223 lines
8.0 KiB
Python
223 lines
8.0 KiB
Python
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 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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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 plotly.subplots import make_subplots
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from custom_utils.custom_utils import _maskFaces
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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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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 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 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 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_curvatures(folder, frame, neighbourhoods=[25], nr_of_labels=6):
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id = frame.split('_')[0]
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frame_nr = int(frame.split('_')[1].split('.')[0])
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mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
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_, faces = _maskFaces(np.zeros((14475,3)), F, mask)
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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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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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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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cloud = PyntCloud.from_file(temp_file)
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curvatures = []
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for neighbourhood in neighbourhoods:
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k_neighbors = np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
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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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bins = get_bins(curvature, nr_of_labels)
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labels = np.linspace(0, bins.shape[0] - 2, bins.shape[0] - 1)
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frame_labels = np.zeros((curvature.shape[0]), dtype=np.float32)
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for j in range(bins.shape[0] - 2):
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frame_labels[(curvature >= bins[j]) * (curvature < bins[j+1])] = labels[j]
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frame_labels[curvature >= bins[-2]] = labels[-1]
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curvatures.append(frame_labels)
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return curvatures
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def visualize_curvature_labels(folder, frame, neighbourhoods):
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id = frame.split('_')[0]
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frame_nr = frame.split('_')[1].split('.')[0]
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mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
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_, faces = _maskFaces(np.zeros((14475,3)), F, mask)
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verts = np.load(os.path.join(folder, 'mesh_posed', f'{id}_{frame_nr}.npz'))['mesh_posed'][mask,:]
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curvature_labels_all = generate_curvatures(folder, frame, neighbourhoods)
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colors_binned_all = []
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for curvature_labels in curvature_labels_all:
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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_all.append(colors_binned)
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fig = make_subplots(rows=1, cols=len(colors_binned_all)+1, start_cell="top-left", specs = [ [{ 'type': 'scene' } for _ in range(len(colors_binned_all)+1)] ])
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fig.add_trace(go.Mesh3d(
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# 8 vertices of a cube
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x=verts[:,0],
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y=verts[:,1],
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z=verts[:,2],
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# i, j and k give the vertices of triangles
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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=np.ones((verts.shape[0],3))*200
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),
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row=1, col=1)
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for i, colors_binned in enumerate(colors_binned_all):
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fig.add_trace(go.Mesh3d(
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# 8 vertices of a cube
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x=verts[:,0],
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y=verts[:,1],
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z=verts[:,2],
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# i, j and k give the vertices of triangles
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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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row=1, col=i+2)
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fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1),
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scene2_aspectmode='manual', scene2_aspectratio=dict(x=1, y=1, z=1),
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scene3_aspectmode='manual', scene3_aspectratio=dict(x=1, y=1, z=1),
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scene4_aspectmode='manual', scene4_aspectratio=dict(x=1, y=1, z=1),
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scene5_aspectmode='manual', scene5_aspectratio=dict(x=1, y=1, z=1),
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)
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helper = dict(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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fig.update_layout(scene = helper,
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scene2 = helper,
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scene3 = helper,
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scene4 = helper,
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scene5 = helper,
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)
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# meshes = [go.Mesh3d(
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# x=x[:,0],
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# y=x[:,1],
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# z=x[:,2],
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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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if __name__ == '__main__':
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is_skirt = True
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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 = '/home/workspace/local-data/valid_skirt'
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nr_of_labels = 6
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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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visualize_curvature_labels(folder, '02572_1', neighbourhoods=[10, 25, 50, 100]) |