optimizing curvature generation
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@@ -4,15 +4,15 @@ import os
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if __name__ == '__main__':
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folder = '/home/workspace/local-data/cape/train/male'
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out_folder = '/home/workspace/local-data/cape_processed/train/male'
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ids = os.listdir(folder)
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folder = '/home/workspace/local-data/train'
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out_folder = '/home/workspace/local-data/train'
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ids = os.listdir(folder + '/mesh_posed')
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count = 0
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step = 1
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step = 15000
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while count < len(ids):
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# 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 &')
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os.system('nohup python curvature_labels_cape.py -if ' + folder + ' -of ' + out_folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'curv_gen_2_cape_train_male_' + str(count) + '_' + str(count+step) + ' > nohup/curv_gen_2_cape_train_male_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
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os.system('nohup python curvature_labels.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'curv_gen_50_train_nons_' + str(count) + '_' + str(count+step) + ' > nohup/curv_gen_50_train_nons_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
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# os.system('python Data/convert_to_uv.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + '')
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count += step
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+56
-49
@@ -4,6 +4,7 @@ import tqdm
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import argparse
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import random
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import string
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import time
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import numpy as np
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import trimesh as tm
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import cmapy as cm
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@@ -40,6 +41,8 @@ def export_to_ply(mesh, file):
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output_file.close()
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def get_closest_n_points(verts, idx, n):
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dist = sp.spatial.distance.cdist(verts[idx, :], verts)
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dist[dist == 0] = 1e9
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@@ -80,35 +83,40 @@ def k_neighbours(k):
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pass
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def calculate_dist(folder, neighbourhood):
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out = dict()
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frames = sorted(os.listdir(os.path.join(folder, 'mesh_posed')))[int(args.start):int(args.end)]
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for frame in tqdm.tqdm(frames):
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id = frame.split('_')[0]
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if id in out.keys():
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continue
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mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
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k_neighbors = np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
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out[id] = k_neighbors
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return out
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def generate_curvatures(folder, neighbourhood):
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ids = os.listdir(folder)[int(args.start):int(args.end)]
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# ids = os.listdir(folder)[int(args.start):int(args.end)]
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frames = sorted(os.listdir(os.path.join(folder, 'mesh_posed')))[int(args.start):int(args.end)]
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os.makedirs(os.path.join(folder, f'mesh_curvature_raw_{neighbourhood}'), exist_ok=True)
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curvatures_all = []
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k_neighbors_cache = calculate_dist(folder, neighbourhood)
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for id in tqdm.tqdm(ids):
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garment_dir = os.path.join(folder, id)
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for frame in tqdm.tqdm(frames):
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if not os.path.isdir(garment_dir):
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continue
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# if os.path.exists(garment_dir + '/curvature_25_raw.npy'):
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# continue
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curvatures_garment = []
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garments = [f.replace('.pc16','') for f in os.listdir(garment_dir) if f.find('_') == -1 and f != 'info.mat' and not f.endswith('.obj')]
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# assert len (garments) == 1
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if len (garments) != 1:
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continue
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garment = garments[0]
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id = frame.split('_')[0]
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frame_nr = frame.split('_')[1].split('.')[0]
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bodies = readPC2(os.path.join(folder, id, 'Body_posed.pc16'), True)['V']
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offsetss = readPC2(os.path.join(folder, id, garment + '_registered.pc16'), True)['V']
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mask = np.squeeze(sio.loadmat(os.path.join(garment_dir, garment + '_mask.mat'))['mask'].astype(bool))
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# bodies = readPC2(os.path.join(folder, id, 'Body_posed.pc16'), True)['V']
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# offsetss = readPC2(os.path.join(folder, id, garment + '_registered.pc16'), True)['V']
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mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
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faces = np.load(os.path.join(folder, 'faces', f'{id}.npy'))
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v = np.load(os.path.join(folder, 'mesh_posed', f'{id}_{frame_nr}.npz'))['mesh_posed'][mask,:]
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_, faces = _maskFaces(bodies[0, :, :], F, mask)
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v = bodies[0, mask, :] + offsetss[0, mask, :]
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m = tm.Trimesh(v, faces, process=False, validate=False)
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outliers = np.array([len(v) for v in m.vertex_neighbors]) == 0
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@@ -117,30 +125,23 @@ def generate_curvatures(folder, neighbourhood):
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outliers_faces = np.concatenate((outliers[:, np.newaxis], outliers_n_neighbours), axis=-1)
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faces = np.concatenate((faces, outliers_faces), axis=0)
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step = 0
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for body, offsets in zip(bodies, offsetss):
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verts = body + offsets
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verts = verts[mask, :]
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verts = v
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m = tm.Trimesh(verts, faces, process=False, validate=False)
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export_to_ply(m, temp_file)
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m = tm.Trimesh(verts, faces, process=False, validate=False)
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export_to_ply(m, temp_file)
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cloud = PyntCloud.from_file(temp_file)
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# cloud.plot(mesh=True)
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cloud = PyntCloud.from_file(temp_file)
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k_neighbors = np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
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ev = cloud.add_scalar_field("eigen_values", k_neighbors=k_neighbors)
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cloud.add_scalar_field("curvature", ev=ev)
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curvature = np.array(cloud.points["curvature(" + str(neighbourhood+1) + ")"])
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curvatures_garment.append(curvature)
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k_neighbors = k_neighbors_cache[id] # np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
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ev = cloud.add_scalar_field("eigen_values", k_neighbors=k_neighbors)
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cloud.add_scalar_field("curvature", ev=ev)
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curvature = np.array(cloud.points["curvature(" + str(neighbourhood+1) + ")"])
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out = np.zeros((14475), dtype=np.float32)
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out[mask] = curvature
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np.savez_compressed(os.path.join(folder, f'mesh_curvature_raw_{neighbourhood}', f'{id}_{frame_nr}.npz'), mesh_curvature_raw=out)
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print(step)
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step += 1
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# curvatures_all.append(np.array(curvatures_garment))
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np.save(garment_dir + '/curvature_25_raw.npy', np.array(curvatures_garment))
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return curvatures_all
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return None
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def get_bins(values, nr_of_bins):
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@@ -282,21 +283,27 @@ def read_raw_curvatures(folder):
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument("-f", "--folder", help = "process the given folder", default=r'/home/workspace/data/dataset/test')
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parser.add_argument("-f", "--folder", help = "process the given folder", default=r'/home/workspace/local-data/train')
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parser.add_argument("-s", "--start", help = "start of range", default=0)
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parser.add_argument("-e", "--end", help = "end of range", default=1500)
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parser.add_argument("-e", "--end", help = "end of range", default=10)
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parser.add_argument("-n", "--name", help = "end of range", default="lhunor")
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args = parser.parse_args()
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F = np.load("FeatureLines/superSMPL/smpl_faces.npy")
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is_skirt = False
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skirt_modifier = '_skirt' if is_skirt else ''
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F = np.load("custom_utils/smpl_faces" + skirt_modifier + ".npy").astype(np.int32)
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# k_neighbours(k=25)
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folder = args.folder
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nr_of_labels = 6
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neighbourhood = 25
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temp_file = 'temp/' + ''.join(random.choice(string.ascii_lowercase) for i in range(10)) + '.ply'
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dist = np.load('MeshUtils/dijsktra.npy')
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# dist = np.load('MeshUtils/dijkstra_skirt.npy')
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neighbourhood = 50
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temp_file = '/home/workspace/local-data/temp/' + ''.join(random.choice(string.ascii_lowercase) for i in range(10)) + '.ply'
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if not is_skirt:
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dist = np.load('custom_data/dijkstra.npy')
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else:
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dist = np.load('custom_data/dijkstra_skirt.npy')
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print('SKIRT' if is_skirt else 'NOT SKIRT')
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generate_curvatures(folder, neighbourhood)
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# curvatures = read_raw_curvatures(folder)
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