easyidp.roi.ROI#
- class easyidp.roi.ROI(target_path=None, **kwargs)#
A Region of Interest (ROI) object, can be either 2D or 3D, often read from shp file.
- __init__(target_path=None, **kwargs)#
The method to initialize the ROI class
- Parameters:
target_path (str | pathlib.Path, optional) – the path to roi file, often is shp file path, by default None
Example
>>> import easyidp as idp >>> test_data = idp.data.TestData() >>> roi = idp.ROI(test_data.shp.lotus_shp, name_field=0) [shp][proj] Use projection [WGS 84] for loaded shapefile [lotus_plots.shp] Read shapefile [lotus_plots.shp]: 100%|███████████| 112/112 [00:00<00:00, 2559.70it/s] >>> roi <easyidp.ROI> with 112 items [0] N1W1 array([[139.54052962, 35.73475194], [139.54055106, 35.73475596], [139.54055592, 35.73473843], [139.54053438, 35.73473446], [139.54052962, 35.73475194]]) [1] N1W2 array([[139.54053488, 35.73473289], [139.54055632, 35.73473691], [139.54056118, 35.73471937], [139.54053963, 35.73471541], [139.54053488, 35.73473289]]) ... [110] S4E6 array([[139.54090456, 35.73453742], [139.540926 , 35.73454144], [139.54093086, 35.7345239 ], [139.54090932, 35.73451994], [139.54090456, 35.73453742]]) [111] S4E7 array([[139.54090986, 35.73451856], [139.54093129, 35.73452258], [139.54093616, 35.73450504], [139.54091461, 35.73450107], [139.54090986, 35.73451856]])
Methods
__init__([target_path])The method to initialize the ROI class
back2raw(recons, **kwargs)Projects several GIS coordintates ROIs (polygons) to all images
change_crs(target_crs)Change the geo coordinates of roi to another crs.
crop(target[, save_folder])Crop several ROIs from the geotiff by given <ROI> object with several polygons and polygon names
get_z_from_dsm(dsm[, mode, kernel, buffer, ...])Get the z values (heights) from DSM for 2D polygon
get_z_from_pcd(pcd[, mode, kernel, buffer])Get the z values (heights) from Point cloud for 2D polygon
is_geo()Returns True if the ROI is geo coordinate.
open(target_path, **kwargs)An advanced wrapper to open ROI without dealing with file format, current support shapefile.shp and labelme.json
read_labelme_json(json_path)read roi from labelme marked json file
read_shp(shp_path[, shp_proj, name_field, ...])read ROI from shp file
read_geojson(geojson_path[, name_field, ...])read ROI from geojson file
Attributes
- back2raw(recons, **kwargs)#
Projects several GIS coordintates ROIs (polygons) to all images
- Parameters:
roi (easyidp.ROI | dict) – the <ROI> object created by easyidp.ROI() or dictionary
save_folder (str, optional) – the folder to save projected preview images and json files, by default “”
distortion_correct (bool, optional) –
Whether do distortion correction, by default True (back to raw image with lens distortion);If back to software corrected images without len distortion, set it to False.(Pix4D support do this operation, seems metashape not supported yet.)ignore (str | None, optional) –
Whether tolerate small parts outside image, check
easyidp.reconstruct.Sensor.in_img_boundary()for more details.None: strickly in image area;x: only y (vertical) in image area, x can outside image;y: only x (horizontal) in image area, y can outside image.
log (bool, optional) – whether print log for debugging, by default False
- Returns:
out_dict – The 2-layer dictionary, the first layer is the roi id, the second layer is the image names contains each roi (
out_dict['roi_id']['image_name'])>>> out_dict = roi.back2raw(...) # find all available images with specified roi (plot), e.g. roi named 'N1W1': >>> one_roi_dict = out_dict['N1W1'] # roi id {'IMG_3457': ..., 'IMG_3458': ..., ...} # find the sepecific roi on specific image, e.g. roi named 'N1W1' on image 'IMG_3457': >>> one_roi_one_img_coord = out_dict["N1W1']['IMG_3457'] array([[ 43.9388228 , 1247.0474214 ], [ 69.04076173, 972.90860296], [ 353.26968458, 993.31308291], [ 328.12327606, 1267.41006845], [ 43.9388228 , 1247.0474214 ]])
Caution
It is recommended to use dict.items() for iteration.
for roi_id, img_dict in out_dict.items(): # roi_id = 'N1W1' # img_dict = out_dict[roi_id] for img_name, coords in img_dict.items(): # img_name = "IMG_3457" # coords = out_dict[roi_id][img_name] print(coords)
Not recommended to use in this way:
for roi_id in out_dict.keys() img_dict = out_dict[roi_id] for img_name in img_dict.keys(): coords = out_dict[roi_id][img_name] print(coords)
- Return type:
dict
Example
Data prepare:
>>> import easyidp as idp >>> lotus = idp.data.Lotus() >>> p4d = idp.Pix4D(lotus.pix4d.project, lotus.photo, lotus.pix4d.param) >>> ms = idp.Metashape(project_path=lotus.metashape.project, chunk_id=0) >>> roi = idp.ROI(lotus.shp, name_field=0) >>> roi = roi[0:3] >>> roi.get_z_from_dsm(lotus.pix4d.dsm)
Then do the backward calculation
>>> out_p4d = roi.back2raw(p4d) >>> out_ms = roi.back2raw(ms) {'N1W1': {'DJI_0479': array([[ 43.91987253, 1247.04066872], [ 69.0221046 , 972.89938018], [ 353.25370817, 993.30409359], [ 328.10701394, 1267.40353364], [ 43.91987253, 1247.04066872]]), 'DJI_0480': array([[ 655.3678591 , 1273.01418098], [ 681.18303761, 996.4866665 ], [ 965.60719523, 1019.55346144], [ 939.89408896, 1296.05588162], [ 655.3678591 , 1273.01418098]]), 'DJI_0481': array([[1024.43757205, 1442.10211955], [1043.51451272, 1159.41597 ], [1331.67724595, 1177.40543929], [1312.55275279, 1460.0493473 ], [1024.43757205, 1442.10211955]]), ... } 'N1W2': {...} ...
See also
easyidp.pix4d.back2raw,easyidp.metashape.back2raw
- change_crs(target_crs)#
Change the geo coordinates of roi to another crs.
- Parameters:
target_crs (pyproj.CRS) – the CRS want convert to.
Example
>>> import easyidp as idp >>> test_data = idp.data.TestData()
Read roi with lon and lat CRS (WGS84)
>>> roi = idp.ROI(test_data.shp.lotus_shp) [shp][proj] Use projection [WGS 84] for loaded shapefile [lotus_plots.shp] >>> roi.crs <Geographic 2D CRS: EPSG:4326> Name: WGS 84 Axis Info [ellipsoidal]: - Lat[north]: Geodetic latitude (degree) - Lon[east]: Geodetic longitude (degree) Area of Use: - name: World. - bounds: (-180.0, -90.0, 180.0, 90.0) Datum: World Geodetic System 1984 ensemble - Ellipsoid: WGS 84 - Prime Meridian: Greenwich
Check the roi coordinates
>>> roi[0] array([[139.54052962, 35.73475194], [139.54055106, 35.73475596], [139.54055592, 35.73473843], [139.54053438, 35.73473446], [139.54052962, 35.73475194]])
Read a geotiff with different CRS (UTM 54N)
>>> dom = idp.GeoTiff(test_data.pix4d.lotus_dom) >>> target_crs = dom.header["crs"] >>> target_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
Change the roi crs (coordiante) from WGS84 to UTM 54N
>>> roi.change_crs(target_crs) >>> roi.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
Check the converted coordiante values
>>> roi[0] array([[ 368017.7565143 , 3955511.08102276], [ 368019.70190232, 3955511.49811902], [ 368020.11263046, 3955509.54636219], [ 368018.15769062, 3955509.13563382], [ 368017.7565143 , 3955511.08102276]])
- crop(target, save_folder=None)#
Crop several ROIs from the geotiff by given <ROI> object with several polygons and polygon names
- Parameters:
target (str | <GeoTiff> object) – the path of dsm, or the GeoTiff object from idp.GeoTiff()
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 None, means not save.
- Returns:
The dictionary with key=id and value=ndarray data
- Return type:
dict,
Example
Data prepare:
>>> import easyidp as idp >>> test_data = idp.data.TestData() >>> roi = idp.ROI(test_data.shp.lotus_shp , name_field=0) >>> roi = roi[0:3] >>> roi.get_z_from_dsm(lotus_full_dsm, mode="point", kernel="mean", buffer=0, keep_crs=False)
Then crop the given DOM, DSM and PointCloud:
>>> lotus_full_dsm = test_data.pix4d.lotus_dsm >>> lotus_full_pcd = test_data.pix4d.lotus_pcd >>> lotus_full_dom = test_data.pix4d.lotus_dom >>> out_dom = roi.crop(lotus_full_dom) >>> out_dsm = roi.crop(lotus_full_dsm) >>> out_pcd = roi.crop(lotus_full_pcd) >>> out_dsm {'N1W1': array([[-10000., -10000., -10000., ..., -10000., -10000., -10000.], [-10000., -10000., -10000., ..., -10000., -10000., -10000.], [-10000., -10000., -10000., ..., -10000., -10000., -10000.], ..., [-10000., -10000., -10000., ..., -10000., -10000., -10000.], [-10000., -10000., -10000., ..., -10000., -10000., -10000.], [-10000., -10000., -10000., ..., -10000., -10000., -10000.]], dtype=float32), 'N1W2': array([[-10000., -10000., -10000., ..., -10000., -10000., -10000.], [-10000., -10000., -10000., ..., -10000., -10000., -10000.], [-10000., -10000., -10000., ..., -10000., -10000., -10000.], ..., [-10000., -10000., -10000., ..., -10000., -10000., -10000.], [-10000., -10000., -10000., ..., -10000., -10000., -10000.], [-10000., -10000., -10000., ..., -10000., -10000., -10000.]], dtype=float32), 'N1W3': array([[-10000., -10000., -10000., ..., -10000., -10000., -10000.], [-10000., -10000., -10000., ..., -10000., -10000., -10000.], [-10000., -10000., -10000., ..., -10000., -10000., -10000.], ..., [-10000., -10000., -10000., ..., -10000., -10000., -10000.], [-10000., -10000., -10000., ..., -10000., -10000., -10000.], [-10000., -10000., -10000., ..., -10000., -10000., -10000.]], dtype=float32)}
Or you can specify the
save_folderparameter to automatically save the cropped results>>> out_dom = roi.crop(lotus_full_dom, save_folder=r"path/to/save/outputs")
See also
- crs#
the CRS that current ROI used.
- get_z_from_dsm(dsm, mode='face', kernel='mean', buffer=0, keep_crs=False)#
Get the z values (heights) from DSM for 2D polygon
- Parameters:
dsm (str | <GeoTiff> object) – the path of dsm, or the GeoTiff object from idp.GeoTiff()
mode (str, optional) –
the mode to calculate z values, by default “face”.
point: get height on each vertex, result in different values for each vertexface: get height on polygon face, result in the same value for each vertex
kernel (str, optional) –
The math kernel to calculate the z value, by default ‘mean’
mean: the mean value inside polygonmin: the minimum value inside polygonmax: the maximum value inside polygonpmin5: 5th percentile mean inside polygonpmin10: 10th percentile mean inside polygonpmax5: 95th percentile mean inside polygonpmax10: 90th percentile mean inside polygon
Note
percentile mean: the mean value of all pixels over/under xth percentile threshold
buffer (float, optional) –
the buffer of ROI, by default 0 (no buffer),0: not using buffer-1: ignore given polygon, using the full dsm to calculate the heightfloat: buffer distance, the unit of buffer follows the DSM coordinates, either pixel or meter.
Note
If
mode="point", will generate a round buffer polygon first, then extract the z-value by this region, but the return will only be a single point.If
mode="face", will buffer the polygon and then calculate the height inside the buffered polygon
keep_crs (bool, optional) –
When the crs is not the save with DSM crs, where change the ROI crs to fit DSM.
False(default): change ROI’s CRS;True: not change ROI’s CRS, only attach the z value to current coordinate.
Example
Data prepare:
>>> import easyidp as idp >>> test_data = idp.data.TestData() >>> roi = idp.ROI(test_data.shp.lotus_shp, name_field=0) >>> roi = roi[0:3] >>> lotus_full_dsm = idp.GeoTiff(test_data.pix4d.lotus_dsm)
Caution
The ROI and DSM, did not share the same CRS.
The ROI is in longitude-latitude coordinate system, unit is degree.
>>> roi.crs <Geographic 2D CRS: EPSG:4326> Name: WGS 84 ... >>> roi [0] N1W1 array([[139.54052962, 35.73475194], [139.54055106, 35.73475596], [139.54055592, 35.73473843], [139.54053438, 35.73473446], [139.54052962, 35.73475194]]) [1] N1W2 array([[139.54053488, 35.73473289], [139.54055632, 35.73473691], [139.54056118, 35.73471937], [139.54053963, 35.73471541], [139.54053488, 35.73473289]]) [2] N1W3 array([[139.54054017, 35.73471392], [139.54056161, 35.73471794], [139.54056647, 35.73470041], [139.54054493, 35.73469644], [139.54054017, 35.73471392]])
While the DSM (and DOM) are in UTM zone 54N, unit is meter.
>>> lotus_full_dsm.crs <Derived Projected CRS: EPSG:32654> Name: WGS 84 / UTM zone 54N ... >>> >>> lotus_full_dsm.header['tie_point'] [368014.54157, 3955518.2747700005]
Different mode examples
The point mode, each point has its unique z value.
>>> roi_temp = roi.copy() >>> roi_temp.get_z_from_dsm(lotus_full_dsm, mode="point", kernel="mean", buffer=0, keep_crs=False) >>> roi_temp <easyidp ROI> with 3 items [0] N1W1 array([[ 368017.7565143 , 3955511.08102276, 97.63990021], [ 368019.70190232, 3955511.49811902, 97.67140198], [ 368020.11263046, 3955509.54636219, 97.75421143], [ 368018.15769062, 3955509.13563382, 97.65288544], [ 368017.7565143 , 3955511.08102276, 97.63990021]]) [1] N1W2 array([[ 368018.20042946, 3955508.96051697, 97.65105438], [ 368020.14581791, 3955509.37761334, 97.65817261], [ 368020.55654627, 3955507.42585654, 97.63339996], [ 368018.601606 , 3955507.01512806, 97.61153412], [ 368018.20042946, 3955508.96051697, 97.65105438]]) [2] N1W3 array([[ 368018.64801755, 3955506.84956301, 97.59950256], [ 368020.59340644, 3955507.26665948, 97.64406586], [ 368021.00413502, 3955505.31490271, 97.64678192], [ 368019.04919431, 3955504.90417413, 97.63285828], [ 368018.64801755, 3955506.84956301, 97.59950256]])
The face mode, all points of one ROI share the same z value.
>>> roi_temp = roi.copy() >>> roi_temp.get_z_from_dsm(lotus_full_dsm, mode="face", kernel="mean", buffer=0, keep_crs=False) >>> roi_temp <easyidp ROI> with 3 items [0] N1W1 array([[ 368017.7565143 , 3955511.08102276, 97.68352509], [ 368019.70190232, 3955511.49811902, 97.68352509], [ 368020.11263046, 3955509.54636219, 97.68352509], [ 368018.15769062, 3955509.13563382, 97.68352509], [ 368017.7565143 , 3955511.08102276, 97.68352509]]) [1] N1W2 array([[ 368018.20042946, 3955508.96051697, 97.59811401], [ 368020.14581791, 3955509.37761334, 97.59811401], [ 368020.55654627, 3955507.42585654, 97.59811401], [ 368018.601606 , 3955507.01512806, 97.59811401], [ 368018.20042946, 3955508.96051697, 97.59811401]]) [2] N1W3 array([[ 368018.64801755, 3955506.84956301, 97.6997757 ], [ 368020.59340644, 3955507.26665948, 97.6997757 ], [ 368021.00413502, 3955505.31490271, 97.6997757 ], [ 368019.04919431, 3955504.90417413, 97.6997757 ], [ 368018.64801755, 3955506.84956301, 97.6997757 ]])
Setting buffer
You can using buffer to calculate z values from a larger area. This will decrease the effects of some extreme noise points on DSM. Especially for the point mode, which is more sensitive to such noise.
Caution
The value here share the same unit as DSM, if your DSM in lon-lat coordinate (e.g. WGS84, EPSG:4326),
buffer=1.0will result in 1.0 degree in longitude and latitude, this is a very large area!>>> roi_temp = roi.copy() >>> roi_temp.get_z_from_dsm(lotus_full_dsm, mode="face", kernel="mean", buffer=1.0, keep_crs=False) >>> roi_temp <easyidp ROI> with 3 items [0] N1W1 array([[ 368017.7565143 , 3955511.08102276, 98.30323792], [ 368019.70190232, 3955511.49811902, 98.30323792], [ 368020.11263046, 3955509.54636219, 98.30323792], [ 368018.15769062, 3955509.13563382, 98.30323792], [ 368017.7565143 , 3955511.08102276, 98.30323792]]) [1] N1W2 array([[ 368018.20042946, 3955508.96051697, 97.6088028 ], [ 368020.14581791, 3955509.37761334, 97.6088028 ], [ 368020.55654627, 3955507.42585654, 97.6088028 ], [ 368018.601606 , 3955507.01512806, 97.6088028 ], [ 368018.20042946, 3955508.96051697, 97.6088028 ]]) [2] N1W3 array([[ 368018.64801755, 3955506.84956301, 97.5995636 ], [ 368020.59340644, 3955507.26665948, 97.5995636 ], [ 368021.00413502, 3955505.31490271, 97.5995636 ], [ 368019.04919431, 3955504.90417413, 97.5995636 ],
keep_crs option
If not keep CRS, the ROI x and y values will also change to the same coordinate with DSM.
If do not want the value change, please setting
keep_crs=True>>> roi_temp = roi.copy() >>> roi_temp.get_z_from_dsm(lotus_full_dsm, mode="point", kernel="mean", buffer=0, keep_crs=True) >>> roi_temp <easyidp ROI> with 3 items [0] N1W1 array([[139.54052962, 35.73475194, 97.63990021], [139.54055106, 35.73475596, 97.67140198], [139.54055592, 35.73473843, 97.75421143], [139.54053438, 35.73473446, 97.65288544], [139.54052962, 35.73475194, 97.63990021]]) [1] N1W2 array([[139.54053488, 35.73473289, 97.65105438], [139.54055632, 35.73473691, 97.65817261], [139.54056118, 35.73471937, 97.63339996], [139.54053963, 35.73471541, 97.61153412], [139.54053488, 35.73473289, 97.65105438]]) [2] N1W3 array([[139.54054017, 35.73471392, 97.59950256], [139.54056161, 35.73471794, 97.64406586], [139.54056647, 35.73470041, 97.64678192], [139.54054493, 35.73469644, 97.63285828], [139.54054017, 35.73471392, 97.59950256]])
See also
- get_z_from_pcd(pcd, mode='face', kernel='mean', buffer=0)#
Get the z values (heights) from Point cloud for 2D polygon
Attention
This function has not been implemented.
See also
- is_geo()#
Returns True if the ROI is geo coordinate.
- Return type:
bool
- open(target_path, **kwargs)#
An advanced wrapper to open ROI without dealing with file format, current support shapefile.shp and labelme.json
- Parameters:
target_path (str) – the path to roi files, current support shapefile.shp and labelme.json
Example
Initialize an empty object:
>>> import easyidp as idp >>> test_data = idp.data.TestData() >>> roi = idp.ROI()
Then you can open a ROI by:
>>> roi.read_shp(test_data.shp.lotus_shp, name_field=0) >>> roi.read_labelme_json(test_data.json.labelme_demo)
Or using this short function:
>>> roi.open(test_data.shp.lotus_shp, name_field=0) >>> roi.open(test_data.json.labelme_demo)
Notes
You can also pass several control parameters in this function, please refer to
read_shp()andread_labelme_json()for more information
- read_geojson(geojson_path, name_field=-1, include_title=False)#
read ROI from geojson file
- Parameters:
geojson_path (str) – the file path of *.geojson
name_field (str or int or list[ str|int ], optional) – by default None, the id or name of shp file fields as output dictionary keys
include_title (bool, optional) – by default False, whether add column name to roi key.
Example
>>> import easyidp as idp >>> test_data = idp.data.TestData() >>> roi = idp.ROI()
Then you can open a ROI by:
>>> roi.read_geojson(test_data.json.geojson_soy, name_field="FID") Read geojson [2023_soybean_field.geojson]: 100%|███████████| 260/260 [00:00<00:00, 218234.75it/s] >>> roi <easyidp.ROI> with 260 items [0] 65 array([[-26384.952573, -28870.678514], [-26384.269447, -28870.522501], [-26385.160022, -28866.622912], [-26385.843163, -28866.778928], [-26384.952573, -28870.678514]]) [1] 97 array([[-26386.065868, -28865.804036], [-26385.382668, -28865.648006], [-26386.273244, -28861.748416], [-26386.956458, -28861.90445 ], [-26386.065868, -28865.804036]]) ... [258] 4 array([[-26404.447166, -28860.770249], [-26405.337854, -28856.870669], [-26406.020223, -28857.026509], [-26405.129644, -28860.926114], [-26404.447166, -28860.770249]]) [259] 1 array([[-26393.693576, -28844.979604], [-26394.58426 , -28841.08004 ], [-26395.26665 , -28841.235885], [-26394.375966, -28845.135449], [-26393.693576, -28844.979604]])
Notes
For more details of these parameters, please refer to
easyidp.jsonfile.read_geojson()See also
- read_labelme_json(json_path)#
read roi from labelme marked json file
- Parameters:
json_path (str) – the path to labelme json file.
Example
>>> import easyidp as idp >>> test_data = idp.data.TestData() >>> json_path = test_data.json.labelme_demo PosixPath('/Users/<user>/Library/Application Support/easyidp.data/data_for_tests/json_test/labelme_demo_img.json') >>> roi = idp.ROI(json_path) >>> roi <easyidp.ROI> with 1 items [0] 1 array([[2447.2392638 , 1369.32515337], [2469.93865031, 1628.2208589 ], [2730.06134969, 1605.52147239], [2703.06748466, 1348.46625767]])
- read_shp(shp_path, shp_proj=None, name_field=-1, include_title=False, encoding='utf-8')#
read ROI from shp file
- Parameters:
shp_path (str) – the file path of *.shp
shp_proj (str | pyproj object) –
by default None, will read automatically from prj file with the same name of shp filename,or give manually byread_shp(..., shp_proj=pyproj.CRS.from_epsg(4326), ...)orread_shp(..., shp_proj=r'path/to/{shp_name}.prj', ...)name_field (str or int or list[ str|int ], optional) – by default None, the id or name of shp file fields as output dictionary keys
include_title (bool, optional) – by default False, whether add column name to roi key.
encoding (str) – by default ‘utf-8’, for some chinese characters, ‘gbk’ may required
Example
>>> import easyidp as idp >>> test_data = idp.data.TestData() >>> roi = idp.ROI()
Then you can open a ROI by:
>>> roi.read_shp(test_data.shp.lotus_shp, name_field=0) [shp][proj] Use projection [WGS 84] for loaded shapefile [lotus_plots.shp] Read shapefile [lotus_plots.shp]: 100%|███████████| 112/112 [00:00<00:00, 2559.70it/s] >>> roi <easyidp.ROI> with 112 items [0] N1W1 array([[139.54052962, 35.73475194], [139.54055106, 35.73475596], [139.54055592, 35.73473843], [139.54053438, 35.73473446], [139.54052962, 35.73475194]]) [1] N1W2 array([[139.54053488, 35.73473289], [139.54055632, 35.73473691], [139.54056118, 35.73471937], [139.54053963, 35.73471541], [139.54053488, 35.73473289]]) ... [110] S4E6 array([[139.54090456, 35.73453742], [139.540926 , 35.73454144], [139.54093086, 35.7345239 ], [139.54090932, 35.73451994], [139.54090456, 35.73453742]]) [111] S4E7 array([[139.54090986, 35.73451856], [139.54093129, 35.73452258], [139.54093616, 35.73450504], [139.54091461, 35.73450107], [139.54090986, 35.73451856]])
Notes
For more details of these parameters, please refer to
easyidp.shp.read_shp()See also
- source#
the source file path of current ROI.