参考TIF文件"""
|
||
filename = filedialog.askopenfilename(
|
||
title="选择参考TIF文件",
|
||
filetypes=[("TIF files", "*.tif"), ("All files", "*.*")]
|
||
)
|
||
if filename:
|
||
self.ref_tif_var.set(filename)
|
||
|
||
def select_bip_dir(self):
|
||
"""选择BIP文件夹"""
|
||
dirname = filedialog.askdirectory(title="选择BIP文件夹")
|
||
if dirname:
|
||
self.bip_dir_var.set(dirname)
|
||
|
||
def select_out_dir(self):
|
||
"""选择输出文件夹"""
|
||
dirname = filedialog.askdirectory(title="选择输出文件夹")
|
||
if dirname:
|
||
self.out_dir_var.set(dirname)
|
||
|
||
def move_up(self):
|
||
"""上移选中的变换方法"""
|
||
selection = self.transform_listbox.curselection()
|
||
if selection and selection[0] > 0:
|
||
idx = selection[0]
|
||
text = self.transform_listbox.get(idx)
|
||
self.transform_listbox.delete(idx)
|
||
self.transform_listbox.insert(idx - 1, text)
|
||
self.transform_listbox.selection_set(idx - 1)
|
||
|
||
def move_down(self):
|
||
"""下移选中的变换方法"""
|
||
selection = self.transform_listbox.curselection()
|
||
if selection and selection[0] < self.transform_listbox.size() - 1:
|
||
idx = selection[0]
|
||
text = self.transform_listbox.get(idx)
|
||
self.transform_listbox.delete(idx)
|
||
self.transform_listbox.insert(idx + 1, text)
|
||
self.transform_listbox.selection_set(idx + 1)
|
||
|
||
def start_processing(self):
|
||
"""开始处理"""
|
||
if self.processing_thread and self.processing_thread.is_alive():
|
||
messagebox.showwarning("警告", "处理正在进行中")
|
||
return
|
||
|
||
# 获取选中的变换方法
|
||
selected_indices = self.transform_listbox.curselection()
|
||
if not selected_indices:
|
||
messagebox.showwarning("警告", "请至少选择一种变换方法")
|
||
return
|
||
|
||
transform_methods = []
|
||
for idx in selected_indices:
|
||
transform_methods.append(self.transform_listbox.get(idx))
|
||
|
||
# 创建配置
|
||
config = Config(
|
||
ref_tif=self.ref_tif_var.get(),
|
||
bip_dir=self.bip_dir_var.get(),
|
||
out_dir=self.out_dir_var.get(),
|
||
matcher_name=self.matcher_var.get(),
|
||
device=self.device_var.get(),
|
||
transform_methods=transform_methods,
|
||
match_max_side=self.match_max_side_var.get(),
|
||
roi_pad_px=self.roi_pad_px_var.get(),
|
||
min_inliers=self.min_inliers_var.get(),
|
||
min_inlier_ratio=self.min_inlier_ratio_var.get()
|
||
)
|
||
|
||
# 重置停止事件
|
||
self.stop_event.clear()
|
||
|
||
# 禁用开始按钮，启用停止按钮
|
||
self.start_btn.config(state=tk.DISABLED)
|
||
self.stop_btn.config(state=tk.NORMAL)
|
||
self.progress_var.set(0)
|
||
self.progress_label.config(text="正在初始化...")
|
||
|
||
# 在后台线程中运行处理
|
||
self.processing_thread = threading.Thread(
|
||
target=self.run_processing,
|
||
args=(config,),
|
||
daemon=True
|
||
)
|
||
self.processing_thread.start()
|
||
|
||
def stop_processing(self):
|
||
"""停止处理"""
|
||
if self.processing_thread and self.processing_thread.is_alive():
|
||
self.stop_event.set()
|
||
self.progress_label.config(text="正在停止...")
|
||
|
||
def run_processing(self, config):
|
||
"""在后台线程中运行处理"""
|
||
try:
|
||
run_batch(config, self.on_progress, self.on_log, self.stop_event)
|
||
except Exception as e:
|
||
self.log_queue.put(f"处理过程中发生错误: {e}")
|
||
finally:
|
||
# 恢复按钮状态
|
||
self.root.after(0, lambda: self.start_btn.config(state=tk.NORMAL))
|
||
self.root.after(0, lambda: self.stop_btn.config(state=tk.DISABLED))
|
||
self.root.after(0, lambda: self.progress_label.config(text="处理完成"))
|
||
|
||
def on_progress(self, current, total, filename):
|
||
"""进度回调"""
|
||
if total > 0:
|
||
progress = (current / total) * 100
|
||
self.root.after(0, lambda: self.progress_var.set(progress))
|
||
self.root.after(0, lambda: self.progress_label.config(text=f"处理中: {filename} ({current}/{total})"))
|
||
|
||
def on_log(self, message):
|
||
"""日志回调"""
|
||
self.log_queue.put(message)
|
||
|
||
def check_log_queue(self):
|
||
"""检查日志队列并更新GUI"""
|
||
try:
|
||
while True:
|
||
message = self.log_queue.get_nowait()
|
||
self.log_text.insert(tk.END, message + '\n')
|
||
self.log_text.see(tk.END)
|
||
except queue.Empty:
|
||
pass
|
||
|
||
# 每100ms检查一次
|
||
self.root.after(100, self.check_log_queue)
|
||
|
||
def clear_log(self):
|
||
"""清空日志"""
|
||
self.log_text.delete(1.0, tk.END)
|
||
|
||
def save_log(self):
|
||
"""保存日志"""
|
||
filename = filedialog.asksaveasfilename(
|
||
title="保存日志",
|
||
defaultextension=".txt",
|
||
filetypes=[("Text files", "*.txt"), ("All files", "*.*")]
|
||
)
|
||
if filename:
|
||
with open(filename, 'w', encoding='utf-8') as f:
|
||
f.write(self.log_text.get(1.0, tk.END))
|
||
|
||
def create_gui():
|
||
"""创建GUI"""
|
||
root = tk.Tk()
|
||
app = RegistrationGUI(root)
|
||
root.mainloop()
|
||
|
||
if __name__ == "__main__":
|
||
if len(sys.argv) > 1 and sys.argv[1] == "--cli":
|
||
# 命令行模式
|
||
main()
|
||
else:
|
||
# 默认GUI模式
|
||
create_gui() |