修改像素精度

.idea/vcs.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="Git" />
+  </component>
+</project>

test V8.py

@@ -61,9 +61,9 @@ logger = logging.getLogger(__name__)
 
 # ---------- 配置 ----------
 # 请根据实际情况修改这些路径
-REF_TIF = r"E:\is2\guidingsahn\result.tif" # 参考 tif 文件路径
+REF_TIF = r"E:\is2\dingshanhu\mask_water.tif" # 参考 tif 文件路径
-BIP_DIR = Path(r"E:\is2\guidingsahn")  # .bip 文件所在文件夹
+BIP_DIR = Path(r"E:\is2\dingshanhu")  # .bip 文件所在文件夹
-OUT_DIR = Path(r"E:\is2\guidingsahn\output")  # 输出文件夹
+OUT_DIR = Path(r"E:\is2\dingshanhu\output")  # 输出文件夹
 
 # 匹配算法选择
 MATCHER_NAME = "matchanything-roma"  # 可选: xfeat-star, loftr, roma, superpoint-lightglue, sift-lightglue 等

test V9.py

@@ -61,7 +61,7 @@ logger = logging.getLogger(__name__)
 
 # ---------- 配置 ----------
 # 请根据实际情况修改这些路径
-REF_TIF = r"E:\is2\guidingsahn\result.tif" # 参考 tif 文件路径
+REF_TIF = r"E:\is2\guidingsahn\mask_water.tif" # 参考 tif 文件路径
 BIP_DIR = Path(r"E:\is2\guidingsahn")  # .bip 文件所在文件夹
 OUT_DIR = Path(r"E:\is2\guidingsahn\output")  # 输出文件夹
 
@@ -157,7 +157,30 @@ def _expand_window(win, pad, max_w, max_h):
     row_off = int(max(0, win.row_off - pad))
     col_end = int(min(max_w, win.col_off + win.width + pad))
     row_end = int(min(max_h, win.row_off + win.height + pad))
-    return rasterio.windows.Window(col_off, row_off, col_end - col_off, row_end - row_off)
+    return rasterio.windows.Window(col_off, row_off, col_end - col_off, col_end - col_off,)
+
+
+def _pixel_size_xy(transform: Affine):
+    rx = float(np.hypot(transform.a, transform.d))
+    ry = float(np.hypot(transform.b, transform.e))
+    if not np.isfinite(rx) or rx <= 0:
+        rx = float(abs(transform.a)) if transform.a != 0 else 1.0
+    if not np.isfinite(ry) or ry <= 0:
+        ry = float(abs(transform.e)) if transform.e != 0 else 1.0
+    return rx, ry
+
+
+def _grid_from_bounds(bounds, res_x: float, res_y: float):
+    left, bottom, right, top = [float(v) for v in bounds]
+    res_x = float(abs(res_x))
+    res_y = float(abs(res_y))
+    w = int(np.ceil((right - left) / max(1e-12, res_x)))
+    h = int(np.ceil((top - bottom) / max(1e-12, res_y)))
+    w = max(1, w)
+    h = max(1, h)
+    out_transform = Affine(res_x, 0.0, left, 0.0, -res_y, top)
+    return out_transform, w, h
+
 
 def estimate_transform(method, k0, k1):
     """统一的变换估计函数，支持多种变换类型"""
@@ -689,41 +712,43 @@ def process_bip_to_tif(bip_path: Path, ref_dataset, matcher, out_dir: Path, stat
                     return False
 
                 bbox_window = rasterio.windows.Window(min_x, min_y, bbox_w, bbox_h)
-                bbox_transform = ref_dataset.window_transform(bbox_window)
+                bounds = rasterio.windows.bounds(bbox_window, transform=ref_dataset.transform)
+
+                res_x, res_y = _pixel_size_xy(src.transform)
+                out_transform, out_w, out_h = _grid_from_bounds(bounds, res_x, res_y)
 
                 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 = src.profile.copy()
                 out_profile.update(
                     driver="ENVI",
                     dtype=src.dtypes[0],
-                    height=bbox_h,
+                    height=out_h,
-                    width=bbox_w,
+                    width=out_w,
                     count=src.count,
-                    transform=bbox_transform,
+                    transform=out_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)
+                        dst_band = np.zeros((out_h, out_w), dtype=np.float32)
                         reproject(
                             source=src_band,
                             destination=dst_band,
                             src_transform=corrected_affine,
                             src_crs=ref_crs,
-                            dst_transform=bbox_transform,
+                            dst_transform=out_transform,
                             dst_crs=ref_crs,
                             src_nodata=src_nodata,
                             dst_nodata=dst_nodata,
-                            resampling=Resampling.bilinear,
+                            resampling=Resampling.nearest,
                         )
 
                         if np.issubdtype(np.dtype(out_profile["dtype"]), np.integer):
@@ -772,46 +797,56 @@ def process_bip_to_tif(bip_path: Path, ref_dataset, matcher, out_dir: Path, stat
                         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)
+                    bounds = rasterio.windows.bounds(bbox_window, transform=ref_dataset.transform)
 
-                    # 子窗口坐标的单应矩阵（输出坐标系是子窗口像素）
+                    res_x, res_y = _pixel_size_xy(src.transform)
-                    T_off = np.array([[1,0,min_x],[0,1,min_y],[0,0,1]], dtype=np.float64)
+                    out_transform, out_w, out_h = _grid_from_bounds(bounds, res_x, res_y)
-                    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 = src.profile.copy()
                     out_profile.update(
                         driver="ENVI",
                         dtype=src.dtypes[0],
-                        height=bbox_h,
+                        height=out_h,
-                        width=bbox_w,
+                        width=out_w,
                         count=src.count,
-                        transform=bbox_transform,
+                        transform=out_transform,
                         crs=ref_crs,
                         interleave="bip",
                         compress=None,
                         nodata=dst_nodata
                     )
 
-                    # 使用 OpenCV 进行单应变换重采样
+                    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,
+                    )
+                    Out = np.array(
+                        [[out_transform.a, out_transform.b, out_transform.c],
+                         [out_transform.d, out_transform.e, out_transform.f],
+                         [0.0, 0.0, 1.0]],
+                        dtype=np.float64,
+                    )
+                    M = np.linalg.inv(Out) @ Rt @ H_full.astype(np.float64)
+
                     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,
+                                src_band,
-                                (bbox_w, bbox_h),
+                                M,
-                                flags=cv2.INTER_LINEAR,
+                                (out_w, out_h),
+                                flags=cv2.INTER_NEAREST,
                                 borderMode=cv2.BORDER_CONSTANT,
-                                borderValue=dst_nodata
+                                borderValue=float(dst_nodata)
-                            )
+                            ).astype(np.float32)
 
                             # 转回目标 dtype
                             if np.issubdtype(np.dtype(out_profile["dtype"]), np.integer):
@@ -957,7 +992,8 @@ def process_bip_to_tif(bip_path: Path, ref_dataset, matcher, out_dir: Path, stat
                                 output_shape=(bbox_h, bbox_w),
                                 mode='constant',
                                 cval=dst_nodata,
-                                preserve_range=True
+                                preserve_range=True,
+                                order=0
                             ).astype(np.float32)
 
                             # 转回目标 dtype
@@ -1060,7 +1096,8 @@ def process_bip_to_tif(bip_path: Path, ref_dataset, matcher, out_dir: Path, stat
                                     output_shape=(bbox_h, bbox_w),
                                     mode='constant',
                                     cval=dst_nodata,
-                                    preserve_range=True
+                                    preserve_range=True,
+                                    order=0
                                 ).astype(np.float32)
 
                                 if np.issubdtype(np.dtype(out_profile["dtype"]), np.integer):
@@ -1091,7 +1128,7 @@ def process_bip_to_tif(bip_path: Path, ref_dataset, matcher, out_dir: Path, stat
                                 # 使用OpenCV的remap进行重采样
                                 dst_band = cv2.remap(
                                     src_band, map_x, map_y,
-                                    interpolation=cv2.INTER_LINEAR,
+                                    interpolation=cv2.INTER_NEAREST,
                                     borderMode=cv2.BORDER_CONSTANT,
                                     borderValue=dst_nodata
                                 )
@@ -1192,44 +1229,44 @@ def process_bip_to_tif(bip_path: Path, ref_dataset, matcher, out_dir: Path, stat
                 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)
+            bounds = rasterio.windows.bounds(bbox_window, transform=ref_dataset.transform)
+
+            res_x, res_y = _pixel_size_xy(src.transform)
+            out_transform, out_w, out_h = _grid_from_bounds(bounds, res_x, res_y)
 
             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 = src.profile.copy()
-            out_profile = ref_dataset.profile.copy()
             out_profile.update(
                 driver="ENVI",
                 dtype=src.dtypes[0],
-                height=bbox_h,
+                height=out_h,
-                width=bbox_w,
+                width=out_w,
                 count=src.count,
-                transform=bbox_transform,  # 使用最小外接矩形的变换
+                transform=out_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)
+                    dst_band = np.zeros((out_h, out_w), dtype=np.float32)
                     reproject(
                         source=src_band,
                         destination=dst_band,
                         src_transform=corrected_affine,
                         src_crs=ref_crs,
-                        dst_transform=bbox_transform,
+                        dst_transform=out_transform,
                         dst_crs=ref_crs,
                         src_nodata=src_nodata,
                         dst_nodata=dst_nodata,
-                        resampling=Resampling.bilinear,
+                        resampling=Resampling.nearest,
                     )
                     # 转回目标 dtype
                     if np.issubdtype(np.dtype(out_profile["dtype"]), np.integer):