def rasterize_line_rois(self, img_src, output=False):
u"""
Convert lines of interests from vector coordinates to pixel coordinates
in the specified source image.
"""
# retrieving necessary information from template image
tpl_ds = gdal.Open(img_src)
tpl_geo_transform = tpl_ds.GetGeoTransform()
tpl_x_size, tpl_y_size = tpl_ds.RasterXSize, tpl_ds.RasterYSize
# opening vector layer to be rasterized
roi_ds = ogr.Open(self.roi_src)
roi_ly = roi_ds.GetLayer(0)
if output:
mem_ds = gdal.GetDriverByName("MEM").Create("", tpl_x_size, tpl_y_size, 1, gdal.GDT_Byte)
mem_ds.SetGeoTransform(tpl_geo_transform)
mem_ds.SetProjection(roi_ly.GetSpatialRef().ExportToWkt())
mem_data = mem_ds.GetRasterBand(1).ReadAsArray()
img_coordinates = dict()
for ft in roi_ly:
fid = ft.GetFID()
print "Working on FID %d..." % fid
gm = ft.GetGeometryRef()
vertices = gm.GetPoints()
pixels = list()
for i in range(0, len(vertices) - 2):
# retrieving real world coordinates of sub-line endpoints
x0, y0 = vertices[i]
x1, y1 = vertices[i + 1]
# converting real-world coordinates to image coordinates
c0 = int((x0 - tpl_geo_transform[0]) / tpl_geo_transform[1])
r0 = int((y0 - tpl_geo_transform[3]) / tpl_geo_transform[5])
c1 = int((x1 - tpl_geo_transform[0]) / tpl_geo_transform[1])
r1 = int((y1 - tpl_geo_transform[3]) / tpl_geo_transform[5])
# finding sequential pixels between current vertices
pixels += geom_utils.retrieve_line_pixels(c0, r0, c1, r1)
# finally eliminating duplicate pixels
else:
pixels = general_utils.ordered_set(pixels)
if output:
for col, row in pixels:
mem_data[row, col] = 255
img_coordinates[fid] = pixels
if output:
gdal_utils.export_data(mem_data, r"z:\mem_out_bresenham.img", tpl_ds)
return img_coordinates
def rasterize_line_rois(self, img_src, output = False):
u"""
Convert lines of interests from vector coordinates to pixel coordinates
in the specified source image.
"""
# retrieving necessary information from template image
tpl_ds = gdal.Open(img_src)
tpl_geo_transform = tpl_ds.GetGeoTransform()
tpl_x_size, tpl_y_size = tpl_ds.RasterXSize, tpl_ds.RasterYSize
# opening vector layer to be rasterized
roi_ds = ogr.Open(self.roi_src)
roi_ly = roi_ds.GetLayer(0)
if output:
mem_ds = gdal.GetDriverByName('MEM').Create("", tpl_x_size, tpl_y_size, 1, gdal.GDT_Byte)
mem_ds.SetGeoTransform(tpl_geo_transform)
mem_ds.SetProjection(roi_ly.GetSpatialRef().ExportToWkt())
mem_data = mem_ds.GetRasterBand(1).ReadAsArray()
img_coordinates = dict()
for ft in roi_ly:
fid = ft.GetFID()
print "Working on FID %d..." % fid
gm = ft.GetGeometryRef()
vertices = gm.GetPoints()
pixels = list()
for i in range(0, len(vertices) - 2):
# retrieving real world coordinates of sub-line endpoints
x0, y0 = vertices[i]
x1, y1 = vertices[i + 1]
# converting real-world coordinates to image coordinates
c0 = int((x0 - tpl_geo_transform[0]) / tpl_geo_transform[1])
r0 = int((y0 - tpl_geo_transform[3]) / tpl_geo_transform[5])
c1 = int((x1 - tpl_geo_transform[0]) / tpl_geo_transform[1])
r1 = int((y1 - tpl_geo_transform[3]) / tpl_geo_transform[5])
# finding sequential pixels between current vertices
pixels += geom_utils.retrieve_line_pixels(c0, r0, c1, r1)
# finally eliminating duplicate pixels
else:
pixels = general_utils.ordered_set(pixels)
if output:
for col, row in pixels:
mem_data[row, col] = 255
img_coordinates[fid] = pixels
if output:
gdal_utils.export_data(mem_data, r"z:\mem_out_bresenham.img", tpl_ds)
return img_coordinates
def rasterize_polygon_rois(self, img_src, output=False):
u"""
Convert polygons of interests from vector coordinates to pixel
coordinates in the specified source image.
"""
# declaring source imagery as template raster data set
tpl_ds = gdal.Open(img_src)
tpl_geo_transform = tpl_ds.GetGeoTransform()
tpl_x_size, tpl_y_size = tpl_ds.RasterXSize, tpl_ds.RasterYSize
# opening vector roi data source
roi_ds = ogr.Open(self.roi_src)
roi_ly = roi_ds.GetLayer(0)
img_coordinates = dict()
if output:
out_ds = gdal.GetDriverByName("MEM").Create("", tpl_x_size, tpl_y_size, 1, gdal.GDT_Int16)
out_ds.SetGeoTransform(tpl_geo_transform)
out_ds.SetProjection(roi_ly.GetSpatialRef().ExportToWkt())
out_data = out_ds.GetRasterBand(1).ReadAsArray()
out_data[:] = -9999
for ft in roi_ly:
fid = ft.GetFID()
print "Working on FID %d..." % fid
mem_ds = gdal.GetDriverByName("MEM").Create("", tpl_x_size, tpl_y_size, 1, gdal.GDT_Byte)
mem_ds.SetGeoTransform(tpl_geo_transform)
mem_ds.SetProjection(roi_ly.GetSpatialRef().ExportToWkt())
where_clause = "WHERE '%s' = %i" % ("FID", fid)
query = "SELECT * FROM '%s' %s" % (roi_ly.GetName(), where_clause)
tmp_ds = ogr.Open(self.roi_src)
sel_ly = tmp_ds.ExecuteSQL(query)
gdal.RasterizeLayer(mem_ds, (1,), sel_ly, burn_values=(255,))
mem_data = mem_ds.GetRasterBand(1).ReadAsArray()
mask = ma.masked_where(mem_data != 255, mem_data)
if output:
out_data[~mask.mask] = fid
pixels = list()
# reversing image shape to make it column-major
shape = list(mask.shape)
shape.reverse()
non_mask_indices = ma.flatnotmasked_contiguous(mask)
# iterating over each slice
if non_mask_indices is None:
continue
for sl in non_mask_indices:
start_pixel = np.unravel_index(sl.start, tuple(shape), order="F")
stop_pixel = np.unravel_index(sl.stop, tuple(shape), order="F")
# print sl, np.unravel_index(sl.start, mask.shape), np.unravel_index(sl.stop, mask.shape)
pixels.append((start_pixel, stop_pixel))
else:
img_coordinates[fid] = pixels
if output:
gdal_utils.export_data(out_data, r"z:\mem_out_raster.img", tpl_ds)
return img_coordinates
def rasterize_polygon_rois(self, img_src, output = False):
u"""
Convert polygons of interests from vector coordinates to pixel
coordinates in the specified source image.
"""
# declaring source imagery as template raster data set
tpl_ds = gdal.Open(img_src)
tpl_geo_transform = tpl_ds.GetGeoTransform()
tpl_x_size, tpl_y_size = tpl_ds.RasterXSize, tpl_ds.RasterYSize
# opening vector roi data source
roi_ds = ogr.Open(self.roi_src)
roi_ly = roi_ds.GetLayer(0)
img_coordinates = dict()
if output:
out_ds = gdal.GetDriverByName('MEM').Create("", tpl_x_size, tpl_y_size, 1, gdal.GDT_Int16)
out_ds.SetGeoTransform(tpl_geo_transform)
out_ds.SetProjection(roi_ly.GetSpatialRef().ExportToWkt())
out_data = out_ds.GetRasterBand(1).ReadAsArray()
out_data[:] = -9999
for ft in roi_ly:
fid = ft.GetFID()
print "Working on FID %d..." % fid
mem_ds = gdal.GetDriverByName('MEM').Create("", tpl_x_size, tpl_y_size, 1, gdal.GDT_Byte)
mem_ds.SetGeoTransform(tpl_geo_transform)
mem_ds.SetProjection(roi_ly.GetSpatialRef().ExportToWkt())
where_clause = "WHERE '%s' = %i" % ('FID', fid)
query = "SELECT * FROM '%s' %s" % (roi_ly.GetName(), where_clause)
tmp_ds = ogr.Open(self.roi_src)
sel_ly = tmp_ds.ExecuteSQL(query)
gdal.RasterizeLayer(mem_ds, (1,), sel_ly, burn_values = (255,))
mem_data = mem_ds.GetRasterBand(1).ReadAsArray()
mask = ma.masked_where(mem_data != 255, mem_data)
if output:
out_data[~mask.mask] = fid
pixels = list()
# reversing image shape to make it column-major
shape = list(mask.shape)
shape.reverse()
non_mask_indices = ma.flatnotmasked_contiguous(mask)
# iterating over each slice
if non_mask_indices is None:
continue
for sl in non_mask_indices:
start_pixel = np.unravel_index(sl.start, tuple(shape), order = 'F')
stop_pixel = np.unravel_index(sl.stop, tuple(shape), order = 'F')
#print sl, np.unravel_index(sl.start, mask.shape), np.unravel_index(sl.stop, mask.shape)
pixels.append((start_pixel, stop_pixel))
else:
img_coordinates[fid] = pixels
if output:
gdal_utils.export_data(out_data, r"z:\mem_out_raster.img", tpl_ds)
return img_coordinates
