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generate curvatures

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luni
2023-02-09 12:43:03 +00:00
parent ccec55d91f
commit db5cb35718
3 changed files with 96 additions and 51 deletions
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custom_data/train_bins_25.npy
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data/curvatures.py
+94 -49
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@@ -88,13 +88,13 @@ def k_neighbours(k):
class Data:
def __init__(self):
self.dist = None
self.k_neighbors = None
self.mask = None
self.faces = None
class Cache:
def __init__(self, folder, neighbourhood, frames):
def __init__(self, folder, neighbourhood, frames, include_neighbours=True):
self.cache = dict()
for frame in tqdm.tqdm(frames):
@@ -107,7 +107,14 @@ class Cache:
self.cache[id] = Data()
mask = read_garment_mask(folder, id, garment) #np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
k_neighbors = np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
if include_neighbours:
k_neighbors = np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
self.cache[id].k_neighbors = k_neighbors
G = tm.graph.vertex_adjacency_graph(m)
for i in range(v.shape[0]):
for k in range(25):
assert nx.has_path(G, i, k_neighbors[i, k]), f'No path between {i} and {k_neighbors[i, k]}'
_, faces = _maskFaces(np.zeros((14475,3)), F, mask) #np.load(os.path.join(folder, 'faces', f'{id}.npy'))
v = read_posed_garment_frame(folder, id, garment, frame_nr)[mask,:]
@@ -117,13 +124,8 @@ class Cache:
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)
m = tm.Trimesh(v, faces, process=False, validate=False)
G = tm.graph.vertex_adjacency_graph(m)
for i in range(v.shape[0]):
for k in range(25):
assert nx.has_path(G, i, k_neighbors[i, k]), f'No path between {i} and {k_neighbors[i, k]}'
self.cache[id].k_neighbors = k_neighbors
self.cache[id].mask = mask
self.cache[id].faces = faces
@@ -153,9 +155,10 @@ def generate_curvatures(folder, neighbourhood, frames):
# ids = os.listdir(folder)[int(args.start):int(args.end)]
# frames = frames[int(args.start):int(args.end)]
# os.makedirs(os.path.join(folder, f'mesh_curvature_raw_{neighbourhood}'), exist_ok=True)
os.makedirs(os.path.join('/'.join(folder.split('/')[:-1]), 'backup', f'mesh_curvature_raw'), exist_ok=True)
cache = Cache(folder, neighbourhood, frames)
filters = tb.Filters(complevel=9, complib='blosc2')
for frame in tqdm.tqdm(frames):
@@ -181,13 +184,16 @@ def generate_curvatures(folder, neighbourhood, frames):
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('/'.join(folder.split('/')[:-1]), 'backup', f'mesh_curvature_raw', f'{id}_{frame_nr}_{garment}.npz'), mesh_curvature_raw=out)
group = h5file.get_node(f'/{id}/{frame_nr}/{garment}')
if f'curvature_raw' in group:
array = h5file.get_node(f'/{id}/{frame_nr}/{garment}/curvature_raw')
array[:] = out
else:
h5file.create_array(group, "curvature_raw", out)
carray = h5file.create_carray(group, "curvature_raw", atom=tb.Float32Atom(), shape=out.shape, filters=filters)
carray[:] = out
return None
@@ -202,50 +208,71 @@ def get_bins(values, nr_of_bins):
def generate_curvature_labels(folder, bins, curvatures, frames, neighbourhood):
cache = Cache(folder, neighbourhood, frames, include_neighbours=False)
os.makedirs(os.path.join(folder, f'mesh_curvature_{neighbourhood}_2'), exist_ok=True)
os.makedirs(os.path.join('/'.join(folder.split('/')[:-1]), 'backup', f'curvature_mesh_global'), exist_ok=True)
os.makedirs(os.path.join('/'.join(folder.split('/')[:-1]), 'backup', f'curvature_mesh_garment'), exist_ok=True)
labels = np.linspace(0, bins.shape[0] - 2, bins.shape[0] - 1)
filters = tb.Filters(complevel=9, complib='blosc2')
for i, frame in enumerate(tqdm.tqdm(frames)):
# id = frame.split('_')[0]
# mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy'))
# print(i)
curvature = curvatures[i]
id = frame.split()[0]
frame_nr = int(frame.split()[1])
garment = frame.split()[2]
frame_labels = np.zeros((curvature.shape[0]), dtype=np.float32)
curvature = curvatures[i][cache[id].mask]
frame_labels = np.zeros((curvature.shape[0]), dtype=np.uint8)
for j in range(bins.shape[0] - 2):
frame_labels[(curvature >= bins[j]) * (curvature < bins[j+1])] = labels[j]
frame_labels[curvature >= bins[-2]] = labels[-1]
out = np.zeros((14475), dtype=np.uint8)
out[cache[id].mask] = frame_labels + 1
np.savez_compressed(os.path.join(folder, f'mesh_curvature_{neighbourhood}_2', f'{frame}.npz'), mesh_curvature=frame_labels)
group = h5file.get_node(f'/{id}/{frame_nr}/{garment}')
if f'curvature_mesh_global' in group:
array = h5file.get_node(f'/{id}/{frame_nr}/{garment}/curvature_mesh_global')
array[:] = out
else:
carray = h5file.create_carray(group, "curvature_mesh_global", atom=tb.UInt8Atom(), shape=out.shape, filters=filters)
carray[:] = out
np.savez_compressed(os.path.join('/'.join(folder.split('/')[:-1]), 'backup', f'curvature_mesh_global', f'{id}_{frame_nr}_{garment}.npz'), mesh_curvature_raw=out)
# writePC2(os.path.join(garment_dir, "curvature_label_" + str(nr_of_labels) + "_" + str(neighbourhood) + ".pc16"), unmasked_label, True)
# redo it with local bins
bins = get_bins([curvature], nr_of_labels)
frame_labels = np.zeros((curvature.shape[0]), dtype=np.uint8)
for j in range(bins.shape[0] - 2):
frame_labels[(curvature >= bins[j]) * (curvature < bins[j+1])] = labels[j]
frame_labels[curvature >= bins[-2]] = labels[-1]
out = np.zeros((14475), dtype=np.uint8)
out[cache[id].mask] = frame_labels + 1
def visualize_curvature_labels(folder):
ids = os.listdir(folder)#[:10]
group = h5file.get_node(f'/{id}/{frame_nr}/{garment}')
if f'curvature_mesh_garment' in group:
array = h5file.get_node(f'/{id}/{frame_nr}/{garment}/curvature_mesh_garment')
array[:] = out
else:
carray = h5file.create_carray(group, "curvature_mesh_garment", atom=tb.UInt8Atom(), shape=out.shape, filters=filters)
carray[:] = out
for id in tqdm.tqdm(ids):
garment_dir = os.path.join(folder, id)
np.savez_compressed(os.path.join('/'.join(folder.split('/')[:-1]), 'backup', f'curvature_mesh_garment', f'{id}_{frame_nr}_{garment}.npz'), mesh_curvature_raw=out)
if not os.path.isdir(garment_dir):
continue
garments = [f.replace('.pc16','') for f in os.listdir(garment_dir) if f.find('_') == -1 and f != 'info.mat' and not f.endswith('.obj')]
# assert len (garments) == 1
if len (garments) != 1:
continue
garment = garments[0]
def visualize_curvature_labels(folder, frames):
cache = Cache(folder, 0, frames, include_neighbours=False)
frame = 10
for frame in tqdm.tqdm(frames):
id = frame.split()[0]
frame_nr = int(frame.split()[1])
garment = frame.split()[2]
bodies = readPC2Frame(os.path.join(folder, id, 'Body_posed.pc16'), frame, True)
offsetss = readPC2Frame(os.path.join(folder, id, garment + '_registered.pc16'), frame, True)
# curvature_labels = readPC2Frame(os.path.join(folder, id, 'curvature_label_6_25.pc16'), frame, True)[:, 0]
curvature_labels = np.load(os.path.join(folder, id, 'curvature_label_6_25.npy'), allow_pickle=True)[frame, :]
mask = np.squeeze(sio.loadmat(os.path.join(garment_dir, garment + '_mask.mat'))['mask'].astype(bool))
mask = cache[id].mask
faces = cache[id].faces
verts = read_posed_garment_frame(folder, id, garment, frame_nr)[mask, :]
curv = h5file.get_node(f'/{id}/{frame_nr}/{garment}/curvature_mesh_global').read()[mask]
verts = offsetss
verts, faces = _maskFaces(verts, F, mask)
# m = tm.Trimesh(verts, faces, process=False, validate=False)
# outliers = np.array([len(v) for v in m.vertex_neighbors]) == 0
@@ -254,12 +281,12 @@ def visualize_curvature_labels(folder):
# outliers_faces = np.concatenate((outliers[:, np.newaxis], outliers_n_neighbours), axis=-1)
# faces = np.concatenate((faces, outliers_faces), axis=0)
colors = np.array([cm.color('jet', 1 - i) for i in np.linspace(0, 1, int(curvature_labels.max()+1))])
colors_binned = np.zeros((curvature_labels.shape[0], 3))
for i in range(int(curvature_labels.max())+1):
print(np.count_nonzero(curvature_labels == i))
colors_binned[curvature_labels == i, :] = colors[i]
colors_binned = colors_binned[mask, :]
colors = np.array([cm.color('jet', 1 - i) for i in np.linspace(0, 1, int(curv.max()))])
colors_binned = np.zeros((curv.shape[0], 3))
for i in range(1, int(curv.max())+1):
# print(np.count_nonzero(curv == i))
colors_binned[curv == i, :] = colors[i-1]
# colors_binned = colors_binned[mask, :]
# m = tm.Trimesh(verts, faces, process=False, validate=False, vertex_colors=colors_binned)
# m.show()
@@ -291,11 +318,25 @@ def visualize_curvature_labels(folder):
def read_raw_curvatures(folder, frames, neighbourhood):
curvatures_all = []
for frame in tqdm.tqdm(frames):
curv = np.load(os.path.join(folder, f'mesh_curvature_raw_{neighbourhood}', f'{frame}.npz'))['mesh_curvature_raw']
id = frame.split()[0]
frame_nr = int(frame.split()[1])
garment = frame.split()[2]
curv = h5file.get_node(f'/{id}/{frame_nr}/{garment}/curvature_raw').read()
curvatures_all.append(curv)
return curvatures_all
def read_raw_curvatures_2(folder, frames, neighbourhood):
curvatures_all = []
for frame in tqdm.tqdm(frames):
id = frame.split()[0]
frame_nr = int(frame.split()[1])
garment = frame.split()[2]
# curv = np.load(os.path.join(folder, f'mesh_curvature_raw_{neighbourhood}', f'{frame}.npz'))['mesh_curvature_raw']
curv = np.load(os.path.join('/'.join(folder.split('/')[:-1]), 'backup', f'mesh_curvature_raw', f'{id}_{frame_nr}_{garment}.npz'))['mesh_curvature_raw']
curvatures_all.append(curv)
return curvatures_all
if __name__ == '__main__':
parser = argparse.ArgumentParser()
@@ -339,14 +380,18 @@ if __name__ == '__main__':
type = folder.split('/')[-1]
h5file = tb.open_file(os.path.join(root, f'{type}_extras.h5'), mode='a')
start = time.perf_counter()
generate_curvatures(folder, neighbourhood, frames)
# 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')
# np.save(f'custom_data/{type}_bins{skirt_modifier}_{neighbourhood}.npy', bins)
# print(bins)
# bins = np.load(f'custom_data/{type}_bins{skirt_modifier}_{neighbourhood}.npy')
# generate_curvature_labels(folder, bins, curvatures, frames, neighbourhood)
# visualize_curvature_labels(folder)
visualize_curvature_labels(folder, frames)
print(time.perf_counter() - start)
h5file.close()
data/preprocessing.py
+1 -1
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@@ -119,7 +119,7 @@ def create_extras_file(folder):
lines = [line.strip() for line in lines]
frames = lines
for frame in tqdm(frames[:1000]):
for frame in tqdm(frames[:3000]):
# add a new empty group to the root group for each frame
id, frame_nr, garment = frame.split()
if not id in h5file.root: