def get_image_views(bin_images, calibrations, with_ref_view=True):
refs_angle_list = list()
if with_ref_view:
refs_angle_list = [routine_select_ref_angle(bin_images["side"])]
image_views = list()
for id_camera in bin_images:
for angle in bin_images[id_camera]:
projection = calibrations[id_camera].get_projection(angle)
image_ref = None
if id_camera == "side" and angle in refs_angle_list:
image_ref = bin_images[id_camera][angle]
inclusive = False
if id_camera == "top":
inclusive = True
image_views.append(
phm_obj.ImageView(bin_images[id_camera][angle],
projection,
inclusive=inclusive,
image_ref=image_ref))
return image_views
def get_image_views_cube_projected(with_ref=False):
plant_number = 1
# ==========================================================================
# Create object
voxels_size = 10
voxels_position = phm_data.build_cube(cube_size=10,
voxels_size=voxels_size,
voxels_position=(0, 0, 0))
assert len(voxels_position) == 1000
volume = len(voxels_position) * (10**3)
assert volume == 1000000
# ==========================================================================
calibrations = phm_data.calibrations(plant_number=plant_number)
shape_image = (2454, 2056)
image_views = list()
for angle in range(0, 360, 30):
projection = calibrations["side"].get_projection(angle)
img = phm_mvr.project_voxel_centers_on_image(voxels_position,
voxels_size, shape_image,
projection)
image_ref = None
if with_ref:
if angle == 0:
img[:] = 0
if angle == 90:
image_ref = img
iv = phm_obj.ImageView(img,
projection,
inclusive=False,
image_ref=image_ref)
image_views.append(iv)
return image_views
def test_reconstruction_3d_1():
# Load images binarize
bin_images = phm_data.bin_images(plant_1_dir)
calibrations = phm_data.calibrations(plant_1_dir)
image_views = list()
for id_camera in bin_images:
for angle in bin_images[id_camera]:
projection = calibrations[id_camera].get_projection(angle)
iv = phm_obj.ImageView(bin_images[id_camera][angle],
projection,
inclusive=False,
image_ref=None)
image_views.append(iv)
voxels_size = 64
error_tolerance = 0
vg = phm_mvr.reconstruction_3d(image_views,
voxels_size=voxels_size,
error_tolerance=error_tolerance)
assert len(vg.voxels_position) > 0
image_views = list()
for id_camera in bin_images:
for angle in bin_images[id_camera]:
projection = calibrations[id_camera].get_projection(angle)
image_ref = None
if id_camera == "side" and angle in refs_angle_list:
image_ref = bin_images[id_camera][angle]
inclusive = False
if id_camera == "top":
inclusive = True
image_views.append(
phm_obj.ImageView(bin_images[id_camera][angle],
projection,
inclusive=inclusive,
image_ref=image_ref))
# Do multi-view reconstruction
voxels_size = 16 # mm
error_tolerance = 0
voxel_grid = phm_mvr.reconstruction_3d(image_views,
voxels_size=voxels_size,
error_tolerance=error_tolerance)
# Save / Load voxel grid
voxel_tar_file = '/home/ubuntu/multiviewReconstruction/plant_{}_size_{}.npz'.format(
plant_number, voxels_size)
voxel_grid.write(voxel_tar_file)
print(voxel_tar_file)
def segmentation(row):
bin_images = dc.get_bin_images(row)
calibrations = dc.get_calibrations(row)
refs_angle_list = [_routine_select_ref_angle(bin_images["side"])]
image_views = list()
for id_camera in bin_images:
for angle in bin_images[id_camera]:
projection = calibrations[id_camera].get_projection(angle)
image_ref = None
if id_camera == "side" and angle in refs_angle_list:
image_ref = bin_images[id_camera][angle]
inclusive = False
if id_camera == "top":
inclusive = True
image_views.append(
phm_obj.ImageView(bin_images[id_camera][angle],
projection,
inclusive=inclusive,
image_ref=image_ref))
# ### 2.2 Do multi-view reconstruction
voxels_size = 16 # mm
error_tolerance = 0
voxel_grid = phm_mvr.reconstruction_3d(image_views,
voxels_size=voxels_size,
error_tolerance=error_tolerance)
#phm_display_notebook.show_voxel_grid(voxel_grid, size=1)
# ## 4. Skeletonization
#
# ### 4.1 Compute Graph and the skeleton
# Compute the skeleton of the plant voxelgrid
# In[12]:
graph = phm_seg.graph_from_voxel_grid(voxel_grid, connect_all_point=True)
voxel_skeleton = phm_seg.skeletonize(voxel_grid, graph)
# ### 2.3 Clean skeleton (filter small elements)
# In[14]:
# Select images
image_projection = list()
for angle in bin_images["side"]:
projection = calibrations["side"].get_projection(angle)
image_projection.append((bin_images["side"][angle], projection))
voxel_skeleton_denoised = phm_seg.segment_reduction(voxel_skeleton,
image_projection,
required_visible=5,
nb_min_pixel=400)
#phm_display_notebook.show_skeleton(voxel_skeleton_denoised, size=1)
# ## 5. Maize Segmentation
#
# ### 5.1 Process
#
# Voxel skeleton are segmented in several label "stem", "mature_leaf", "growing_leaf" and "unknow"
# In[16]:
vms = phm_seg.maize_segmentation(voxel_skeleton_denoised, graph)
vmsi = phm_seg.maize_analysis(vms)
return vmsi