更新通用工具集，修改分块计算边界以及合并分块模块
tian jiax
parent
e6468adc8a
commit
0d96100f69
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@ -220,6 +220,83 @@ class BlockProcess:
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count += 1
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return True
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def cut_new(self, in_dir, out_dir, file_type=['tif', 'tiff'], out_type='tif', out_size=2048):
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"""
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:param in_dir:存放待裁剪的影像文件夹，不用指定到tif文件
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:param out_dir:存放裁剪结果的影像文件夹
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:param file_type:待裁剪的影像文件类型tif、tiff、bmp、jpg、png等等
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:param out_type:裁剪结果影像文件类型
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:param out_size:裁剪尺寸，裁剪为n*n的方形
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:return: True or Flase
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20230831修改 ----tjx
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"""
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if not os.path.exists(out_dir):
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os.makedirs(out_dir)
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data_dir_list, _ = self.get_file_names(in_dir, file_type)
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count = 0
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for each_dir in data_dir_list:
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name_suffix = os.path.basename(each_dir)
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img_name = os.path.splitext(name_suffix)[0]
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# gdal读取方法
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image = self.__get_band_array(each_dir, 1)
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block_x = int(np.ceil(image.shape[1] / out_size))
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block_y = int(np.ceil(image.shape[0] / out_size)) # todo 修改分块
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for i in range(block_y):
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for j in range(block_x):
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start_x = j * out_size
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start_y = i * out_size
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end_x = image.shape[1] if (j + 1) * out_size > image.shape[1] else (j + 1) * out_size
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end_y = image.shape[0] if (i + 1) * out_size > image.shape[0] else (i + 1) * out_size
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out_dir_images = os.path.join(out_dir, img_name + '_' + str(start_x) + '_' + str(end_x) + '_' + str(start_y) + '_' + str(
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end_y) + '.' + out_type)
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# print(out_dir_images)
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# cut_factor_row = int(np.ceil(image.shape[0] / out_size))
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# cut_factor_clo = int(np.ceil(image.shape[1] / out_size))
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# for i in range(cut_factor_row):
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# for j in range(cut_factor_clo):
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#
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# if i == cut_factor_row - 1:
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# i = image.shape[0] / out_size - 1
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# else:
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# pass
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#
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# if j == cut_factor_clo - 1:
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# j = image.shape[1] / out_size - 1
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# else:
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# pass
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#
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# start_x = int(np.rint(i * out_size))
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# start_y = int(np.rint(j * out_size))
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# end_x = int(np.rint((i + 1) * out_size))
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# end_y = int(np.rint((j + 1) * out_size))
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# out_dir_images = os.path.join(out_dir, img_name + '_' + str(start_x) + '_' + str(end_x) + '_' + str(start_y) + '_' + str(
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# end_y) + '.' + out_type)
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# + '/' + img_name \
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# + '_' + str(start_x) + '_' + str(end_x) + '_' + str(start_y) + '_' + str(
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# end_y) + '.' + out_type
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# temp_image = image[start_x:end_x, start_y:end_y]
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# out_image = Image.fromarray(temp_data)
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# out_image = Image.fromarray(temp_image)
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# out_image.save(out_dir_images)
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data = ImageHandler.get_data(each_dir)
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if ImageHandler.get_bands(each_dir) > 1:
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# temp_data = data[:,start_x:end_x, start_y:end_y]
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temp_data = data[:,start_y:end_y, start_x:end_x]
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else:
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# temp_data = data[start_x:end_x, start_y:end_y]
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temp_data = data[start_y:end_y, start_x:end_x]
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ImageHandler.write_img(out_dir_images, '', [0, 0, 0, 0, 0, 0], temp_data)
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count += 1
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return True
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def combine(self, data_dir, w, h, out_dir, out_type='tif', file_type=['tif', 'tiff'], datetype='float16'):
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"""
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:param data_dir: 存放待裁剪的影像文件夹，不用指定到tif文件
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@ -261,6 +338,70 @@ class BlockProcess:
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count += 1
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return True
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# todo 20230901 修改分块同步修改合并代码
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def combine_new(self, data_dir, w, h, out_dir, out_type='tif', file_type=['tif', 'tiff'], datetype='float16'):
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"""
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:param data_dir: 存放待裁剪的影像文件夹，不用指定到tif文件
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:param w 拼接影像的宽度，
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:param h 拼接影像的高度
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:param out_dir: 存放裁剪结果的影像文件夹
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:param out_type: 裁剪结果影像文件类型
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:param file_type: 待裁剪的影像文件类型
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:param datetype:数据类型 int8，int16，float16，float32 等
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:return: True or Flase
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"""
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if not os.path.exists(out_dir):
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os.makedirs(out_dir)
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img_dir, img_name = self.get_file_names(data_dir, file_type)
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dir_dict = self.get_same_img(img_dir, img_name)
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count = 0
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for key in dir_dict.keys():
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dir_list = dir_dict[key]
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bands = ImageHandler.get_bands(dir_list[0])
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if bands > 1:
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temp_label = np.zeros(shape=(bands, h, w), dtype=datetype)
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for item in dir_list:
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name_split = item.split('_')
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x_start = int(name_split[-4])
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x_end = int(name_split[-3])
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y_start = int(name_split[-2])
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y_end = int(name_split[-1].split('.')[0])
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# img = Image.open(item)
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img = ImageHandler.get_band_array(item, 1)
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img = np.array(img)
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temp_label[:, y_start:y_end, x_start:x_end] = img
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img_name = key + '.' + out_type
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new_out_dir = os.path.join(out_dir, img_name)
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ImageHandler.write_img(new_out_dir, '', [0, 0, 0, 0, 0, 0], temp_label)
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# label = Image.fromarray(temp_label)
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# label.save(new_out_dir)
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count += 1
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else:
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temp_label = np.zeros(shape=(h, w), dtype=datetype)
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for item in dir_list:
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name_split = item.split('_')
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x_start = int(name_split[-4])
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x_end = int(name_split[-3])
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y_start = int(name_split[-2])
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y_end = int(name_split[-1].split('.')[0])
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# img = Image.open(item)
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img = ImageHandler.get_band_array(item, 1)
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img = np.array(img)
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temp_label[y_start:y_end, x_start:x_end] = img
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img_name = key + '.' + out_type
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new_out_dir = os.path.join(out_dir, img_name)
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ImageHandler.write_img(new_out_dir, '', [0, 0, 0, 0, 0, 0], temp_label)
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# label = Image.fromarray(temp_label)
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# label.save(new_out_dir)
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count += 1
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return True
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def combine_Tif(self, data_dir, w, h, out_dir, proj, geo, out_type='tif', file_type=['tif', 'tiff'],
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datetype='float16'):
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"""
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@ -298,30 +439,30 @@ class BlockProcess:
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img_name = key + '.' + out_type
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new_out_dir = os.path.join(out_dir,img_name)
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image_handler.write_img(new_out_dir, proj, geo, temp_label)
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count += 1
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return True
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# if __name__ == '__main__':
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# bp = BlockProcess()
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# # cut
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# data_dir = r"D:\DATA\testdata1"
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# out_dir = r"D:\DATA\testdata1\cut255"
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# file_type = ['tif']
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# out_type = 'tif'
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# cut_size = 2048
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#
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# # bp.cut(data_dir, out_dir, file_type, out_type, cut_size)
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# # combine
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# data_dir=r"D:\Workspace\SoilMoisture\Temporary\test"
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# w= 4626
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# h= 2313
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# out_dir=r"D:\Workspace\SoilMoisture\Temporary\combine"
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# out_type='tif'
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# file_type=['tif']
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# src_path = r"D:\Workspace\SoilMoisture\Temporary\preprocessed\HH_preprocessed.tif"
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# datetype = bp.get_tif_dtype(src_path)
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# bp.combine(data_dir, w, h, out_dir, out_type, file_type, datetype)
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# # # cut
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# data_dir = r"D:\BaiduNetdiskDownload\HF\cut"
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# out_dir = r"D:\BaiduNetdiskDownload\HF\cut_out"
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# file_type = ['tif']
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# out_type = 'tif'
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# cut_size = 512
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#
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# bp.cut_new(data_dir, out_dir, file_type, out_type, cut_size)
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# # # combine
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# # data_dir=r"D:\Workspace\SoilMoisture\Temporary\test"
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# w= 5043
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# h= 1239
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# out_dirs=r"D:\BaiduNetdiskDownload\HF\cut_outs"
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# # out_type='tif'
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# # file_type=['tif']
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# datetype = 'float'
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# # src_path = r"D:\Workspace\SoilMoisture\Temporary\preprocessed\HH_preprocessed.tif"
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# # datetype = bp.get_tif_dtype(src_path)
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# bp.combine_new(out_dir, w, h, out_dirs, out_type, file_type, datetype)
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#
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# # 添加地理信息
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@ -423,12 +423,14 @@ class ImageHandler:
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outband = dataset.GetRasterBand(1)
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outband.WriteArray(im_data)
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if no_data != 'null':
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outband.SetNoDataValue(no_data)
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outband.SetNoDataValue(np.double(no_data))
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outband.FlushCache()
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else:
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for i in range(im_bands):
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outband = dataset.GetRasterBand(1 + i)
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outband.WriteArray(im_data[i])
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if no_data != 'null':
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outband.SetNoDataValue(np.double(no_data))
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outband.FlushCache()
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# outRaster.GetRasterBand(i + 1).WriteArray(array[i])
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del dataset
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@ -624,11 +626,13 @@ class ImageHandler:
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temp = np.zeros((2, lon_arr.shape[0], lon_arr.shape[1]), dtype=float)
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temp[0, :, :] = lon_arr
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temp[1, :, :] = lat_arr
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self.write_img(ori_sim, '', [0.0, 1.0, 0.0, 0.0, 0.0, 1.0], temp)
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self.write_img(ori_sim, '', [0.0, 1.0, 0.0, 0.0, 0.0, 1.0], temp, '0')
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# if __name__ == '__main__':
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# path = r"I:\MicroWorkspace\product\C-SAR\Ortho\Output\GF3B_MYC_QPSI_003581_E120.6_N31.3_20220729_L1A_AHV_L10000073024_RPC\RPC_ori_sim.tif"
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# s = ImageHandler().get_scope_ori_sim(path)
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# path = r'D:\BaiduNetdiskDownload\GZ\lon.rdr'
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# path2 = r'D:\BaiduNetdiskDownload\GZ\lat.rdr'
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# path3 = r'D:\BaiduNetdiskDownload\GZ\lon_lat.tif'
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# s = ImageHandler().band_merge(path, path2, path3)
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# print(s)
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# pass
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