easyidp.geotiff.GeoTiff

class easyidp.geotiff.GeoTiff(file_path: str | Path | None = None, imarray: ndarray | None = None, header: dict | None = None, mask: ndarray | None = None)

A easy GeoTiff class warpped on rasterio

__init__(file_path: str | Path | None = None, imarray: ndarray | None = None, header: dict | None = None, mask: ndarray | None = None)

The method to initialize the GeoTiff class

パラメータ:

• tif_path (None| str | pathlib.Path, optional) -- the path to geotiff file, by default None, specify if need to open existing geotiff file

• imarray (np.ndarray) -- The pixel data of GeoTIFF if format of (height, width, bands)

• header (dict) -- The profile / meta information of geotiff file, including size, bands, CRS, etc

• Transparent_layer (None | int, optional) -- the transparent or alpha layer of given GeoTiff file, by default None | None: no alpha layer | 0-x : specific alpha layer | -1 : the last layer

サンプル

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

Methods

__init__([file_path, imarray, header, mask])	The method to initialize the GeoTiff class
convert_from_affine([target_bounds])	Convert from affine rotation storage to standard storage.
convert_to_affine()	Convert from standard storage to affine rotation storage.
crop_polygon(polygon_hv[, is_geo, ...])	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
crop_shapely_polygon(shapely_polygon[, ...])	Crop a given polygon from geotiff, the base function of cropping geotiff
geo2pixel(polygon_hv[, return_index])	Convert geo coordinate (lon, lat) to geotiff pixel coordinate (horizontal, vertical).
has_data()	Check if the geotiff has data
open(tif_path)	Open and get the meta information (header) from geotiff
pixel2geo(polygon_hv)	Convert geotiff pixel coordinate or index (horizontal, vertical) to geo coordinate (x, y).
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
save(save_path[, overwrite, apply_mask, ...])	Save GeoTiff as tiff file with proper nodata/mask handling.
set_mask_polygon(polygon[, is_geo])	Set mask from polygon coordinates.

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
has_alpha	Check if this GeoTiff has an alpha channel.
height	A quick access to self.header['height'], please access the header dict to change value
imarray	Access to the pixel values in the type of numpy ndarray
mask	Boolean mask where True indicates valid (non-nodata) pixels.
mask_polygon	Get the mask as polygon coordinates.
mask_polygon_geo	Get mask polygon in geo coordinates.
mask_polygon_pixel	Get mask polygon in pixel coordinates.
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
use_affine	Check if this GeoTiff uses affine rotation storage.
width	A quick access to self.header['width'], please access the header dict to change value
file_path	The file path of current GeoTiff, pathlib.Path object
header	The Geotiff meta infomation

convert_from_affine(target_bounds: tuple | None = None) → GeoTiff

Convert from affine rotation storage to standard storage.

Returns a NEW GeoTiff object with the rotated imarray transformed back to axis-aligned coordinates, with a mask for the valid region. The original object is not modified.

パラメータ:

target_bounds (tuple, optional) -- (min_x, min_y, max_x, max_y) for the output image bounds. If None, uses the bounding box of mask_polygon.

戻り値:

New GeoTiff object in standard mode. Returns self if already standard.

戻り値の型:

GeoTiff

例外:

ValueError -- If no mask polygon is available for conversion.

サンプル

>>> gtiff = idp.GeoTiff('affine_file.tif')
>>> gtiff.use_affine
True
>>> standard_gtiff = gtiff.convert_from_affine()
>>> standard_gtiff.use_affine
False
>>> gtiff.use_affine  # Original unchanged
True

convert_to_affine() → GeoTiff

Convert from standard storage to affine rotation storage.

Returns a NEW GeoTiff object with imarray aligned to the mask polygon rectangle, and transform including rotation. The original object is not modified.

Requires mask_polygon to be a valid rectangle (4 vertices, 90° angles).

戻り値:

New GeoTiff object in affine mode. Returns self if already affine.

戻り値の型:

GeoTiff

例外:

ValueError -- If no mask polygon is set or polygon is not a valid rectangle.

サンプル

>>> gtiff = idp.GeoTiff('input.tif')
>>> gtiff.set_mask_polygon(rect_coords, is_geo=True)
>>> affine_gtiff = gtiff.convert_to_affine()
>>> affine_gtiff.use_affine
True
>>> gtiff.use_affine  # Original unchanged
False

crop_polygon(polygon_hv, is_geo=True, save_path: str | Path | None = None, return_geotiff: bool = False, use_affine: bool = False)

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 | pathlib.Path, optional) -- if given, will save the cropped as *.tif file to path, by default None

• return_geotiff (bool, optional) -- if specify to True, will return idp.GeoTiff object instead of ndarray

• use_affine (bool, optional) -- if True, use affine rotation storage for cropped results (only if ROI is a rectangle). Forces return_geotiff=True.

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: int, top: int, w: int, h: int, is_geo: bool = True, save_path: str | Path | None = None, return_geotiff: bool = False, use_affine: bool = False)

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) -- Coordinates of the top left corner of the desired crop.

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

• h (int) -- Desired crop height.

• w (int) -- Desired crop width.

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

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

• return_geotiff (bool, optional) -- if specify to True, will return idp.GeoTiff object instead of ndarray

• use_affine (bool, optional) -- if True, use affine rotation storage for cropped results (only if ROI is a rectangle). Forces return_geotiff=True.

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

crop_rois(roi, is_geo=True, save_folder=None, return_geotiff: bool = False, use_affine: bool = False)

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.

• return_geotiff (bool, optional) -- if specify to True, will return idp.GeoTiff object in dictionary values instead of ndarray. Note that if use_affine=True, this will be forced to True.

• use_affine (bool, optional) -- if True, use affine rotation storage for cropped results (only if ROI is a rectangle). Forces return_geotiff=True.

戻り値:

The dictionary with key=id and value=ndarray data (or GeoTiff object)

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)

crop_shapely_polygon(shapely_polygon: Polygon, save_path: str | Path | None = None, return_geotiff: bool = False, use_affine: bool = False)

Crop a given polygon from geotiff, the base function of cropping geotiff

パラメータ:

• shapely_polygon (shapely.geometry.Polygon) -- The polygon to crop, in geo coordinate

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

• return_geotiff (bool, optional) -- if specify to True, will return idp.GeoTiff object instead of ndarray

• use_affine (bool, optional) -- if True, use affine rotation storage for cropped results (only if ROI is a rectangle). Forces return_geotiff=True.

戻り値の型:

idp.GeoTiff object

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, pathlib.Path object

>>> 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: ndarray, return_index=False) → ndarray

Convert geo coordinate (lon, lat) to geotiff pixel coordinate (horizontal, vertical). A warpper of rasterio.io.DatasetReader.transform() and rasterio.io.DatasetReader.index()

パラメータ:

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

• return_index (bool, default false) -- if false: will get float coordinates -> (23.5, 27.8) if true: will get int pixel index -> (23, 27)

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]])

property has_alpha: bool

Check if this GeoTiff has an alpha channel.

This property reads the colorinterp (color interpretation) from the GeoTiff header to determine if an alpha mask is present. The colorinterp field is extracted from the TIFF metadata by rasterio, which maps to the GDAL/TIFF PHOTOMETRIC and EXTRASAMPLES tags.

戻り値:

True if the GeoTiff has an alpha band, False otherwise.

戻り値の型:

bool

サンプル

>>> import easyidp as idp
>>> test_data = idp.data.TestData()
>>> dom = idp.GeoTiff(test_data.pix4d.lotus_dom)
>>> dom.has_alpha
True
>>> dsm = idp.GeoTiff(test_data.pix4d.lotus_dsm)
>>> dsm.has_alpha
False

has_data() → bool

Check if the geotiff has data

header

The Geotiff meta infomation

>>> dom.header
{'height': 5752, 'width': 5490, 'dim': 4, 'nodata': 0, 'dtype': dtype('uint8'),
 '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
 'profile': <dict `rasterio.io.DatasetReader.profile`>
}
>>> dom.header["height"]
5752

注意

Since v2.0.2, this function backend has been switched from tifffile to rasterio to improve the performance, Some of the key tags in header like 'tags', 'photometric', 'planarconfig', 'compress' has been deprecated.

property height

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

property imarray

Access to the pixel values in the type of numpy ndarray

property mask: ndarray | None

Boolean mask where True indicates valid (non-nodata) pixels.

Shape is (height, width). When mask_polygon is set, the binary mask is computed from the polygon. Otherwise, falls back to computing from alpha channel or nodata values.

This property is lazy-computed and cached for efficiency.

戻り値:

Boolean mask array with shape (height, width), or None if no data.

戻り値の型:

np.ndarray | None

サンプル

>>> import easyidp as idp
>>> test_data = idp.data.TestData()
>>> dsm = idp.GeoTiff(test_data.pix4d.lotus_dsm)
>>> mask = dsm.mask
>>> mask.shape
(5752, 5490)
>>> mask.dtype
dtype('bool')

property mask_polygon: ndarray | None

Get the mask as polygon coordinates.

Returns the polygon in its stored coordinate type (geo or pixel). Use mask_polygon_geo or mask_polygon_pixel for specific types.

戻り値:

(n, 2) polygon coordinates, or None if not set.

戻り値の型:

np.ndarray | None

property mask_polygon_geo: ndarray | None

Get mask polygon in geo coordinates.

Converts from pixel coords if needed using the transform.

戻り値:

(n, 2) geo coordinates, or None if no polygon.

戻り値の型:

np.ndarray | None

property mask_polygon_pixel: ndarray | None

Get mask polygon in pixel coordinates.

Converts from geo coords if needed using the transform.

戻り値:

(n, 2) pixel coordinates, or None if no polygon.

戻り値の型:

np.ndarray | None

property nodata

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

open(tif_path: str | 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)

pixel2geo(polygon_hv)

Convert geotiff pixel coordinate or index (horizontal, vertical) to geo coordinate (x, y). A warpper of rasterio.io.DatasetReader.xy() and rasterio.io.DatasetReader.transform()

パラメータ:

points_hv (numpy nx2 array) -- [horizontal, vertical] points if dtype is np.floating, view as pixel coordinate if dtype is np.integer, view as pixel index (return left upper corner of pixel)

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]])

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
>>> dsm = idp.GeoTiff(test_data.pix4d.lotus_dsm)

Query one point by tuple

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

Query one point by list

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

Query several points by list

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

参考

easyidp.geotiff.point_query

polygon_math(polygon_hv: ndarray | None = None, is_geo=True, kernel='mean')

Calculate the valus inside given polygon

パラメータ:

• polygon_hv (numpy nx2 array | None, optional) -- (horizontal, vertical) points. If None, the calculation will be performed on the entire image. Defaults to None.

• 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.

save(save_path: str | Path, overwrite: bool = False, apply_mask: bool = True, use_affine: bool = False) → bool

Save GeoTiff as tiff file with proper nodata/mask handling.

Mask is only applied during save. Previous crop operations preserve full rectangular data, allowing further calculations on edge pixels.

The save strategy depends on data type: - DSM: uses nodata value (-32767.0) - RGB/RGBA: uses alpha channel - MS/MSA (multispectral): adds alpha channel to protect original data

When use_affine=True and mask_polygon is a rectangle, the image is saved with affine rotation in the transform (no mask needed). The current object is NOT modified (unless it was already in affine mode).

パラメータ:

• save_path (str | Path) -- The file path to save the geotiff

• overwrite (bool, optional) -- If True, overwrite existing file without prompting, by default False

• apply_mask (bool, optional) -- If True and mask exists, apply mask appropriately, by default True

• use_affine (bool, optional) -- "Convert and Save" mode. If True, try to use affine rotation storage for rectangular masks. If the object is ALREADY in affine mode (self.use_affine=True), this parameter is ignored (no double-conversion). Requires mask_polygon to be a valid rectangle (4 vertices, 90° angles). By default False.

戻り値:

True if save succeeded, False if cancelled

戻り値の型:

bool

サンプル

>>> import easyidp as idp
>>> test_data = idp.data.TestData()
>>> dsm = idp.GeoTiff(test_data.pix4d.lotus_dsm)
>>> dsm.save('output_dsm.tif', overwrite=True)
True

property scale

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

set_mask_polygon(polygon: ndarray, is_geo: bool = True) → None

Set mask from polygon coordinates.

パラメータ:

• polygon (np.ndarray) -- (n, 2) polygon vertices. Auto-closes if first != last.

• is_geo (bool, optional) -- True if polygon is in geo coordinates, False for pixel. By default True.

サンプル

>>> gtiff = idp.GeoTiff(...)
>>> roi_coords = np.array([[x1, y1], [x2, y2], ...])
>>> gtiff.set_mask_polygon(roi_coords, is_geo=True)

property tie_point

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

property use_affine: bool

Check if this GeoTiff uses affine rotation storage.

When True, the transform contains rotation and the entire image is valid (no mask needed). This is detected from the transform's b and d coefficients being non-zero.

戻り値:

True if using affine rotation storage.

戻り値の型:

bool

property width

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