easyidp.geotiff.geo2pixel#

easyidp.geotiff.geo2pixel(points_hv, header, return_index=False)#

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

Parameters:

points_hv (numpy nx2 array) – [horizontal, vertical] points
header (dict) – the geotiff head dictionary from get_header()
return_index (bool, default false) – if false: will get float coordinates -> (23.5, 27.8) if true: will get int pixel index -> (23, 27)

Returns:

pixel position of these points (horizontal, vertical)

Return type:

ndarray

Notes

Please note: gis UTM coordinate, horizontal is x axis, vertical is y axis, origin at left upper.

To crop image ndarray:

the first columns is vertical pixel (along height),
the second columns is horizontal pixel number (along width),
the third columns is 3 or 4 bands (RGB, alpha),
the x and y is reversed compared with gis coordinates.

This function has already do this reverse, so that you can use the output directly.

Example

# manual specify header just as example (no need to open geotiff)
>>> header = {'width': 19436, 'height': 31255, 'dim':4,
              'scale': [0.001, 0.001], 'nodata': None,
              'tie_point': [484576.70205, 3862285.5109300003],
              'proj': pyproj.CRS.from_string("WGS 84 / UTM zone 53N")}
# prepare coord data (no need to read)
>>> gis_coord = np.asarray([
        [ 484593.67474654, 3862259.42413431],
        [ 484593.41064743, 3862259.92582402],
        [ 484593.64841806, 3862260.06515117],
        [ 484593.93077419, 3862259.55455913],
        [ 484593.67474654, 3862259.42413431]])
# get the results
>>> idp.geotiff.geo2pixel(gis_coord, header, return_index=True)
array([[16972, 26086],
       [16708, 25585],
       [16946, 25445],
       [17228, 25956],
       [16972, 26086]])