From 2f7b90725eac431bbe49d76166926e1a05df1b12 Mon Sep 17 00:00:00 2001
From: duxin <duxin@example.com>
Date: Wed, 9 Sep 2026 10:33:28 +0800
Subject: [PATCH] =?UTF-8?q?fix:=20=E9=87=87=E6=A0=B7=E7=82=B9=E5=9C=B0?=
 =?UTF-8?q?=E5=9B=BE=E5=BA=95=E5=9B=BERGB=E6=94=B9=E7=94=A8GDAL=20buf?=
 =?UTF-8?q?=E9=99=8D=E9=87=87=E6=A0=B7=E9=98=B2OOM?=
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- _read_hyperspectral 原整幅读3波段再np.stack(34914x19177 float32 ~7.5GiB),现将预览最长边限制到2048,用 ReadAsArray(buf_xsize/buf_ysize) 抽样
- 返回'显示坐标系'(原点不变、像元尺寸×抽样比),geo_to_pixel 直接得显示像素,比例尺/指北针一致;移除下采样暂被禁用分支
---
 src/postprocessing/point_map.py | 24 ++++++++++++++++++------
 1 file changed, 18 insertions(+), 6 deletions(-)

diff --git a/src/postprocessing/point_map.py b/src/postprocessing/point_map.py
index a77c6bc..2f0dcad 100644
--- a/src/postprocessing/point_map.py
+++ b/src/postprocessing/point_map.py
@@ -107,16 +107,20 @@ class SamplingPointMap:
             else:
                 rgb_bands = [0, 0, 0]
 
-        if downsample and (width > 2000 or height > 2000):
-            print(f"  ⚠ 下采样暂被禁用，使用原始分辨率: {width}x{height}")
-            sample_factor = 1
+        # ★ 底图仅供预览：整幅高光谱 RGB 在 34914x19177 全分辨率下 np.stack(float32)
+        #   会一次分配 ~7.5GiB 导致 OOM。统一用 GDAL buf 抽样，预览最长边 ≤ MAX_DIM。
+        _MAX_DIM = 2048
+        if max(width, height) <= _MAX_DIM:
+            buf_w, buf_h = width, height
         else:
-            sample_factor = 1
+            _scale = _MAX_DIM / max(width, height)
+            buf_w, buf_h = max(1, int(width * _scale)), max(1, int(height * _scale))
 
         rgb_data = []
         for band_idx in rgb_bands:
             band = dataset.GetRasterBand(band_idx + 1)
-            band_data = band.ReadAsArray().astype(np.float32)
+            # buf_xsize/buf_ysize：由 GDAL 在读取时抽样，Python 只驻留 buf 大小的数组
+            band_data = band.ReadAsArray(buf_xsize=buf_w, buf_ysize=buf_h).astype(np.float32)
             rgb_data.append(band_data)
 
         if len(rgb_data) == 3:
@@ -128,7 +132,15 @@ class SamplingPointMap:
         projection = dataset.GetProjection()
         dataset = None
 
-        return image_array, geotransform, projection, width, height, sample_factor
+        # 返回“显示坐标系”：原点不变，像元尺寸按抽样比例放大；
+        # 下游 _geo_to_pixel 据此直接得到显示像素坐标，无需再按 sample_factor 除一次。
+        disp_gt = list(geotransform)
+        if buf_w != width:
+            disp_gt[1] = geotransform[1] * (width / float(buf_w))
+        if buf_h != height:
+            disp_gt[5] = geotransform[5] * (height / float(buf_h))
+        sample_factor = 1  # 缩放已并入 disp_gt
+        return image_array, tuple(disp_gt), projection, buf_w, buf_h, sample_factor
 
     def _read_sampling_points(self, csv_path: str) -> pd.DataFrame:
         """智能读取采样点，自动识别模糊列名，允许UTM坐标，自动修复颠倒坐标"""