def test_graph():
plant_number = 1
voxels_size = 16
voxel_grid = phm_data.voxel_grid(plant_number=plant_number,
voxels_size=voxels_size)
graph = phm_seg.graph_from_voxel_grid(voxel_grid)
def test_meshing():
plant_number = 1
voxels_size = 16
dir_path = os.path.join(os.path.dirname(os.path.realpath(__file__)),
"../data")
print(dir_path)
voxel_grid = phm_data.voxel_grid(dir_path,
plant_number=plant_number,
voxels_size=voxels_size)
image_3d = voxel_grid.to_image_3d()
vertices, faces = phm_mesh.meshing(image_3d,
smoothing_iteration=2,
reduction=0.95,
verbose=True)
assert 100 <= len(vertices) <= 500, len(vertices)
assert 200 <= len(faces) <= 1000, len(faces)
poly_data = phm_mesh.from_vertices_faces_to_vtk_poly_data(vertices, faces)
vtk_image_data = phm_mesh.voxelization(poly_data, voxels_size=voxels_size)
voxels_position = phm_mesh.from_vtk_image_data_to_voxels_center(
vtk_image_data)
assert len(voxels_position) >= 1000
def test_read_write():
plant_number = 1
voxels_size = 8
voxel_grid = phm_data.voxel_grid(plant_number=plant_number,
voxels_size=voxels_size)
filename = 'test.npz'
voxel_grid.write_to_npz(filename)
vg = phm_obj.VoxelGrid.read_from_npz(filename)
os.remove(filename)
def test_maize():
plant_number = 3
voxels_size = 4
voxel_grid = phm_data.voxel_grid(plant_number=plant_number,
voxels_size=voxels_size)
graph = phm_seg.graph_from_voxel_grid(voxel_grid)
voxel_skeleton = phm_seg.skeletonize(voxel_grid, graph)
vms = phm_seg.maize_segmentation(voxel_skeleton, graph)
vmsi = phm_seg.maize_analysis(vms)
def test_running():
plant_number = 1
voxels_size = 16
bin_images = phm_data.bin_images(plant_number=plant_number)
calibrations = phm_data.calibrations(plant_number=plant_number)
voxel_grid = phm_data.voxel_grid(plant_number=plant_number,
voxels_size=voxels_size)
graph = phm_seg.graph_from_voxel_grid(voxel_grid)
voxel_skeleton = phm_seg.skeletonize(voxel_grid, graph)
image_projection = list()
for angle in [0, 120, 270]:
projection = calibrations["side"].get_projection(angle)
image_projection.append((bin_images["side"][angle], projection))
voxel_skeleton_reduced = phm_seg.segment_reduction(voxel_skeleton,
image_projection,
required_visible=1,
nb_min_pixel=100)
def test_running():
bin_images = phm_data.bin_images(plant_1_dir)
calibrations = phm_data.calibrations(plant_1_dir)
voxel_grid = phm_data.voxel_grid(data_dir, 1, 32)
# Load images binarize
graph = phm_seg.graph_from_voxel_grid(voxel_grid)
voxel_skeleton = phm_seg.skeletonize(voxel_grid, graph)
image_projection = list()
for angle in [0, 120, 270]:
projection = calibrations["side"].get_projection(angle)
image_projection.append((bin_images["side"][angle], projection))
voxel_skeleton = phm_seg.segment_reduction(
voxel_skeleton, image_projection,
required_visible=1,
nb_min_pixel=100)
def test_time_graph():
plant_number = 1
voxels_size = 16
voxel_grid = phm_data.voxel_grid(plant_number=plant_number,
voxels_size=voxels_size)
# print(voxel_grid.voxels_position)
voxels_size = int(voxel_grid.voxels_size)
voxels_position = map(tuple, list(voxel_grid.voxels_position))
voxel_grid = phm_obj.VoxelGrid(voxels_position, voxels_size)
number_of_loop = 2
best_time = float('inf')
for i in range(number_of_loop):
t0 = time.time()
graph = phm_seg.graph_from_voxel_grid(voxel_grid)
best_time = min(best_time, float(time.time() - t0))
print("{number_of_loop} loop, best of {number_of_loop}: "
"{best_time}s per loop".format(best_time=best_time,
number_of_loop=number_of_loop))
def test_meshing():
plant_number = 1
voxels_size = 16
voxel_grid = phm_data.voxel_grid(plant_number=plant_number,
voxels_size=voxels_size)
image_3d = voxel_grid.to_image_3d()
vertices, faces = phm_mesh.meshing(image_3d,
smoothing_iteration=2,
reduction=0.95,
verbose=True)
assert 100 <= len(vertices) <= 500, len(vertices)
assert 200 <= len(faces) <= 1000, len(faces)
poly_data = phm_mesh.from_vertices_faces_to_vtk_poly_data(vertices, faces)
vtk_image_data = phm_mesh.voxelization(poly_data, voxels_size=voxels_size)
voxels_position = phm_mesh.from_vtk_image_data_to_voxels_center(
vtk_image_data)
assert len(voxels_position) >= 1000
#
# ==============================================================================
from __future__ import division, print_function
import time
import openalea.phenomenal.data as phm_data
import openalea.phenomenal.object as phm_obj
import openalea.phenomenal.segmentation as phm_seg
# import openalea.phenomenal.display as phm_display
# ==============================================================================
plant_number = 2
voxels_size = 8
bin_images = phm_data.bin_images(plant_number=plant_number)
calibrations = phm_data.calibrations(plant_number=plant_number)
voxel_grid = phm_data.voxel_grid(plant_number=plant_number,
voxels_size=voxels_size)
init_start = time.time()
# ==============================================================================
start = time.time()
graph = phm_seg.graph_from_voxel_grid(voxel_grid)
print("time processing , graph_from_voxel_grid : {}".format(time.time() -
start))
start = time.time()
voxel_skeleton = phm_seg.skeletonize(voxel_grid, graph)
print("time processing , skeletonize : {}".format(time.time() - start))
start = time.time()
image_projection = list()
