def image_to_array(i):
'''
Converts a Python Imaging Library (PIL) array to a gdalnumeric image.
'''
a = gdalnumeric.fromstring(i.tobytes(), 'b')
a.shape = i.im.size[1], i.im.size[0]
return a
def image_to_array(i):
'''
Converts a Python Imaging Library (PIL) array to a gdalnumeric image.
'''
a = gdalnumeric.fromstring(i.tobytes(), 'b')
a.shape = i.im.size[1], i.im.size[0]
return a
def imageToArray(i):
"""
Converts a Python Imaging Library array to a
gdalnumeric image.
"""
a=gdalnumeric.fromstring(i.tostring(),'b')
a.shape=i.im.size[1], i.im.size[0]
return a
def image_to_array_byte(i: Image) -> np.ndarray:
"""
Converts a Python Imaging Library array to a
gdalnumeric image.
"""
a = gdalnumeric.fromstring(i.tobytes(), dtype=np.int8)
a.shape = i.im.size[1], i.im.size[0]
return a
def imageToArray(self,i):
"""
Convert the rasterized clipper shapefile
to a mask for use within GDAL.
"""
a=gdalnumeric.fromstring(i.tostring(),'b')
a.shape=i.im.size[1], i.im.size[0]
return a
def imageToArray(self, i):
"""
Convert the rasterized clipper shapefile
to a mask for use within GDAL.
"""
a = gdalnumeric.fromstring(i.tostring(), 'b')
a.shape = i.im.size[1], i.im.size[0]
return a
def image_to_array(pil_array):
"""
Converts a Python Imaging Library (PIL) array to a
gdalnumeric image.
"""
gdal_numeric_array = gdalnumeric.fromstring(pil_array.tobytes(), 'b')
gdal_numeric_array.shape = pil_array.im.size[1], pil_array.im.size[0]
return gdal_numeric_array
def imageToArray(i):
"""
Converts a Python Imaging Library array to a
gdalnumeric image.
"""
a = gdalnumeric.fromstring(i.tobytes(), 'b')
a.shape = i.im.size[1], i.im.size[0]
return a
def imageToArray(i):
# """
# Converts a Python Imaging Library array to a
# gdalnumeric image.
# """
# a=gdalnumeric.fromstring(i.tostring(),'b')
a = gdalnumeric.fromstring(str(i), 'b')
a.shape = i.im.size[1], i.im.size[0]
return a
def imageToArray(img):
"""
将 PIL.Image 转换为 gdalnumeric array 格式\n
参数：\n
img - (PIL.Image)输入图片\n
返回值：\n
"""
array = gdalnumeric.fromstring(img.tobytes(), 'b')
array.shape = img.im.size[1], img.im.size[0]
return array
def imageToArray(i):
"""
将PIL数组转换成gdalnumeric图像
:param i:
:return:
"""
print(type(i))
# a = gdalnumeric.fromstring(i.tostring(), 'b')
a = gdalnumeric.fromstring(i.tobytes(), 'b')
a.shape = i.im.size[1], i.im.size[0]
return a
def imageToArray(i):
a = gdalnumeric.fromstring(i.tobytes(), 'b')
a.shape = i.im.size[1], i.im.size[0]
return a
def image_to_array(i):
"""Converts a PIL array to a gdalnumeric image."""
a = gdalnumeric.fromstring(i.tobytes(), 'b')
a.shape = i.im.size[1], i.im.size[0]
return a
def image_to_array(i):
a = gdalnumeric.fromstring(i.tostring(), 'b')
a.shape = i.im.size[1], i.im.size[0]
return a
def maskShapefile(tile, shp, buffer_size = 0., field = None, value = None, location_id = False):
"""
Rasterize points, lines or polygons from a shapefile to match ALOS mosaic data.
Args:
tile: An ALOS tile (biota.LoadTile())
shp: Path to a shapefile consisting of points, lines and/or polygons. This does not have to be in the same projection as ds
buffer_size: Optionally specify a buffer to add around features of the shapefile, in meters.
field: Optionally specify a single shapefile field to extract (you must also specify its value)
value: Optionally specify a single shapefile field value to extract (you must also specify the field name)
location_id: Set True to return a unique ID for each masked shape. Note: This is not zero indexed, but starts at 1.
Returns:
A numpy array with a boolean (or integer) mask delineating locations inside and outside the shapefile and optional buffer.
"""
import shapefile
from osgeo import gdalnumeric
assert np.logical_or(np.logical_and(field == None, value == None), np.logical_and(field != None, value != None)), "If specifying field or value, both must be defined. At present, field = %s and value = %s"%(str(field), str(value))
shp = os.path.expanduser(shp)
assert os.path.exists(shp), "Shapefile %s does not exist in the file system."%shp
# Determine the size of the buffer in degrees
buffer_size_degrees = buffer_size / (((tile.xRes * tile.xSize) + (tile.yRes * tile.ySize)) / 2.)
# Determine size of buffer to place around lines/polygons
buffer_px = int(round(buffer_size_degrees / tile.geo_t[1]))
# Create output image. Add a buffer around the image array equal to the maxiumum dilation size. This means that features just outside ALOS tile extent can contribute to dilated mask.
rasterPoly = Image.new("I", (tile.xSize + (buffer_px * 2), tile.ySize + (buffer_px * 2)), 0)
rasterize = ImageDraw.Draw(rasterPoly)
# The shapefile may not have the same CRS as ALOS mosaic data, so this will generate a function to reproject points.
coordTransform = _coordinateTransformer(shp)
# Read shapefile
sf = shapefile.Reader(shp)
# Get shapes
shapes = np.array(sf.shapes())
# If extracting a mask for just a single field.
if field != None:
shapes = shapes[getField(shp, field) == value]
# For each shape in shapefile...
for n, shape in enumerate(shapes):
# Get shape bounding box
if shape.shapeType == 1 or shape.shapeType == 11:
# Points don't have a bbox, calculate manually
sxmin = np.min(np.array(shape.points)[:,0])
sxmax = np.max(np.array(shape.points)[:,0])
symin = np.min(np.array(shape.points)[:,1])
symax = np.max(np.array(shape.points)[:,1])
else:
sxmin, symin, sxmax, symax = shape.bbox
# Transform bounding box points
sxmin, symin, z = coordTransform.TransformPoint(sxmin, symin)
sxmax, symax, z = coordTransform.TransformPoint(sxmax, symax)
# Go to the next record if out of bounds
if sxmax < tile.geo_t[0] - buffer_size_degrees: continue
if sxmin > tile.geo_t[0] + (tile.geo_t[1] * tile.xSize) + buffer_size_degrees: continue
if symax < tile.geo_t[3] + (tile.geo_t[5] * tile.ySize) + buffer_size_degrees: continue
if symin > tile.geo_t[3] - buffer_size_degrees: continue
#Separate polygons with list indices
n_parts = len(shape.parts) #Number of parts
indices = shape.parts #Get indices of shapefile part starts
indices.append(len(shape.points)) #Add index of final vertex
# Catch to allow use of point shapefiles, which don't have parts
if shape.shapeType == 1 or shape.shapeType == 11:
n_parts = 1
points = shape.points
for part in range(n_parts):
if shape.shapeType != 1 and shape.shapeType != 11:
start_index = shape.parts[part]
end_index = shape.parts[part+1]
points = shape.points[start_index:end_index] #Map coordinates
pixels = [] #Pixel coordinantes
# Transform coordinates to pixel values
for p in points:
# First update points from shapefile projection to ALOS mosaic projection
lon, lat, z = coordTransform.TransformPoint(p[0], p[1])
# Then convert map to pixel coordinates using geo transform
pixels.append(_world2Pixel(tile.geo_t, lon, lat, buffer_size = buffer_size_degrees))
# Draw the mask for this shape...
# if a point...
if shape.shapeType == 0 or shape.shapeType == 1 or shape.shapeType == 11:
rasterize.point(pixels, n+1)
# a line...
elif shape.shapeType == 3 or shape.shapeType == 13:
rasterize.line(pixels, n+1)
# or a polygon.
elif shape.shapeType == 5 or shape.shapeType == 15:
rasterize.polygon(pixels, n+1)
else:
print('Shapefile type %s not recognised!'%(str(shape.shapeType)))
#Converts a Python Imaging Library array to a gdalnumeric image.
mask = gdalnumeric.fromstring(rasterPoly.tobytes(),dtype=np.uint32)
mask.shape = rasterPoly.im.size[1], rasterPoly.im.size[0]
# If any buffer pixels are slected, dilate the masked area by buffer_px pixels
if buffer_px > 0:
mask = dilateMask(mask, buffer_px, location_id = location_id)
# Get rid of image buffer
mask = mask[buffer_px:mask.shape[0]-buffer_px, buffer_px:mask.shape[1]-buffer_px]
if location_id == False:
# Get rid of record numbers
mask = mask > 0
return mask
def image_to_array(i):
a = gdalnumeric.fromstring(i.tostring(), 'b')
a.shape = i.im.size[1], i.im.size[0]
return a
# рисуем киев на image чтобы сделать mask
t1 = timer()
points = []
pixels = []
kiev_points = kiev.GetGeometryRef(0)
pixels = Stream(range(kiev_points.GetPointCount())) \
.map( lambda i: (kiev_points.GetX(i), kiev_points.GetY(i)) ) \
.map( lambda x, y: world2pixel(transform, x,y) ).list()
rasterPoly = Image.new("L", (pxWidth, pxHeight), 1)
rasterize = ImageDraw.Draw(rasterPoly)
rasterize.polygon(pixels, 0)
mask = gdalnumeric.fromstring(rasterPoly.tobytes(),'b')
mask.shape=rasterPoly.im.size[1], rasterPoly.im.size[0]
print "Create mask %.3f sec" % (timer() - t1)
# Clip the image using the mask
clip = gdalnumeric.choose(mask, (clip, 0)).astype(gdalnumeric.uint16)
gdalnumeric.SaveArray(clip, "kiev.tif", format="GTiff", prototype=raster_path)
dataset = gdal.GetDriverByName('GTiff')\
.Create('kiev2.tif', pxWidth, pxHeight, 1, gdal.GDT_Byte)
dataset.SetGeoTransform(transform)
dataset.SetProjection(str(raster_proj))
def loadShapefile(shp, md_dest, field='', field_values=''):
"""
Rasterize polygons from a shapefile to match a specified CRS.
Args:
shp: Path to a shapefile consisting of points, lines and/or polygons. This does not have to be in the same projection as ds.
md_dest: A metadata file from sen2mosaic.core.Metadata().
field: Field name to include as part of mask. Defaults to all. If specifying an field, you must also specify an field_value.
field_values: Field value or list of values (from 'field') to include in the mask. Defaults to all values. If specifying an field_value, you must also specify a 'field'.
Returns:
A numpy array with a boolean mask delineating locations inside (True) and outside (False) the shapefile given attribute and attribute_value.
"""
if field != '' or field_values != '':
assert field != '' and field_values != '', "Both `attribute` and `attribute_value` must be specified."
shp = os.path.expanduser(shp)
assert os.path.exists(
shp), "Shapefile %s does not exist in the file system." % shp
# Allow input of one or more attribute values
if type(field_values) == str: field_values = [field_values]
def _coordinateTransformer(shp, EPSG_out):
"""
Generates function to transform coordinates from a source shapefile CRS to EPSG.
Args:
shp: Path to a shapefile.
Returns:
A function that transforms shapefile points to EPSG.
"""
driver = ogr.GetDriverByName('ESRI Shapefile')
ds = driver.Open(shp)
layer = ds.GetLayer()
spatialRef = layer.GetSpatialRef()
# Create coordinate transformation
inSpatialRef = osr.SpatialReference()
inSpatialRef.ImportFromWkt(spatialRef.ExportToWkt())
outSpatialRef = osr.SpatialReference()
outSpatialRef.ImportFromEPSG(EPSG_out)
coordTransform = osr.CoordinateTransformation(inSpatialRef,
outSpatialRef)
return coordTransform
def _world2Pixel(geo_t, x, y):
"""
Uses a gdal geomatrix (ds.GetGeoTransform()) to calculate the pixel location of a geospatial coordinate.
Modified from: http://geospatialpython.com/2011/02/clip-raster-using-shapefile.html.
Args:
geo_t: A gdal geoMatrix (ds.GetGeoTransform().
x: x coordinate in map units.
y: y coordinate in map units.
buffer_size: Optionally specify a buffer size. This is used when a buffer has been applied to extend all edges of an image, as in rasterizeShapfile().
Returns:
A tuple with pixel/line locations for each input coordinate.
"""
ulX = geo_t[0]
ulY = geo_t[3]
xDist = geo_t[1]
yDist = geo_t[5]
pixel = int((x - ulX) / xDist)
line = int((y - ulY) / yDist)
return (pixel, line)
def _getField(shp, field):
'''
Get values from a field in a shapefile attribute table.
Args:
shp: A string pointing to a shapefile
field: A string with the field name of the attribute of interest
Retuns:
An array containing all the values of the specified attribute
'''
assert os.path.isfile(shp), "Shapefile %s does not exist." % shp
# Read shapefile
sf = shapefile.Reader(shp)
# Get the column number of the field of interest
for n, this_field in enumerate(sf.fields[1:]):
fieldname = this_field[0]
if fieldname == field:
field_n = n
assert 'field_n' in locals(
), "Attribute %s not found in shapefile." % str(field)
# Extract data type from shapefile. Interprets N (int), F (float) and C (string), sets others to string.
this_dtype = sf.fields[1:][field_n][1]
if this_dtype == 'N':
dtype = np.int
elif this_dtype == 'F':
dtype = np.float32
elif this_dtype == 'C':
dtype = np.str
else:
dtype = np.str
value_out = []
# Cycle through records:
for s in sf.records():
value_out.append(s[field_n])
return np.array(value_out, dtype=dtype)
# Create output image
rasterPoly = Image.new("I", (md_dest.ncols, md_dest.nrows), 0)
rasterize = ImageDraw.Draw(rasterPoly)
# The shapefile may not have the same CRS as the data, so this will generate a function to reproject points.
coordTransform = _coordinateTransformer(shp, md_dest.EPSG_code)
# Read shapefile
sf = shapefile.Reader(shp)
# Get names of fields
fields = sf.fields[1:]
field_names = [field[0] for field in fields]
# Get shapes
shapes = np.array(sf.shapes())
# If extracting a mask for just a single field.
if field != '':
shape_values = _getField(shp, field)
field_values = np.array(field_values).astype(shape_values.dtype)
shapes = shapes[np.isin(shape_values, field_values)]
# For each shape in shapefile...
for n, shape in enumerate(shapes):
#if field != '' and shape_values[n] not in field_values:
# continue
# Get shape bounding box
if shape.shapeType == 1 or shape.shapeType == 11:
# Points don't have a bbox, calculate manually
sxmin = np.min(np.array(shape.points)[:, 0])
sxmax = np.max(np.array(shape.points)[:, 0])
symin = np.min(np.array(shape.points)[:, 1])
symax = np.max(np.array(shape.points)[:, 1])
else:
sxmin, symin, sxmax, symax = shape.bbox
# Transform points
sxmin, symin, z = coordTransform.TransformPoint(sxmin, symin)
sxmax, symax, z = coordTransform.TransformPoint(sxmax, symax)
# Go to the next record if out of bounds
geo_t = md_dest.geo_t
if sxmax < geo_t[0]: continue
if sxmin > geo_t[0] + (geo_t[1] * md_dest.ncols): continue
if symax < geo_t[3] + (geo_t[5] * md_dest.nrows): continue
if symin > geo_t[3]: continue
#Separate polygons with list indices
n_parts = len(shape.parts) #Number of parts
indices = shape.parts #Get indices of shapefile part starts
indices.append(len(shape.points)) #Add index of final vertex
for part in range(n_parts):
if shape.shapeType != 1 and shape.shapeType != 11:
start_index = shape.parts[part]
end_index = shape.parts[part + 1]
points = shape.points[start_index:end_index] #Map coordinates
pixels = [] #Pixel coordinantes
# Transform coordinates to pixel values
for p in points:
# First update points from shapefile projection to image projection
x, y, z = coordTransform.TransformPoint(p[0], p[1])
# Then convert map to pixel coordinates using geo transform
pixels.append(_world2Pixel(md_dest.geo_t, x, y))
# Draw the mask for this shape...
# if a point...
if shape.shapeType == 0 or shape.shapeType == 1 or shape.shapeType == 11:
rasterize.point(pixels, n + 1)
# a line...
elif shape.shapeType == 3 or shape.shapeType == 13:
rasterize.line(pixels, n + 1)
# or a polygon.
elif shape.shapeType == 5 or shape.shapeType == 15:
rasterize.polygon(pixels, n + 1)
else:
print('Shapefile type %s not recognised!' %
(str(shape.shapeType)))
#Converts a Python Imaging Library array to a gdalnumeric image.
mask = gdalnumeric.fromstring(rasterPoly.tobytes(), dtype=np.uint32)
mask.shape = rasterPoly.im.size[1], rasterPoly.im.size[0]
return mask
def pilArrayToGDALnumeric(self, pA):
gdalArray=gdalnumeric.fromstring(pA.tostring(),'b')
gdalArray=shape=pA.im.size[1], pA.im.size[0]
return a
