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import os
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import sys
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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 numpy as np
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import trimesh as tm
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import cmapy as cm
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import scipy as sp
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import scipy.io as sio
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import plotly.graph_objects as go
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from pyntcloud import PyntCloud
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from scipy.sparse import csr_matrix, coo_matrix
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from scipy.sparse.csgraph import floyd_warshall, dijkstra
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from custom_utils.supersmpl_io import readPC2, readPC2Frame, writePC2, readOBJ
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from custom_utils.custom_utils import _maskFaces
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# from FeatureLines.superSMPL.smpl import SMPL
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def get_body():
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smpl = SMPL('FeatureLines/superSMPL')
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pose = np.zeros((1, 72), dtype=np.float32)
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beta = np.zeros((1, 11), dtype=np.float32)
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R = smpl.run_rodrigues(pose)
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T, _, _ = smpl.run(R, beta, True)
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return np.squeeze(T)
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def get_skirt_body():
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body = readOBJ('FeatureLines/superSMPL_skirt.obj')
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return body[0]
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def export_to_ply(mesh, file):
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ply = tm.exchange.ply.export_ply(mesh, encoding='binary', vertex_normal=True, include_attributes=True)
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output_file = open(file, "wb+")
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output_file.write(ply)
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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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idx_sort = np.argsort(dist, axis=-1)
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closest = idx_sort[:, :n]
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return closest
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def histedges_equalN(x, nbin):
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npt = len(x)
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return np.interp(np.linspace(0, npt, nbin + 1),
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np.arange(npt),
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np.sort(x))
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def k_neighbours(k):
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body = get_skirt_body()
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adj = np.ones((14475, 14475)) * 9999 # ???
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diagonal = np.linspace(0, 14474, 14475, dtype=np.int32)
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m = tm.Trimesh(body, F, process=False, validate=False)
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edges = m.edges_unique
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lengths = m.edges_unique_length
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adj[edges[:, 0], edges[:, 1]] = lengths
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adj[edges[:, 1], edges[:, 0]] = lengths
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adj[diagonal, diagonal] = 0
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adj = csr_matrix(adj)
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dist = dijkstra(adj, directed=False, unweighted=False, limit=0.5)
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np.save('MeshUtils/dijkstra_skirt.npy', dist)
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# dist = floydWarshall(adj, 14475)
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# idx_sort = np.argsort(dist, axis=-1)
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# closest = idx_sort[:, :k]
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pass
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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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curvatures_all = []
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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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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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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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_, 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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outliers = np.nonzero(outliers)[0]
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outliers_n_neighbours = get_closest_n_points(v, outliers, 2)
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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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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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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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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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def get_bins(values, nr_of_bins):
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flattened = []
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for c in values:
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flattened.extend(c.flatten())
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flattened = np.array(flattened)
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bins = histedges_equalN(flattened, nr_of_bins)
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return bins
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def generate_curvature_labels(folder, bins, curvatures):
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ids = os.listdir(folder)#[:10]
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labels = np.linspace(0, bins.shape[0] - 2, bins.shape[0] - 1)
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i = 0
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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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if not os.path.isdir(garment_dir):
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continue
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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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mask = np.squeeze(sio.loadmat(os.path.join(garment_dir, garment + '_mask.mat'))['mask'].astype(bool))
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curvature = curvatures[i]
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i += 1
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curvature_label = np.zeros((curvature.shape[0], curvature.shape[1]))
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frame = 0
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for frame in range(curvature.shape[0]):
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frame_labels = np.zeros((curvature.shape[1]), dtype=np.float32)
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for j in range(bins.shape[0] - 2):
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frame_labels[(curvature[frame, :] >= bins[j]) * (curvature[frame, :] < bins[j+1])] = labels[j]
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frame_labels[curvature[frame, :] >= bins[-2]] = labels[-1]
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curvature_label[frame, :] = frame_labels
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frame += 1
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unmasked_label = np.zeros((curvature.shape[0], 14475), dtype=np.float32)
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unmasked_label[:, mask] = curvature_label
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np.save(os.path.join(garment_dir, "curvature_label_" + str(nr_of_labels) + "_" + str(neighbourhood) + ".npy"), unmasked_label)
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# writePC2(os.path.join(garment_dir, "curvature_label_" + str(nr_of_labels) + "_" + str(neighbourhood) + ".pc16"), unmasked_label, True)
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def visualize_curvature_labels(folder):
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ids = os.listdir(folder)#[:10]
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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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if not os.path.isdir(garment_dir):
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continue
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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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frame = 10
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bodies = readPC2Frame(os.path.join(folder, id, 'Body_posed.pc16'), frame, True)
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offsetss = readPC2Frame(os.path.join(folder, id, garment + '_registered.pc16'), frame, True)
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# curvature_labels = readPC2Frame(os.path.join(folder, id, 'curvature_label_6_25.pc16'), frame, True)[:, 0]
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curvature_labels = np.load(os.path.join(folder, id, 'curvature_label_6_25.npy'), allow_pickle=True)[frame, :]
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mask = np.squeeze(sio.loadmat(os.path.join(garment_dir, garment + '_mask.mat'))['mask'].astype(bool))
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verts = offsetss
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verts, faces = _maskFaces(verts, F, mask)
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# m = tm.Trimesh(verts, 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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# outliers = np.nonzero(outliers)[0]
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# outliers_n_neighbours = get_closest_n_points(verts, outliers, 2)
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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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colors = np.array([cm.color('jet', 1 - i) for i in np.linspace(0, 1, int(curvature_labels.max()+1))])
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colors_binned = np.zeros((curvature_labels.shape[0], 3))
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for i in range(int(curvature_labels.max())+1):
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print(np.count_nonzero(curvature_labels == i))
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colors_binned[curvature_labels == i, :] = colors[i]
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colors_binned = colors_binned[mask, :]
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# m = tm.Trimesh(verts, faces, process=False, validate=False, vertex_colors=colors_binned)
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# m.show()
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x = verts
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meshes = [go.Mesh3d(
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x=x[:,0],
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y=x[:,2],
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z=x[:,1],
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i = faces[:,0],
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j = faces[:,1],
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k = faces[:,2],
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vertexcolor=colors_binned
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),]
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fig = go.Figure(data=meshes)
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fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1))
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fig.update_layout(
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scene = dict(
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xaxis = dict(nticks=4, range=[-1,1],),
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yaxis = dict(nticks=4, range=[-1,1],),
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zaxis = dict(nticks=4, range=[-1,1],),)
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)
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fig.show()
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print()
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def read_raw_curvatures(folder):
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ids = os.listdir(folder)[int(args.start):int(args.end)]
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curvatures_all = []
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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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if not os.path.isdir(garment_dir):
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continue
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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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curv = np.load(garment_dir + '/curvature_25_raw.npy')
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curvatures_all.append(curv)
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return curvatures_all
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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("-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("-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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# 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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generate_curvatures(folder, neighbourhood)
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# curvatures = read_raw_curvatures(folder)
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# bins = get_bins(curvatures, nr_of_labels)
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# np.save('train_bins_skirt.npy', bins)
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# bins = np.load('train_bins.npy')
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# generate_curvature_labels(folder, bins, curvatures)
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# visualize_curvature_labels(folder)
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pass
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