def save_or_plot_subsample(args, subsample, filename, img_name=None):
if args.save_tensor == 1: torch.save(subsample, filename) # save as tensor
elif args.save_tensor == 0: # save as numpy array
if args.sequence_name == 'DeepCloth':
np.savetxt(filename, subsample)
else:
np.savetxt(filename, subsample.to(torch.device("cpu")).numpy())
elif args.save_tensor == 2:
functions_plot.plot_RGB_and_landmarks(subsample[:, 0],
subsample[:, 1],
image_path=img_name,
axis_on='off',
visible_colour='y')
if args.contour == 1:
functions_plot.show_for_seconds(milliseconds=400000)
# Load vertex data
variables = get_variables_from_vertex_full_Dataframe(
sequence_name=sequence_name,
dataset_number=dataset_number,
group_number=group_number,
animation_frame=animation_frame,
RT_extended=RT_extended,
reordered=reordered,
submesh_idx=submesh_idx,
verbose=0)
u_visible = variables['u_visible']
v_visible = variables['v_visible']
u_occluded = variables['u_occluded']
v_occluded = variables['v_occluded']
# Plot RGB and landmarks
fig = functions_plot.plot_RGB_and_landmarks(
u_visible=u_visible,
v_visible=v_visible,
u_occluded=u_occluded,
v_occluded=v_occluded,
sequence_name=sequence_name,
dataset_number=dataset_number,
group_number=group_number,
animation_frame=animation_frame,
marker_size=0.01)
file_name = os.path.join(
directory_name,
'Group' + group_number + '_frame' + animation_frame + '.png')
fig.savefig(file_name)
# animation_frame = '1'
submesh_num_vertices_horizontal = 52
submesh_num_vertices_vertical = 103
verbose = 0
input_png_name = os.path.join('Renders' + sequence_name + dataset_number,
'Group.' + group_number,
animation_frame + '.png')
u, v = convert_contour_to_uv_from_img_name(input_png_name=input_png_name,
verbose=verbose)
# Plot uv of contour without straight lines on RGB
fig = functions_plot.plot_RGB_and_landmarks(
u_visible=u,
v_visible=v,
sequence_name=sequence_name,
dataset_number=dataset_number,
group_number=group_number,
animation_frame=animation_frame,
marker_size=50 / submesh_num_vertices_vertical)
plt.show()
# Access all pixels from contour, without omitting the ones which describe straight lines
def append_missing_new_value(w, i, l):
""" update w (u or v) adding the missing points between w[i] and w[i+1].
l = len of the original w before undergoing any updates at all"""
sign = 1 if w[(i + 1) % l] > w[i % l] else -1
for k in range(sign, w[(i + 1) % l] - w[i % l], sign):
w = np.append(w, w[i] + k)
return w
text_name = os.path.join(directory_name, 'uv_width_height_rectROI_' + str(int(animation_frame)) +'.txt')
uvwh = np.genfromtxt(fname=text_name, dtype='int', delimiter=' ', skip_header=1)
u_crop_corner = uvwh[0]
v_crop_corner = uvwh[1]
width_crop = uvwh[2]
height_crop = uvwh[3]
u_visible_crop = u_visible - u_crop_corner
v_visible_crop = v_visible - v_crop_corner
u_occluded_crop = u_occluded - u_crop_corner
v_occluded_crop = v_occluded - v_crop_corner
ROI_image_path = os.path.join(directory_name, str(int(animation_frame)) + '_rectROI.png')
functions_plot.plot_RGB_and_landmarks(u_visible=u_visible_crop, v_visible=v_visible_crop,
u_occluded=u_occluded_crop, v_occluded=v_occluded_crop,
image_path = ROI_image_path)
plt.title('Plot RGB (from file path) and landmarks\ncropped image - (u,v)=(0,0) on upper-left corner')
###
### Plot RGB and landmarks on a cropped sample - loading RGB from transformed dataset
###
# Cropping option:
# crop_centre_or_ROI==0: centre crop.
# crop_centre_or_ROI==1: Squared box containing the towel.
# crop_centre_or_ROI==2: Rectangular box containing the towel.
import functions_plot
args = functions_data_processing.parser()
DeepCloth_example = os.path.join('DeepCloth2', 'train', 'train_non-text',
'imgs', '027996.png')
TowelWall_example = os.path.join('RendersTowelWall11', 'Group.003',
'17.png')
for input_png_name in [DeepCloth_example, TowelWall_example]:
uv_towel = find_uv_from_img_name(input_png_name=input_png_name,
verbose=0,
sequence_name=args.sequence_name)
print(uv_towel.shape)
# Plot uv on RGB
fig = functions_plot.plot_RGB_and_landmarks(u_visible=uv_towel[:, 0],
v_visible=uv_towel[:, 1],
image_path=input_png_name)
plt.show()
uv_towel = find_uv_from_img_name(input_png_name=input_png_name,
verbose=0,
numpy_or_tensor=1,
sequence_name=args.sequence_name)
print(uv_towel.shape)
# Plot uv on RGB
fig = functions_plot.plot_RGB_and_landmarks(u_visible=uv_towel[:, 0],
v_visible=uv_towel[:, 1],
image_path=input_png_name)
plt.show()
show_BnW_n_original_video(