openalea.phenomenal.data.data

Functions

bin_images(name_dir)	According to the plant number return a dict[id_camera][angle] of numpy array of the loader binary image.
calibrations(name_dir)	According to name_dir return a dict[id_camera] of camera calibration object
chessboard_images(name_dir)	According to the plant number return a dict[id_camera][angle] of numpy array of the loader binary image.
chessboards(name_dir)	According to name_dir return a dict[id_camera] of camera calibration object
data_dir(name_dir[, dtype])
do_calibration(name_dir)	Regenerate calibration of cameras
image_points(name_dir)	According to name_dir return a dict[id_camera] of camera calibration object
mesh_mccormik_plant(name_dir)	According to name_dir return the mesh of plant from the McCormik paper
new_calibrations(name_dir)	According to name_dir return a camera calibration object
path_bin_images(name_dir)	According to the plant number return a dict[id_camera][angle] containing filename of the binary image.
path_chessboard_images(name_dir)	According to the plant number return a dict[id_camera][angle] containing filename of the raw image.
path_raw_images(name_dir)	According to the plant number return a dict[id_camera][angle] containing filename of the raw image.
raw_images(name_dir)	According to the plant number return a dict[id_camera][angle] of numpy array of the loader raw image.
synthetic_plant(name_dir[, registration_point])	According to name_dir return the mesh plant and skeleton of the
tutorial_data_binarization_mask()	Return the list of required images to process the notebook tutorial on binarization.
voxel_grid(name_dir[, plant_number, voxels_size])	According to the plant number and the voxel size desired return the voxel_grid of the plant.