easyidp.visualize.draw_backward_one_roi#
- easyidp.visualize.draw_backward_one_roi(proj, result_dict, buffer=40, title=None, save_as=None, show=False, color='red', alpha=0.5, dpi=72)#
Plot one ROI results on all available images.
- パラメータ:
proj (easyidp.Recons) -- The 3D reconstruction project object
result_dict (dict) --
The dictionary of one ROI backward to all images.e.g.{"IMG_2345": np.array([...]), "IMG_2346": np.array([])}
buffer (int, optional) -- The pixel buffer number around the backward ROI results, by default 40
title (str, optional) -- The image title displayed on the top, by default None ->
Projection on [img_name]
save_as (str, optional) -- file path to save the output figure, by default None
show (bool, optional) -- whether display (in jupyter notebook) or popup (in command line) the figure, by default False
color (str, optional) -- the polygon line color, by default 'red'
alpha (float, optional) -- the polygon transparency, by default 0.5
dpi (int, optional) -- the dpi of produced figure, by default 72
サンプル
Data prepare:
>>> import easyidp as idp >>> lotus = idp.data.Lotus() >>> roi = idp.ROI(lotus.shp, name_field='plot_id') >>> roi.get_z_from_dsm(lotus.metashape.dsm) >>> ms = idp.Metashape(lotus.metashape.project, chunk_id=0) >>> img_dict_ms = roi.back2raw(ms)
Then use this code to show the results of ROI [N1W1]:
>>> idp.visualize.draw_backward_one_roi(ms, img_dict_ms['N1W1'], save_as="draw_backward_one_roi.png")
It will get the following figure: