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import numpy as np
import argparse
import glob
import os
from functools import partial
import vispy
import scipy.misc as misc
from tqdm import tqdm
import yaml
import time
import sys
from mesh import write_ply, read_ply, output_3d_photo
from utils import get_MiDaS_samples, read_MiDaS_depth
import torch
import cv2
from skimage.transform import resize
import imageio
import copy
from networks import Inpaint_Color_Net, Inpaint_Depth_Net, Inpaint_Edge_Net
from MiDaS.run import run_depth
from boostmonodepth_utils import run_boostmonodepth
from MiDaS.monodepth_net import MonoDepthNet
import MiDaS.MiDaS_utils as MiDaS_utils
from bilateral_filtering import sparse_bilateral_filtering
parser = argparse.ArgumentParser()
parser.add_argument('--config', type=str, default='argument.yml',help='Configure of post processing')
args = parser.parse_args()
config = yaml.load(open(args.config, 'r'))
if config['offscreen_rendering'] is True:
vispy.use(app='egl')
os.makedirs(config['mesh_folder'], exist_ok=True)
os.makedirs(config['video_folder'], exist_ok=True)
os.makedirs(config['depth_folder'], exist_ok=True)
sample_list = get_MiDaS_samples(config['src_folder'], config['depth_folder'], config, config['specific'])
normal_canvas, all_canvas = None, None
if isinstance(config["gpu_ids"], int) and (config["gpu_ids"] >= 0):
device = config["gpu_ids"]
else:
device = "cpu"
print(f"running on device {device}")
for idx in tqdm(range(len(sample_list))):
depth = None
sample = sample_list[idx]
print("Current Source ==> ", sample['src_pair_name'])
mesh_fi = os.path.join(config['mesh_folder'], sample['src_pair_name'] +'.ply')
image = imageio.imread(sample['ref_img_fi'])
print(f"Running depth extraction at {time.time()}")
if config['use_boostmonodepth'] is True:
run_boostmonodepth(sample['ref_img_fi'], config['src_folder'], config['depth_folder'])
elif config['require_midas'] is True:
run_depth([sample['ref_img_fi']], config['src_folder'], config['depth_folder'],
config['MiDaS_model_ckpt'], MonoDepthNet, MiDaS_utils, target_w=640)
if 'npy' in config['depth_format']:
config['output_h'], config['output_w'] = np.load(sample['depth_fi']).shape[:2]
else:
config['output_h'], config['output_w'] = imageio.imread(sample['depth_fi']).shape[:2]
frac = config['longer_side_len'] / max(config['output_h'], config['output_w'])
config['output_h'], config['output_w'] = int(config['output_h'] * frac), int(config['output_w'] * frac)
config['original_h'], config['original_w'] = config['output_h'], config['output_w']
if image.ndim == 2:
image = image[..., None].repeat(3, -1)
if np.sum(np.abs(image[..., 0] - image[..., 1])) == 0 and np.sum(np.abs(image[..., 1] - image[..., 2])) == 0:
config['gray_image'] = True
else:
config['gray_image'] = False
image = cv2.resize(image, (config['output_w'], config['output_h']), interpolation=cv2.INTER_AREA)
depth = read_MiDaS_depth(sample['depth_fi'], 3.0, config['output_h'], config['output_w'])
mean_loc_depth = depth[depth.shape[0]//2, depth.shape[1]//2]
if not(config['load_ply'] is True and os.path.exists(mesh_fi)):
vis_photos, vis_depths = sparse_bilateral_filtering(depth.copy(), image.copy(), config, num_iter=config['sparse_iter'], spdb=False)
depth = vis_depths[-1]
model = None
torch.cuda.empty_cache()
print("Start Running 3D_Photo ...")
print(f"Loading edge model at {time.time()}")
depth_edge_model = Inpaint_Edge_Net(init_weights=True)
depth_edge_weight = torch.load(config['depth_edge_model_ckpt'],
map_location=torch.device(device))
depth_edge_model.load_state_dict(depth_edge_weight)
depth_edge_model = depth_edge_model.to(device)
depth_edge_model.eval()
print(f"Loading depth model at {time.time()}")
depth_feat_model = Inpaint_Depth_Net()
depth_feat_weight = torch.load(config['depth_feat_model_ckpt'],
map_location=torch.device(device))
depth_feat_model.load_state_dict(depth_feat_weight, strict=True)
depth_feat_model = depth_feat_model.to(device)
depth_feat_model.eval()
depth_feat_model = depth_feat_model.to(device)
print(f"Loading rgb model at {time.time()}")
rgb_model = Inpaint_Color_Net()
rgb_feat_weight = torch.load(config['rgb_feat_model_ckpt'],
map_location=torch.device(device))
rgb_model.load_state_dict(rgb_feat_weight)
rgb_model.eval()
rgb_model = rgb_model.to(device)
graph = None
print(f"Writing depth ply (and basically doing everything) at {time.time()}")
rt_info = write_ply(image,
depth,
sample['int_mtx'],
mesh_fi,
config,
rgb_model,
depth_edge_model,
depth_edge_model,
depth_feat_model)
if rt_info is False:
continue
rgb_model = None
color_feat_model = None
depth_edge_model = None
depth_feat_model = None
torch.cuda.empty_cache()
if config['save_ply'] is True or config['load_ply'] is True:
verts, colors, faces, Height, Width, hFov, vFov = read_ply(mesh_fi)
else:
verts, colors, faces, Height, Width, hFov, vFov = rt_info
print(f"Making video at {time.time()}")
videos_poses, video_basename = copy.deepcopy(sample['tgts_poses']), sample['tgt_name']
top = (config.get('original_h') // 2 - sample['int_mtx'][1, 2] * config['output_h'])
left = (config.get('original_w') // 2 - sample['int_mtx'][0, 2] * config['output_w'])
down, right = top + config['output_h'], left + config['output_w']
border = [int(xx) for xx in [top, down, left, right]]
normal_canvas, all_canvas = output_3d_photo(verts.copy(), colors.copy(), faces.copy(), copy.deepcopy(Height), copy.deepcopy(Width), copy.deepcopy(hFov), copy.deepcopy(vFov),
copy.deepcopy(sample['tgt_pose']), sample['video_postfix'], copy.deepcopy(sample['ref_pose']), copy.deepcopy(config['video_folder']),
image.copy(), copy.deepcopy(sample['int_mtx']), config, image,
videos_poses, video_basename, config.get('original_h'), config.get('original_w'), border=border, depth=depth, normal_canvas=normal_canvas, all_canvas=all_canvas,
mean_loc_depth=mean_loc_depth)
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