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import argparse
import sys
sys.path.append("..")
sys.path.append(".")
import numpy as np
import mcubes
import os
import torch
import trimesh
from datasets.SingleView_dataset import Object_PartialPoints_MultiImg
from datasets.transforms import Scale_Shift_Rotate
from models import get_model
from pathlib import Path
import open3d as o3d
from configs.config_utils import CONFIG
import cv2
from util.misc import MetricLogger
import scipy
from pyTorchChamferDistance.chamfer_distance import ChamferDistance
from util.projection_utils import draw_proj_image
from util import misc
import time
dist_chamfer=ChamferDistance()
def pc_metrics(p1, p2, space_ext=2, fscore_param=0.01, scale=.5):
""" p2: reference ponits
(B, N, 3)
"""
p1, p2, space_ext = p1 * scale, p2 * scale, space_ext * scale
f_thresh = space_ext * fscore_param
#print(p1.shape,p2.shape)
d1, d2, _, _ = dist_chamfer(p1, p2)
#print(d1.shape,d2.shape)
d1sqrt, d2sqrt = (d1 ** .5), (d2 ** .5)
chamfer_L1 = d1sqrt.mean(axis=-1) + d2sqrt.mean(axis=-1)
chamfer_L2 = d1.mean(axis=-1) + d2.mean(axis=-1)
precision = (d1sqrt < f_thresh).sum(axis=-1).float() / p1.shape[1]
recall = (d2sqrt < f_thresh).sum(axis=-1).float() / p2.shape[1]
#print(precision,recall)
fscore = 2 * torch.div(recall * precision, recall + precision)
fscore[fscore == float("inf")] = 0
return chamfer_L1,chamfer_L2,fscore
if __name__ == "__main__":
parser = argparse.ArgumentParser('this script can be used to compute iou fscore chamfer distance before icp align', add_help=False)
parser.add_argument('--configs',type=str,required=True)
parser.add_argument('--output_folder', type=str, default="../output_result/Triplane_diff_parcond_0926")
parser.add_argument('--dm-pth',type=str)
parser.add_argument('--ae-pth',type=str)
parser.add_argument('--data-pth', type=str,default="../")
parser.add_argument('--save_mesh',action="store_true",default=False)
parser.add_argument('--save_image',action="store_true",default=False)
parser.add_argument('--save_par_points', action="store_true", default=False)
parser.add_argument('--save_proj_img',action="store_true",default=False)
parser.add_argument('--save_surface',action="store_true",default=False)
parser.add_argument('--reso',default=128,type=int)
parser.add_argument('--category',nargs="+",type=str)
parser.add_argument('--eval_cd',action="store_true",default=False)
parser.add_argument('--use_augmentation',action="store_true",default=False)
parser.add_argument('--world_size', default=1, type=int,
help='number of distributed processes')
parser.add_argument('--local_rank', default=-1, type=int)
parser.add_argument('--dist_on_itp', action='store_true')
parser.add_argument('--dist_url', default='env://',
help='url used to set up distributed training')
parser.add_argument('--device', default='cuda',
help='device to use for training / testing')
args = parser.parse_args()
misc.init_distributed_mode(args)
config_path=args.configs
config=CONFIG(config_path)
dataset_config=config.config['dataset']
dataset_config['data_path']=args.data_pth
if "arkit" in args.category[0]:
split_filename=dataset_config['keyword']+'_val_par_img.json'
else:
split_filename='val_par_img.json'
transform = None
if args.use_augmentation:
transform=Scale_Shift_Rotate(jitter_partial=False,jitter=False,use_scale=False,angle=(-10,10),shift=(-0.1,0.1))
dataset_val = Object_PartialPoints_MultiImg(dataset_config['data_path'], split_filename=split_filename,categories=args.category,split="val",
transform=transform, sampling=False,
num_samples=1024, return_surface=True,ret_sample=True,
surface_sampling=True, par_pc_size=dataset_config['par_pc_size'],surface_size=100000,
load_proj_mat=True,load_image=True,load_org_img=True,load_triplane=None,par_point_aug=None,replica=1)
batch_size=1
num_tasks = misc.get_world_size()
global_rank = misc.get_rank()
val_sampler = torch.utils.data.DistributedSampler(
dataset_val, num_replicas=num_tasks, rank=global_rank,
shuffle=False) # shu
dataloader_val=torch.utils.data.DataLoader(
dataset_val,
sampler=val_sampler,
batch_size=batch_size,
num_workers=10,
shuffle=False,
)
output_folder=args.output_folder
device = torch.device('cuda')
ae_config=config.config['model']['ae']
dm_config=config.config['model']['dm']
ae_model=get_model(ae_config).to(device)
if args.category[0] == "all":
dm_config["use_cat_embedding"]=True
else:
dm_config["use_cat_embedding"] = False
dm_model=get_model(dm_config).to(device)
ae_model.eval()
dm_model.eval()
ae_model.load_state_dict(torch.load(args.ae_pth)['model'])
dm_model.load_state_dict(torch.load(args.dm_pth)['model'])
density = args.reso
gap = 2.2 / density
x = np.linspace(-1.1, 1.1, int(density + 1))
y = np.linspace(-1.1, 1.1, int(density + 1))
z = np.linspace(-1.1, 1.1, int(density + 1))
xv, yv, zv = np.meshgrid(x, y, z,indexing='ij')
grid = torch.from_numpy(np.stack([xv, yv, zv]).astype(np.float32)).view(3, -1).transpose(0, 1)[None].to(device,non_blocking=True)
metric_logger=MetricLogger(delimiter=" ")
header = 'Test:'
with torch.no_grad():
for data_batch in metric_logger.log_every(dataloader_val,10, header):
# if data_iter_step==100:
# break
partial_name = data_batch['partial_name']
class_name = data_batch['class_name']
model_ids=data_batch['model_id']
surface=data_batch['surface']
proj_matrices=data_batch['proj_mat']
sample_points=data_batch["points"].cuda().float()
labels=data_batch["labels"].cuda().float()
sample_input=dm_model.prepare_sample_data(data_batch)
#t1 = time.time()
sampled_array = dm_model.sample(sample_input,num_steps=36).float()
#t2 = time.time()
#sample_time = t2 - t1
#print("sampling time %f" % (sample_time))
sampled_array = torch.nn.functional.interpolate(sampled_array, scale_factor=2, mode="bilinear")
for j in range(sampled_array.shape[0]):
if args.save_mesh | args.save_par_points | args.save_image:
object_folder = os.path.join(output_folder, class_name[j], model_ids[j])
Path(object_folder).mkdir(parents=True, exist_ok=True)
'''calculate iou'''
sample_point=sample_points[j:j+1]
sample_output=ae_model.decode(sampled_array[j:j + 1],sample_point)
sample_pred=torch.zeros_like(sample_output)
sample_pred[sample_output>=0.0]=1
label=labels[j:j+1]
intersection = (sample_pred * label).sum(dim=1)
union = (sample_pred + label).gt(0).sum(dim=1)
iou = intersection * 1.0 / union + 1e-5
iou = iou.mean()
metric_logger.update(iou=iou.item())
if args.use_augmentation:
tran_mat=data_batch["tran_mat"][j].numpy()
mat_save_path='{}/tran_mat.npy'.format(object_folder)
np.save(mat_save_path,tran_mat)
if args.eval_cd:
grid_list=torch.split(grid,128**3,dim=1)
output_list=[]
#t3=time.time()
for sub_grid in grid_list:
output_list.append(ae_model.decode(sampled_array[j:j + 1],sub_grid))
output=torch.cat(output_list,dim=1)
#t4=time.time()
#decoding_time=t4-t3
#print("decoding time:",decoding_time)
logits = output[j].detach()
volume = logits.view(density + 1, density + 1, density + 1).cpu().numpy()
verts, faces = mcubes.marching_cubes(volume, 0)
verts *= gap
verts -= 1.1
#print("vertice max min",np.amin(verts,axis=0),np.amax(verts,axis=0))
m = trimesh.Trimesh(verts, faces)
'''calculate fscore and chamfer distance'''
result_surface,_=trimesh.sample.sample_surface(m,100000)
gt_surface=surface[j]
assert gt_surface.shape[0]==result_surface.shape[0]
result_surface_gpu = torch.from_numpy(result_surface).float().cuda().unsqueeze(0)
gt_surface_gpu = gt_surface.float().cuda().unsqueeze(0)
_,chamfer_L2,fscore=pc_metrics(result_surface_gpu,gt_surface_gpu)
metric_logger.update(chamferl2=chamfer_L2*1000.0)
metric_logger.update(fscore=fscore)
if args.save_mesh:
m.export('{}/{}_mesh.ply'.format(object_folder, partial_name[j]))
if args.save_par_points:
par_point_input = data_batch['par_points'][j].numpy()
#print("input max min", np.amin(par_point_input, axis=0), np.amax(par_point_input, axis=0))
par_point_o3d = o3d.geometry.PointCloud()
par_point_o3d.points = o3d.utility.Vector3dVector(par_point_input[:, 0:3])
o3d.io.write_point_cloud('{}/{}.ply'.format(object_folder, partial_name[j]), par_point_o3d)
if args.save_image:
image_list=data_batch["org_image"]
for idx,image in enumerate(image_list):
image=image[0].numpy().astype(np.uint8)
if args.save_proj_img:
proj_mat=proj_matrices[j,idx].numpy()
proj_image=draw_proj_image(image,proj_mat,result_surface)
proj_save_path = '{}/proj_{}.jpg'.format(object_folder, idx)
cv2.imwrite(proj_save_path,proj_image)
save_path='{}/{}.jpg'.format(object_folder, idx)
cv2.imwrite(save_path,image)
if args.save_surface:
surface=gt_surface.numpy().astype(np.float32)
surface_o3d = o3d.geometry.PointCloud()
surface_o3d.points = o3d.utility.Vector3dVector(surface[:, 0:3])
o3d.io.write_point_cloud('{}/surface.ply'.format(object_folder), surface_o3d)
metric_logger.synchronize_between_processes()
print('* iou {ious.global_avg:.3f}'
.format(ious=metric_logger.iou))
if args.eval_cd:
print('* chamferl2 {chamferl2s.global_avg:.3f}'
.format(chamferl2s=metric_logger.chamferl2))
print('* fscore {fscores.global_avg:.3f}'
.format(fscores=metric_logger.fscore))
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