luni/cvpr
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

save

Browse Source
This commit is contained in:
luni
2023-02-13 10:03:19 +00:00
parent 11a8c7e63e
commit 19938273c3
3 changed files with 40 additions and 40 deletions
Download Patch File Download Diff File Expand all files Collapse all files
curvature_analysis.py
+6 -6
View File
@@ -67,7 +67,7 @@ def get_bins(values, nr_of_bins):
return bins
def generate_curvatures(folder, frame, bins, neighbourhoods=[25], nr_of_labels=6):
def generate_curvatures(folder, frame, bins, neighbourhoods=[25], nr_of_labels=[6]):
id = frame.split('_')[0]
frame_nr = int(frame.split('_')[1].split('.')[0])
@@ -91,14 +91,14 @@ def generate_curvatures(folder, frame, bins, neighbourhoods=[25], nr_of_labels=6
cloud = PyntCloud.from_file(temp_file)
curvatures = []
for neighbourhood in neighbourhoods:
for neighbourhood, nr_labels in zip(neighbourhoods, nr_of_labels):
k_neighbors = np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
#k_neighbors = k_neighbors_cache[id] # np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
ev = cloud.add_scalar_field("eigen_values", k_neighbors=k_neighbors)
cloud.add_scalar_field("curvature", ev=ev)
curvature = np.array(cloud.points["curvature(" + str(neighbourhood+1) + ")"])
bins_local = bins[neighbourhood] if bins is not None else get_bins(curvature, nr_of_labels)
bins_local = bins[neighbourhood] if bins is not None else get_bins(curvature, nr_labels)
labels = np.linspace(0, bins_local.shape[0] - 2, bins_local.shape[0] - 1)
frame_labels = np.zeros((curvature.shape[0]), dtype=np.float32)
@@ -111,7 +111,7 @@ def generate_curvatures(folder, frame, bins, neighbourhoods=[25], nr_of_labels=6
return curvatures
def visualize_curvature_labels(folder, frame, bins, neighbourhoods):
def visualize_curvature_labels(folder, frame, bins, neighbourhoods, nr_of_labels):
id = frame.split('_')[0]
frame_nr = frame.split('_')[1].split('.')[0]
@@ -119,7 +119,7 @@ def visualize_curvature_labels(folder, frame, bins, neighbourhoods):
_, faces = _maskFaces(np.zeros((14475,3)), F, mask)
verts = np.load(os.path.join(folder, 'mesh_posed', f'{id}_{frame_nr}.npz'))['mesh_posed'][mask,:]
curvature_labels_all = generate_curvatures(folder, frame, bins, neighbourhoods)
curvature_labels_all = generate_curvatures(folder, frame, bins, neighbourhoods, nr_of_labels)
colors_binned_all = []
for curvature_labels in curvature_labels_all:
@@ -232,4 +232,4 @@ if __name__ == '__main__':
else:
dist = np.load('custom_data/dijkstra_skirt.npy')
visualize_curvature_labels(folder, '02572_1', bins, neighbourhoods=[10, 25, 100])
visualize_curvature_labels(folder, '02572_1', bins, neighbourhoods=[25, 25, 25], nr_of_labels=[3,6,12])
curvature_label_parallel.py
+5 -5
View File
@@ -4,17 +4,17 @@ import os
if __name__ == '__main__':
folder = '/home/workspace/data/dataset/test_skirt_ready_2'
# folder = '/home/workspace/data/dataset/test_skirt_ready_2'
# folder = '/home/workspace/data/dataset/local-cache/train_skirt'
# folder = '/home/workspace/local-data/valid_skirt'
folder = '/home/workspace/local-data/valid_skirt'
ids = os.listdir(folder + '/mesh_posed')
count = 0
step = 2500
step = 10000
while count < len(ids):
while count < 20*step:
# while count < 10000:
# os.system('nohup python MeshUtils/curvature_labels.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'curv_train_' + str(count) + '_' + str(count+step) + ' > curv_train_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
os.system('nohup python curvature_labels.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'curv_gen_2_100_label_test_skirt_' + str(count) + '_' + str(count+step) + ' > nohup/curv_gen_2_100_label_test_skirt_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
os.system('nohup python curvature_labels.py -f ' + folder + ' -s ' + str(0) + ' -e ' + str(0 + step) + ' -n ' + 'speed_test_' + str(count) + '_' + str(count+step) + ' > nohup/speed/speed_test_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
# os.system('python Data/convert_to_uv.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + '')
count += step
curvature_labels.py
+29 -29
View File
@@ -106,40 +106,40 @@ def generate_curvatures(folder, neighbourhood, frames):
k_neighbors_cache = calculate_dist(folder, neighbourhood, frames)
for frame in tqdm.tqdm(frames):
# for frame in tqdm.tqdm(frames):
id = frame.split('_')[0]
frame_nr = frame.split('_')[1].split('.')[0]
# id = frame.split('_')[0]
# frame_nr = frame.split('_')[1].split('.')[0]
# bodies = readPC2(os.path.join(folder, id, 'Body_posed.pc16'), True)['V']
# offsetss = readPC2(os.path.join(folder, id, garment + '_registered.pc16'), True)['V']
mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
_, faces = _maskFaces(np.zeros((14475,3)), F, mask) #np.load(os.path.join(folder, 'faces', f'{id}.npy'))
v = np.load(os.path.join(folder, 'mesh_posed', f'{id}_{frame_nr}.npz'))['mesh_posed'][mask,:]
# # bodies = readPC2(os.path.join(folder, id, 'Body_posed.pc16'), True)['V']
# # offsetss = readPC2(os.path.join(folder, id, garment + '_registered.pc16'), True)['V']
# mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
# _, faces = _maskFaces(np.zeros((14475,3)), F, mask) #np.load(os.path.join(folder, 'faces', f'{id}.npy'))
# v = np.load(os.path.join(folder, 'mesh_posed', f'{id}_{frame_nr}.npz'))['mesh_posed'][mask,:]
m = tm.Trimesh(v, faces, process=False, validate=False)
# m = tm.Trimesh(v, faces, process=False, validate=False)
outliers = np.array([len(v) for v in m.vertex_neighbors]) == 0
outliers = np.nonzero(outliers)[0]
outliers_n_neighbours = get_closest_n_points(v, outliers, 2)
outliers_faces = np.concatenate((outliers[:, np.newaxis], outliers_n_neighbours), axis=-1)
faces = np.concatenate((faces, outliers_faces), axis=0)
# outliers = np.array([len(v) for v in m.vertex_neighbors]) == 0
# outliers = np.nonzero(outliers)[0]
# outliers_n_neighbours = get_closest_n_points(v, outliers, 2)
# outliers_faces = np.concatenate((outliers[:, np.newaxis], outliers_n_neighbours), axis=-1)
# faces = np.concatenate((faces, outliers_faces), axis=0)
verts = v
# verts = v
m = tm.Trimesh(verts, faces, process=False, validate=False)
export_to_ply(m, temp_file)
# m = tm.Trimesh(verts, faces, process=False, validate=False)
# export_to_ply(m, temp_file)
cloud = PyntCloud.from_file(temp_file)
# cloud = PyntCloud.from_file(temp_file)
k_neighbors = k_neighbors_cache[id] # np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
ev = cloud.add_scalar_field("eigen_values", k_neighbors=k_neighbors)
cloud.add_scalar_field("curvature", ev=ev)
curvature = np.array(cloud.points["curvature(" + str(neighbourhood+1) + ")"])
out = np.zeros((14475), dtype=np.float32)
out[mask] = curvature
# k_neighbors = k_neighbors_cache[id] # np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
# ev = cloud.add_scalar_field("eigen_values", k_neighbors=k_neighbors)
# cloud.add_scalar_field("curvature", ev=ev)
# curvature = np.array(cloud.points["curvature(" + str(neighbourhood+1) + ")"])
# out = np.zeros((14475), dtype=np.float32)
# out[mask] = curvature
np.savez_compressed(os.path.join(folder, f'mesh_curvature_raw_{neighbourhood}', f'{id}_{frame_nr}.npz'), mesh_curvature_raw=out)
# np.savez_compressed(os.path.join(folder, f'mesh_curvature_raw_{neighbourhood}', f'{id}_{frame_nr}.npz'), mesh_curvature_raw=out)
return None
@@ -285,12 +285,12 @@ if __name__ == '__main__':
# lines = [line.strip() for line in lines]
# frames = lines[int(args.start):int(args.end)]
# generate_curvatures(folder, neighbourhood, frames)
curvatures = read_raw_curvatures(folder, frames, neighbourhood)
generate_curvatures(folder, neighbourhood, frames)
# curvatures = read_raw_curvatures(folder, frames, neighbourhood)
# bins = get_bins(curvatures, nr_of_labels)
# np.save(f'custom_data/train_bins_skirt_{neighbourhood}.npy', bins)
bins = np.load(f'custom_data/train_bins_skirt_{neighbourhood}.npy')
generate_curvature_labels(folder, bins, curvatures, frames, neighbourhood)
# bins = np.load(f'custom_data/train_bins_skirt_{neighbourhood}.npy')
# generate_curvature_labels(folder, bins, curvatures, frames, neighbourhood)
# visualize_curvature_labels(folder)
pass