311 lines
11 KiB
Python
311 lines
11 KiB
Python
import glob
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import os
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from data.supersmpl_io import readPC2
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os.environ["CUDA_VISIBLE_DEVICES"] = "3"
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import sys
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import shutil
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import tqdm
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import random
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import math
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import numpy as np
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import scipy as sp
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import scipy.io as sio
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from custom_utils.smpl import SMPL
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import plotly.graph_objects as go
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from custom_utils.custom_utils import _maskFaces
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def create_no_orient_body(folder):
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ids = os.listdir(folder)
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F = np.load("custom_utils/smpl_faces.npy")
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smpl = SMPL()
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for id in tqdm.tqdm(ids):
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if not os.path.isdir(os.path.join(folder, id)):
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continue
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# if os.path.exists(os.path.join(folder, id, 'mesh_posed_body_no_orient')):
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# continue
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info = sio.loadmat(os.path.join(folder, id, 'info.mat'))
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pose, shape, gender = info['poses'], info['shape'], info['gender']
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# if gender == 1:
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# continue
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pose = np.transpose(pose, [1, 0]).astype(np.float32)
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if len(os.listdir(os.path.join(folder, id, 'mesh_posed_body_no_orient'))) == pose.shape[0]:
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continue
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pose[:, :3] = 0
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beta = np.squeeze(np.concatenate((shape, gender), axis=1)).astype(np.float32)
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R = smpl.run_rodrigues(pose)
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T, _, _ = smpl.run(R, np.tile(beta[np.newaxis, :], [pose.shape[0], 1]), True)
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T = T.numpy()
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os.makedirs(os.path.join(folder, id, 'mesh_posed_body_no_orient'), exist_ok=True)
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for i in range(pose.shape[0]):
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np.savez_compressed(os.path.join(folder, id, 'mesh_posed_body_no_orient', id + '_' + str(i) + '.npz'), mesh_posed_body_no_orient = T[i, :, :])
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def create_no_shape_unposed(folder):
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import tensorflow as tf
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ids = os.listdir(folder)
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smpl = SMPL()
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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(os.path.join(folder, id, 'mesh_unposed_no_shape')):
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# continue
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info = sio.loadmat(os.path.join(folder, id, 'info.mat'))
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# if len(os.listdir(os.path.join(folder, id, 'mesh_unposed_no_shape_one_hot'))) == info['poses'].shape[1]:
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# continue
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shape = info['shape']
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gender = int(info['gender'][0,0] > 0)
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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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assert gender == info['gender'][0,0]
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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.load(os.path.join(folder, id, garment + '_mask.npy'))
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shape_offsets = tf.tensordot(shape.astype(np.float32), smpl.shapedirs[gender], axes=[[1], [2]]).numpy()
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os.makedirs(os.path.join(folder, id, 'mesh_unposed_no_shape_one_hot'), exist_ok=True)
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for i in range(info['poses'].shape[1]):
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mesh_unposed = np.load(os.path.join(garment_dir, 'mesh_unposed_one_hot', id + '_' + str(i) + '.npz'))
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mesh_unposed = mesh_unposed['mesh_unposed']
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mesh_unposed[mask, :] = mesh_unposed[mask, :] - np.squeeze(shape_offsets)[smpl.to_keep, :][mask, :]
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np.savez_compressed(os.path.join(folder, id, 'mesh_unposed_no_shape_one_hot', id + '_' + str(i) + '.npz'), mesh_unposed_no_shape = mesh_unposed)
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def check_if_skirt(folder):
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ids = os.listdir(folder)
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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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# garment = garments[0]
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# assert garment != 'Skirt'
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assert "Skirt" not in garments
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def gather_ids(folder, file_name):
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ids = os.listdir(folder)
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to_print = []
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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 len(id) == 5:
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to_print.append(id)
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to_print.sort()
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with open(file_name, 'w') as f:
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f.write('\n'.join(to_print))
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def verify_poses(folder):
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ids = os.listdir(folder)
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for id in tqdm.tqdm(ids):
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if not os.path.isdir(os.path.join(folder, id)):
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continue
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garments = [f.replace('_registered.pc16','') for f in os.listdir(os.path.join(folder, id)) if f.endswith('_registered.pc16') and not f.startswith('Combined')]
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assert len (garments) == 1
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info = sio.loadmat(os.path.join(folder, id, 'info.mat'))
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poses = info['poses']
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verts = readPC2(os.path.join(folder, id, garments[0] + '_registered.pc16'), True)['V']
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# print(id)
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# assert type(poses) == np.ndarray and poses.shape[0] == 72 and poses.shape[1] == verts.shape[0], 'mv ' + os.path.join(folder, id) + ' /home/workspace/data/dataset/invalid/'
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if not (type(poses) == np.ndarray and poses.shape[0] == 72 and poses.shape[1] == verts.shape[0]):
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shutil.move(os.path.join(folder, id), '/home/workspace/data/dataset/invalid/')
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print(id)
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def fix_split_garment_generated_data(folder):
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ids = os.listdir(folder)
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for id in tqdm.tqdm(ids):
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if not os.path.isdir(os.path.join(folder, id)):
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continue
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garments = [f.replace('_registered.pc16','') for f in os.listdir(os.path.join(folder, id)) if f.endswith('_registered.pc16') and not f.startswith('Combined')]
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assert len (garments) == 1
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if len(id) == 6 and id.endswith('2'):
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frames_folder = os.path.join(folder, id, 'mesh_posed_body_no_orient') # os.path.join(folder, id, 'mesh_unposed_no_shape')
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frames = os.listdir(frames_folder)
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frame_id = frames[0].split('_')[0]
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if len(frame_id) == 6:
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assert frame_id == id, 'Wrong id found!'
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if len(frame_id) == 5 and id.startswith(frame_id):
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for frame in frames:
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frame_nr = frame.split('.')[0].split('_')[1]
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shutil.move(os.path.join(frames_folder, frame), os.path.join(frames_folder, id + '_' + frame_nr + '.npz'))
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def create_garment_type_table():
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folder = '/home/workspace/data/dataset'
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# sub_folders = ['train', 'validation', 'test', 'train_skirt', 'valid_skirt', 'test_skirt']
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# sub_folders = ['train', 'validation', 'train_skirt', 'valid_skirt']
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sub_folders = ['test_skirt']
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# sub_folders = ['test', 'test_skirt']
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garment_type_table = dict()
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for sub_f in sub_folders:
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ids = os.listdir(os.path.join(folder, sub_f))
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for id in tqdm.tqdm(ids):
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if not os.path.isdir(os.path.join(folder, sub_f, id)):
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continue
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garments = [f.replace('_registered.pc16','') for f in os.listdir(os.path.join(folder, sub_f, id)) if f.endswith('_registered.pc16') and not f.startswith('Combined')]
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assert len (garments) == 1
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if id in garment_type_table.keys():
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print(id + ': ' + garments[0] + ' ' + garment_type_table[id])
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garment_type_table[id] = garments[0]
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np.save('custom_data/garment_type_table_test.npy', garment_type_table)
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def generate_faces():
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folder = '/home/workspace/data/dataset/test_skirt_ready'
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output_folder = '/home/workspace/data/results/support/faces_skirt'
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F = np.load("custom_utils/smpl_faces_skirt.npy")
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os.makedirs(output_folder, exist_ok=True)
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ids = os.listdir(os.path.join(folder, 'pose_shape'))
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ids = [id.split('_')[0] for id in ids]
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ids = set(ids)
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for id in tqdm.tqdm(ids):
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mask = np.load(os.path.join(folder, 'mesh_mask', id + '.npy')).astype(bool)
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_, faces = _maskFaces(np.zeros((14475, 3), np.float32), F, mask)
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np.save(os.path.join(output_folder, id + '.npy'), faces.astype(np.int32))
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def generate_frames_file(folder, ratio=0.1):
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all_frames = os.listdir(os.path.join(folder, 'pose_shape'))
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ids = [frame.split('_')[0] for frame in all_frames]
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all_frames = [frame.split('.')[0] for frame in all_frames]
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ids = sorted(list(set(ids)))
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garment_type_table = np.load('custom_data/garment_type_table_train.npy', allow_pickle=True).item()
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random.shuffle(ids)
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sub_ids = ids[:int(len(ids) * ratio)]
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selected = []
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for id in tqdm.tqdm(sub_ids):
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if garment_type_table[id] != 'Dress':
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continue
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if len(selected) > 30000:
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break
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# frames = glob.glob(os.path.join(folder, 'pose_shape', id + '_*'))
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frames = [frame for frame in all_frames if frame.startswith(id + '_')]
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frames = [frame.split('/')[-1].split('.')[0] for frame in frames]
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selected.extend(frames)
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with open(os.path.join(folder, 'frames_dress_30k.txt'), 'w') as f:
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for i, frame in enumerate(selected):
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f.write(frame + '\n')
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def generate_frames_file_filtered_cape():
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folder = '/home/workspace/local-data/cape_processed/train/female'
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all_frames = os.listdir(os.path.join(folder, 'pose_shape'))
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ids = [frame.split('.')[0] for frame in all_frames]
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all_frames = ['.'.join(frame.split('.')[:-1]) for frame in all_frames]
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ids = sorted(list(set(ids)))
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seqs_train_00032_SS = ['00032_shortshort_hips', '00032_shortshort_shoulders_mill']
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seqs_test_00032_SS = [ '00032_shortshort_tilt_twist_left']
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seqs_train_00032_SL = ['00032_shortlong_hips', '00032_shortlong_shoulders_mill']
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seqs_test_00032_SL = [ '00032_shortlong_tilt_twist_left']
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seqs_train_00032_SL_2 = ['00032_shortlong_hips', '00032_shortlong_tilt_twist_left']
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seqs_test_00032_SL_2 = ['00032_shortlong_shoulders_mill']
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seqs_train_03223_SL_2 = ['03223_shortlong_hips', '03223_shortlong_tilt_twist_left']
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seqs_test_03223_SL_2 = ['03223_shortlong_shoulders_mill']
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seqs_train_03223_SS_2 = ['03223_shortshort_hips', '03223_shortshort_tilt_twist_left']
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seqs_test_03223_SS_2 = ['03223_shortshort_shoulders_mill']
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ids = [id for id in ids if id in seqs_train_03223_SS_2]
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random.shuffle(ids)
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sub_ids = ids
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selected = []
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for id in tqdm.tqdm(sub_ids):
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# frames = glob.glob(os.path.join(folder, 'pose_shape', id + '_*'))
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frames = [frame for frame in all_frames if frame.startswith(id + '.')]
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# frames = [frame.split('/')[-1].split('.')[0] for frame in frames]
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selected.extend(frames)
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random.shuffle(selected)
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with open(os.path.join(folder, 'frames_03223_SS_train_2.txt'), 'w') as f:
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for i, frame in enumerate(selected):
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f.write(frame + '\n')
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if __name__ == '__main__':
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src_folder = '/home/workspace/local-data/train_skirt' #cloth3d_skirt
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# src_folder = r'D:\Work\Dataset\validation_2'
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dest_folder = '/home/workspace/data/dataset/validation'
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# create_no_orient_body(src_folder)
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# fix_split_garment_generated_data(src_folder)
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# create_no_shape_unposed(src_folder)
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# check_if_skirt(src_folder)
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# gather_ids(src_folder, 'ids/test_non_skirt_ids.txt')
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# verify_poses(src_folder)
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# create_garment_type_table()
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# generate_faces()
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generate_frames_file(src_folder, ratio=1)
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# generate_frames_file_filtered_cape()
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