image_tile.py
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# coding=utf-8
#author: 4N
#createtime: 2021/3/24
#email: nheweijun@sina.com
from app.util import *
import traceback
from osgeo import gdal
from osgeo.gdal import *
from numpy import ndarray
import numpy
from flask import Response
import io
import os
from PIL import Image
from app.util.slice_scheme import slice_scheme
import time
import cv2
from .image_tile_center import create_by_opencv
def api(level,row,col):
result = {}
parameter: dict = get_parameter()
try:
bands = [1, 2, 3]
image_list=[
{"origin_extent":[111.604613312, 29.0171545762, 111.653989358, 29.0531633509],
"path" : os.path.join(os.path.dirname(__file__), "data", "江南_03.tif"),
"xysize":[47646,28201],
"max_level":7},
{"origin_extent": [111.705644552, 28.9864085959, 111.737115887, 29.079435995],
"path": os.path.join(os.path.dirname(__file__), "data", "江南_01.tif"),
"xysize":[30116, 73219],
"max_level":8},
{"origin_extent": [111.639350712, 28.9170588759, 111.751508603, 29.032941696],
"path": os.path.join(os.path.dirname(__file__), "data", "江南_02.tif"),
"xysize": [108488, 90777],
"max_level": 9}
]
slice_para = {'rows': 256.0, 'cols': 256.0, 'x': -180.0, 'y': 90.0, 'dpi': 96.0,
'0': {'scale': 590995186.11750006, 'resolution': 1.4062500000000004},
'1': {'scale': 295497593.05875003, 'resolution': 0.7031250000000002},
'2': {'scale': 147748796.52937502, 'resolution': 0.3515625000000001},
'3': {'scale': 73874398.26468751, 'resolution': 0.17578125000000006},
'4': {'scale': 36937199.132343754, 'resolution': 0.08789062500000003},
'5': {'scale': 18468599.566171877, 'resolution': 0.043945312500000014},
'6': {'scale': 9234299.783085939, 'resolution': 0.021972656250000007},
'7': {'scale': 4617149.891542969, 'resolution': 0.010986328125000003},
'8': {'scale': 2308574.9457714846, 'resolution': 0.005493164062500002},
'9': {'scale': 1154287.4728857423, 'resolution': 0.002746582031250001},
'10': {'scale': 577143.7364428712, 'resolution': 0.0013732910156250004},
'11': {'scale': 288571.8682214356, 'resolution': 0.0006866455078125002},
'12': {'scale': 144285.9341107178, 'resolution': 0.0003433227539062501},
'13': {'scale': 72142.9670553589, 'resolution': 0.00017166137695312505},
'14': {'scale': 36071.48352767945, 'resolution': 8.583068847656253e-05},
'15': {'scale': 18035.741763839724, 'resolution': 4.2915344238281264e-05},
'16': {'scale': 9017.870881919862, 'resolution': 2.1457672119140632e-05},
'17': {'scale': 4508.935440959931, 'resolution': 1.0728836059570316e-05},
'18': {'scale': 2254.4677204799655, 'resolution': 5.364418029785158e-06},
'19': {'scale': 1127.2338602399827, 'resolution': 2.682209014892579e-06},
'20': {'scale': 563.6169301199914, 'resolution': 1.3411045074462895e-06}}
if parameter.get("leaflet"):
slice_para = {'rows': 256.0, 'cols': 256.0, 'x': -180.0, 'y': 90.0, 'dpi': 96.0,
'0': {'scale': 295497593.05875003, 'resolution': 0.7031250000000002},
'1': {'scale': 147748796.52937502, 'resolution': 0.3515625000000001},
'2': {'scale': 73874398.26468751, 'resolution': 0.17578125000000006},
'3': {'scale': 36937199.132343754, 'resolution': 0.08789062500000003},
'4': {'scale': 18468599.566171877, 'resolution': 0.043945312500000014},
'5': {'scale': 9234299.783085939, 'resolution': 0.021972656250000007},
'6': {'scale': 4617149.891542969, 'resolution': 0.010986328125000003},
'7': {'scale': 2308574.9457714846, 'resolution': 0.005493164062500002},
'8': {'scale': 1154287.4728857423, 'resolution': 0.002746582031250001},
'9': {'scale': 577143.7364428712, 'resolution': 0.0013732910156250004},
'10': {'scale': 288571.8682214356, 'resolution': 0.0006866455078125002},
'11': {'scale': 144285.9341107178, 'resolution': 0.0003433227539062501},
'12': {'scale': 72142.9670553589, 'resolution': 0.00017166137695312505},
'13': {'scale': 36071.48352767945, 'resolution': 8.583068847656253e-05},
'14': {'scale': 18035.741763839724, 'resolution': 4.2915344238281264e-05},
'15': {'scale': 9017.870881919862, 'resolution': 2.1457672119140632e-05},
'16': {'scale': 4508.935440959931, 'resolution': 1.0728836059570316e-05},
'17': {'scale': 2254.4677204799655, 'resolution': 5.364418029785158e-06},
'18': {'scale': 1127.2338602399827, 'resolution': 2.682209014892579e-06},
'19': {'scale': 563.6169301199914, 'resolution': 1.3411045074462895e-06}}
# 转换参数
new_para ={}
for key in parameter.keys():
new_para[key.lower()] = parameter[key]
parameter=new_para
if parameter.get("tilematrix"):
if parameter.get("tilematrix").__contains__(":"):
level = int(parameter.get("tilematrix").split(":")[-1])
else:
level = int(parameter.get("tilematrix"))
if parameter.get("tilerow"):
row = int(parameter.get("tilerow"))
if parameter.get("tilecol"):
col = int(parameter.get("tilecol"))
image_type = parameter.get("format") if parameter.get("format") else "image/png"
quality = int(parameter.get("quality")) if parameter.get("quality") else 30
extent = slice_scheme.get_polygon(slice_para, level, row, col)
pixel_array = numpy.zeros((256, 256,3), dtype=int)
ceng = 2
for band in bands:
empty = numpy.zeros((256, 256), dtype=int)+65536
for im in image_list:
# 自决定金字塔等级
xysize = im.get("xysize")
origin_extent = im.get("origin_extent")
max_level = im.get("max_level")
# 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]:
pass
# 空间范围相交
else:
image_level = determine_level(xysize, origin_extent, extent, max_level)
path = im.get("path")
# print(image_level)
image: Dataset = gdal.Open(path, 0)
band_data: Band = image.GetRasterBand(band)
if image_level == -1:
overview = band_data
else:
try:
overview: Band = band_data.GetOverview(image_level)
except:
raise Exception("该影像不存在该级别的金字塔数据！")
ox = overview.XSize
oy = overview.YSize
# 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]:
t1 = time.time()
# 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)
t2 = time.time()
# print(t2-t1)
overview_raster:ndarray = overview.ReadAsArray(off_x,off_y,x_g,y_g,256,256)
t3 = time.time()
# print(t3-t2)
mask1 = numpy.zeros((256, 256), dtype=int)
mask2 = numpy.zeros((256, 256), dtype=int)
mask1[overview_raster == 65536] = 1
mask2[overview_raster != 65536] = 1
empty = empty*mask1+overview_raster*mask2
t4 = time.time()
# print(t4-t3)
# 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]) / (256 * 1.0)
out_grid_y = (extent[3] - extent[1]) / (256 * 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 = overview.ReadAsArray(off_x,off_y,x_g,y_g,out_x_g,out_y_g)
mask1 = numpy.zeros((out_y_g,out_x_g), dtype=int)
mask2 = numpy.zeros((out_y_g,out_x_g), dtype=int)
mask1[overview_raster == 65536] = 1
mask2[overview_raster != 65536] = 1
empty[out_off_y:out_off_y + out_y_g, out_off_x:out_off_x + out_x_g] = empty[out_off_y:out_off_y + out_y_g,out_off_x:out_off_x + out_x_g]*mask1 + overview_raster*mask2
# 关闭句柄
del image
# opencv 的颜色排列为GBR
pixel_array[:,:,ceng]=empty
ceng-=1
t5 = time.time()
# print(t4-t3)
#将图片生成在内存中,然后直接返回response
im_data = create_by_opencv(image_type, pixel_array, quality)
return Response(im_data, mimetype=image_type.lower())
except Exception as e:
print(traceback.format_exc())
result["state"] = -1
result["message"] = e.__str__()
return result
def determine_level(xysize,origin_extent,extent,max_level):
x = xysize[0]
y = xysize[1]
level = -1
pixel = x*y * (((extent[2]-extent[0])*(extent[3]-extent[1]))/((origin_extent[2]-origin_extent[0])*(origin_extent[3]-origin_extent[1])))
while pixel>100000 and level<max_level-1:
level+=1
x=x/2
y=y/2
pixel = x * y * (((extent[2] - extent[0]) * (extent[3] - extent[1])) / (
(origin_extent[2] - origin_extent[0]) * (origin_extent[3] - origin_extent[1])))
return level
def create_by_pil(image_type,pixel_list,quality):
buffer = io.BytesIO()
if image_type.__eq__("image/jpeg") or image_type.__eq__("image/jpg"):
im_type = "jpeg"
data = list(zip(pixel_list[0][0], pixel_list[1][0], pixel_list[2][0]))
image_out = Image.new("RGB", (256, 256))
else:
im_type = "png"
four = [0 if x.__eq__(65536) else 255 for x in pixel_list[0][0]]
data = list(zip(pixel_list[0][0], pixel_list[1][0], pixel_list[2][0], four))
image_out = Image.new("RGBA", (256, 256))
t6 = time.time()
image_out.putdata(data)
image_out.save(buffer, im_type, quality=quality, optimize=True)
im_data = buffer.getvalue()
buffer.close()
t7 = time.time()
return im_data
api_doc={
"tags":["影像接口"],
"parameters":[
],
"responses":{
200:{
"schema":{
"properties":{
}
}
}
}
}