From 178fa676ca3c4e9604e2631e7d4675fa5d9a8caa Mon Sep 17 00:00:00 2001 From: Hunor Laczko Date: Mon, 15 May 2023 17:14:49 +0000 Subject: [PATCH] general save --- .gitignore | 4 +- custom_data/body.obj | 82731 ++++++++++++++++++++++++++++++ custom_data/body_color_init.ply | Bin 0 -> 1158534 bytes data/dataset.py | 8 +- data/pose_dataset.py | 125 + data/render.py | 41 +- docker/requirements.txt | 3 +- models/pose_encoder.py | 90 +- scripts/pose_encoder_train.py | 188 + speed.py | 15 +- speed_2.py | 56 + test.py | 9 +- test/perspective.py | 149 + utils/pose_coloring.py | 138 +- 14 files changed, 83507 insertions(+), 50 deletions(-) create mode 100644 custom_data/body.obj create mode 100644 custom_data/body_color_init.ply create mode 100644 data/pose_dataset.py create mode 100644 scripts/pose_encoder_train.py create mode 100644 speed_2.py create mode 100644 test/perspective.py diff --git a/.gitignore b/.gitignore index 9969755..3964981 100644 --- a/.gitignore +++ b/.gitignore @@ -162,4 +162,6 @@ cython_debug/ .vscode/ temp/ -dataset/ \ No newline at end of file +dataset/ +wandb_login.py +custom_data/dijsktra.npy \ No newline at end of file diff --git a/custom_data/body.obj b/custom_data/body.obj new file mode 100644 index 0000000..524cb11 --- /dev/null +++ b/custom_data/body.obj @@ -0,0 +1,82731 @@ +# https://github.com/mikedh/trimesh +v 0.04335548 0.68859708 0.05844480 +v 0.03779455 0.67568338 0.06814519 +v 0.04818223 0.67041469 0.05964312 +v 0.05391985 0.67944694 0.04867428 +v 0.04515740 0.66203868 0.06776497 +v 0.03530443 0.66441774 0.07395065 +v 0.05497590 0.66274160 0.05107146 +v 0.05990269 0.66858232 0.03939099 +v 0.06188988 0.62586761 0.04714679 +v 0.06566367 0.62008619 0.03828504 +v 0.06457852 0.63240141 0.03742200 +v 0.05998698 0.63498563 0.04628241 +v 0.06048878 0.59995413 0.04775262 +v 0.05461406 0.59265542 0.05730211 +v 0.05653401 0.58236635 0.04872594 +v 0.06123716 0.58933830 0.03788940 +v 0.02313602 0.60930705 0.07785596 +v 0.02824266 0.60291362 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np.load('custom_data/pose_colors.npy') + self.textures = TexturesVertex(verts_features=torch.from_numpy(colors).to(device)[None, :, :]) + self.cam = torch.tensor([0.0, 0.0, 1.0], device=device) + + + def forward(self, verts: torch.Tensor, faces: torch.Tensor): + cameras, K = get_camera(self.cam, [480, 640], verts.get_device()) + + raster_settings = RasterizationSettings( + image_size=(480, 640), + blur_radius=0.0, + faces_per_pixel=1, + bin_size=0 + ) + + renderer = MeshRenderer( + rasterizer=MeshRasterizer(cameras=cameras, raster_settings=raster_settings), + shader=HardPhongShader(device=verts.get_device(), cameras=cameras) + ) + # create a pytorch3d vertex texture from the colors + + output = renderer(Meshes(verts[None,:,:], faces[None,:,:], textures=self.textures)) + + + return output diff --git a/docker/requirements.txt b/docker/requirements.txt index ff217cf..e350a9d 100644 --- a/docker/requirements.txt +++ b/docker/requirements.txt @@ -5,4 +5,5 @@ scipy pandas pytorch_msssim kornia -wandb \ No newline at end of file +wandb +trimesh \ No newline at end of file diff --git a/models/pose_encoder.py b/models/pose_encoder.py index c095598..232356a 100644 --- a/models/pose_encoder.py +++ b/models/pose_encoder.py @@ -1,8 +1,8 @@ import os import sys -os.environ["CUDA_VISIBLE_DEVICES"] = '7' -os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" +# os.environ["CUDA_VISIBLE_DEVICES"] = '3' +# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" import torch from torch import nn, Tensor @@ -77,12 +77,12 @@ class ConvNeXtReverseMaskPose(nn.Module): self.layers = nn.Sequential(*layers_list[:-1]).to(device) self.stage_pose = nn.Sequential(*layers_list[-1:]).to(device) - for cnf in block_setting[-1:]: - # Bottlenecks - stage: List[nn.Module] = [] - for _ in range(cnf.num_layers): - stage.append(block(cnf.input_channels, layer_scale, 0)) - self.stage_mask = nn.Sequential(*stage).to(device) + # for cnf in block_setting[-1:]: + # # Bottlenecks + # stage: List[nn.Module] = [] + # for _ in range(cnf.num_layers): + # stage.append(block(cnf.input_channels, layer_scale, 0)) + # self.stage_mask = nn.Sequential(*stage).to(device) self.last = nn.Conv2d( block_setting[-1].input_channels, @@ -90,12 +90,12 @@ class ConvNeXtReverseMaskPose(nn.Module): kernel_size=3, padding='same', ).to(device) - self.last_mask = nn.Conv2d( - block_setting[-1].input_channels, - 1, - kernel_size=3, - padding='same', - ).to(device) + # self.last_mask = nn.Conv2d( + # block_setting[-1].input_channels, + # 1, + # kernel_size=3, + # padding='same', + # ).to(device) for m in self.modules(): @@ -114,12 +114,13 @@ class ConvNeXtReverseMaskPose(nn.Module): x = self.first(input) f = self.layers(x) x = self.stage_pose(f) - pose = self.last(x) + pose = torch.sigmoid(self.last(x)) - x = self.stage_mask(f) - mask = self.last_mask(x) + # x = self.stage_mask(f) + # mask = torch.sigmoid(self.last_mask(x)) - return pose, mask + # return pose, mask + return pose, torch.zeros((x.shape[0], 1, x.shape[2], x.shape[3]), device=x.device) def forward(self, x) -> Tensor: return self._forward_impl(x) @@ -178,11 +179,58 @@ class PoseEncoder(nn.Module): rotation = torch.squeeze(rotation, dim=1) rotation = torch.softmax(rotation, dim=1) - x = torch.einsum('bij,bkj->bik', x, rotation) + # x = torch.einsum('bij,bkj->bik', x, rotation) + x = torch.einsum('bij,bjk->bik', x, rotation) x = x.view(x.size(0), x.size(1), 16, 16) - pose, mask = self.decoder(x) + pose, mask_out = self.decoder(x) + # mask_out = torch.round(mask_out) + # mask_out = (mask_out > 0.5).to(torch.int64) + # pose = pose * (mask_out > 0.5).to(torch.float32) - return pose, mask, rotation + # return pose, mask_out, rotation + x = pose + return x, torch.zeros((x.shape[0], 1, x.shape[2], x.shape[3]), device=x.device), torch.zeros((x.shape[0], 256, 256), device=x.device) + +class DumbPoseEncoder(nn.Module): + def __init__(self, in_channels=7, out_channels=3, device='cuda'): + super(DumbPoseEncoder, self).__init__() + + # Encoder + self.encoder_conv1 = nn.Conv2d(in_channels, 64, kernel_size=3, padding=1) + self.encoder_conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1) + self.encoder_conv3 = nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1) + self.encoder_conv4 = nn.Conv2d(256, 512, kernel_size=3, padding=1) + self.encoder_pool = nn.MaxPool2d(kernel_size=2, stride=2) + + # Decoder + self.decoder_upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) + self.decoder_conv1 = nn.Conv2d(512, 256, kernel_size=3, padding=1) + self.decoder_upsample_2 = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) + self.decoder_conv2 = nn.Conv2d(256, 128, kernel_size=3, padding=1) + self.decoder_upsample_3 = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) + self.decoder_conv3 = nn.Conv2d(128, 64, kernel_size=3, padding=1) + self.decoder_conv4 = nn.Conv2d(64, out_channels, kernel_size=3, padding=1) + + def forward(self, x): + # Encoder + mask, pose_in, pose_out = x + x = torch.cat([mask, pose_in, pose_out], dim=1) + x1 = nn.functional.relu(self.encoder_conv1(x)) + x = self.encoder_pool(x1) + x = nn.functional.relu(self.encoder_conv2(x)) + x = nn.functional.relu(self.encoder_conv3(x)) + x = nn.functional.relu(self.encoder_conv4(x)) + + # Decoder + x = self.decoder_upsample(x) + x = nn.functional.relu(self.decoder_conv1(x)) + x = self.decoder_upsample_2(x) + x = nn.functional.relu(self.decoder_conv2(x)) + x = self.decoder_upsample_3(x) + x = nn.functional.relu(self.decoder_conv3(x)) + x = nn.functional.sigmoid(self.decoder_conv4(x)) + + return x, torch.zeros((x.shape[0], 1, x.shape[2], x.shape[3]), device=x.device), torch.zeros((x.shape[0], 256, 256), device=x.device) if __name__ == '__main__': diff --git a/scripts/pose_encoder_train.py b/scripts/pose_encoder_train.py new file mode 100644 index 0000000..50614de --- /dev/null +++ b/scripts/pose_encoder_train.py @@ -0,0 +1,188 @@ +import os +import sys + +os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID" +os.environ["CUDA_VISIBLE_DEVICES"] = '4' #config['gpu'] +import torch + +sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..') + +import importlib +import argparse +parser = argparse.ArgumentParser(description='') +parser.add_argument('--name', type=str) +parser.add_argument('--config', type=str) +parser.add_argument('--debug', action='store_true') +args = vars(parser.parse_args()) + +# config_module = importlib.import_module(args['config']) +# config = config_module.config + + +import wandb +import torch.optim as optim +import torch.nn as nn +from torch.utils.data import DataLoader +# from torchgeometry.losses.dice import DiceLoss +# from torchmetrics import Dice + +from data.pose_dataset import Dataset +from utils.utils import Accumulator +from models.pose_encoder import PoseEncoder as PoseEncoder +from models.convnext import ConvNeXt +import plotly.express as ex + +from pytorch_msssim import ssim, ms_ssim, SSIM, MS_SSIM +ms_ssim_module = MS_SSIM(data_range=10, size_average=True, channel=1) + + +def init_wandb(): + run = wandb.init(project='iccv', name=config['name'], notes=config['notes'], dir='/tmp', tags=config['tags']) + artifact = wandb.Artifact('code', type='code') + artifact.add_file('scripts/cvae.py') + artifact.add_file('losses/losses.py') + artifact.add_file('models/base.py') + artifact.add_file('models/cond_encoder.py') + artifact.add_file('models/convnext.py') + artifact.add_file('models/cvae_model.py') + artifact.add_file('data/dataset.py') + config_file_names = [key for key in sys.modules.keys() if key.startswith('configs.')] + for config_file_name in config_file_names: + file_name = '/'.join(config_file_name.split('.')) + '.py' + if os.path.exists(file_name): + artifact.add_file(file_name) + run.log_artifact(artifact) + + +class SymmetricLoss(nn.Module): + def __init__(self): + super(SymmetricLoss, self).__init__() + + def forward(self, x): + # Check the difference between the matrix and its transpose + diff = x - x.transpose(1, 2) + # Compute the Frobenius norm of the difference + loss = torch.norm(diff, p='fro', dim=(1, 2)) + # Return the average loss over the batch + return torch.mean(loss) + + +def train_step(batch): + model.train() + + pred_pose, pred_mask, pred_rotation = model([batch['mask'], batch['pose'], batch['pose_out']]) + + mse = loss_fn(pred_pose, batch['pose_out']) + mse_mask = 0 #loss_fn_mask(pred_mask.squeeze(1), batch['mask_out'].squeeze(1)) + symm_rotation = 0 #loss_fn_rotation(pred_rotation) + loss = mse #+ mse_mask + symm_rotation + print(f'Loss: {loss} MSE: {mse} MSE Mask: {mse_mask} Symm Rotation: {symm_rotation}') + + optimizer.zero_grad() + loss.backward() + optimizer.step() + + return { 'train/loss': loss, 'train/mse': mse, 'train/mse_mask': mse_mask, 'train/symm_rotation': symm_rotation } + + +def valid_step(batch): + model.eval() + with torch.inference_mode(): + pred_pose, pred_mask, pred_rotation = model([batch['mask'], batch['pose'], batch['pose_out']]) + + mse = loss_fn(pred_pose, batch['pose_out']) + mse_mask = loss_fn_mask(pred_mask, batch['mask_out']) + symm_rotation = loss_fn_rotation(pred_rotation) + loss = mse + mse_mask + symm_rotation + + + return { 'valid/loss': loss, 'valid/mse': mse, 'valid/mse_mask': mse_mask, 'valid/symm_rotation': symm_rotation } + + +def plot_everything(batch, type: str): + pred_pose, pred_mask, pred_rotation = model([batch['mask'], batch['pose'], batch['pose_out']]) + + mask = torch.permute(batch['mask'], (0, 2, 3, 1)) + mask_out = torch.permute(batch['mask_out'], (0, 2, 3, 1)) + pose = torch.permute(batch['pose'], (0, 2, 3, 1)) + pose_out = torch.permute(batch['pose_out'], (0, 2, 3, 1)) + pred_mask = torch.permute(pred_mask, (0, 2, 3, 1)).detach() + pred_pose = torch.permute(pred_pose, (0, 2, 3, 1)).detach() + + masks = torch.concat((mask, mask_out, pred_mask), axis=2).squeeze(-1) + poses = torch.concat((pose, pose_out, pred_pose), axis=2) + masks_imgs = [wandb.Image((masks[i].cpu().numpy() + 1) / 2.0) for i in range(pred_pose.shape[0])] + poses_imgs = [wandb.Image((poses[i].cpu().numpy() + 1) / 2.0) for i in range(pred_pose.shape[0])] + return { ** {f'{type}/masks': masks_imgs, f'{type}/poses': poses_imgs, } } + + +if __name__ == '__main__': + if not args['debug']: + init_wandb() + + device = "cuda" if torch.cuda.is_available() else "cpu" + + batch_size = 1 + use_cache = False + + train_dataset = Dataset('/data-net/dataset/cloth3d/train', 'frames_00005_single.txt', device=device, use_cache=use_cache) + valid_dataset = Dataset('/data-net/dataset/cloth3d/train', 'frames_00005_single.txt', device=device, use_cache=use_cache) + train_dataloader = DataLoader(train_dataset, batch_size=batch_size, drop_last=True, num_workers=0) + valid_dataloader = DataLoader(valid_dataset, batch_size=batch_size, drop_last=True, num_workers=0) + train_accumulator = Accumulator() + valid_accumulator = Accumulator() + + model = PoseEncoder(device=device).to(device) + + loss_fn = nn.MSELoss() + loss_fn_mask = nn.BCELoss(reduction='mean') + loss_fn_rotation = SymmetricLoss() + optimizer = optim.Adam(model.parameters(), lr=0.001) + lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.999, verbose=False) + # optimizer = optim.SGD(model.parameters(), lr=0.1) + + iteration = 0 + epoch = 0 + while True: + + for batch in train_dataloader: + train_loss = train_step(batch) + train_accumulator.update(train_loss) + + if iteration % 1000 == 0 or (iteration < 1000 and iteration % 100 == 0): + to_log = dict({ **train_loss, **{ 'current_step': iteration } }) + if iteration % 5000 == 0 or (iteration < 5000 and iteration % 1000 == 0): + train_figs = plot_everything(batch, 'train') + to_log = { **to_log, **train_figs } + # if iteration == 0: + # to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } } + + if not args['debug']: + wandb.log(to_log, step=iteration) + train_accumulator.reset() + iteration += 1 + # print(f'Iteration {iteration} finished') + + epoch += 1 + print(f'Epoch {epoch} finished') + + if not iteration % 1000 == 0: + continue + + for batch in valid_dataloader: + valid_loss = valid_step(batch) + valid_accumulator.update(valid_loss) + + + to_log = dict({ **valid_loss, **{ 'current_step': iteration } }) + valid_figs = plot_everything(batch, 'train') + to_log = { **to_log, **valid_figs } + valid_accumulator.reset() + + if not args['debug']: + wandb.log(to_log, step=iteration) + + + + + pass \ No newline at end of file diff --git a/speed.py b/speed.py index 132632a..6a3c228 100644 --- a/speed.py +++ b/speed.py @@ -2,12 +2,11 @@ import numpy as np import time if __name__ == '__main__': - start = time.perf_counter() - a, b = np.random.rand(1024, 1024), np.random.rand(1024, 1024) - print(f'init time: {time.perf_counter() - start}') - start = time.perf_counter() - - c = np.dot(a, b) - - print(f'dot time: {time.perf_counter() - start}') + l = [] + while True: + start = time.perf_counter() + a, b = np.random.rand(10240, 1024), np.random.rand(10240, 1024) + l.append([a,b]) + c = a * b + print(f'init time: {time.perf_counter() - start}') diff --git a/speed_2.py b/speed_2.py new file mode 100644 index 0000000..64d9919 --- /dev/null +++ b/speed_2.py @@ -0,0 +1,56 @@ +# import sys +# import numpy as np +# from timeit import Timer +# print(np.__version__, sys.version) +# # results sensitive to hardware cache size +# n = 5 +# sizes = [2**20, 2**21, 2**22, 2**23] +# stmts = [ +# 'np.zeros(({},))', +# 'np.random.rand({})', +# 'np.linspace(0,1,{})', +# 'np.exp(np.zeros(({},)))', +# ] +# for stmt in stmts: +# for size in sizes: +# s = stmt.format(size) +# print(s+':') +# t = Timer(s, globals=globals()).timeit(n) +# print(f'\t{size/n/t} elements/second') +#!/usr/bin/env python3 + + + +import sys +import time +import concurrent.futures +import numpy as np + +SIZE = 16 * 1024 * 1024 +PASSES = 100 +THREADS = 64 + +print(np.__version__, sys.version) +with open('/sys/kernel/mm/transparent_hugepage/enabled') as f: + print(f.read()) + +def work(thread_id): + arrs = [] + for i in range(PASSES): + arrs.append(np.ones(SIZE, np.uint8)) + np.stack(arrs) + +with concurrent.futures.ThreadPoolExecutor(THREADS) as pool: + start = time.monotonic() + list(pool.map(work, range(THREADS))) + stop = time.monotonic() + print(stop - start) + + +# import time +# import numpy as np +# start = time.monotonic() +# N_GB = 10 +# a = np.ones(N_GB * 1024 * 1024 * 1024, 'ubyte') +# stop = time.monotonic() +# print(stop - start) \ No newline at end of file diff --git a/test.py b/test.py index d57de5a..97501f6 100644 --- a/test.py +++ b/test.py @@ -1,5 +1,6 @@ import os -os.environ["CUDA_VISIBLE_DEVICES"] = '6' +os.environ["CUDA_VISIBLE_DEVICES"] = '0' +os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" import sys @@ -199,7 +200,7 @@ def normals_reconst_loss(y_pred, normals_gt, mask): def train_step(batch): - # plot3d_with_mesh(batch['depth_image'][:,None,:,:], batch['mesh_posed'], batch['info'], batch['root_dist'], batch['scale'], batch['depth_mask'], batch['root_joint_pixel'], batch['joints'], batch['original'], batch['original_mask']) + plot3d_with_mesh(batch['depth_image'][:,None,:,:], batch['mesh_posed'], batch['info'], batch['root_dist'], batch['scale'], batch['depth_mask'], batch['root_joint_pixel'], batch['joints'], batch['original'], batch['original_mask']) # plot3d(batch['depth_image'][:,None,:,:], batch['mesh_posed'], batch['info'], batch['root_dist'], batch['scale'], batch['depth_mask'], batch['root_joint_pixel']) ### Training model.train() # train mode is on by default after construction @@ -275,8 +276,8 @@ if __name__ == '__main__': batch_size = 1 use_cache = True - train_dataset = Dataset('/home/workspace/data/dataset/cloth3d/train', '/home/workspace/data/dataset/cloth3drgb/train', 'frames_00005_single.txt', random_view=random_view, device=device, use_cache=use_cache) - valid_dataset = Dataset('/home/workspace/data/dataset/cloth3d/train', '/home/workspace/data/dataset/cloth3drgb/train', 'frames_00005_single.txt', random_view=random_view, device=device, use_cache=use_cache) + train_dataset = Dataset('/data-net/dataset/cloth3d/train', '/data-net/dataset/cloth3drgb/train', 'frames_00005_single.txt', random_view=random_view, device=device, use_cache=use_cache) + valid_dataset = Dataset('/data-net/dataset/cloth3d/train', '/data-net/dataset/cloth3drgb/train', 'frames_00005_single.txt', random_view=random_view, device=device, use_cache=use_cache) train_dataloader = DataLoader(train_dataset, batch_size=batch_size, drop_last=True, num_workers=0) valid_dataloader = DataLoader(valid_dataset, batch_size=batch_size, drop_last=True, num_workers=0) train_accumulator = Accumulator() diff --git a/test/perspective.py b/test/perspective.py new file mode 100644 index 0000000..d7ba0ef --- /dev/null +++ b/test/perspective.py @@ -0,0 +1,149 @@ +import os +import sys + +os.environ["CUDA_VISIBLE_DEVICES"] = '5' + +import math +import time +import torch +import kornia +import pytorch3d +import torchvision as tv +import numpy as np +import scipy.io as sio +import plotly.express as ex + +from torch.utils.data import Dataset + + +sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..') +from data.render import Render, DepthRenderer +from superSMPL.IO import readPC2Frame, writePC2 +from utils.data_utils import Frame, read_frames_file, mask_faces, get_normals_from_depth, get_camera, filter_info, resize_image +from cloth3d.read import DataReader +from cloth3d.util import quads2tris, zRotMatrix +import kornia +import plotly.graph_objects as go +from data.render import intrinsic +from simpleicp import SimpleICP, PointCloud + + +def depth_to_3d(depth, depth_mask): + K = torch.tensor(intrinsic()[None, :, :], device='cuda') + pc_full = kornia.geometry.depth.depth_to_3d(depth, K, normalize_points=True) + pc = [pc_full[i,:,depth_mask[i]] for i in range(len(depth_mask))] + # x = torch.sum(torch.stack(depth_mask), dim=0).cpu().numpy() == len(depth_mask) + # choice = [0,0] + # while x[choice[0], choice[1]] == False: + # choice = (np.random.random(2) * [480, 640]).astype(int) + # for i in range(len(pc)): + # pc[i] = pc[i] - pc_full[i][:,choice[0], choice[1]][:,None] + return pc + + +def perspective_test(folder, folder_rgb, frames_file='frames_all.txt', device='cuda', use_cache=True, fix_camera=True): + transforms = tv.transforms.ToTensor() + frames = read_frames_file(os.path.join(folder, frames_file)) + render = DepthRenderer() + body_faces = np.load('custom_data/smpl_faces.npy') + reader = DataReader(folder, 'superSMPL') + + frame : Frame = frames[0] + + path = os.path.join(folder, frame.id) + info = reader.read_info(frame.id) + # info['zrot'] = torch.pi / 2 + torch.pi/4 + + zrot = info['zrot'] # if not self.random_view else (((np.random.random(1) * 2) -1) * np.pi)[0] + trans = info['trans'][:,frame.frame_nr].astype(np.float32) + # mesh_posed = readPC2Frame(os.path.join(path, frame.garment + '_registered.pc16'), frame.frame_nr, True) + + mesh_posed = reader.read_garment_vertices(frame.id, frame.garment, frame.frame_nr, trans, zrot) + mesh_posed = torch.tensor(mesh_posed, device=device) + # mesh_posed_no_trans = reader.read_garment_vertices(frame.id, frame.garment, frame.frame_nr, None, zrot) + # mesh_posed_no_trans = torch.tensor(mesh_posed_no_trans, device=device) + + # raw_mask = sio.loadmat(os.path.join(path, frame.garment + '_mask.mat'))['mask'].reshape((-1)) + # mesh_mask = raw_mask > 0 #np.load(os.path.join(path, frame.garment + '_mask.npy')) + # TODO: what's the difference between masking and reading .obj file. is the obj file the not registered version? + # faces = mask_faces(self.body_faces, mesh_mask) + faces = quads2tris(reader.read_garment_topology(frame.id, frame.garment)) + # body = readPC2Frame(os.path.join(path, 'Body_posed.pc16'), frame.frame_nr, True) + body, body_faces, root_joint, joints, joints_no_trans = reader.read_human(frame.id, frame.frame_nr, zrot, use_trans=True) + body_no_trans, _, _, _, _ = reader.read_human(frame.id, frame.frame_nr, zrot, use_trans=False) + body = torch.tensor(body.astype(np.float32), device=device) + body_no_trans = torch.tensor(body_no_trans.astype(np.float32), device=device) + body_faces = quads2tris(body_faces) + + joints = torch.tensor(joints[0], device=device).to(torch.float32) + + offsets = [[1, -0.9, 0], [1, 0.9, 0], [1, 0, 0.4], [1, 0, -0.4], [1,-0.9,0.4], [1, 0.9, -0.4]] + # x: - = closer, + = farther (+-0.5) + # y: - = to the right, + = to the left (+-1) + # z: - = up, + = down (+-0.5) + + combineds = [] + for offset in offsets: + combineds.append(torch.concat((body, mesh_posed)) - torch.tensor(offset, device=body.get_device())[None,:]) + # combined_no_trans = torch.concat((body_no_trans, mesh_posed_no_trans[mesh_mask, :])) + combined_faces = torch.Tensor(np.concatenate((body_faces, faces + body.shape[0]))).to(device) + + depth_images = [] + depth_masks = [] + for i, combined in enumerate(combineds): + start = time.perf_counter() + cam = info['camLoc'].copy() + if not fix_camera: + cam = cam - offsets[i] + depth_image, depth_mask, _, _, _, _ = render(combined, combined_faces, cam) + print(f'pytorch3d time: {time.perf_counter() - start}') + depth_images.append(depth_image) + depth_masks.append(depth_mask) + + # depth_mask_body = (pix_to_face <= body_faces.shape[0]) * depth_mask + # depth_mask_garment = (pix_to_face > body_faces.shape[0]) * depth_mask + + # rgb_image = tv.io.read_image(os.path.join(folder_rgb, frame.id, f'{frame.frame_nr:04d}.png'), mode=tv.io.ImageReadMode.RGB).to(torch.float32).to(device) + + all_depth = torch.stack(depth_images) + pointclouds = depth_to_3d(all_depth[:,None,:,:], depth_masks) + + all_depth_merged = torch.sum(all_depth, dim=0) + ex.imshow(all_depth_merged.cpu()).show() + + mesh_data = [] + for pc in pointclouds: + pc = pc.cpu().numpy() + mesh_data.append(go.Scatter3d(x=pc[0,:], y=pc[1,:], z=pc[2,:], marker=dict(size=3, line=dict(width=0)), mode='markers'),) + + go.Figure(data = mesh_data).show() + + pc_0 = pointclouds[0] - torch.mean(pointclouds[0], dim=1)[:,None] + pc_fix = PointCloud(torch.permute(pc_0,[1,0]).cpu().numpy(), columns=["x", "y", "z"]) + pc_registered = [np.transpose(pc_0.cpu().numpy(), (1,0))] + for pc in pointclouds[1:]: + pc = pc - torch.mean(pc, dim=1)[:,None] + # pc_reg = torch.permute(pc,[1,0]).cpu().numpy() + pc_mov = PointCloud(torch.permute(pc,[1,0]).cpu().numpy(), columns=["x", "y", "z"]) + icp = SimpleICP() + icp.add_point_clouds(pc_fix, pc_mov) + _, pc_reg, _, _ = icp.run(max_iterations=50, correspondences=10000, min_change=0.5, neighbors=50) + pc_registered.append(pc_reg) + # pass + + mesh_data = [] + for pc in pc_registered: + pc = np.transpose(pc, (1,0)) + mesh_data.append(go.Scatter3d(x=pc[0,:], y=pc[1,:], z=pc[2,:], marker=dict(size=3, line=dict(width=0)), mode='markers'),) + + go.Figure(data = mesh_data).show() + pass + + +if __name__ == '__main__': + device = "cuda" if torch.cuda.is_available() else "cpu" + fix_camera = True + perspective_test('/data-net/dataset/cloth3d/train', '/data-net/dataset/cloth3drgb/train', 'frames_00005.txt', fix_camera=fix_camera, device=device) + + + pass diff --git a/utils/pose_coloring.py b/utils/pose_coloring.py index 85f3777..6925cbc 100644 --- a/utils/pose_coloring.py +++ b/utils/pose_coloring.py @@ -3,7 +3,7 @@ import sys sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..') os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID" -os.environ["CUDA_VISIBLE_DEVICES"] = '0' +os.environ["CUDA_VISIBLE_DEVICES"] = '3' # os.environ["CUDA_LAUNCH_BLOCKING"] = '1' import torch @@ -58,6 +58,98 @@ class PoseRenderer(Module): return output +def get_dijsktra(m: trimesh.Trimesh) -> np.ndarray: + edge_lengths = m.edges_unique_length + # create and adjacency matrix based on the edge_lengths + adjacency_matrix = np.zeros((m.vertices.shape[0], m.vertices.shape[0]), dtype=np.float32) + adjacency_matrix[m.edges_unique[:,0], m.edges_unique[:,1]] = edge_lengths + adjacency_matrix[m.edges_unique[:,1], m.edges_unique[:,0]] = edge_lengths + # calculate the distance between all vertices using dijsktra algorithm + from scipy.sparse import csr_matrix + from scipy.sparse.csgraph import dijkstra + dists = csr_matrix(adjacency_matrix) + dists = dijkstra(dists, directed=False) + + return dists + + +def get_distances(vertices, edges): + from scipy.sparse import csr_matrix + from scipy.sparse.csgraph import dijkstra + num_vertices = vertices.shape[0] + + edge_lengths = np.linalg.norm(vertices[edges[:, 0]] - vertices[edges[:,1]], axis=-1) + adjacency_matrix = csr_matrix((edge_lengths.flatten(), (edges[:, 0], edges[:, 1])), shape=(num_vertices, num_vertices)) + + # Compute the distances between all vertices using Dijkstra's algorithm + distances = dijkstra(adjacency_matrix, directed=False) + + return distances + + +def get_distances2(vertices, faces): + from scipy.sparse import coo_matrix, csgraph + + num_vertices = vertices.shape[0] + num_faces = faces.shape[0] + + # Calculate the edge lengths + edge_lengths = np.linalg.norm(vertices[faces[:, np.newaxis]] - vertices[faces], axis=2) + + # Create the adjacency matrix as a COO matrix + row_indices = np.repeat(faces.flatten(), 3) + col_indices = np.tile(faces.flatten(), 3) + data = np.tile(edge_lengths.flatten(), 3) + adjacency_matrix = coo_matrix((data, (row_indices, col_indices)), shape=(num_vertices, num_vertices)) + + # Compute the distances between all vertices using Dijkstra's algorithm + distances = csgraph.dijkstra(adjacency_matrix, directed=False) + + return distances + + +def interpolate_colors(distance_matrix, colored_indices, colored_colors): + from scipy.spatial import distance + all_colors = np.array(np.meshgrid(np.arange(0,256), np.arange(0,256), np.arange(0,256))).T.reshape(-1,3) + color_map = dict() + for idx, color in enumerate(all_colors): + color_map[tuple(color)] = color + + num_vertices = distance_matrix.shape[0] + + # Create an array to store the interpolated colors + interpolated_colors = np.zeros((num_vertices, 3), dtype=np.uint8) + + # Assign the given colors to the corresponding vertices + for index, color in zip(colored_indices, colored_colors): + interpolated_colors[index] = color + + # Interpolate colors for the remaining vertices + for vertex in range(num_vertices): + if vertex not in colored_indices: + # Find the distances from the current vertex to the colored vertices + distances = distance_matrix[vertex, colored_indices] + + # Calculate the weights for color interpolation based on inverse distances + weights = 1.0 / distances + + # Normalize the weights to sum up to 1 + weights /= np.sum(weights) + + # Interpolate the colors based on the weights + color = np.dot(weights, colored_colors).astype(np.uint8) + + # get the closest color to this from color_map if this color is not available + if tuple(color) not in color_map: + color = color_map[min(color_map.keys(), key=lambda x: distance.euclidean(x, color))] + + interpolated_colors[vertex] = color + + return interpolated_colors + + + + if __name__ == '__main__': smpl = SMPL(path='superSMPL', device='cuda') @@ -75,20 +167,54 @@ if __name__ == '__main__': # and rotate it 90 degress along the z axis body_to_render = torch.matmul(torch.tensor([[0,1,0],[-1,0,0],[0,0,1]], dtype=torch.float32, device='cuda'), body_to_render.T).T - out = renderer(body_to_render, torch.from_numpy(smpl.f_all).to(body.get_device())) - ex.imshow(out[0,:,:,:3].cpu().numpy()).show() + # out = renderer(body_to_render, torch.from_numpy(smpl.f_all).to(body.get_device())) + # ex.imshow(out[0,:,:,:3].cpu().numpy()).show() body = body.cpu().numpy()[0] faces = smpl.f_all + # m = trimesh.Trimesh(vertices=body, faces=faces, process=False, validate=False) + # # export the mesh to an obj file + # m.export('custom_data/body.obj') + # imoprt the mesh from the obj file + m = trimesh.load('custom_data/body_color_init.ply', process=False, validate=False) + color_init = np.array(m.visual.vertex_colors)[:,:3] + colored_vertices = {} + for idx, color in enumerate(color_init): + if np.all(color == 255): + continue + colored_vertices[idx] = color + + # dist = get_distances(body, m.edges) + # # dist = compute_distances(body, faces) + # np.save('custom_data/dijsktra.npy', dist) + + dist = np.load('custom_data/dijsktra.npy') + colors = interpolate_colors(dist, list(colored_vertices.keys()), list(colored_vertices.values())) + + weights = dist[0,:] + # define a gradient rgb colors for body based on weights that is not just gray + colors = np.zeros((body.shape[0], 3)) + colors[:,0] = weights + colors[:,1] = weights + colors[:,2] = weights + colors = colors * 255 + colors = colors.astype(np.uint8) + + # get the distances between all vertices using dijsktra algorithm without get_dijsktra + # from scipy.sparse import csr_matrix + # from scipy.sparse.csgraph import dijkstra + + + # define a gradinet color cube for the body that goes from green to red based on the coordinates of the vertices and not their indices # colors = np.zeros((body.shape[0], 3)) # colors[:,0] = (body[:,0] - np.min(body[:,0])) / (np.max(body[:,0]) - np.min(body[:,0])) # colors[:,1] = (body[:,1] - np.min(body[:,1])) / (np.max(body[:,1]) - np.min(body[:,1])) # colors[:,2] = (body[:,2] - np.min(body[:,2])) / (np.max(body[:,2]) - np.min(body[:,2])) - # fig = go.Figure(data=[go.Mesh3d(x=body[:,0], y=body[:,2], z=body[:,1], i=faces[:,0], j=faces[:,1], k=faces[:,2], vertexcolor=colors)]) - # fig.update_layout(scene=dict(xaxis=dict(range=[-1,1]), yaxis=dict(range=[-1,1]), zaxis=dict(range=[-1,1]))) - # fig.show() + fig = go.Figure(data=[go.Mesh3d(x=body[:,0], y=body[:,2], z=body[:,1], i=faces[:,0], j=faces[:,2], k=faces[:,1], vertexcolor=colors)]) + fig.update_layout(scene=dict(xaxis=dict(range=[-1,1]), yaxis=dict(range=[-1,1]), zaxis=dict(range=[-1,1]))) + fig.show() # np.save('custom_data/pose_colors.npy', colors.astype(np.float32)) # same as a scatter plot