def rectifyRaster(src, dst_filename, dst_proj, gcps, tmp_filename='tmpfile_remove.tif', resampling_type='near',\
data_type='Float32', output_format='GTiff', dst_options=['COMPRESS=LZW']):
"""
Takes GCPs created using gdal api, adds them to the not rectified image, and rectifies it.
* resampling_type keywords are: 'near','bilinear', 'cubic', 'cubicspline', 'lanczos'
* data_type keywords are: 'Byte', 'Float32', 'Float64', 'UInt16' , 'UInt32', 'Int16', 'Int32'
"""
# legacy: dictionary of possible settings for resampling_type
# resTypes = { 'near':'GRA_NearestNeighbour', 'bilinear':'GRA_Bilinear', 'cubic':'GRA_Cubic', \
# 'cubicspline':'GRA_CubicSpline','lanczos':'GRA_Lanczos' }
driver = gdal.GetDriverByName(output_format)
# dst = driver.Create(dst_filename, src.RasterXSize, src.RasterYSize, src.RasterCount, \
# gdal.GetDataTypeByName(data_type), dst_options)
dst = driver.CreateCopy(tmp_filename, src, 1, dst_options)
dst.SetGCPs(gcps, src.GetProjection())
dst.SetGeoTransform(gdal.GCPsToGeoTransform(gcps))
dst.SetProjection(dst_proj)
del dst, src # flush
# @FIX find a way to transform the data with Python bindings
#'-srcnodata','0', '-dstnodata','None',
subprocess.call([
'gdalwarp', '-r', resampling_type, '-ot', data_type, '-co',
'COMPRESS=LZW', '-q', tmp_filename, dst_filename
])
os.unlink(tmp_filename) # remove the tmp file created during execution
def convert(self):
options=self.map.options
if options.after_name:
name_patt=self.name
elif options.after_map:
name_patt=self.map.file
else:
name_patt=self.img_file
base = dst_path(name_patt, options.dst_dir)
if options.long_name:
base += ' - ' + "".join([c for c in self.name
if c .isalpha() or c.isdigit() or c in '-_.() '])
dst_dir=os.path.split(base)[0]
out_format='VRT'
ext='.'+out_format.lower()
try:
start_dir=os.getcwd()
if dst_dir:
os.chdir(dst_dir)
dst_file = os.path.abspath(os.path.basename(base+ext)) # output file
dst_drv = gdal.GetDriverByName(out_format)
dst_ds = dst_drv.CreateCopy(
dst_file.encode(locale.getpreferredencoding()),
self.raster_ds,
0
)
dst_ds.SetProjection(self.srs)
#double x = 0.0, double y = 0.0, double z = 0.0, double pixel = 0.0,
#double line = 0.0, char info = "", char id = ""
gcps=[gdal.GCP(c[0],c[1],0,p[0],p[1],'',i) for i,p,c in self.refs]
dst_ds.SetGCPs(gcps,self.refs.srs())
dst_geotr=gdal.GCPsToGeoTransform(gcps) # if len(gcps) < 5 else (0.0, 1.0, 0.0, 0.0, 0.0, 1.0)
dst_ds.SetGeoTransform(dst_geotr)
poly,gmt_data=self.cut_poly(dst_ds)
if poly:
dst_ds.SetMetadataItem('CUTLINE',poly)
if self.name:
dst_ds.SetMetadataItem('DESCRIPTION',self.name.encode('utf-8'))
dst_ds = None # close dataset
# re_sub_file(dst_file, [
# ('^.*<GeoTransform>.*\n',''),
# ('^.*<SRS>.*\n','')
# ])
finally:
self.raster_ds = None
os.chdir(start_dir)
if options.get_cutline: # print cutline then return
print poly
return
if gmt_data and options.cut_file: # create shapefile with a cut polygon
with open(base+'.gmt','w+') as f:
f.write(gmt_data)
return dst_file
def SetGCPsToGeoTransform(cornerPointUL, cornerPointUR, cornerPointLR,
cornerPointLL, frameCenterLon, frameCenterLat):
''' Make Geotranform from pixel to lon lat coordinates '''
gcps = []
global gcornerPointUL
gcornerPointUL = cornerPointUL
global gcornerPointUR
gcornerPointUR = cornerPointUR
global gcornerPointLR
gcornerPointLR = cornerPointLR
global gcornerPointLL
gcornerPointLL = cornerPointLL
global gframeCenterLat
gframeCenterLat = frameCenterLat
global gframeCenterLon
gframeCenterLon = frameCenterLon
global geotransform
geotransform = gdal.GCPsToGeoTransform(gcps)
src = np.float64(
np.array([[0.0, 0.0], [xSize, 0.0], [xSize, ySize], [0.0, ySize]]))
dst = np.float64(
np.array([cornerPointUL, cornerPointUR, cornerPointLR, cornerPointLL]))
geotransform = from_points(src, dst)
if geotransform is None:
qgsu.showUserAndLogMessage(QCoreApplication.translate(
"QgsFmvUtils", 'Unable to extract a geotransform.'),
onlyLog=True)
return
def test_gcps2gt_1():
gt = gdal.GCPsToGeoTransform(
_list2gcps([(0.0, 0.0, 400000, 370000), (100.0, 0.0, 410000, 370000),
(100.0, 200.0, 410000, 368000)]))
assert gdaltest.geotransform_equals(
gt, (400000.0, 100.0, 0.0, 370000.0, 0.0, -10.0), 0.000001)
def print_layer_list(names, sds):
print "----- Accessible layers: -----"
out_names = [s.rsplit(':', 1)[1] for s in names]
for ind, name in enumerate(out_names):
geotrans = gdal.GCPsToGeoTransform(sds[ind].GetGCPs())
if geotrans:
print "%s. %s" % (ind, name)
def main(argv):
if len(argv) < 2:
print("Usage: gcps2wld.py source_file")
return 1
filename = argv[1]
dataset = gdal.Open(filename)
if dataset is None:
print('Unable to open %s' % filename)
return 1
gcps = dataset.GetGCPs()
if gcps is None or not gcps:
print('No GCPs found on file ' + filename)
return 1
geotransform = gdal.GCPsToGeoTransform(gcps)
if geotransform is None:
print('Unable to extract a geotransform.')
return 1
print(geotransform[1])
print(geotransform[4])
print(geotransform[2])
print(geotransform[5])
print(geotransform[0] + 0.5 * geotransform[1] + 0.5 * geotransform[2])
print(geotransform[3] + 0.5 * geotransform[4] + 0.5 * geotransform[5])
def test_gcps2gt_3():
approx_ok = 0
gt = gdal.GCPsToGeoTransform(
_list2gcps([(0.0, 0.0, 400000, 370000), (100.0, 0.0, 410000, 370000),
(100.0, 200.0, 410000, 368000),
(0.0, 200.0, 400000, 360000)]), approx_ok)
assert gt is None, 'Expected failure when no good solution.'
def test_gcps2gt_2():
gt = gdal.GCPsToGeoTransform(
_list2gcps([(0.0, 0.0, 400000, 370000), (100.0, 0.0, 410000, 370000),
(100.0, 200.0, 410000, 368000),
(0.0, 200.0, 400000, 368000.01)]))
assert gdaltest.geotransform_equals(
gt, (400000.0, 100.0, 0.0, 370000.0025, -5e-05, -9.999975), 0.000001)
def SetGCPsToGeoTransform(cornerPointUL, cornerPointUR, cornerPointLR,
cornerPointLL, frameCenterLon, frameCenterLat, ele):
''' Make Geotranform from pixel to lon lat coordinates '''
gcps = []
global gcornerPointUL, gcornerPointUR, gcornerPointLR, gcornerPointLL, gframeCenterLat, gframeCenterLon, geotransform_affine, geotransform
# TEMP FIX : If have elevation the geotransform is wrong
if ele:
del cornerPointUL[-1]
del cornerPointUR[-1]
del cornerPointLR[-1]
del cornerPointLL[-1]
gcornerPointUL = cornerPointUL
gcornerPointUR = cornerPointUR
gcornerPointLR = cornerPointLR
gcornerPointLL = cornerPointLL
gframeCenterLat = frameCenterLat
gframeCenterLon = frameCenterLon
Height = GetFrameCenter()[2]
gcp = gdal.GCP(cornerPointUL[1], cornerPointUL[0], Height, 0, 0,
"Corner Upper Left", "1")
gcps.append(gcp)
gcp = gdal.GCP(cornerPointUR[1], cornerPointUR[0], Height, xSize, 0,
"Corner Upper Right", "2")
gcps.append(gcp)
gcp = gdal.GCP(cornerPointLR[1], cornerPointLR[0], Height, xSize, ySize,
"Corner Lower Right", "3")
gcps.append(gcp)
gcp = gdal.GCP(cornerPointLL[1], cornerPointLL[0], Height, 0, ySize,
"Corner Lower Left", "4")
gcps.append(gcp)
gcp = gdal.GCP(frameCenterLon, frameCenterLat, Height, xSize / 2,
ySize / 2, "Center", "5")
gcps.append(gcp)
geotransform_affine = gdal.GCPsToGeoTransform(gcps)
src = np.float64(
np.array([[0.0, 0.0], [xSize, 0.0], [xSize, ySize], [0.0, ySize]]))
dst = np.float64(
np.array([cornerPointUL, cornerPointUR, cornerPointLR, cornerPointLL]))
try:
geotransform = from_points(src, dst)
except Exception:
pass
if geotransform is None:
qgsu.showUserAndLogMessage("",
"Unable to extract a geotransform.",
onlyLog=True)
return
def __init__(self, dataset):
super(CoordinateMapper, self).__init__()
projection = ''
self._geotransform = None
if dataset.GetGCPCount():
try:
projection = dataset.GetGCPProjection()
gcps = dataset.GetGCPs()
gcps = _fixedGCPs(gcps) # @TODO: remove
self._geotransform = gdal.GCPsToGeoTransform(gcps)
except Exception:
_log.warning(
'unable to retrieve geo-transform and '
'projection from GCPs for %s', dataset)
if not self._geotransform:
projection = dataset.GetProjection()
if not projection:
projection = dataset.GetProjectionRef()
self._geotransform = dataset.GetGeoTransform()
if not projection:
raise InvalidProjection('unable to get a valid projection')
#sref = osr.SpatialReference(projection) # do not work for Pymod API
sref = osr.SpatialReference()
sref.ImportFromWkt(projection)
if not sref.IsGeographic():
sref_target = osr.SpatialReference()
sref_target.SetWellKnownGeogCS(self.geogCS)
self._srTransform = osr.CoordinateTransformation(sref, sref_target)
else:
self._srTransform = None
# Xgeo = GT(0) + Xpixel*GT(1) + Yline*GT(2)
# Ygeo = GT(3) + Xpixel*GT(4) + Yline*GT(5)
#
# -- -- -- -- -- -- -- --
# | Xgeo | | m11 m12 | | Xpixel | | xoffset |
# | | = | | * | | + | |
# | Ygeo | | m21 m22 | | Yline | | yoffset |
# -- -- -- -- -- -- -- --
xoffset, m11, m12, yoffset, m21, m22 = self._geotransform
_log.debug('geotransform = %s', self._geotransform)
# Direct transform
M = np.array(((m11, m12), (m21, m22)))
C = np.array(([xoffset], [yoffset]))
self._direct_transform = (M, C)
# Invrse transform
M = np.linalg.inv(M)
C = -np.dot(M, C)
self._inverse_transform = (M, C)
def process(self, annotation_files, grid_pts=100, writeback=False):
"""
entry point into class functionality
"""
# read annotation xml into dictionary
for f in annotation_files:
doc = self.readAnnotationFile(f)
if doc is not None:
# get scene image dimensions
print('Densifying geolocation grid in annotation file: {}'.
format(f))
dims = self.getDimensions(doc)
# extract tie points from annotation schema and reproject to mercator
gcps = {'latlon': self.getTiePoints(doc)}
gcps['mercator'] = self.reprojectTiePoints(
gcps['latlon'], {
'source': self._proj['latlon'],
'target': self._proj['mercator']
})
# create denser tie point grid
x_grid, y_grid = self.getDenseGrid(dims, grid_pts)
dense_grid = {
'pixel': x_grid.flatten(),
'line': y_grid.flatten()
}
# interpolate parameter values onto dense grid
dense_grid.update(
self.interpolateFields(
doc, (gcps['latlon']['pixel'], gcps['latlon']['line']),
(x_grid, y_grid)))
# interpolate gcps onto dense grid
geo_transform = gdal.GCPsToGeoTransform(gcps['mercator'])
print('{}: Mean sum of squares: {} m '.format(
os.path.basename(f),
self.computeError(gcps['mercator'], geo_transform)))
dense_grid['gcps'] = self.interpolateTiePoints(
geo_transform, dense_grid['pixel'], dense_grid['line'])
# optionally visualize dense grid map coordinates
if writeback:
self.plotDenseGrid(dense_grid, grid_pts)
# write denser tie point grid to updated annotation file and geotiff
self.writeAnnotationFile(doc, dense_grid)
# self.writeImageFile( doc, dense_grid )
print('... OK!')
return
def SetGCPsToGeoTransform(cornerPointUL, cornerPointUR, cornerPointLR,
cornerPointLL, frameCenterLon, frameCenterLat, ele):
''' Make Geotranform from pixel to lon lat coordinates '''
gcps = []
gv.setCornerUL(cornerPointUL)
gv.setCornerUR(cornerPointUR)
gv.setCornerLR(cornerPointLR)
gv.setCornerLL(cornerPointLL)
gv.setFrameCenter(frameCenterLat, frameCenterLon)
xSize = gv.getXSize()
ySize = gv.getYSize()
Height = GetFrameCenter()[2]
gcp = gdal.GCP(cornerPointUL[1], cornerPointUL[0], Height, 0, 0,
"Corner Upper Left", "1")
gcps.append(gcp)
gcp = gdal.GCP(cornerPointUR[1], cornerPointUR[0], Height, xSize, 0,
"Corner Upper Right", "2")
gcps.append(gcp)
gcp = gdal.GCP(cornerPointLR[1], cornerPointLR[0], Height, xSize, ySize,
"Corner Lower Right", "3")
gcps.append(gcp)
gcp = gdal.GCP(cornerPointLL[1], cornerPointLL[0], Height, 0, ySize,
"Corner Lower Left", "4")
gcps.append(gcp)
gcp = gdal.GCP(frameCenterLon, frameCenterLat, Height, xSize / 2,
ySize / 2, "Center", "5")
gcps.append(gcp)
at = gdal.GCPsToGeoTransform(gcps)
gv.setAffineTransform(at)
src = np.float64(
np.array([[0.0, 0.0], [xSize, 0.0], [xSize, ySize], [0.0, ySize],
[xSize / 2.0, ySize / 2.0]]))
dst = np.float64(
np.array([[cornerPointUL[0], cornerPointUL[1]],
[cornerPointUR[0], cornerPointUR[1]],
[cornerPointLR[0], cornerPointLR[1]],
[cornerPointLL[0], cornerPointLL[1]],
[frameCenterLat, frameCenterLon]]))
try:
geotransform, _ = findHomography(src, dst)
gv.setTransform(geotransform)
except Exception:
pass
if geotransform is None:
qgsu.showUserAndLogMessage("",
"Unable to extract a geotransform.",
onlyLog=True)
return
def gcps2gt_4():
gt = gdal.GCPsToGeoTransform(_list2gcps([
(0.0, 0.0, 400000, 370000),
]))
if gt is not None:
gdaltest.post_reason('Expected failure for single GCP.')
return 'failure'
return 'success'
def gcps2gt_1():
gt = gdal.GCPsToGeoTransform(
_list2gcps([(0.0, 0.0, 400000, 370000), (100.0, 0.0, 410000, 370000),
(100.0, 200.0, 410000, 368000)]))
if not gdaltest.geotransform_equals(
gt, (400000.0, 100.0, 0.0, 370000.0, 0.0, -10.0), 0.000001):
return 'failure'
return 'success'
def do_rgs(data_time_value, rgs_file_path):
""" 配准
:param data_time_value: dict 原始图像的信息-GCJ02
{
"time" : "", #时间
"timestamp" : "", #时间戳
"url" : "", #下载链接
"extent" : "", #范围
"req_pnt" : "", #该瓦片请求的点坐标
"file_path" : "" #文件的path
}
:param rgs_file_path: str 配准后保存的路径
:return: rgs_data_item: dict/None
{
"extent" : "", #范围-WGS84
"file_path" : "", #该文件的路径
}
"""
# 将tiff保存到配准文件夹
origin_file_path = data_time_value["file_path"]
im = Image.open(origin_file_path)
im.save(rgs_file_path)
# 转换extent
extent_gcj02 = data_time_value[
"extent_gcj02"] #[21.532796186275732, 115.14559029127062, 25.66504821372427, 119.65495970872936]
extent_wgs84 = gcj02towgs84_extent(
extent_gcj02
) #[21.53519080438208, 115.14078044126353, 25.66805217057752, 119.65050502755392]
x1, y1, x2, y2 = extent_wgs84
# 用extent_wgs84对rgs_file_path文件进行配准
ds = gdal.Open(rgs_file_path, gdal.GA_Update) #打开影像
if not ds:
return None
# 创建坐标系
srs = osr.SpatialReference()
srs.SetWellKnownGeogCS('WGS84')
# 相关信息
rows = ds.RasterYSize # 行数
cols = ds.RasterXSize # 列数
# 创建地面控制点：经度、纬度、z，照片列数，照片行数
gcps = [
gdal.GCP(y1, x2, 0, 0, 0), # 左上
gdal.GCP(y2, x2, 0, cols - 1, 0), # 右上
gdal.GCP(y1, x1, 0, 0, rows - 1), # 左下
gdal.GCP(y2, x1, 0, cols - 1, rows - 1) # 右下
]
ds.SetGCPs(gcps, srs.ExportToWkt())
ds.SetProjection(srs.ExportToWkt())
ds.SetGeoTransform(gdal.GCPsToGeoTransform(gcps))
del ds
return {"extent": extent_wgs84, "file_path": rgs_file_path}
def gcps2gt_2():
gt = gdal.GCPsToGeoTransform(
_list2gcps([(0.0, 0.0, 400000, 370000), (100.0, 0.0, 410000, 370000),
(100.0, 200.0, 410000, 368000),
(0.0, 200.0, 400000, 368000.01)]))
if not gdaltest.geotransform_equals(
gt,
(400000.0, 100.0, 0.0, 370000.0025, -5e-05, -9.999975), 0.000001):
return 'fail'
return 'success'
def gcps2gt_3():
approx_ok = 0
gt = gdal.GCPsToGeoTransform(
_list2gcps([(0.0, 0.0, 400000, 370000), (100.0, 0.0, 410000, 370000),
(100.0, 200.0, 410000, 368000),
(0.0, 200.0, 400000, 360000)]), approx_ok)
if gt is not None:
gdaltest.post_reason('Expected failure when no good solution.')
return 'failure'
return 'success'
def geographic_info(ds, srsout=None):
# Read geotransform matrix and source reference system
srs = osr.SpatialReference()
if ds.GetGCPCount():
gcps = ds.GetGCPs()
srs.ImportFromWkt(ds.GetGCPProjection())
geomatrix = gdal.GCPsToGeoTransform(gcps)
else:
gcps = []
if not ds.GetProjection():
raise ValueError('no geographic info in "%s"' %
ds.GetDescription())
srs.ImportFromWkt(ds.GetProjection())
geomatrix = ds.GetGeoTransform()
if geomatrix == (0.0, 1.0, 0.0, 0.0, 0.0, 1.0):
raise ValueError('no geographic info in "%s"' % ds.GetDescription())
# Set the target reference system
srsout = makesrs(srsout)
# Instantiate the coordinate transformer
transformer = osr.CoordinateTransformation(srs, srsout)
# dataset corners setup
corners = []
for id_, (pixel, line) in enumerate((
(0, 0),
(0, ds.RasterYSize - 1),
(ds.RasterXSize - 1, ds.RasterYSize - 1),
(ds.RasterXSize - 1, 0))):
X = geomatrix[0] + geomatrix[1] * pixel + geomatrix[2] * line
Y = geomatrix[3] + geomatrix[4] * pixel + geomatrix[5] * line
# Shift to the center of the pixel
X += geomatrix[1] / 2.0
Y += geomatrix[5] / 2.0
Z = 0.
(x, y, z) = transformer.TransformPoint(X, Y, Z)
gcp = gdal.GCP(x, y, z, pixel, line, '', str(id_ + 1))
corners.append(gcp)
# convert GCPs to the targer srs
outgcps = []
for gcp in gcps:
(x, y, z) = transformer.TransformPoint(gcp.GCPX, gcp.GCPY, gcp.GCPZ)
gcp = gdal.GCP(x, y, z, gcp.GCPPixel, gcp.GCPLine, gcp.Info, gcp.Id)
outgcps.append(gcp)
return corners, outgcps
def SetGCPsToGeoTransform(cornerPointUL, cornerPointUR, cornerPointLR,
cornerPointLL, frameCenterLon, frameCenterLat):
''' Make Geotranform from pixel to lon lat coordinates '''
gcps = []
global gcornerPointUL
gcornerPointUL = cornerPointUL
global gcornerPointUR
gcornerPointUR = cornerPointUR
global gcornerPointLR
gcornerPointLR = cornerPointLR
global gcornerPointLL
gcornerPointLL = cornerPointLL
global gframeCenterLat
gframeCenterLat = frameCenterLat
global gframeCenterLon
gframeCenterLon = frameCenterLon
global geotransform
global geotransform_affine
Height = GetFrameCenter()[2]
gcp = gdal.GCP(cornerPointUL[1], cornerPointUL[0], Height, 0, 0,
"Corner Upper Left", "1")
gcps.append(gcp)
gcp = gdal.GCP(cornerPointUR[1], cornerPointUR[0], Height, xSize, 0,
"Corner Upper Right", "2")
gcps.append(gcp)
gcp = gdal.GCP(cornerPointLR[1], cornerPointLR[0], Height, xSize, ySize,
"Corner Lower Right", "3")
gcps.append(gcp)
gcp = gdal.GCP(cornerPointLL[1], cornerPointLL[0], Height, 0, ySize,
"Corner Lower Left", "4")
gcps.append(gcp)
gcp = gdal.GCP(frameCenterLon, frameCenterLat, Height, xSize / 2,
ySize / 2, "Center", "5")
gcps.append(gcp)
geotransform_affine = gdal.GCPsToGeoTransform(gcps)
src = np.float64(
np.array([[0.0, 0.0], [xSize, 0.0], [xSize, ySize], [0.0, ySize]]))
dst = np.float64(
np.array([cornerPointUL, cornerPointUR, cornerPointLR, cornerPointLL]))
geotransform = from_points(src, dst)
if geotransform is None:
qgsu.showUserAndLogMessage("",
"Unable to extract a geotransform.",
onlyLog=True)
return
def test_gcps2gt_8():
gt = gdal.GCPsToGeoTransform(_list2gcps([
(0.01, 0.04, -87.05528672907, 39.22759504228),
(0.01, 2688.02, -86.97079900719, 39.27075713986),
(4031.99, 2688.04, -87.05960736744, 39.37569137000),
(1988.16, 1540.80, -87.055069186699924, 39.304963106777514),
(1477.41, 2400.83, -87.013419295885001, 39.304705030894979),
(1466.02, 2376.92, -87.013906298363295, 39.304056190007913),
]))
gt_expected = (-87.056612873288, -2.232795668658e-05, 3.178617809303e-05,
39.227856615716, 2.6091510188921e-05, 1.596921026218e-05)
assert gdaltest.geotransform_equals(gt, gt_expected, 0.00001)
def gcps2gt_7():
gt = gdal.GCPsToGeoTransform(_list2gcps([
(400000, 370000, 400000, 370000),
(410000, 368000, 410000, 368000),
(410000, 370000, 410000, 370000),
(400000, 368000, 400000, 368000),
]))
if not gdaltest.geotransform_equals(
gt, (0.0, 1.0, 0.0, 0.0, 0.0, 1.0), 0.000001):
return 'fail'
return 'success'
def SetGCPsToGeoTransform(cornerPointUL, cornerPointUR, cornerPointLR,
cornerPointLL, frameCenterLon, frameCenterLat):
''' Make Geotranform from pixel to lon lat coordinates '''
gcps = []
global gcornerPointUL
gcornerPointUL = cornerPointUL
global gcornerPointUR
gcornerPointUR = cornerPointUR
global gcornerPointLR
gcornerPointLR = cornerPointLR
global gcornerPointLL
gcornerPointLL = cornerPointLL
global gframeCenterLat
gframeCenterLat = frameCenterLat
global gframeCenterLon
gframeCenterLon = frameCenterLon
Height = 0
gcp = gdal.GCP(cornerPointUL[1], cornerPointUL[0], Height, 0, 0,
"Corner Upper Left", "1")
gcps.append(gcp)
gcp = gdal.GCP(cornerPointUR[1], cornerPointUR[0], Height, xSize, 0,
"Corner Upper Right", "2")
gcps.append(gcp)
gcp = gdal.GCP(cornerPointLR[1], cornerPointLR[0], Height, xSize, ySize,
"Corner Lower Right", "3")
gcps.append(gcp)
gcp = gdal.GCP(cornerPointLL[1], cornerPointLL[0], Height, 0, ySize,
"Corner Lower Left", "4")
gcps.append(gcp)
gcp = gdal.GCP(frameCenterLon, frameCenterLat, Height, xSize / 2,
ySize / 2, "Center", "5")
gcps.append(gcp)
global geotransform
geotransform = gdal.GCPsToGeoTransform(gcps)
if geotransform is None:
qgsu.showUserAndLogMessage(QCoreApplication.translate(
"QgsFmvUtils", 'Unable to extract a geotransform.'),
onlyLog=True)
return
def copy_and_transform(file_name, sds):
format = "GTiff"
print "Format: GTiff"
driver = gdal.GetDriverByName(format)
file_name += '.tif'
print "File name: %s" % file_name
driver = gdal.GetDriverByName(format)
dst_ds = driver.CreateCopy(file_name, sds, 1)
print "Status: Created"
geotrans = gdal.GCPsToGeoTransform(sds.GetGCPs())
print "Geotrans: ", geotrans
if geotrans:
# geotrans = (20.776085394125101, 0.061360126841990, 0, 65.899963721633995, 0, -0.061360126841990)
dst_ds.SetGeoTransform(geotrans)
print "Status: Transformed"
def georef(input_file,
reference_file,
output_file,
feature_name,
flann,
transform_type,
gcp_output_shp,
gcp_output_txt,
tile,
offset,
ratio=0.75,
convert=False):
"""Georeference the input using the training image and save the result in outputFile.
A ratio can be given to select more or less matches (defaults to 0.75)."""
detector, norm = init_feature(feature_name)
train, kp_train, des_train = getImageKeyPointsAndDescriptors(
reference_file, detector, tile, offset, convert)
query, kp_query, des_query = getImageKeyPointsAndDescriptors(
input_file, detector, tile, offset, convert)
two_sides_matches = match(norm, flann, des_train, des_query, ratio)
MIN_MATCH_COUNT = 3
affine = True
if len(two_sides_matches) > MIN_MATCH_COUNT:
src_pts = np.float32([
kp_train[m.queryIdx].pt for m in two_sides_matches
]).reshape(-1, 1, 2)
dst_pts = np.float32([
kp_query[m.trainIdx].pt for m in two_sides_matches
]).reshape(-1, 1, 2)
if affine:
M, mask = cv.estimateAffine2D(src_pts, dst_pts)
else:
M, mask = cv.findHomography(src_pts, dst_pts, cv.RANSAC)
matchesMask = mask.ravel().tolist()
ds = gdal.Open(reference_file)
x_offset, px_w, rot1, y_offset, px_h, rot2 = ds.GetGeoTransform()
logging.debug("%s : Transform: %f, %f, %f, %f, %f, %f",
datetime.datetime.now(), x_offset, px_w, rot1, y_offset,
px_h, rot2)
gcp_list = []
geo_t = ds.GetGeoTransform()
# gcp_string = ''
logging.debug("%s : %d matches", datetime.datetime.now(),
len(matchesMask))
for i, good_match in enumerate(matchesMask):
if good_match == 1:
p1 = kp_train[two_sides_matches[i].queryIdx].pt
p2 = kp_query[two_sides_matches[i].trainIdx].pt
pp = gdal.ApplyGeoTransform(geo_t, p1[0], p1[1])
logging.debug(f"GCP geot = (%f,%f) -> (%f,%f)", p1[0], p1[1],
pp[0], pp[1])
logging.debug(f"Matched with (%f,%f)", p2[0], p2[1])
z = 0
# info = "GCP from pixel %f, %f" % (p1[0], p1[1])
gcp = gdal.GCP(pp[0], pp[1], z, p2[0], p2[1]) # , info, i)
# print ("GCP = (" + str(p2[0]) +","+ str(p2[1]) + ") -> (" + str(pp[0]) +","+ str(pp[1]) + ")")
# gcp_string += ' -gcp '+" ".join([str(p2[0]),str(p2[1]),str(pp[0]), str(pp[1])])
gcp_list.append(gcp)
logging.debug("%s : %d GCPs", datetime.datetime.now(), len(gcp_list))
translate_t = gdal.GCPsToGeoTransform(gcp_list)
translate_inv_t = gdal.InvGeoTransform(translate_t)
logging.debug(len(translate_t))
logging.debug("geotransform = %s", translate_t)
logging.debug(len(translate_inv_t))
logging.debug("invgeotransform = %s", translate_inv_t)
# trans_gcp_list = []
dst_gcp_list = []
mapResiduals = 0.0
geoResiduals = 0.0
for gcp in gcp_list:
# Inverse geotransform to get the corresponding pixel
pix = gdal.ApplyGeoTransform(translate_inv_t, gcp.GCPX, gcp.GCPY)
logging.debug("GCP = (%d,%d) -> (%d,%d)", gcp.GCPPixel,
gcp.GCPLine, gcp.GCPX, gcp.GCPY)
logging.debug(" => (%d,%d)", pix[0], pix[1])
map_dX = gcp.GCPPixel - pix[0]
map_dY = gcp.GCPLine - pix[1]
map_residual = map_dX * map_dX + map_dY * map_dY
mapResiduals = mapResiduals + map_residual
# trans_gcp_list.append(pix)
# Apply the transform to get the GCP location in the output SRS
pp = gdal.ApplyGeoTransform(translate_t, gcp.GCPPixel, gcp.GCPLine)
z = 0
out_gcp = gdal.GCP(pp[0], pp[1], z, gcp.GCPPixel, gcp.GCPLine)
logging.debug("GCP = (%d,%d) -> (%d,%d)", out_gcp.GCPPixel,
out_gcp.GCPLine, pp[0], pp[1])
# gcp_string += ' -gcp '+" ".join([str(p2[0]),str(p2[1]),str(pp[0]), str(pp[1])])
dX = gcp.GCPX - pp[0]
dY = gcp.GCPY - pp[1]
residual = dX * dX + dY * dY
geoResiduals = geoResiduals + residual
logging.debug("map residual = %f, %f = %f", map_dX, map_dY,
map_residual)
logging.debug("residual = %f, %f = %f", dX, dY, residual)
dst_gcp_list.append(out_gcp)
logging.debug(f"map residuals %s", mapResiduals)
logging.debug(f"geo residuals %s", geoResiduals)
# print (gcp_string)
# h,w = train.shape
# pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)
# dst = cv.perspectiveTransform(pts,M)
# img2 = cv.polylines(query,[np.int32(dst)],True,255,3, cv.LINE_AA)
src_ds = gdal.Open(input_file)
# translate and warp the inputFile using GCPs and polynomial of order 1
dst_ds = gdal.Translate('',
src_ds,
outputSRS=ds.GetProjection(),
GCPs=gcp_list,
format='MEM')
tps = False
if transform_type == 'poly1':
polynomialOrder = 1
gdal.Warp(output_file,
dst_ds,
tps=tps,
polynomialOrder=polynomialOrder,
dstNodata=1)
elif transform_type == 'poly2':
polynomialOrder = 2
gdal.Warp(output_file,
dst_ds,
tps=tps,
polynomialOrder=polynomialOrder,
dstNodata=1)
elif transform_type == 'poly3':
polynomialOrder = 3
gdal.Warp(output_file,
dst_ds,
tps=tps,
polynomialOrder=polynomialOrder,
dstNodata=1)
elif transform_type == 'tps':
tps = True
gdal.Warp(output_file, dst_ds, tps=tps, dstNodata=1)
else:
polynomialOrder = 1
gdal.Warp(output_file,
dst_ds,
tps=tps,
polynomialOrder=polynomialOrder,
dstNodata=1)
if gcp_output_shp is not None:
saveGCPsAsShapefile(dst_gcp_list,
osr.SpatialReference(ds.GetProjection()),
gcp_output_shp)
saveGCPsAsText(dst_gcp_list, gcp_output_txt)
draw_params = dict(
matchColor=(0, 255, 0), # draw matches in green color
singlePointColor=None,
matchesMask=matchesMask, # draw only inliers
flags=2)
img3 = cv.drawMatches(train, kp_train, query, kp_query,
two_sides_matches, None, **draw_params)
return img3
else:
logging.error("Not enough matches are found - %d/%d",
len(two_sides_matches), MIN_MATCH_COUNT)
def __getmetadata__(self, f=None):
'''Populate the metadata'''
if not f: f = self.fileinfo['filepath']
self._gdaldataset = geometry.OpenDataset(f)
led = glob.glob(os.path.dirname(f) +
'/[Ll][Ee][Aa][Dd]*')[0] #volume file
meta = open(led, 'rb').read()
'''
metadata has 4 records, each is 4320 (LS) or 6120 (SPOT) bytes long:
File descriptor record;
Scene header record;
Map projection (scene-related) ancillary record;
Radiometric transformation ancillary record.
'''
#Record 2 - Scene header record
record = 2
recordlength = 4320 #LS 5
satellite = utilities.readbinary(meta, (record - 1) * recordlength,
309, 324)
#Scene ID, path/row & image date/time
start, stop = 37, 52
sceneid = utilities.readbinary(meta, (record - 1) * recordlength,
start, stop)
start, stop = 165, 180
pathrow = utilities.readbinary(meta, (record - 1) * recordlength,
start, stop)[1:]
start, stop = 117, 148
imgdate = utilities.readbinary(meta, (record - 1) * recordlength,
start, stop)
self.metadata['imgdate'] = time.strftime(
utilities.datetimeformat,
time.strptime(imgdate[0:14], '%Y%m%d%H%M%S')) #ISO 8601
#Ascending/descending flag
start, stop = 357, 372
if utilities.readbinary(meta, (record - 1) * recordlength, start,
stop) == 'D':
self.metadata['orbit'] = 'Descending'
else:
self.metadata['orbit'] = 'Ascending'
#Processing level
start, stop = 1573, 1588
self.metadata['level'] = utilities.readbinary(
meta, (record - 1) * recordlength, start, stop)
#Bands
start, stop = 1653, 1659
bands = []
actbands = utilities.readbinary(meta, (record - 1) * recordlength,
start, stop)
for i in range(0, 7): #Loop thru the 7 LS 5 bands
if actbands[i] == '1': bands.append(str(i + 1))
self._gdaldataset.GetRasterBand(i + 1).SetNoDataValue(0)
#Record 3 - Map projection (scene-related) ancillary record
record = 3
#Bands, rows & columns and rotation
nbands = int(self._gdaldataset.RasterCount)
start, stop = 333, 348
ncols = float(
utilities.readbinary(meta, (record - 1) * recordlength, start,
stop))
start, stop = 349, 364
nrows = float(
utilities.readbinary(meta, (record - 1) * recordlength, start,
stop))
start, stop = 445, 460
self.metadata['rotation'] = float(
utilities.readbinary(meta, (record - 1) * recordlength, start,
stop))
if abs(self.metadata['rotation']) < 1:
self.metadata['orientation'] = 'Map oriented'
self.metadata['rotation'] = 0.0
else:
self.metadata['orientation'] = 'Path oriented'
#Sun elevation and azimuth
start, stop = 605, 620
self.metadata['sunelevation'] = float(
utilities.readbinary(meta, (record - 1) * recordlength, start,
stop))
start, stop = 621, 636
self.metadata['sunazimuth'] = float(
utilities.readbinary(meta, (record - 1) * recordlength, start,
stop))
#geometry.CellSizes
start, stop = 365, 396
(cellx, celly) = map(
float,
utilities.readbinary(meta, (record - 1) * recordlength, start,
stop).split())
start, stop = 397, 412
projection = utilities.readbinary(meta, (record - 1) * recordlength,
start, stop).split()
datum = projection[0]
zone = projection[1]
# lat/lons
start, stop = 765, 892
coords = utilities.readbinary(meta, (record - 1) * recordlength, start,
stop).split()
uly, ulx, ury, urx, lry, lrx, lly, llx = map(float, coords)
ext = [[ulx, uly], [urx, ury], [lrx, lry], [llx, lly], [ulx, uly]]
if int(zone) != 0:
# UTM
type = 'UTM'
units = 'm'
#start,stop = 637,764
#coords = utilities.readbinary(meta,(record-1)*recordlength,start,stop).split()
#uly,ulx,ury,urx,lry,lrx,lly,llx = map(float, coords)
#ext=[[ulx,uly],[urx,ury],[lrx,lry],[llx,lly],[ulx,uly]]
if datum == 'GDA94': epsg = int('283' + zone)
elif datum == 'AGD66': epsg = int('202' + zone)
elif datum == 'WGS84': epsg = int('327' + zone)
else: #Assume
type = 'GEO'
units = 'deg'
if datum == 'GDA94': epsg = 4283
else: epsg = 4326 #Assume WGS84
gcps = []
i = 0
lr = [[0, 0], [ncols, 0], [ncols, nrows], [0, nrows]]
while i < len(ext) - 1: #don't need the last xy pair
gcp = gdal.GCP()
gcp.GCPPixel, gcp.GCPLine = lr[i]
gcp.GCPX, gcp.GCPY = ext[i]
gcp.Id = str(i)
gcps.append(gcp)
i += 1
geotransform = gdal.GCPsToGeoTransform(gcps)
cellx, celly = geometry.CellSize(geotransform)
rotation = geometry.Rotation(geotransform)
srs = osr.SpatialReference()
srs.ImportFromEPSG(epsg)
srs = srs.ExportToWkt()
self.metadata['satellite'] = satellite
if satellite == 'LANDSAT-5':
self.metadata['sensor'] = 'TM'
self.metadata['filetype'] = 'CEOS/Landsat CCRS Format'
else:
self.metadata['sensor'] = 'HRV'
self.metadata['filetype'] = 'CEOS/SPOT CCRS Format'
self.metadata['filesize'] = sum(
[os.path.getsize(file) for file in self.filelist])
self.metadata['srs'] = srs
self.metadata['epsg'] = epsg
self.metadata['units'] = units
self.metadata['cols'] = ncols
self.metadata['rows'] = nrows
self.metadata['nbands'] = nbands
self.metadata['bands'] = ','.join(bands)
self.metadata['nbits'] = 8
self.metadata['datatype'] = 'Byte'
self.metadata['nodata'] = 0
self.metadata['cellx'], self.metadata['celly'] = map(
float, [cellx, celly])
self.metadata['UL'] = '%s,%s' % tuple(ext[0])
self.metadata['UR'] = '%s,%s' % tuple(ext[1])
self.metadata['LR'] = '%s,%s' % tuple(ext[2])
self.metadata['LL'] = '%s,%s' % tuple(ext[3])
metadata = self._gdaldataset.GetMetadata()
self.metadata['metadata'] = '\n'.join(
['%s: %s' % (m, hdf_self.metadata[m]) for m in metadata])
self.metadata['compressionratio'] = 0
self.metadata['compressiontype'] = 'None'
self.extent = ext
def __getmetadata__(self, f=None):
'''Read Metadata for ASTER HDF images as GDAL doesn't.'''
if not f: f = self.fileinfo['filepath']
hdf_sd = self._gdaldataset.GetSubDatasets()
hdf_sd = [sd for sd, sz in hdf_sd if 'ImageData' in sd]
hdf_md = self._hdf_md
#sd,sz = hdf_sd[0]
sd = hdf_sd[0]
sd = geometry.OpenDataset(sd)
nbands = len(hdf_sd)
ncols = []
nrows = []
nbits = []
bands = []
datatypes = []
cellxy = []
for i in range(0, len(hdf_md['PROCESSEDBANDS']), 2):
band = hdf_md['PROCESSEDBANDS'][i:i + 2]
if i / 2 + 1 <= 4:
bands.append('VNIR' + band)
cellxy.append('15')
elif i / 2 + 1 <= 10:
bands.append('SWIR' + band)
cellxy.append('30')
else:
bands.append('TIR' + band)
cellxy.append('90')
if band.isdigit(): band = str(int(band)) #Get rid of leading zero
cols, rows, bytes = map(
int, hdf_md['IMAGEDATAINFORMATION%s' % band].split(','))
if bytes == 1: datatypes.append('Byte')
elif bytes == 2: datatypes.append('UInt16')
ncols.append(str(cols))
nrows.append(str(rows))
nbits.append(str(bytes * 8))
ncols = ','.join(ncols)
nrows = ','.join(nrows)
nbits = ','.join(nbits)
bands = ','.join(bands)
datatypes = ','.join(datatypes)
cellxy = ','.join(cellxy)
uly, ulx = [float(xy) for xy in hdf_md['UPPERLEFT'].split(',')]
ury, urx = [float(xy) for xy in hdf_md['UPPERRIGHT'].split(',')]
lry, lrx = [float(xy) for xy in hdf_md['LOWERRIGHT'].split(',')]
lly, llx = [float(xy) for xy in hdf_md['LOWERLEFT'].split(',')]
ext = [[ulx, uly], [urx, ury], [lrx, lry], [llx, lly], [ulx, uly]]
#SRS reported by GDAL is slightly dodgy, GDA94 is not recognised and doesn't set the North/South properly
#Get it anyway so we can work out if it's GDA94 based on the spheroid
srs = sd.GetGCPProjection()
src_srs = osr.SpatialReference(srs)
tgt_srs = osr.SpatialReference()
geogcs = osr.SpatialReference()
if src_srs.GetAttrValue('SPHEROID') == 'GRS 1980':
geogcs.ImportFromEPSG(4283) #Assume 'GDA94'
else:
geogcs.ImportFromEPSG(4326) #Assume 'WGS84'
tgt_srs.CopyGeogCSFrom(geogcs)
if hdf_md['PROCESSINGLEVELID'].upper() == '1A':
units = 'deg'
else:
#projparams=map(float, hdf_md['PROJECTIONPARAMETERS1'].split(','))
if hdf_md['MPMETHOD1'] == 'UTM': #Universal Transverse Mercator
if uly < 0:
bNorth = False #GDAL doesn't set the North/South properly
else:
bNorth = True
nZone = int(hdf_md['UTMZONECODE1'])
tgt_srs.SetUTM(nZone, bNorth)
units = 'm'
#Other projections not (yet?) implemented...
#elif hdf_md['MPMETHOD1'] == 'PS':#Polar Stereographic
# #dfCenterLon = ? GTCP projection params don't list cenlon/lat for PS
# dfCenterLat = ?
# dfScale = ?
# tgt_srs.SetPS(dfCenterLat,dfCenterLon,dfScale,0.0,0.0)
#elif hdf_md['MPMETHOD1'] == 'LAMCC':#Lambert Conformal Conic
# dfCenterLon = ?
# dfCenterLat = ?
# dfStdP1 = ?
# dfStdP2 = ?
# tgt_srs.SetLCC(dfStdP1,dfStdP2,dfCenterLat,dfCenterLon,0,0)
#elif hdf_md['MPMETHOD1'] == 'SOM':#Space Oblique Mercator
# dfCenterLon = ?
# dfCenterLat = ?
# srs.SetMercator(dfCenterLat,dfCenterLon,0,0,0)
#elif hdf_md['MPMETHOD1'] == 'EQRECT':#Equi-Rectangular
# dfCenterLon = ?
# dfCenterLat = ?
# tgt_srs.SetMercator(dfCenterLat,dfCenterLon,0,0,0)
else: #Assume Geog
units = 'deg'
srs = tgt_srs.ExportToWkt()
self.metadata['UL'] = '%s,%s' % tuple(ext[0])
self.metadata['UR'] = '%s,%s' % tuple(ext[1])
self.metadata['LR'] = '%s,%s' % tuple(ext[2])
self.metadata['LL'] = '%s,%s' % tuple(ext[3])
self.metadata['metadata'] = '\n'.join(
['%s: %s' % (m, hdf_md[m]) for m in hdf_md])
self.metadata['satellite'] = 'Terra'
self.metadata['sensor'] = 'ASTER'
self.metadata['filetype'] = self._gdaldataset.GetDriver(
).ShortName + '/' + self._gdaldataset.GetDriver().LongName + ' (ASTER)'
self.metadata['sceneid'] = hdf_md['ASTERSCENEID']
self.metadata['level'] = hdf_md['PROCESSINGLEVELID']
if '-' in hdf_md['CALENDARDATE']: imgdate = hdf_md['CALENDARDATE']
else:
imgdate = time.strftime(utilities.dateformat,
time.strptime(hdf_md['CALENDARDATE'],
'%Y%m%d')) #ISO 8601
imgtime = hdf_md.get('TIMEOFDAY')
if imgtime:
self.metadata['imgdate'] = time.strftime(
utilities.datetimeformat,
time.strptime(imgdate + imgtime[0:6],
'%Y-%m-%d%H%M%S')) #ISO 8601
else:
self.metadata['imgdate'] = imgdate
#self.metadata['imgdate'] = hdf_md['CALENDARDATE']
self.metadata['cloudcover'] = float(hdf_md['SCENECLOUDCOVERAGE'])
if hdf_md['FLYINGDIRECTION'] == 'DE':
self.metadata['orbit'] = 'Descending'
else:
self.metadata['orbit'] = 'Ascending'
self.metadata['rotation'] = float(
hdf_md.get('MAPORIENTATIONANGLE',
hdf_md.get('SCENEORIENTATIONANGLE')))
if abs(self.metadata['rotation']) < 1.0:
self.metadata['orientation'] = 'Map oriented'
else:
self.metadata['orientation'] = 'Path oriented'
self.metadata['sunazimuth'], self.metadata['sunelevation'] = map(
float, hdf_md['SOLARDIRECTION'].split(','))
self.metadata['viewangle'] = float(hdf_md['POINTINGANGLE'])
self.metadata['cols'] = ncols
self.metadata['rows'] = nrows
self.metadata['nbands'] = nbands
self.metadata['datatype'] = datatypes
self.metadata['nbits'] = nbits
self.metadata['nodata'] = ','.join(['0' for i in range(0, nbands)])
self.metadata['bands'] = bands
self.metadata['resampling'] = hdf_md.get(
'RESMETHOD1') #Assume same for all...
self.metadata['srs'] = srs
self.metadata['epsg'] = spatialreferences.IdentifyAusEPSG(srs)
self.metadata['units'] = units
self.metadata['cellx'], self.metadata['celly'] = cellxy, cellxy
#Geotransform
ext = [[ulx, uly], [urx, ury], [lrx, lry], [llx, lly], [ulx, uly]]
ncols = map(int, str(ncols).split(','))
nrows = map(int, str(nrows).split(','))
cellx, celly = [], []
j = 0
while j < len(ncols):
gcps = []
i = 0
lr = [[0, 0], [ncols[j], 0], [ncols[j], nrows[j]], [0, nrows[j]]]
while i < len(ext) - 1: #don't need the last xy pair
gcp = gdal.GCP()
gcp.GCPPixel, gcp.GCPLine = lr[i]
gcp.GCPX, gcp.GCPY = ext[i]
gcp.Id = str(i)
gcps.append(gcp)
i += 1
j += 1
geotransform = gdal.GCPsToGeoTransform(gcps)
x, y = geometry.CellSize(geotransform)
cellx.append(str(x))
celly.append(str(abs(y)))
self.metadata['cellx'] = ','.join(cellx)
self.metadata['celly'] = ','.join(celly)
srs = osr.SpatialReference()
srs.ImportFromEPSG(4326)
self.metadata['srs'] = srs.ExportToWkt()
self.metadata['UL'] = '%s,%s' % tuple(ext[0])
self.metadata['UR'] = '%s,%s' % tuple(ext[1])
self.metadata['LR'] = '%s,%s' % tuple(ext[2])
self.metadata['LL'] = '%s,%s' % tuple(ext[3])
self.metadata['metadata'] = '\n'.join(
['%s: %s' % (m, hdf_md[m]) for m in hdf_md])
self.metadata['filesize'] = sum(
[os.path.getsize(file) for file in self.filelist])
self.metadata['compressionratio'] = 0
self.metadata['compressiontype'] = 'None'
self.extent = ext
#Build gdaldataset object for overviews
vrtcols = ncols[0]
vrtrows = nrows[0]
#vrtbands=[sd for sd,sn in hdf_sd[0:4]]#The 4 VNIR bands
vrtbands = hdf_sd[0:4] #The 4 VNIR bands
vrt = geometry.CreateSimpleVRT(vrtbands, vrtcols, vrtrows,
datatypes.split(',')[0])
self._gdaldataset = geometry.OpenDataset(vrt)
for i in range(1, 5):
self._gdaldataset.GetRasterBand(i).SetNoDataValue(0)
def test_gcps2gt_4():
gt = gdal.GCPsToGeoTransform(_list2gcps([
(0.0, 0.0, 400000, 370000),
]))
assert gt is None, 'Expected failure for single GCP.'
def getGCP(reference_file,
kp_query,
kp_train,
two_sides_matches,
min_matches=3):
if len(two_sides_matches) > min_matches:
src_pts = np.float32([
kp_train[m.queryIdx].pt for m in two_sides_matches
]).reshape(-1, 1, 2)
dst_pts = np.float32([
kp_query[m.trainIdx].pt for m in two_sides_matches
]).reshape(-1, 1, 2)
M, mask = cv.findHomography(src_pts, dst_pts, cv.RANSAC)
matchesMask = mask.ravel().tolist()
ds = gdal.Open(reference_file)
x_offset, px_w, rot1, y_offset, px_h, rot2 = ds.GetGeoTransform()
logging.debug("%s : Transform: %f, %f, %f, %f, %f, %f",
datetime.datetime.now(), x_offset, px_w, rot1, y_offset,
px_h, rot2)
gcp_list = []
geo_t = ds.GetGeoTransform()
# gcp_string = ''
logging.debug("%s : %d matches", datetime.datetime.now(),
len(matchesMask))
for i, good_match in enumerate(matchesMask):
if good_match == 1:
p1 = kp_train[two_sides_matches[i].queryIdx].pt
p2 = kp_query[two_sides_matches[i].trainIdx].pt
pp = gdal.ApplyGeoTransform(geo_t, p1[0], p1[1])
logging.debug(f"GCP geot = (%f,%f) -> (%f,%f)", p1[0], p1[1],
pp[0], pp[1])
logging.debug(f"Matched with (%f,%f)", p2[0], p2[1])
z = 0
# info = "GCP from pixel %f, %f" % (p1[0], p1[1])
gcp = gdal.GCP(pp[0], pp[1], z, p2[0], p2[1]) # , info, i)
# print ("GCP = (" + str(p2[0]) +","+ str(p2[1]) + ") -> (" + str(pp[0]) +","+ str(pp[1]) + ")")
# gcp_string += ' -gcp '+" ".join([str(p2[0]),str(p2[1]),str(pp[0]), str(pp[1])])
gcp_list.append(gcp)
logging.debug("%s : %d GCPs", datetime.datetime.now(), len(gcp_list))
translate_t = gdal.GCPsToGeoTransform(gcp_list)
translate_inv_t = gdal.InvGeoTransform(translate_t)
logging.debug(len(translate_t))
logging.debug("geotransform = %s", translate_t)
logging.debug(len(translate_inv_t))
logging.debug("invgeotransform = %s", translate_inv_t)
# trans_gcp_list = []
dst_gcp_list = []
mapResiduals = 0.0
geo_residuals = 0.0
for gcp in gcp_list:
# Inverse geotransform to get the corresponding pixel
pix = gdal.ApplyGeoTransform(translate_inv_t, gcp.GCPX, gcp.GCPY)
logging.debug("GCP = (%d,%d) -> (%d,%d)", gcp.GCPPixel,
gcp.GCPLine, gcp.GCPX, gcp.GCPY)
logging.debug(" => (%d,%d)", pix[0], pix[1])
map_dX = gcp.GCPPixel - pix[0]
map_dY = gcp.GCPLine - pix[1]
map_residual = map_dX * map_dX + map_dY * map_dY
mapResiduals = mapResiduals + map_residual
# Apply the transform to get the GCP location in the output SRS
pp = gdal.ApplyGeoTransform(translate_t, gcp.GCPPixel, gcp.GCPLine)
z = 0
out_gcp = gdal.GCP(pp[0], pp[1], z, gcp.GCPPixel, gcp.GCPLine)
logging.debug("GCP = (%d,%d) -> (%d,%d)", out_gcp.GCPPixel,
out_gcp.GCPLine, pp[0], pp[1])
dX = gcp.GCPX - pp[0]
dY = gcp.GCPY - pp[1]
residual = dX * dX + dY * dY
geo_residuals = geo_residuals + residual
logging.debug("map residual = %f, %f = %f", map_dX, map_dY,
map_residual)
logging.debug("residual = %f, %f = %f", dX, dY, residual)
dst_gcp_list.append(out_gcp)
logging.debug(f"map residuals %s", mapResiduals)
logging.debug(f"geo residuals %s", geo_residuals)
return ds.GetProjection(), gcp_list, dst_gcp_list
else:
logging.error("Not enough matches are found - %d/%d",
len(two_sides_matches), min_matches)
return None, None, None
from osgeo import gdal
if len(sys.argv) < 2:
print("Usage: gcps2wld.py source_file")
sys.exit(1)
filename = sys.argv[1]
dataset = gdal.Open(filename)
if dataset is None:
print('Unable to open %s' % filename)
sys.exit(1)
gcps = dataset.GetGCPs()
if gcps is None or not gcps:
print('No GCPs found on file ' + filename)
sys.exit(1)
geotransform = gdal.GCPsToGeoTransform(gcps)
if geotransform is None:
print('Unable to extract a geotransform.')
sys.exit(1)
print(geotransform[1])
print(geotransform[4])
print(geotransform[2])
print(geotransform[5])
print(geotransform[0] + 0.5 * geotransform[1] + 0.5 * geotransform[2])
print(geotransform[3] + 0.5 * geotransform[4] + 0.5 * geotransform[5])
def modify_src_raster(self):
'convert to RGB(A) if required'
#----------------------------
override_srs = self.options.srs
src_bands = self.src_ds.RasterCount
band1 = self.src_ds.GetRasterBand(1)
if src_bands == 1 and band1.GetColorInterpretation(
) == GCI_PaletteIndex: # source is a paletted raster
transparency = None
if self.base_resampling == 'NearestNeighbour' and self.resampling == Image.NEAREST:
# check if src can be rendered in paletted mode
color_table = band1.GetColorTable()
ncolors = color_table.GetCount()
palette = [
color_table.GetColorEntry(i) for i in range(ncolors)
]
r, g, b, a = zip(*palette)
pil_palette = flatten(zip(
r, g, b)) # PIL doesn't support RGBA palettes
if self.options.dst_nodata is not None:
transparency = int(self.options.dst_nodata.split(',')[0])
elif min(a) == 0:
transparency = a.index(0)
elif ncolors < 256:
pil_palette += [0, 0, 0
] # the last color index is a transparency
transparency = len(pil_palette) / 3 - 1
ld('transparency', transparency)
if transparency is not None: # render in paletted mode
self.transparency = transparency
self.palette = pil_palette
ld('self.palette', self.palette)
else: # convert src to rgb VRT
src_geotr = self.src_ds.GetGeoTransform()
src_proj = txt2proj4(self.src_ds.GetProjection())
gcps = self.src_ds.GetGCPs()
if gcps:
ld('src GCPsToGeoTransform', gdal.GCPsToGeoTransform(gcps))
if not src_proj and gcps:
src_proj = txt2proj4(self.src_ds.GetGCPProjection())
if override_srs is not None:
src_proj = txt2proj4(override_srs)
override_srs = None
ld('src_proj', src_proj, 'src geotr', src_geotr)
assert src_proj, 'The source does not have a spatial reference system assigned'
if not src_geotr or src_geotr == (0.0, 1.0, 0.0, 0.0, 0.0,
1.0):
geotr_txt = ''
else:
geotr_txt = geotr_templ % src_geotr
gcplst_txt = ''
if gcps:
gcp_lst = '\n'.join(
(gcp_templ %
(g.Id, g.GCPPixel, g.GCPLine, g.GCPX, g.GCPY, g.GCPZ)
for g in gcps))
gcp_proj = txt2proj4(self.src_ds.GetGCPProjection(
)) if override_srs is None else src_proj
gcplst_txt = gcplst_templ % (gcp_proj, gcp_lst)
metadata = self.src_ds.GetMetadata()
ld('metadata', metadata)
if metadata:
mtd_lst = [
xml_txt('MDI', metadata[mdkey], 4, key=mdkey)
for mdkey in metadata
]
meta_txt = meta_templ % '\n'.join(mtd_lst)
else:
meta_txt = ''
xsize, ysize = (self.src_ds.RasterXSize,
self.src_ds.RasterYSize)
blxsize, blysize = band1.GetBlockSize()
band_lst = ''.join(
(band_templ % {
'band': band,
'color': color,
'src': cgi.escape(self.src_path, quote=True),
'srcband': 1,
'xsize': xsize,
'ysize': ysize,
'blxsize': blxsize,
'blysize': blysize,
} for band, color in ((1, 'Red'), (2, 'Green'), (3,
'Blue'))))
vrt_txt = vrt_templ % {
'xsize': xsize,
'ysize': ysize,
'metadata': meta_txt,
'srs': (srs_templ % src_proj) if src_proj else '',
'geotr': geotr_txt,
'gcp_list': gcplst_txt,
'band_list': band_lst,
}
src_vrt = os.path.abspath(
os.path.join(self.dest,
self.base + '.src.vrt')) # auxilary VRT file
self.temp_files.append(src_vrt)
self.src_path = src_vrt
with open(src_vrt, 'w') as f:
f.write(vrt_txt.encode('utf-8'))
self.src_ds = gdal.Open(src_vrt, GA_ReadOnly)
# finished with a paletted raster
if override_srs is not None: # src SRS needs to be relpaced
src_vrt = os.path.join(self.dest,
self.base + '.src.vrt') # auxilary VRT file
self.temp_files.append(src_vrt)
self.src_path = src_vrt
vrt_drv = gdal.GetDriverByName('VRT')
self.src_ds = vrt_drv.CreateCopy(src_vrt,
src_ds) # replace src dataset
ld('override_srs', override_srs, 'txt2wkt(override_srs)',
txt2wkt(override_srs))
self.src_ds.SetProjection(
txt2wkt(override_srs)) # replace source SRS
gcps = self.src_ds.GetGCPs()
if gcps:
self.src_ds.SetGCPs(gcps, txt2wkt(override_srs))
