更新裁剪方法，通过地距映射表进行斜距影像裁剪

tool/algorithm/transforml1a/SAR_geo/SAR_GEO.pyx

@@ -2,7 +2,7 @@ import os
 cimport cython # 必须导入
 import numpy as np##必须为c类型和python类型的数据都申明一个np
 cimport numpy as np # 必须为c类型和python类型的数据都申明一个np
-from libc.math cimport pi
+from libc.math cimport pi,ceil,floor
 from scipy.interpolate import griddata
 
 
@@ -86,17 +86,54 @@ cpdef np.ndarray[double,ndim=2] cut_L1A_img(np.ndarray[double,ndim=3]  ori2geo_i
     while i<height:
         j=0
         while j<width:
-            temp_p.x=ori2geo_img[0,i,j]
+            temp_p.x=ori2geo_img[0,i,j] # temp_p 
-            temp_p.y=ori2geo_img[1,i,j]
+            temp_p.y=ori2geo_img[1,i,j] # temp_p 
             if rayCasting(temp_p,roi_list)==1:
                 mask[i,j]=1
             else:
                 mask[i,j]=np.nan
             j=j+1
         i=i+1
-
     return mask
 
+cpdef np.ndarray[double,ndim=2] gereratorMask(np.ndarray[double,ndim=1]  rlist,np.ndarray[double,ndim=1] clist,np.ndarray[double,ndim=2]  mask):
+    cdef int rcount=rlist.shape[0]
+    cdef int ccount=clist.shape[0]
+    cdef int count=rcount if rcount<ccount else ccount
+    cdef int i=0
+    cdef int j=0
+    cdef int temp_row=0
+    cdef int temp_col=0
+    cdef int height=mask.shape[0]
+    cdef int width=mask.shape[1]
+    while i<count:
+
+        # 1
+        temp_row=int(ceil(rlist[i]))
+        temp_col=int(ceil(clist[i]))
+
+        if temp_row>=0 and temp_col>=0 and  temp_row<height and temp_col<width:
+            mask[temp_row,temp_col]=1
+
+        # 2
+        temp_row=int(floor(rlist[i]))
+        temp_col=int(ceil(clist[i]))
+        if temp_row>=0 and temp_col>=0 and  temp_row<height and temp_col<width:
+            mask[temp_row,temp_col]=1
+
+        # 3
+        temp_row=int(ceil(rlist[i]))
+        temp_col=int(floor(clist[i]))
+        if temp_row>=0 and temp_col>=0 and  temp_row<height and temp_col<width:
+            mask[temp_row,temp_col]=1
+        # 4
+        temp_row=int(floor(rlist[i]))
+        temp_col=int(floor(clist[i]))
+        if temp_row>=0 and temp_col>=0 and  temp_row<height and temp_col<width:
+            mask[temp_row,temp_col]=1
+        i=i+1        
+
+    return mask
 
 
 cdef double distance_powe(Point p1,Point p2):

tool/algorithm/transforml1a/transHandle.py

@@ -4,13 +4,14 @@ import numpy as np
 import scipy
 from scipy.interpolate import griddata, RegularGridInterpolator
 import logging
-import pyresample as pr
+# import pyresample as pr
 # 插值模块
 from pyresample.bilinear import NumpyBilinearResampler
 from pyresample import geometry
 from pyresample.geometry import AreaDefinition
 from osgeo import osr
 import os
+import math
 
 # os.environ['PROJ_LIB'] = r"D:\Anaconda\envs\micro\Lib\site-packages\osgeo\data\proj"
 
@@ -26,6 +27,7 @@ def griddata_geo(points, data, lon_grid, lat_grid, method, i, end_i):
     grid_data = grid_data[:, :, 0]
     return [i, end_i, grid_data]
 
+
 def griddataBlock(start_x, len_x, start_y, len_y, grid_data_input, grid_x, grid_y, method):
     grid_x = grid_x.reshape(-1)
     grid_y = grid_y.reshape(-1)
@@ -82,10 +84,12 @@ class polyfit2d_U:
 
 
 class TransImgL1A:
-    def __init__(self, ori_sim_path, roi):
+    def __init__(self, ori_sim_path, roi, l1a_height, l1a_width):
         self._begin_r, self._begin_c, self._end_r, self._end_c = 0, 0, 0, 0
         self.ori2geo_img = None
         self._mask = None
+        self.l1a_height = l1a_height
+        self.l1a_width = l1a_width
         self._min_lon, self._max_lon, self._min_lat, self._max_lat = 0, 0, 0, 0
         self.init_trans_para(ori_sim_path, roi)
 
@@ -94,7 +98,6 @@ class TransImgL1A:
         data = [(self._begin_r + row, self._begin_c + col) for (row, col) in zip(rowcol[0], rowcol[1])]
         return data
 
-
     def get_lonlat_points(self):
         lon = self.ori2geo_img[0, :, :][np.where(self._mask == 1)]
         lat = self.ori2geo_img[1, :, :][np.where(self._mask == 1)]
@@ -104,39 +107,118 @@ class TransImgL1A:
     ######################
     # 插值方法
     ######################
-    def init_trans_para(self, ori_sim_path, roi):
+    def init_trans_para(self, sim_ori_path, roi):
-        """裁剪L1a_img
+        """裁剪L1a_img --裁剪L1A影像
+        --- 修改 ori_sim  变换为 sim_ori
         Args:
             src_img_path (_type_): 原始L1A影像
             cuted_img_path (_type_): 待裁剪对象
             roi (_type_): 裁剪roi
         """
-        ori2geo_img = ImageHandle.ImageHandler.get_data(ori_sim_path)
+        ori2geo_img_height = ImageHandle.ImageHandler.get_img_height(sim_ori_path)
+        ori2geo_img_width = ImageHandle.ImageHandler.get_img_width(sim_ori_path)
+        ori2geo_img = ImageHandle.ImageHandler.get_data(sim_ori_path)
+        ori2geo_gt = ImageHandle.ImageHandler.get_geotransform(sim_ori_path)
         point_list = np.array(roi)
         min_lon = np.nanmin(point_list[:, 0])
         max_lon = np.nanmax(point_list[:, 0])
         min_lat = np.nanmin(point_list[:, 1])
         max_lat = np.nanmax(point_list[:, 1])
         self._min_lon, self._max_lon, self._min_lat, self._max_lat = min_lon, max_lon, min_lat, max_lat
+        # 根据 min_lon max_lon
+        # 根据 min_lat max_lat
 
-        r_c_list = np.where(
+        (x_min, y_min) = ImageHandle.ImageHandler.lat_lon_to_pixel(sim_ori_path, (min_lon, min_lat))
-            (ori2geo_img[0, :, :] >= min_lon) & (ori2geo_img[0, :, :] <= max_lon)
+        (x_max, y_max) = ImageHandle.ImageHandler.lat_lon_to_pixel(sim_ori_path, (max_lon, max_lat))
-            & (ori2geo_img[1, :, :] >= min_lat) & (ori2geo_img[1, :, :] <= max_lat))  #
 
-        if len(r_c_list) == 0 or r_c_list[0] == [] or r_c_list[1] == [] or np.array(r_c_list).size == 0:
+        xmin = x_min if x_min < x_max else x_max
+        xmax = x_min if x_min > x_max else x_max
+
+        ymin = y_min if y_min < y_max else y_max
+        ymax = y_min if y_min > y_max else y_max
+
+        xmin = int(math.floor(xmin))  # 列号
+        xmax = int(math.ceil(xmax))   # 因为python 的索引机制
+        # xmax = int(math.ceil(xmax)) + 1  # 因为python 的索引机制
+        ymin = int(math.floor(ymin))  # 行号
+        ymax = int(math.ceil(ymax))   # 因为pytohn的索引机制
+        # ymax = int(math.ceil(ymax)) + 1  # 因为pytohn的索引机制
+
+        # 处理最大最小范围
+        xmin = 0 if 0 > xmin else xmin
+        ymin = 0 if 0 > ymin else ymin
+        xmax = ori2geo_img_width if ori2geo_img_width > xmax else xmax
+        ymax = ori2geo_img_height if ori2geo_img_height > ymax else ymax
+
+        # 判断条件
+        xmax = xmax + 1 if xmax == xmin else xmax
+        ymax = ymax + 1 if ymax == ymin else ymax
+
+        if ymax <= ymin or xmax <= xmin or ymax > ori2geo_img_height or xmax > ori2geo_img_width or xmin < 0 or ymin < 0 or xmin > ori2geo_img_width or ymin > ori2geo_img_height or ymax < 0 or xmax < 0:
             msg = 'csv_roi:' + str(roi) + 'not in box,please revise csv data！'
             print(msg)
         else:
+            r_arr = ori2geo_img[0, ymin:ymax, xmin:xmax]
+            c_arr = ori2geo_img[1, ymin:ymax, xmin:xmax]
+
+            # 构建坐标矩阵
+            ori2geo_mask_r_count = ymax - ymin
+            ori2geo_mask_c_count = xmax - xmin
+
+            lon_lat_arr = np.ones((2, ori2geo_mask_r_count, ori2geo_mask_c_count))
+            col_arr = np.arange(xmin, xmax) * np.ones((ori2geo_mask_r_count, ori2geo_mask_c_count))
+            row_arr = ((np.arange(ymin, ymax)) * np.ones((ori2geo_mask_c_count, ori2geo_mask_r_count))).T
+
+            img_geotrans = ori2geo_gt
+            lon_arr = img_geotrans[0] + img_geotrans[1] * col_arr + img_geotrans[2] * row_arr
+            lat_arr = img_geotrans[3] + img_geotrans[4] * col_arr + img_geotrans[5] * row_arr
+            lon_lat_arr[0, :, :] = lon_arr
+            lon_lat_arr[1, :, :] = lat_arr
+
             # print("csv_roi:")
             # print(roi)
-            r_min = np.nanmin(r_c_list[0])
+            r_min = np.floor(np.nanmin(r_arr))  # 获取 L1A 的行列号范围
-            r_max = np.nanmax(r_c_list[0])
+            r_max = np.ceil(np.nanmax(r_arr)) + 1
-            c_min = np.nanmin(r_c_list[1])
+            c_min = np.floor(np.nanmin(c_arr))
-            c_max = np.nanmax(r_c_list[1])
+            c_max = np.ceil(np.nanmax(c_arr)) + 1
-            self.ori2geo_img = ori2geo_img[:, r_min:r_max + 1, c_min:c_max + 1]
+
-            # 开始调用组件 计算
+            # 判断是否越界
+            r_min = 0 if r_min < 0 else r_min
+            r_max = self.l1a_height if r_max > self.l1a_height else r_max
+            c_min = 0 if c_min < 0 else c_min
+            c_max = self.l1a_width if c_max > self.l1a_width else c_max
+
+            # 判断条件
+            r_max = r_max + 1 if r_min == r_max else r_max
+            c_max = c_max + 1 if c_min == c_max else c_max
+            if r_max <= r_min or c_max <= c_min or r_max > self.l1a_height or c_max > self.l1a_width or r_min < 0 or c_min < 0 or c_min > self.l1a_width or r_min > self.l1a_height or r_max < 0 or c_max < 0:
+                msg = 'csv_roi:' + str(roi) + 'not in box,please revise csv data！'
+            else:
+                pass
+                mask_geo = SAR_GEO.cut_L1A_img(lon_lat_arr, point_list)  # 在地理坐标系下裁剪对应影像
+
+                mask_geo = mask_geo.reshape(-1)
+                r_arr = r_arr.reshape(-1)
+                c_arr = c_arr.reshape(-1)
+
+                mask_geo_idx = np.where(mask_geo == 1)[0]
+                if mask_geo_idx.shape[0] == 0:
+                    msg = 'csv_roi:' + str(roi) + 'not in box,please revise csv data！'
+                    print(msg)
+                else:
+                    r_idx = r_arr[mask_geo_idx]
+                    c_idx = c_arr[mask_geo_idx]
+
+                    r_idx = r_idx - r_min  # offset row
+                    c_idx = c_idx - c_min  # offset col
+                    r_count = r_max - r_min  # 行数
+                    c_count = c_max - c_min  # 列数
+
+                    #
+                    mask_l1a = np.zeros((r_count, c_count)) * np.nan  # 创建目标大小的行列号
+                    mask = SAR_GEO.gereratorMask(r_idx.astype(np.float64), c_idx.astype(np.float64).astype(np.float64),
+                                                 mask_l1a)  # 这个函数修改了
 
-            mask = SAR_GEO.cut_L1A_img(self.ori2geo_img.astype(np.float64), point_list)
                     self._begin_r = r_min
                     self._end_r = r_max
                     self._begin_c = c_min
@@ -146,7 +228,7 @@ class TransImgL1A:
     def cut_L1A(self, in_path, out_path):
         img = ImageHandle.ImageHandler.get_data(in_path)
         if len(img.shape) == 3:
-            cut_img = img[:, self._begin_r:self._end_r + 1, self._begin_c:self._end_c + 1]
+            cut_img = img[:, self._begin_r:self._end_r, self._begin_c:self._end_c]
             cut_img[0, :, :] = cut_img[0, :, :] * self._mask
             cut_img[1, :, :] = cut_img[1, :, :] * self._mask
             ImageHandle.ImageHandler.write_img(out_path, '', [0, 0, 0, 0, 0, 0], cut_img)
@@ -303,8 +385,7 @@ class TransImgL1A:
             f_c = scipy.interpolate.interp2d(r_c_list[:, 2], r_c_list[:, 3], r_c_list[:, 1], kind='linear')
             tar_get_r = f_r(p[0], p[1])[0]
             tar_get_c = f_c(p[0], p[1])[0]
-            if tar_get_r < ori2geo_tif.shape[1] and tar_get_c < ori2geo_tif.shape[
+            if tar_get_r < ori2geo_tif.shape[1] and tar_get_c < ori2geo_tif.shape[2] and tar_get_r>=0 and tar_get_c>=0:
-                2] and tar_get_r >= 0 and tar_get_c >= 0:
                 lon_temp = ori2geo_tif[0, int(round(tar_get_r)), int(round(tar_get_c))]
                 lon_lat = ori2geo_tif[1, int(round(tar_get_r)), int(round(tar_get_c))]
                 # 增加条件筛选
@@ -313,9 +394,12 @@ class TransImgL1A:
                 result.append([-1, -1])
         return result
 
-    def tran_lonlats_to_L1A_rowcols(self, meas_data, ori_sim_path):
+    def tran_lonlats_to_L1A_rowcols(self, meas_data, ori_sim_path, row, col):
         lonlats = []
         data_roi = []
+        rowcols = []
+        measdata_list = []
+        data_sim = ImageHandle.ImageHandler.get_all_band_array(ori_sim_path)
         for data in meas_data:
             lon = float(data[1])
             lat = float(data[2])
@@ -323,12 +407,22 @@ class TransImgL1A:
                 lonlats.append([lon, lat])
                 data_roi.append(data)
 
-        rowcols = self.tran_lonlats_to_rowcols(lonlats, ori_sim_path)
+        for lonlat in lonlats:
-        measdata_list = []
+            (x, y) = ImageHandle.ImageHandler.lat_lon_to_pixel(ori_sim_path, lonlat)
+            rowcols.append([x, y])
+
         for data, rowcol in zip(data_roi, rowcols):
-            if (rowcol[0] != -1 and rowcol[1] != -1):
+            img_x = round(data_sim[round(rowcol[1]), round(rowcol[0]), 0])
-                measdata_list.append(
+            img_y = round(data_sim[round(rowcol[1]), round(rowcol[0]), 1])
-                    [round(rowcol[0]) - self._begin_r, round(rowcol[1]) - self._begin_c, float(data[3])])
+            if (img_x > 0 and img_x < row and img_y > 0 and img_y < col):
+                measdata_list.append([img_x, img_y, float(data[3])])
+
+        # rowcols = self.tran_lonlats_to_rowcols(lonlats, ori_sim_path)
+        # measdata_list = []
+        # for data, rowcol in zip(data_roi, rowcols):
+        #     if (rowcol[0] != -1 and rowcol[1] != -1):
+        #         measdata_list.append(
+        #             [round(rowcol[0]) - self._begin_r, round(rowcol[1]) - self._begin_c, float(data[3])])
         return measdata_list
 
     @staticmethod
@@ -536,8 +630,8 @@ if __name__ == '__main__':
     """
     # roi_Extend = [[102.12, 33.879], [102.327, 33.879], [102.327, 33.66], [102.12, 31.45]]
     ori_sim_data = ImageHandle.ImageHandler.get_data(ori_sim)
-    lon = ori_sim_data[0,:,:]
+    lon = ori_sim_data[0, :, :]
-    lat = ori_sim_data[1,:,:]
+    lat = ori_sim_data[1, :, :]
     min_lon = np.nanmin(lon)
     max_lon = np.nanmax(lon)
     min_lat = np.nanmin(lat)