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regenerate curv based on new binning

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luni
2023-01-28 23:37:08 +00:00
parent feadeb3b5a
commit 6bfd18f3c7
7 changed files with 24 additions and 21 deletions
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curvature_labels.py
+8 -8
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@@ -155,7 +155,7 @@ def get_bins(values, nr_of_bins):
def generate_curvature_labels(folder, bins, curvatures, frames, neighbourhood):
os.makedirs(os.path.join(folder, f'mesh_curvature_{neighbourhood}'), exist_ok=True)
os.makedirs(os.path.join(folder, f'mesh_curvature_{neighbourhood}_2'), exist_ok=True)
labels = np.linspace(0, bins.shape[0] - 2, bins.shape[0] - 1)
for i, frame in enumerate(tqdm.tqdm(frames)):
@@ -169,7 +169,7 @@ def generate_curvature_labels(folder, bins, curvatures, frames, neighbourhood):
frame_labels[(curvature >= bins[j]) * (curvature < bins[j+1])] = labels[j]
frame_labels[curvature >= bins[-2]] = labels[-1]
np.savez_compressed(os.path.join(folder, f'mesh_curvature_{neighbourhood}', f'{frame}.npz'), mesh_curvature=frame_labels)
np.savez_compressed(os.path.join(folder, f'mesh_curvature_{neighbourhood}_2', f'{frame}.npz'), mesh_curvature=frame_labels)
# writePC2(os.path.join(garment_dir, "curvature_label_" + str(nr_of_labels) + "_" + str(neighbourhood) + ".pc16"), unmasked_label, True)
@@ -251,7 +251,7 @@ def read_raw_curvatures(folder, frames, neighbourhood):
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("-f", "--folder", help = "process the given folder", default=r'/home/workspace/data/dataset/test_skirt_ready_2')
parser.add_argument("-f", "--folder", help = "process the given folder", default=r'/home/workspace/data/dataset/local-cache/train_skirt')
parser.add_argument("-s", "--start", help = "start of range", default=0)
parser.add_argument("-e", "--end", help = "end of range", default=30000)
parser.add_argument("-n", "--name", help = "end of range", default="lhunor")
@@ -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('train_bins_skirt.npy', bins)
# bins = np.load('custom_data/train_bins_skirt.npy')
# generate_curvature_labels(folder, bins, curvatures, frames, neighbourhood)
# 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)
# visualize_curvature_labels(folder)
pass