easyidp.geotiff.GeoTiff

class easyidp.geotiff.GeoTiff(tif_path='')

A GeoTiff class, consisted by header information and file path to raw file.

__init__(tif_path='')

The method to initialize the GeoTiff class

パラメータ:

tif_path (str | pathlib.Path, optional) -- the path to geotiff file, by default ""

サンプル

>>> import easyidp as idp
>>> test_data = idp.data.TestData()
>>> dom = idp.GeoTiff(test_data.pix4d.lotus_dom)

Methods

__init__([tif_path])	The method to initialize the GeoTiff class
crop_polygon(polygon_hv[, is_geo, save_path])	Crop a given polygon from geotiff
crop_rectangle(left, top, w, h[, is_geo, ...])	Extract a rectangle regeion crop from a GeoTIFF image file.
crop_rois(roi[, is_geo, save_folder])	Crop several ROIs from the geotiff by given <ROI> object with several polygons and polygon names
geo2pixel(polygon_hv)	Convert geotiff pixel coordinate (horizontal, vertical) to point cloud xyz coordinate (x, y, z)
has_data()	Return True if current objects has geotiff infomation
pixel2geo(polygon_hv)	Convert geotiff pixel coordinate (horizontal, vertical) to point cloud xyz coordinate (x, y, z)
point_query(points_hv[, is_geo])	Get the pixel value of given point(s)
polygon_math(polygon_hv[, is_geo, kernel])	Calculate the valus inside given polygon
read_geotiff(tif_path)	Open and get the meta information (header) from geotiff

Attributes

crs	A quick access to self.header['crs'], please access the header dict to change value
dim	A quick access to self.header['dim'], please access the header dict to change value
height	A quick access to self.header['height'], please access the header dict to change value
nodata	A quick access to self.header['nodata'], please access the header dict to change value
scale	A quick access to self.header['scale'], please access the header dict to change value
tie_point	A quick access to self.header['tie_point'], please access the header dict to change value
width	A quick access to self.header['width'], please access the header dict to change value
file_path	The file path of current GeoTiff
header	The Geotiff meta infomation
imarray	The numpy ndarray of GeoTiff images

crop_polygon(polygon_hv, is_geo=True, save_path=None)

Crop a given polygon from geotiff

パラメータ:

• polygon_hv (numpy nx2 array) -- (horizontal, vertical) points

• is_geo (bool, optional) -- whether the given polygon is pixel coords on imarray or geo coords (default)

• save_path (str, optional) -- if given, will save the cropped as *.tif file to path, by default None

戻り値:

The cropped numpy pixels imarray

戻り値の型:

imarray_out

サンプル

Prepare data:

>>> import easyidp as idp
>>> test_data = idp.data.TestData()

# prepare geotiff
>>> dom = idp.GeoTiff(test_data.pix4d.lotus_dom)

# prepare polygon
>>> roi = idp.ROI(test_data.shp.lotus_shp, name_field=0)
>>> roi = roi[0]
>>> roi.change_crs(dom.crs)
>>> roi
array([[ 368017.7565143 , 3955511.08102276],
       [ 368019.70190232, 3955511.49811902],
       [ 368020.11263046, 3955509.54636219],
       [ 368018.15769062, 3955509.13563382],
       [ 368017.7565143 , 3955511.08102276]])

Use this function:

>>> imarray = dom.crop_polygon(roi, is_geo=True)
>>> imarray.shape
(320, 319, 4)

If you want to save the previous as new GeoTiff:

>>> save_tiff = "path/to/save/cropped.tif"
>>> imarray = obj.crop_polygon(polygon_hv, is_geo=True, save_path=save_tiff)

crop_rectangle(left, top, w, h, is_geo=True, save_path=None)

Extract a rectangle regeion crop from a GeoTIFF image file.

(0,0)
o--------------------------
|           ^
|           | top
|           v
| <-------> o=============o  ^
|   left    |<---- w ---->|  |
|           |             |  h
|           |             |  |
|           o=============o  v

パラメータ:

• top (int | float) -- Coordinates of

• left (int | float) -- Coordinates of the top left corner of the desired crop.

• h (int | float) -- Desired crop height.

• w (int | float) -- Desired crop width.

• is_geo (bool, optional) -- whether the given polygon is pixel coords on imarray or geo coords (default)

• save_path (str, optional) -- if given, will save the cropped as *.tif file to path

戻り値:

Extracted crop.

戻り値の型:

ndarray

サンプル

>>> import easyidp as idp
>>> test_data = idp.data.TestData()

>>> obj = idp.GeoTiff(test_data.pix4d.lotus_dom)
>>> out = obj.crop_rectangle(left=434, top=918, w=320, h=321, is_geo=False)
>>> out.shape
(321, 320, 4)

注釈

It is not recommended to use without specifying parameters like this:

crop_rectiange(434, 918, 320, 321)

It is hard to know the exactly order

注意

subfunction easyidp.geotiff.tifffile_crop() has the order (top, left, h, w) which is too heavy to change it.

参考

easyidp.geotiff.tifffile_crop

crop_rois(roi, is_geo=True, save_folder=None)

Crop several ROIs from the geotiff by given <ROI> object with several polygons and polygon names

パラメータ:

• roi (easyidp.ROI | dict) -- the <ROI> object created by easyidp.ROI(), or dictionary with multiple polygons. If you just need crop single polygon with ndarray coordinates, please use GeoTiff.crop_polygon() instead.

• is_geo (bool, optional) -- whether the given polygon is pixel coords on imarray or geo coords (default)

• save_folder (str, optional) -- the folder to save cropped images, use ROI indices as file_names, by default "", means not save.

戻り値:

The dictionary with key=id and value=ndarray data

戻り値の型:

dict,

サンプル

Prepare data:

>>> import easyidp as idp
>>> test_data = idp.data.TestData()

# prepare dom geotiff
>>> dom = idp.GeoTiff(test_data.pix4d.lotus_dom)

# prepare several ROIs
>>> roi = idp.ROI(test_data.shp.lotus_shp, name_field=0)
>>> roi = roi[0:3]    # only use 3 for quick example
>>> roi.change_crs(obj.crs)   # transform to the same CRS like DOM
{0: array([[ 368017.7565143 , 3955511.08102276],
           [ 368019.70190232, 3955511.49811902],
           [ 368020.11263046, 3955509.54636219],
           [ 368018.15769062, 3955509.13563382],
           [ 368017.7565143 , 3955511.08102276]]),
 1: array([[ 368018.20042946, 3955508.96051697],
           [ 368020.14581791, 3955509.37761334],
           [ 368020.55654627, 3955507.42585654],
           [ 368018.601606  , 3955507.01512806],
           [ 368018.20042946, 3955508.96051697]]),
 2: array([[ 368018.64801755, 3955506.84956301],
           [ 368020.59340644, 3955507.26665948],
           [ 368021.00413502, 3955505.31490271],
           [ 368019.04919431, 3955504.90417413],
           [ 368018.64801755, 3955506.84956301]])}

Use this function:

>>> out_dict = obj.crop_rois(roi)
{"N1W1": array[...], "N1W3": array[...], ...}

>>> out_dict["N1W1"].shape
(320, 319, 4)

If you want automatically save geotiff results to specific folder:

>>> tif_out_folder = "./cropped_geotiff"
>>> os.mkdir(tif_out_folder)
>>> out_dict = obj.crop_rois(roi, save_folder=tif_out_folder)

property crs

A quick access to self.header['crs'], please access the header dict to change value

property dim

A quick access to self.header['dim'], please access the header dict to change value

file_path

The file path of current GeoTiff

>>> dom.file_path
PosixPath('/Users/<user>/Library/Application Support/easyidp.data/data_for_tests/pix4d/lotus_tanashi_full/hasu_tanashi_20170525_Ins1RGB_30m_transparent_mosaic_group1.tif')

geo2pixel(polygon_hv)

Convert geotiff pixel coordinate (horizontal, vertical) to point cloud xyz coordinate (x, y, z)

パラメータ:

points_hv (numpy nx2 array) -- [horizontal, vertical] points

戻り値の型:

The ndarray pixel position of these points (horizontal, vertical)

サンプル

Prepare data:

>>> import easyidp as idp
>>> test_data = idp.data.TestData()

# prepare the roi data
>>> roi = idp.ROI(test_data.shp.lotus_shp, name_field=0)
>>> dom = idp.GeoTiff(test_data.pix4d.lotus_dom)
>>> roi.change_crs(dom.crs)
>>> roi_test = roi[111]
array([[ 368051.75902187, 3955484.68169527],
       [ 368053.70441367, 3955485.09879908],
       [ 368054.11515079, 3955483.14704415],
       [ 368052.16020711, 3955482.73630818],
       [ 368051.75902187, 3955484.68169527]])

Use this function:

>>> roi_test_pixel = dom.geo2pixel(roi_test)
array([[5043.01515811, 4551.90714551],
       [5306.6183839 , 4495.38901391],
       [5362.27381938, 4759.85445164],
       [5097.37630191, 4815.50973136],
       [5043.01515811, 4551.90714551]])

参考

easyidp.geotiff.geo2pixel

has_data()

Return True if current objects has geotiff infomation

戻り値の型:

bool

サンプル

>>> import easyidp as idp
>>> test_data = idp.data.TestData()

>>> aaa = idp.GeoTiff()
>>> aaa.has_data()
False
>>> aaa.read_geotiff(test_data.pix4d.lotus_dom)
>>> aaa.has_data()
True

>>> bbb = idp.GeoTiff(test_data.pix4d.lotus_dom)
>>> bbb.has_data()
True

header

The Geotiff meta infomation

>>> dom.header
{'height': 5752, 'width': 5490, 'dim': 4, 'nodata': 0, 'dtype': dtype('uint8'),
'tags': <tifffile.TiffTags @0x00007FB1E8C3FFD0>, 'photometric': <PHOTOMETRIC.RGB: 2>,
'planarconfig': <PLANARCONFIG.CONTIG: 1>, 'compress': <COMPRESSION.LZW: 5>,
'scale': [0.00738, 0.00738], 'tie_point': [368014.54157, 3955518.2747700005],
'crs': <Derived Projected CRS: EPSG:32654>
        Name: WGS 84 / UTM zone 54N
        Axis Info [cartesian]:
        - E[east]: Easting (metre)
        - N[north]: Northing (metre)
        Area of Use:
        - name: Between 138°E and 144°E, northern hemisphere between equator and 84°N, onshore and offshore. Japan. Russian Federation.
        - bounds: (138.0, 0.0, 144.0, 84.0)
        Coordinate Operation:
        - name: UTM zone 54N
        - method: Transverse Mercator
        Datum: World Geodetic System 1984 ensemble
        - Ellipsoid: WGS 84
        - Prime Meridian: Greenwich
}
>>> dom.header["height"]
5752

property height

A quick access to self.header['height'], please access the header dict to change value

imarray

The numpy ndarray of GeoTiff images

property nodata

A quick access to self.header['nodata'], please access the header dict to change value

pixel2geo(polygon_hv)

Convert geotiff pixel coordinate (horizontal, vertical) to point cloud xyz coordinate (x, y, z)

パラメータ:

points_hv (numpy nx2 array) -- [horizontal, vertical] points

戻り値の型:

The ndarray pixel position of these points (horizontal, vertical)

サンプル

Prepare data:

>>> import easyidp as idp
>>> test_data = idp.data.TestData()

# prepare the roi data
>>> roi = idp.ROI(test_data.shp.lotus_shp, name_field=0)
>>> dom = idp.GeoTiff(test_data.pix4d.lotus_dom)
>>> roi.change_crs(dom.crs)
>>> roi_test = roi[111]
>>> roi_test_pixel = dom.geo2pixel(roi_test)
array([[5043.01515811, 4551.90714551],
       [5306.6183839 , 4495.38901391],
       [5362.27381938, 4759.85445164],
       [5097.37630191, 4815.50973136],
       [5043.01515811, 4551.90714551]])

Use this function:

>>> roi_test_back = dom.pixel2geo(roi_test_pixel)
array([[ 368051.75902187, 3955484.68169527],
       [ 368053.70441367, 3955485.09879908],
       [ 368054.11515079, 3955483.14704415],
       [ 368052.16020711, 3955482.73630818],
       [ 368051.75902187, 3955484.68169527]])

参考

easyidp.geotiff.pixel2geo

point_query(points_hv, is_geo=True)

Get the pixel value of given point(s)

パラメータ:

• points_hv (tuple | list | nx2 ndarray) --

  The coordinates of qurey points, in order (horizontal, vertical)

• is_geo (bool, optional) --

  The given polygon is geo coords ( True , default) or pixel coords ( False ) on imarray.

戻り値:

the obtained pixel value (RGB or height)

戻り値の型:

ndarray

サンプル

Prequirements

>>> import easyidp as idp
>>> dom = idp.GeoTiff(test_data.pix4d.lotus_dom)

Query one point by tuple

>>> # one point tuple
>>> pts = (368023.004, 3955500.669)
>>> dom.point_query(pts, is_geo=True)
array([97.45558])

Query one point by list

>>> # one point list
>>> pts = [368023.004, 3955500.669]
>>> dom.point_query(pts, is_geo=True)
array([97.45558])

Query several points by list

>>> pts = [
...    [368022.581, 3955501.054],
...    [368024.032, 3955500.465]
... ]
>>> dom.point_query(pts, is_geo=True)
array([97.624344, 97.59617])

Query several points by numpy

>>> pts = np.array([
...    [368022.581, 3955501.054],
...    [368024.032, 3955500.465]
... ])
>>> dom.point_query(pts, is_geo=True)
array([97.624344, 97.59617])

参考

easyidp.geotiff.point_query

polygon_math(polygon_hv, is_geo=True, kernel='mean')

Calculate the valus inside given polygon

パラメータ:

• polygon_hv (numpy nx2 array | 'all') -- (horizontal, vertical) points

• is_geo (bool, optional) -- whether the given polygon is pixel coords on imarray or geo coords (default)

• kernel (str, optional) -- The method to calculate polygon summary, options are: ["mean", "min", "max", "pmin5", "pmin10", "pmax5", "pmax10"], please check notes section for more details.

メモ

Option details for kernel parameter:

• "mean": the mean value inside polygon

• "min": the minimum value inside polygon

• "max": the maximum value inside polygon

• "pmin5": 5th [percentile mean]_ inside polygon

• "pmin10": 10th [percentile mean]_ inside polygon

• "pmax5": 95th [percentile mean]_ inside polygon

• "pmax10": 90th [percentile mean]_ inside polygon

サンプル

Prepare data:

>>> import easyidp as idp
>>> test_data = idp.data.TestData()

# prepare the roi data
>>> roi = idp.ROI(test_data.shp.lotus_shp, name_field=0)
>>> dsm = idp.GeoTiff(test_data.pix4d.lotus_dsm)
>>> roi.change_crs(dsm.crs)
>>> roi_test = roi[111]
array([[ 368051.75902187, 3955484.68169527],
       [ 368053.70441367, 3955485.09879908],
       [ 368054.11515079, 3955483.14704415],
       [ 368052.16020711, 3955482.73630818],
       [ 368051.75902187, 3955484.68169527]])

Use this function:

>>> dsm.polygon_math(roi_test, is_geo=True, kernel="mean")
97.20491

>>> dsm.polygon_math(roi_test, is_geo=True, kernel="pmax10")
97.311844

注意

This function is initially designed for doing some simple calculations one band (layer) geotiff.

If you applying this function on RGB color geotiff, it will return the calculated results of each layer

>>> dom.polygon_math(roi_test, is_geo=True, kernel="pmax10")
array([139.97428808, 161.36439038, 122.30964888, 255.        ])

The four values are RGBA four color channels.

read_geotiff(tif_path)

Open and get the meta information (header) from geotiff

パラメータ:

tif_path (str | pathlib.Path) -- the path to geotiff file

サンプル

Though this function can be used by:

>>> import easyidp as idp
>>> test_data = idp.data.TestData()
>>> dom = idp.GeoTiff()
>>> dom.read_geotiff(test_data.pix4d.lotus_dom)

It is highly recommended to specify the geotiff path when initializing the geotiff object:

>>> dom = idp.GeoTiff(test_data.pix4d.lotus_dom)

property scale

A quick access to self.header['scale'], please access the header dict to change value

property tie_point

A quick access to self.header['tie_point'], please access the header dict to change value

property width

A quick access to self.header['width'], please access the header dict to change value