565 lines
25 KiB
Python
565 lines
25 KiB
Python
import os
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import sys
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import glob
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import tqdm
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import math
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import time
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import random
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import shutil
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import collections
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import argparse
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import threading
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import numpy as np
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import scipy as sp
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import trimesh as tm
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import cmapy as cm
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import scipy.io as sio
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import scipy.interpolate as spi
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import matplotlib.pyplot as plt
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import scipy.stats as stat
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import cv2
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import plotly.express as ex
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import plotly.graph_objects as go
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os.environ["CUDA_VISIBLE_DEVICES"] = "2"
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def add_triangle_centers():
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pass
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def dilate(img):
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uv_mask_2 = img != 0
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m = img.min()
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# norm = img.max() - img.min()
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img = (img - m) * uv_mask_2 #* (np.invert(uv_mask_2) * -1 + uv_mask_2)
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size = 1
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shape = cv2.MORPH_CROSS # cv.MORPH_CROSS cv.MORPH_ELLIPSE MORPH_RECT
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element = cv2.getStructuringElement(shape, (2 * size + 1, 2 * size + 1), (size, size))
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result = cv2.dilate(img, element, iterations=1)
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result = (result + m) * uv_mask
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return result
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def erode(img):
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# m = img.min()
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# norm = img.max() - img.min()
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# img = (img - m)
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size = 1
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shape = cv2.MORPH_CROSS # cv.MORPH_CROSS cv.MORPH_ELLIPSE MORPH_RECT
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element = cv2.getStructuringElement(shape, (2 * size + 1, 2 * size + 1), (size, size))
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result = cv2.erode(img, element, iterations=2)
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# result = result + m
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return result
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def chunks(lst, n):
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"""Yield successive n-sized chunks from lst."""
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for i in range(0, len(lst), n):
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yield lst[i:i + n]
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def convert_to_uv(res, m2uv, uv_map, verts, neighbours=10, mask=None, method='rbf', garment_uv_mask=None):
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verts_uv = np.zeros((res * 2, res, 3))
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uv_values = np.zeros((uv_map.shape[0], 3))
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uv_values[m2uv[:, 0]] = verts
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uv_values[m2uv[:, 1]] += verts
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uv_values = uv_values[uv_map_mask, :] # add garment mask
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uv_values /= uv_counter[:, np.newaxis]
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return_garment_uv_mask = False
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if mask is not None:
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garment_mask = np.zeros((uv_map.shape[0]), dtype=bool)
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garment_mask[m2uv[:, 0]] = mask
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garment_mask[m2uv[:, 1]] += mask
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garment_mask = garment_mask[uv_map_mask] # add garment mask
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else:
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garment_mask = np.sum(uv_values, axis=-1) != 0
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# test
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verts_uv[uv_map_discreet_unique[:, 0], uv_map_discreet_unique[:, 1], :] = uv_values
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# verts_uv_save = verts_uv.copy()
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# garment_mask = np.sum(uv_values, axis=-1) != 0
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unique_uv_values = uv_values[garment_mask, :]
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uv_map_discreet_unique_unique = uv_map_discreet_unique[garment_mask, :]
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rbf_dest_current = rbf_dest
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if garment_uv_mask is None and mask is not None:
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garment_uv_mask = np.zeros((res * 2, res))
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garment_uv_mask[uv_map_discreet_unique_unique[:, 0], uv_map_discreet_unique_unique[:, 1]] = 255
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garment_uv_mask = cv2.morphologyEx(garment_uv_mask, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_RECT, (4 * 2 + 1, 4 * 2 + 1), (4, 4)), iterations=4)
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garment_uv_mask_border = garment_uv_mask.astype(bool)
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garment_uv_mask = cv2.dilate(garment_uv_mask, cv2.getStructuringElement(cv2.MORPH_RECT, (2 * 2 + 1, 2 * 2 + 1), (2, 2)), iterations=2).astype(bool)
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garment_uv_mask_border = ~garment_uv_mask_border & garment_uv_mask
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garment_uv_mask = garment_uv_mask * uv_mask[:,:,0]
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rbf_dest_current = rbf_dest * garment_uv_mask
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return_garment_uv_mask = True
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rbf_dest_current = np.nonzero(rbf_dest_current)
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rbf_dest_current = np.array(rbf_dest_current).T
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# uv_verts_uniq = np.array(np.nonzero(np.sum(verts_uv, axis=-1))).T
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if method == 'rbf':
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interpolator = spi.RBFInterpolator(y=uv_map_discreet_unique_unique, d=unique_uv_values, neighbors=neighbours, kernel='linear')
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elif method == 'nearest':
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interpolator = spi.NearestNDInterpolator(x=uv_map_discreet_unique_unique, y=unique_uv_values)
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else:
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raise NotImplementedError
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final = interpolator(rbf_dest_current)
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result = np.zeros((res * 2, res, 3))
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# not sure, shouldn't be needed
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# rbf_dest[:, [0,1]] = rbf_dest[:, [1, 0]]
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result[rbf_dest_current[:, 0], rbf_dest_current[:, 1], :] = final
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result[uv_map_discreet_unique[:, 0], uv_map_discreet_unique[:, 1], :] = uv_values
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if garment_uv_mask is not None:
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result = result * garment_uv_mask[:,:,np.newaxis]
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# x = verts_uv #+ eroded
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# uv_mask_2 = verts_uv != 0
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# y = result
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# fig = plt.figure(figsize=(15, 15))
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# ax = fig.add_subplot(121)
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# # im = ((x - x.min()) / (x.max() - x.min())) * uv_mask_2
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# im = ((x + 2) * 64 / 255) * uv_mask_2
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# ax.imshow(im)
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# ax = fig.add_subplot(122)
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# # im = ((y - y.min()) / (y.max() - y.min())) * uv_mask
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# im = ((y + 2) * 64 / 255) * uv_mask # * (rbf_dest + garment_uv_mask)[:,:,np.newaxis]
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# ax.imshow(im)
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# plt.show()
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if return_garment_uv_mask:
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return result, garment_uv_mask, garment_uv_mask_border
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else:
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return result
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def convert_to_uv_batch(res, m2uv, uv_map, verts, neighbours=15, mask=None, method='rbf', garment_uv_mask=None, garment_uv_mask_border=None, id=None):
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batch = len(verts)
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# verts_uv = np.zeros((batch, res * 2, res, 3))
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# test
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# verts_uv[:, uv_map_discreet_unique[:, 0], uv_map_discreet_unique[:, 1], :] = uv_values
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# verts_uv_save = verts_uv.copy()
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assert method == 'rbf'
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# unique_uv_values_mask = np.sum(uv_values[0, :, :], axis=-1) != 0
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# unique_uv_values = uv_values[:, unique_uv_values_mask, :]
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# uv_map_discreet_unique_unique = uv_map_discreet_unique[unique_uv_values_mask, :]
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assert mask is not None
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garment_mask = np.zeros((uv_map.shape[0]), dtype=bool)
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garment_mask[m2uv[:, 0]] = mask
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garment_mask[m2uv[:, 1]] += mask
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garment_mask = garment_mask[uv_map_mask] # add garment mask
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rbf_dest_current = rbf_dest
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# add back mesh vertices that are not part of the garment
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uv_map_discreet_unique_unique_inv = uv_map_discreet_unique[np.invert(garment_mask), :]
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rbf_dest_fill = np.zeros((res * 2, res), dtype=bool)
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rbf_dest_fill[uv_map_discreet_unique_unique_inv[:, 0], uv_map_discreet_unique_unique_inv[:, 1]] = True
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assert not (garment_uv_mask is None and mask is not None)
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assert garment_uv_mask is not None
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rbf_dest_current = (rbf_dest + rbf_dest_fill) * garment_uv_mask + garment_uv_mask_border
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rbf_dest_current = np.nonzero(rbf_dest_current)
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rbf_dest_current = np.array(rbf_dest_current).T
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for i in range(batch):
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print('processing frame: ' + str(i))
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uv_values = np.zeros((uv_map.shape[0], 3))
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uv_values[m2uv[:, 0]] = verts[i]
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uv_values[m2uv[:, 1]] += verts[i]
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uv_values = uv_values[uv_map_mask, :]
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uv_values /= uv_counter[:, np.newaxis]
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unique_uv_values = uv_values[garment_mask, :]
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uv_map_discreet_unique_unique = uv_map_discreet_unique[garment_mask, :]
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interpolator = spi.RBFInterpolator(y=uv_map_discreet_unique_unique, d=unique_uv_values[:,:], neighbors=neighbours, kernel='linear')
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result = np.zeros((res * 2, res, 3), np.float32)
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result[rbf_dest_current[:, 0], rbf_dest_current[:, 1], :] = interpolator(rbf_dest_current)
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result[uv_map_discreet_unique_unique[:, 0], uv_map_discreet_unique_unique[:, 1], :] = unique_uv_values[:,:]
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if garment_uv_mask is not None:
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result *= garment_uv_mask[:,:,np.newaxis]
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np.savez_compressed(os.path.join(local_folder, 'uv_posed_no_orient', id + '_' + str(i) + '.npz'), uv_posed_no_orient=result)
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# np.savez_compressed(os.path.join(local_folder, 'uv_unposed_no_shape_one_hot_2', id + '_' + str(i) + '.npz'), uv_unposed_no_shape=result)
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def convert_to_uv_batch_label(res, m2uv, uv_map, values, neighbours=15, mask=None, method='rbf', garment_uv_mask=None, garment_uv_mask_border=None):
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batch = values.shape[0]
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values = (values + 1) * mask
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values_uv = np.zeros((batch, res * 2, res))
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missing = np.zeros((res * 2, res))
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uv_values = np.zeros((batch, uv_map.shape[0]))
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uv_values[:, m2uv[:, 0]] = values
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uv_values[:, m2uv[:, 1]] = values
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uv_values = uv_values[:, uv_map_mask]
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return_garment_uv_mask = False
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# test
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# verts_uv_save = verts_uv.copy()
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# unique_uv_values_mask = np.sum(uv_values[0, :, :], axis=-1) != 0
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# unique_uv_values = uv_values[:, unique_uv_values_mask, :]
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# uv_map_discreet_unique_unique = uv_map_discreet_unique[unique_uv_values_mask, :]
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if mask is not None:
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garment_mask = np.zeros((uv_map.shape[0]), dtype=bool)
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garment_mask[m2uv[:, 0]] = mask
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garment_mask[m2uv[:, 1]] += mask
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garment_mask = garment_mask[uv_map_mask] # add garment mask
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else:
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raise Exception
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# uv_values = uv_values * garment_mask
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# test
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values_uv[:, uv_map_discreet_unique[:, 0], uv_map_discreet_unique[:, 1]] = uv_values
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missing[uv_map_discreet_unique[:, 0], uv_map_discreet_unique[:, 1]] = (uv_values[0,:] == 0)
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unique_uv_values = uv_values[:, garment_mask]
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uv_map_discreet_unique_unique = uv_map_discreet_unique[garment_mask, :]
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# rbf_dest_current = rbf_dest.astype(bool) + garment_uv_mask_border * uv_mask[:,:,0]
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if garment_uv_mask is None and mask is not None:
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garment_uv_mask = np.zeros((res * 2, res))
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garment_uv_mask[uv_map_discreet_unique_unique[:, 0], uv_map_discreet_unique_unique[:, 1]] = 255
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garment_uv_mask = cv2.morphologyEx(garment_uv_mask, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_RECT, (2 * 2 + 1, 2 * 2 + 1), (2, 2)), iterations=4)
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garment_uv_mask = cv2.dilate(garment_uv_mask, cv2.getStructuringElement(cv2.MORPH_RECT, (2 * 2 + 1, 2 * 2 + 1), (2, 2)), iterations=2).astype(bool)
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return_garment_uv_mask = True
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rbf_dest_current = rbf_dest * garment_uv_mask + missing * garment_uv_mask
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rbf_dest_current = np.nonzero(rbf_dest_current)
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rbf_dest_current = np.array(rbf_dest_current).T
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# uv_verts_uniq = np.array(np.nonzero(np.sum(verts_uv, axis=-1))).T
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final = []
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for i in range(batch):
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print('processing frame: ' + str(i))
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interpolator = spi.NearestNDInterpolator(x=uv_map_discreet_unique_unique, y=unique_uv_values[i,:])
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final.append(interpolator(rbf_dest_current))
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final = np.asarray(final)
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result = np.zeros((batch, res * 2, res))
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result[:, uv_map_discreet_unique[:, 0], uv_map_discreet_unique[:, 1]] = uv_values
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result[:, rbf_dest_current[:, 0], rbf_dest_current[:, 1]] = final
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if garment_uv_mask is not None:
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result = result * garment_uv_mask[np.newaxis,:,:]
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if return_garment_uv_mask:
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return result, garment_uv_mask
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else:
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return result
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def m2uv_array(m2uv):
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result = np.zeros((14475, 2), dtype=np.int32)
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for k, v in m2uv.items():
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if len(v) == 2:
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result[k] = list(v)
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elif len(v) == 1:
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x = v.pop()
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result[k] = [x, x]
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else:
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assert False
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return result
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def get_uv2m(m2uv):
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pass
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def uv_to_mesh(uv, m2uv):
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res = uv.shape[0]
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mesh = np.zeros((14475, 3))
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uv_pixels = uv_map_discreet[m2uv]
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# counter = np.ones((14475))
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# counter[m2uv[:, 0] == m2uv[:, 1]] = 2
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mesh = np.mean(uv[uv_pixels[:, :, 0], uv_pixels[:, :, 1], :], axis=1) # / counter[:, np.newaxis]
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return mesh
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pass
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def verify_reconstruction(orig, reconstructed):
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error = np.sqrt(np.sum((orig - reconstructed) ** 2, axis=-1))
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colors = np.array([cm.color('jet', 1 - i) for i in error])
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error_sum = np.sum(error)
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verts_uv = np.zeros((uv_res * 2, uv_res, 3))
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uv_values = np.zeros((uv_map.shape[0], 3))
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uv_values[m2uv[:, 0]] = colors
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uv_values[m2uv[:, 1]] += colors
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uv_counter = np.zeros((uv_map.shape[0]))
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uv_counter[m2uv[:, 0]] = 1
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uv_counter[m2uv[:, 1]] += 1
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uv_values /= uv_counter[:, np.newaxis]
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verts_uv[uv_map_discreet[:, 0], uv_map_discreet[:, 1], :] = uv_values
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fig = plt.figure(figsize=(15, 15))
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ax = fig.add_subplot(111)
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ax.scatter(uv_map[:, 0], uv_map[:, 1], s=5, c=uv_values/255)
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plt.show()
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fig = plt.figure(figsize=(15, 15))
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ax = fig.add_subplot(121, projection='3d')
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x = orig
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ax.set_xlim3d(-1, 1)
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ax.set_ylim3d(-1, 1)
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ax.set_zlim3d(-1, 1)
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ax.set_xlabel("X")
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ax.set_ylabel("Y")
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ax.set_zlabel("Z")
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ax.scatter(x[:, 0], x[:, 1], x[:, 2], s=5)
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ax = fig.add_subplot(122, projection='3d')
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x = reconstructed#[error > 0.1, :]
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ax.set_xlim3d(-1, 1)
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ax.set_ylim3d(-1, 1)
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ax.set_zlim3d(-1, 1)
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ax.set_xlabel("X")
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ax.set_ylabel("Y")
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ax.set_zlabel("Z")
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ax.scatter(x[:, 0], x[:, 1], x[:, 2], s=5, c=colors/255) #[error > 0.1, :]
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plt.show()
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pass
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# https://stackoverflow.com/questions/38187286/find-unique-pairs-in-list-of-pairs
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def unique_pairs(a):
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a.sort(axis=1)
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b = np.ascontiguousarray(a).view(
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np.dtype((np.void, a.dtype.itemsize * a.shape[1]))
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)
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_, inv, ct = np.unique(b, return_inverse=True, return_counts=True)
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return ct[inv] == 1
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def unique_uv_map_mask(uv_map_discreet):
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mask = np.zeros((uv_map_discreet.shape[0]), dtype=bool)
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temp = np.zeros((uv_res * 2, uv_res))
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for p in uv_map_discreet:
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temp[p[0], p[1]] += 1
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# temp = temp == 1
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for i, p in enumerate(uv_map_discreet):
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if temp[p[0], p[1]] == 1:
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mask[i] = True
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elif temp[p[0], p[1]] == 2:
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mask[i] = True
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temp[p[0], p[1]] = 0
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return mask
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def test(verts, faces, values):
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uv_values = np.zeros((uv_map.shape[0], 3))
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uv_values[m2uv[:, 0]] = values
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uv_values[m2uv[:, 1]] += values
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uv_counter = np.zeros((uv_map.shape[0]))
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uv_counter[m2uv[:, 0]] = 1
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uv_counter[m2uv[:, 1]] += 1
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uv_values /= uv_counter[:, np.newaxis]
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x = uv_values
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verts = np.concatenate((verts, np.zeros((15068, 1))), axis=-1)
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im = ((x - x.min()) / (x.max() - x.min())) * 255
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tm.Trimesh(verts, faces, vertex_colors=im.astype(np.int32)).show()
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument("-f", "--folder", help = "end of range", default='/home/workspace/data/dataset/test_skirt_ready_2')
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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=30000)
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parser.add_argument("-n", "--name", help = "end of range", default='lhunor')
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parser.add_argument("-sk", "--skirt", help = "end of range", default=1)
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args = parser.parse_args()
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# smpl = SMPL('FeatureLines/superSMPL')
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local_folder = args.folder
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os.makedirs(os.path.join(local_folder, 'uv_posed_no_orient'), exist_ok=True)
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# os.makedirs(os.path.join(local_folder, 'uv_unposed_no_shape_one_hot_2'), exist_ok=True)
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print("skirt" if args.skirt == 1 else "not skirt")
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# F = np.load("D:\\Work\\Code\\featurelines\\FeatureLines\\superSMPL\\smpl_faces.npy")
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F = np.load("custom_utils/smpl_faces" + ("_skirt" if args.skirt == 1 else "") + ".npy")
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# data = np.load("Blender/uv_data_1.npz", allow_pickle=True)
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# uv_faces = data.f.Ft
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# folder = 'Z:\\Work\\Huni\\Barcelona\\Dataset\\train'
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uv_map = np.load('custom_utils/stretched_uv_map_' + ("skirt" if args.skirt == 1 else "3") + '.npy')
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m2uv = np.load('custom_utils/m2uv' + ("_skirt" if args.skirt == 1 else "") + '.npy', allow_pickle=True).item()
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m2uv = m2uv_array(m2uv)
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uv_mask = np.load('custom_utils/mask_256x512' + ("_skirt" if args.skirt == 1 else "") + '.npy')
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curvature_label_type = '6_25 '
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uv_res = 256
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uv_mask = (cv2.resize(uv_mask * 255.0, (uv_res, uv_res * 2)) / 255).astype(bool)[:, :, np.newaxis]
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# uv_mask_dilate = cv2.erode(uv_mask.astype(np.float32), cv2.getStructuringElement(cv2.MORPH_CROSS, (2 * 2 + 1, 2 * 2 + 1), (2, 2)), iterations=1).astype(bool)[:, :, np.newaxis]
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# uv_mask_border = uv_mask * np.invert(uv_mask_dilate)
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uv_mask = cv2.dilate(uv_mask.astype(np.float32), cv2.getStructuringElement(cv2.MORPH_RECT, (2 * 2 + 1, 2 * 2 + 1), (2, 2)), iterations=2).astype(bool)[:, :, np.newaxis]
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uv_map_discreet = np.floor(uv_map * uv_res).astype(np.int32)
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uv_map_mask = unique_uv_map_mask(uv_map_discreet)
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uv_counter = np.zeros((uv_map.shape[0]))
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uv_counter[m2uv[:, 0]] = 1
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uv_counter[m2uv[:, 1]] += 1
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uv_counter = uv_counter[uv_map_mask]
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uv_map_discreet_unique = uv_map_discreet[uv_map_mask, :]
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rbf_dest = np.ones((uv_res * 2, uv_res))
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rbf_dest[uv_map_discreet_unique[:, 0], uv_map_discreet_unique[:, 1]] = 0
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rbf_dest[np.invert(uv_mask[:,:,0])] = 0
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# rbf_dest = np.nonzero(rbf_dest)
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# rbf_dest = np.array(rbf_dest).T
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# rbf_dest[:, [0,1]] = rbf_dest[:, [1, 0]]
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# rbf_dest = rbf_dest[unique_pairs(rbf_dest), :] # not good, unique_pairs doesn't consider coord order
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# ids = os.listdir(os.path.join(local_folder, 'mesh_static'))[int(args.start):int(args.end)]
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# ids = [id.split('.')[0] for id in ids]
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is_skirt = True if args.skirt == 1 else False
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skirt_modifier = '_skirt' if is_skirt else ''
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folder = args.folder
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frames_file = 'frames' + skirt_modifier + '_10p.txt'
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neighbourhood = 10
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# frames = []
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# with open(os.path.join(folder, frames_file), 'r') as f:
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# lines = f.readlines()
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# lines = [line.strip() for line in lines]
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# frames = lines#[int(args.start):int(args.end)]
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# ids = [frame.split('_')[0] for frame in frames]
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print(f'params: -f {args.folder} -s {args.start} -e {args.end} -n {args.name} -skirt {args.skirt} -neigh {neighbourhood}')
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ids = os.listdir(os.path.join(local_folder, 'pose_shape'))
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ids = [id.split('_')[0] for id in ids]
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ids = sorted(list(set(ids)))[int(args.start):int(args.end)]
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for id in tqdm.tqdm(ids):
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# if os.path.exists(os.path.join(folder, id, 'uv_unposed', garment + '_0.npz')):
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# continue
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# uv_verts_static = np.load(os.path.join(folder, id, garment + '_offsets_static_uv.npy'))
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# uv_verts_static = ((uv_verts_static - uv_verts_static.min()) / (uv_verts_static.max() - uv_verts_static.min()))
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# plt.imshow(uv_verts_static)
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# plt.show()
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# pass
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frames_len = glob.glob(os.path.join(local_folder, 'pose_shape', id + '_*'))
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frames_len = len(frames_len)
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assert frames_len <= 300
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# offsets = sio.loadmat(os.path.join(folder, id, garment + '_offsets_static.mat'))['label'][:,:3]
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verts_static = np.load(os.path.join(local_folder, 'mesh_static', id + '.npz'))['mesh_static']
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mask = np.load(os.path.join(local_folder, 'mesh_mask', id + '.npy')).astype(bool)
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# body = readPC2(os.path.join(folder, id, 'Body_posed.pc16'), True)['V']
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# gt = readPC2(os.path.join(folder, id, garment + '_registered.pc16'), True)['V']
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curvature = np.stack([np.load(os.path.join(local_folder, f'mesh_curvature_{neighbourhood}_2', f'{id}_{i}.npz'))['mesh_curvature'] for i in range(frames_len)])
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# mesh_unposed = [np.load(os.path.join(folder, id, 'mesh_unposed', id + '_' + str(i) + '.npz')) for i in range(gt.shape[0])]
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# mesh_unposed = np.stack([mesh_unposed[i]['mesh_unposed'] for i in range(gt.shape[0])])
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# mesh_unposed_no_shape = [np.load(os.path.join(folder, id, 'mesh_unposed_no_shape', id + '_' + str(i) + '.npz')) for i in range(gt.shape[0])]
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# mesh_unposed_no_shape = np.stack([mesh_unposed_no_shape[i]['mesh_unposed_no_shape'] for i in range(gt.shape[0])])
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# body_no_orient = [np.load(os.path.join(folder, id, 'mesh_posed_body_no_orient', id + '_' + str(i) + '.npz')) for i in range(gt.shape[0])]
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# body_no_orient = np.stack([body_no_orient[i]['mesh_posed_body_no_orient'] for i in range(gt.shape[0])])
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# mesh_unposed_no_shape_one_hot = [np.load(os.path.join(local_folder, 'mesh_unposed_no_shape_one_hot', id + '_' + str(i) + '.npz')) for i in range(frames)]
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# mesh_unposed_no_shape_one_hot = [mesh_unposed_no_shape_one_hot[i]['mesh_unposed_no_shape'] for i in range(frames)]
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# mesh_posed_no_orient = [np.load(os.path.join(local_folder, 'mesh_posed_no_orient', id + '_' + str(i) + '.npz')) for i in range(frames)]
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# mesh_posed_no_orient = [mesh_posed_no_orient[i]['mesh_posed_no_orient'] for i in range(frames)]
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# verts_dynamic = gt
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# TODO: check if have to mask out non garment vertices or not? are they the same vertices erpeated many times? if remove, have to interpolate from almost nothing?
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# test(data.f.Vt, data.f.Ft, verts)
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# test(uv_map, data.f.Ft, verts)
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start = time.perf_counter()
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uv_verts_static, garment_uv_mask, garment_uv_mask_border = convert_to_uv(uv_res, m2uv, uv_map, verts_static, neighbours=3, mask=mask, method='rbf', garment_uv_mask=None)
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uv_verts_static = uv_verts_static.astype(np.float32)
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# uv_body_static = convert_to_uv(uv_res, m2uv, uv_map, body_static, neighbours=3, mask=None, method='rbf', garment_uv_mask=None).astype(np.float32)
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# uv_body = convert_to_uv_batch(uv_res, m2uv, uv_map, body[:, :, :], neighbours=3, mask=None, method='rbf', garment_uv_mask=None, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
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# uv_posed_body_no_orient = convert_to_uv_batch(uv_res, m2uv, uv_map, body_no_orient[:, :, :], neighbours=3, mask=None, method='rbf', garment_uv_mask=None, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
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# uv_gt = convert_to_uv_batch(uv_res, m2uv, uv_map, gt[:, :, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
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uv_curvature = convert_to_uv_batch_label(uv_res, m2uv, uv_map, curvature[:, :], neighbours=3, mask=mask, method='nearest', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
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# uv_unposed = convert_to_uv_batch(uv_res, m2uv, uv_map, mesh_unposed[:, :, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
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|
# uv_unposed_no_shape = convert_to_uv_batch(uv_res, m2uv, uv_map, mesh_unposed_no_shape[:, :, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
|
|
# uv_unposed_no_shape_one_hot = convert_to_uv_batch(uv_res, m2uv, uv_map, mesh_unposed_no_shape_one_hot, neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border, id=id)
|
|
# uv_posed_no_orient = convert_to_uv_batch(uv_res, m2uv, uv_map, mesh_posed_no_orient, neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border, id=id)
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|
# uv_shaped_body = convert_to_uv(uv_res, m2uv, uv_map, shaped_body, neighbours=3, mask=None, method='rbf', garment_uv_mask=None).astype(np.float32)
|
|
print(time.perf_counter() - start)
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|
|
|
# start = time.perf_counter()
|
|
# np.save(os.path.join(folder, id, garment + '_offsets_static_uv.npy'), uv_verts_static)
|
|
# np.save(os.path.join(folder, id, 'body_static_uv.npy'), uv_body_static)
|
|
# np.save(os.path.join(folder, id, 'mask_uv.npy'), garment_uv_mask)
|
|
|
|
# os.makedirs(os.path.join(folder, id, 'body_posed_uv'), exist_ok=True)
|
|
# for i in range(uv_body.shape[0]):
|
|
# np.savez_compressed(os.path.join(folder, id, 'body_posed_uv', garment + '_' + str(i) + '.npz'), uv_body=uv_body[i, :, :, :])
|
|
|
|
# os.makedirs(os.path.join(folder, id, 'uv_posed_body_no_orient'), exist_ok=True)
|
|
# for i in range(uv_posed_body_no_orient.shape[0]):
|
|
# np.savez_compressed(os.path.join(folder, id, 'uv_posed_body_no_orient', garment + '_' + str(i) + '.npz'), uv_posed_body_no_orient=uv_posed_body_no_orient[i, :, :, :])
|
|
|
|
# os.makedirs(os.path.join(folder, id, 'gt_uv'), exist_ok=True)
|
|
# for i in range(uv_gt.shape[0]):
|
|
# np.savez_compressed(os.path.join(folder, id, 'gt_uv', garment + '_' + str(i) + '.npz'), uv_gt=uv_gt[i, :, :, :])
|
|
|
|
os.makedirs(os.path.join(folder, f'uv_curvature_{neighbourhood}_2'), exist_ok=True)
|
|
for i in range(uv_curvature.shape[0]):
|
|
np.savez_compressed(os.path.join(folder, f'uv_curvature_{neighbourhood}_2', id + '_' + str(i) + '.npz'), curvature_uv=uv_curvature[i, :, :])
|
|
|
|
# os.makedirs(os.path.join(folder, id, 'uv_unposed'), exist_ok=True)
|
|
# for i in range(uv_unposed.shape[0]):
|
|
# np.savez_compressed(os.path.join(folder, id, 'uv_unposed', garment + '_' + str(i) + '.npz'), uv_unposed=uv_unposed[i, :, :, :])
|
|
|
|
# os.makedirs(os.path.join(folder, id, 'uv_unposed_no_shape'), exist_ok=True)
|
|
# for i in range(uv_unposed_no_shape.shape[0]):
|
|
# np.savez_compressed(os.path.join(folder, id, 'uv_unposed_no_shape', garment + '_' + str(i) + '.npz'), uv_unposed_no_shape=uv_unposed_no_shape[i, :, :, :])
|
|
|
|
# os.makedirs(os.path.join(folder, id, 'uv_unposed_no_shape_one_hot'), exist_ok=True)
|
|
# for i in range(uv_unposed_no_shape_one_hot.shape[0]):
|
|
# np.savez_compressed(os.path.join(folder, id, 'uv_unposed_no_shape_one_hot', garment + '_' + str(i) + '.npz'), uv_unposed_no_shape=uv_unposed_no_shape_one_hot[i, :, :, :])
|
|
|
|
# os.makedirs(os.path.join(folder, id, 'uv_shaped_body'), exist_ok=True)
|
|
# np.savez_compressed(os.path.join(folder, id, 'uv_shaped_body.npz'), uv_shaped_body=uv_shaped_body)
|
|
|
|
# print(time.perf_counter() - start)
|
|
|
|
# mesh = uv_to_mesh(uv_verts_static, m2uv)
|
|
# verify_reconstruction(verts_static * mask[:, np.newaxis], mesh * mask[:, np.newaxis])
|
|
|
|
# mesh = uv_to_mesh(uv_body[0,:,:], m2uv)
|
|
# verify_reconstruction(body[0,:,:] * mask[:, np.newaxis], mesh * mask[:, np.newaxis])
|
|
|
|
pass
|
|
|