ImageData.py
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# coding=utf-8
#author: 4N
#createtime: 2021/10/15
#email: nheweijun@sina.com
from app.util import *
from osgeo import gdal
from osgeo.gdal import *
from numpy import ndarray
import numpy
from app.modules.service.image.util.ThriftConnect import ThriftConnect,ThriftPool
import json
import gzip
class ImageData:
def __init__(self,image_server,image):
self.image_server = image_server
self.image = image
def get_data(self, extent, bands, height, width):
if self.image_server.__eq__("本地服务器"):
data = self.get_local_wms_data(extent, bands, height, width)
elif self.image_server.__eq__("None"):
data = numpy.zeros((height, width, 3), dtype=int) + 65536
else:
data = self.get_remote_wms_data(extent, bands, height, width)
return data
def get_remote_wms_data(self, extent, bands, height, width):
'''
通过RPC获取远程数据
:param image:
:param extent:
:param bands:
:return:
'''
# 需要做thrift连接的缓存，连接池
thrift_connect = ThriftConnect(self.image_server)
image_extent = self.image.extent
data = thrift_connect.client.getData(self.image.path, extent, json.loads(image_extent), bands, width, height)
thrift_connect.close()
data = gzip.decompress(data)
data = numpy.frombuffer(data, dtype='int64')
data = data.reshape((height, width, 3))
return data
def get_remote_wms_data_cpp(self, image_server, image, extent, bands, height, width):
'''
通过RPC获取远程数据
:param image:
:param extent:
:param bands:
:return:
'''
# 需要做thrift连接的缓存，连接池
thrift_connect = ThriftConnect(image_server)
image_extent = image.extent
data = thrift_connect.client.getData(image.path, extent, json.loads(image_extent), bands, width, height)
thrift_connect.close()
return data
def get_local_wms_data(self, extent, bands, height, width):
'''
获取本地数据
:param image:
:param extent:
:param bands:
:return:
'''
img: Dataset = gdal.Open(self.image.path, 0)
origin_extent = json.loads(self.image.extent)
# 超出空间范围
if extent[2] < origin_extent[0] or extent[0] > origin_extent[2] or extent[1] > origin_extent[3] or extent[
3] < origin_extent[1]:
empty = numpy.zeros((height, width, 3), dtype=int) + 65536
# 空间范围相交
else:
ox = img.RasterXSize
oy = img.RasterYSize
# 网格大小
grid_x = (origin_extent[2] - origin_extent[0]) / (ox * 1.0)
grid_y = (origin_extent[3] - origin_extent[1]) / (oy * 1.0)
# 完全在影像范围内
if extent[0] > origin_extent[0] and extent[1] > origin_extent[1] and extent[2] < \
origin_extent[2] and extent[3] < origin_extent[3]:
# 网格偏移量
off_x = math.floor((extent[0] - origin_extent[0]) / grid_x)
off_y = math.floor((origin_extent[3] - extent[3]) / grid_y)
# 截取后网格个数
x_g = math.ceil((extent[2] - extent[0]) / grid_x)
y_g = math.ceil((extent[3] - extent[1]) / grid_y)
empty = img.ReadRaster(off_x, off_y, x_g, y_g, 256, 256, band_list = bands)
img.ReadAsArray()
# 部分相交
else:
inter_extent = [0, 0, 0, 0]
inter_extent[0] = origin_extent[0] if origin_extent[0] > extent[0] else extent[0]
inter_extent[1] = origin_extent[1] if origin_extent[1] > extent[1] else extent[1]
inter_extent[2] = origin_extent[2] if origin_extent[2] < extent[2] else extent[2]
inter_extent[3] = origin_extent[3] if origin_extent[3] < extent[3] else extent[3]
# 网格偏移量
off_x = math.floor((inter_extent[0] - origin_extent[0]) / grid_x)
off_y = math.floor((origin_extent[3] - inter_extent[3]) / grid_y)
# 截取后网格个数
x_g = math.floor((inter_extent[2] - inter_extent[0]) / grid_x)
y_g = math.floor((inter_extent[3] - inter_extent[1]) / grid_y)
# 相对于出图的偏移量
# 出图的网格大小
out_grid_x = (extent[2] - extent[0]) / (width * 1.0)
out_grid_y = (extent[3] - extent[1]) / (height * 1.0)
out_off_x = int(math.ceil((inter_extent[0] - extent[0]) / out_grid_x))
out_off_y = int(math.ceil((extent[3] - inter_extent[3]) / out_grid_y))
out_x_g = int(math.floor((inter_extent[2] - inter_extent[0]) / out_grid_x))
out_y_g = int(math.floor((inter_extent[3] - inter_extent[1]) / out_grid_y))
# 相交部分在出图的哪个位置
overview_raster: ndarray = img.ReadAsArray(off_x, off_y, x_g, y_g, out_x_g,
out_y_g)
dat = numpy.zeros((height, width, 3), dtype=int) + 65536
dat[out_off_y:out_off_y + out_y_g, out_off_x:out_off_x + out_x_g] = overview_raster
empty = dat
return empty