176 lines
6.6 KiB
Python
176 lines
6.6 KiB
Python
import os
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import sys
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sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
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import importlib
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import argparse
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parser = argparse.ArgumentParser(description='')
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parser.add_argument('--name', type=str)
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parser.add_argument('--config', type=str)
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parser.add_argument('--debug', action='store_true')
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parser.add_argument('--iteration', type=str, default='')
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parser.add_argument('-g', '--gpu', type=str)
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args = vars(parser.parse_args())
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if args['debug']:
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from configs.iccvw.pyr_64_128_cape_f_2 import config
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else:
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config_module = importlib.import_module(args['config'])
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config = config_module.config
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os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
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os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
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if args['debug']:
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os.environ["CUDA_LAUNCH_BLOCKING"] = '1'
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import time
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import argparse
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import wandb
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import tqdm
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import random
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import numpy as np
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import plotly.graph_objects as go
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import plotly.express as ex
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from PIL import Image
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import torch
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import torch.nn as nn
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import torch.optim as optim
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import torch.nn.functional as F
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from tqdm import tqdm
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from data.dataset import Dataset
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from data.utils import Frame
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from data.cloth3d_io import readPC2Frame, readPC2
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from utils.checkpoints import CheckpointSaver, load_model
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from torch.utils.data import DataLoader
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from utils.utils import text_to_html
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from utils.postprocess import fix_collision
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from losses.losses import collision_loss, normal_loss, kl_loss
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from custom_utils.custom_utils import UV2Mesh, Accumulator, plot_3d, get_summary_html, maskFacesBatch, get_summary_str, plot_3d_collision, group_inputs_for_levels
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from plotly.subplots import make_subplots
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def calculate():
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errors = []
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for frame in tqdm(frames):
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# import plotly.graph_objects as go
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# mesh_pred = mesh_pred.cpu().numpy()
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# mask = batch['mesh_mask'].cpu().numpy()
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# i = 3
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# fig = go.Figure(data=[go.Scatter3d(x=mesh_pred[i,mask[i],0], y=mesh_pred[i,mask[i],2], z=mesh_pred[i,mask[i],1], mode='markers', marker=dict(size=1))])
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# fig.update_layout(scene_aspectmode='cube')
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# fig.show()
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pred = np.load(f'{out_folder}/{frame}.npz')['arr_0']
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gt = readPC2Frame(f'{config["test_dir"]}/mesh_posed_no_orient/{frame.id}_{frame.garment}.pc16', frame.frame_nr, True)
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# mask = np.load(f'{config["test_dir"]}/mesh_mask/{frame.id}_{frame.garment}.npy')
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fix_data = np.load(f'{config["test_dir"]}/mesh_fixed/{frame.id}_{frame.garment}.npz')
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mask_convert = fix_data['mas_convert']
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new_mask = fix_data['mask']
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pred = pred[new_mask]
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gt = gt[mask_convert]
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error = np.linalg.norm(pred - gt, axis=1).mean()
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errors.append((str(frame), error))
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# save errors to a csv file
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with open(f'{results_folder}/errors/{name}.csv', 'w') as f:
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for frame, error in errors:
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frame_str = ','.join(str(frame).split('_'))
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f.write(f'{frame_str},{error}\n')
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def calculate_cape():
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from smpl.smpl_torch import SMPL
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errors = []
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clothing_verts_idx = np.load(os.path.join('custom_data', 'clothing_verts_idx.npy'))
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cape_mask = np.zeros((6890), bool)
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cape_mask[clothing_verts_idx] = True
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smpl = SMPL(path='smpl', orig_smpl=True, device='cpu')
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for frame in tqdm(frames):
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# import plotly.graph_objects as go
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# mesh_pred = mesh_pred.cpu().numpy()
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# mask = batch['mesh_mask'].cpu().numpy()
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# i = 3
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# fig = go.Figure(data=[go.Scatter3d(x=mesh_pred[i,mask[i],0], y=mesh_pred[i,mask[i],2], z=mesh_pred[i,mask[i],1], mode='markers', marker=dict(size=1))])
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# fig.update_layout(scene_aspectmode='cube')
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# fig.show()
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if not os.path.exists(f'{out_folder}/{frame}.npz'):
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print('missed one')
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continue
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pred = np.load(f'{out_folder}/{frame}.npz')['arr_0']
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gt = np.load(f'{config["test_dir"]}/mesh_unposed_no_shape/{frame.id}.{frame.frame_nr}.npz')['mesh_unposed_no_shape']
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pose_shape = np.load(f'{config["test_dir"]}/pose_shape/{frame.id}.{frame.frame_nr}.npy').astype(np.float32)
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pose, shape = pose_shape[:72], pose_shape[72:]
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gender = int(shape[10] >= 0.5)
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shape_offsets = torch.tensordot(torch.from_numpy(shape[:10][None, :]), smpl.shapedirs[gender], dims=[[1], [2]])[0,smpl.to_keep,:].numpy()
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gt_shaped = gt + shape_offsets
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min_body = np.load(os.path.join('/data-local/127_2/cape_processed', 'minimal_body_shape', frame.id.split('_')[0], frame.id.split('_')[0] + '_minimal.npy'))
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offsets_pred = pred[cape_mask[smpl.to_keep],:] - min_body[clothing_verts_idx,:]
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offsets_orig = gt_shaped[cape_mask[smpl.to_keep],:] - min_body[clothing_verts_idx,:]
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error = np.linalg.norm(offsets_pred - offsets_orig, axis=1).mean()
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errors.append((str(frame), error))
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# import plotly.graph_objects as go
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# fig = go.Figure(data=[go.Scatter3d(x=pred[:,0], y=pred[:,2], z=pred[:,1], mode='markers', marker=dict(size=1))])
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# fig.add_trace(go.Scatter3d(x=gt[:,0], y=gt[:,2], z=gt[:,1], mode='markers', marker=dict(size=1)))
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# fig.update_layout(scene_aspectmode='cube')
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# fig.update_layout(scene=dict(xaxis=dict(range=[-1,1]), yaxis=dict(range=[-1,1]), zaxis=dict(range=[-1,1])))
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# fig.show()
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# save errors to a csv file
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with open(f'{results_folder}/errors/{name}.csv', 'w') as f:
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for frame, error in errors:
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split = str(frame).split('_')
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if not config['is_cape']:
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frame_str = ','.join(split)
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else:
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id = '_'.join(split[:-2])
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frame_str = f'{id},{split[-2]},{split[-1]}'
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f.write(f'{frame_str},{error}\n')
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if __name__ == '__main__':
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skirt_modifier = '_skirt' if config['is_skirt'] else ''
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name = config['name']
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results_folder = '/data-local/127_2/results'
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if args['iteration'] == '':
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folders = [f for f in os.listdir(results_folder) if os.path.isdir(os.path.join(results_folder, f)) and f.startswith(config['name'])]
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assert len(folders) == 1
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name = folders[0]
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else:
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name = f'{name}_{args["iteration"]}'
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out_folder = f'{results_folder}/{name}'
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os.makedirs(f'{results_folder}/errors', exist_ok=True)
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if config['is_cape']:
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from data.dataset_cape import read_frames
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frames = read_frames(config['test_dir'], shuffle=False, is_cape=True)
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calculate_cape()
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else:
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frames_file = config['test_frames']
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with open(os.path.join(config['test_dir'], frames_file), 'r') as f:
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frames = f.readlines()
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frames = [Frame(frames.split()[0], int(frames.split()[1]), frames.split()[2]) for frames in frames]
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calculate() |