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from __future__ import absolute_import, division, print_function
import torch
import torch.nn as nn
from torch.autograd import Variable
import os, sys, errno
import argparse
import time
import numpy as np
import cv2
import matplotlib.pyplot as plt
from tqdm import tqdm
import open3d as o3d
from utils import post_process_depth, D_to_cloud, flip_lr, inv_normalize
from networks.NewCRFDepth import NewCRFDepth
def convert_arg_line_to_args(arg_line):
for arg in arg_line.split():
if not arg.strip():
continue
yield arg
parser = argparse.ArgumentParser(description='IEBins PyTorch implementation.', fromfile_prefix_chars='@')
parser.convert_arg_line_to_args = convert_arg_line_to_args
parser.add_argument('--model_name', type=str, help='model name', default='iebins')
parser.add_argument('--encoder', type=str, help='type of encoder, base07, large07, tiny07', default='large07')
parser.add_argument('--data_path', type=str, help='path to the data', required=True)
parser.add_argument('--filenames_file', type=str, help='path to the filenames text file', required=True)
parser.add_argument('--input_height', type=int, help='input height', default=480)
parser.add_argument('--input_width', type=int, help='input width', default=640)
parser.add_argument('--max_depth', type=float, help='maximum depth in estimation', default=10)
parser.add_argument('--checkpoint_path', type=str, help='path to a specific checkpoint to load', default='')
parser.add_argument('--dataset', type=str, help='dataset to train on', default='nyu')
parser.add_argument('--do_kb_crop', help='if set, crop input images as kitti benchmark images', action='store_true')
parser.add_argument('--pred_clouds', help='if set, pred cloud points', action='store_true')
parser.add_argument('--save_viz', help='if set, save visulization of the outputs', action='store_true')
if sys.argv.__len__() == 2:
arg_filename_with_prefix = '@' + sys.argv[1]
args = parser.parse_args([arg_filename_with_prefix])
else:
args = parser.parse_args()
if args.dataset == 'kitti' or args.dataset == 'nyu':
from dataloaders.dataloader import NewDataLoader
model_dir = os.path.dirname(args.checkpoint_path)
sys.path.append(model_dir)
def get_num_lines(file_path):
f = open(file_path, 'r')
lines = f.readlines()
f.close()
return len(lines)
def test(params):
"""Test function."""
args.mode = 'test'
dataloader = NewDataLoader(args, 'test')
model = NewCRFDepth(version='large07', inv_depth=False, max_depth=args.max_depth)
model = torch.nn.DataParallel(model)
checkpoint = torch.load(args.checkpoint_path)
model.load_state_dict(checkpoint['model'])
model.eval()
model.cuda()
num_params = sum([np.prod(p.size()) for p in model.parameters()])
print("Total number of parameters: {}".format(num_params))
num_test_samples = get_num_lines(args.filenames_file)
with open(args.filenames_file) as f:
lines = f.readlines()
print('now testing {} files with {}'.format(num_test_samples, args.checkpoint_path))
pred_depths = []
pred_clouds = []
start_time = time.time()
with torch.no_grad():
for _, sample in enumerate(tqdm(dataloader.data)):
image = Variable(sample['image'].cuda())
inv_K_p = Variable(sample['inv_K_p'].cuda())
b, _, h, w = image.shape
depth_to_cloud = D_to_cloud(b, h, w).cuda()
# Predict
pred_depths_r_list, _, _ = model(image)
post_process = True
if post_process:
image_flipped = flip_lr(image)
pred_depths_r_list_flipped, _, _ = model(image_flipped)
pred_depth = post_process_depth(pred_depths_r_list[-1], pred_depths_r_list_flipped[-1])
if args.pred_clouds:
if args.dataset == 'nyu':
color = inv_normalize(image[0, :, :, :]).permute(1, 2, 0)[45:472, 43:608, :].reshape(-1, 3).cpu().numpy()
points = depth_to_cloud(pred_depth, inv_K_p).reshape(1, h, w, 3)[:, 45:472, 43:608, :].reshape(1, -1, 3)
points = points.cpu().numpy().squeeze()
else:
color = inv_normalize(image[0, :, :, :]).permute(1, 2, 0).reshape(-1, 3).cpu().numpy()
points = depth_to_cloud(pred_depth, inv_K_p)
points = points.cpu().numpy().squeeze()
pc = o3d.geometry.PointCloud()
pc.points = o3d.utility.Vector3dVector(points)
pc.colors = o3d.utility.Vector3dVector(color)
pred_clouds.append(pc)
pred_depth = pred_depth.cpu().numpy().squeeze()
if args.do_kb_crop:
height, width = 352, 1216
top_margin = int(height - 352)
left_margin = int((width - 1216) / 2)
pred_depth_uncropped = np.zeros((height, width), dtype=np.float32)
pred_depth_uncropped[top_margin:top_margin + 352, left_margin:left_margin + 1216] = pred_depth
pred_depth = pred_depth_uncropped
pred_depths.append(pred_depth)
elapsed_time = time.time() - start_time
print('Elapesed time: %s' % str(elapsed_time))
print('Done.')
save_name = 'models/result_' + args.model_name
print('Saving result pngs..')
if not os.path.exists(save_name):
try:
os.mkdir(save_name)
os.mkdir(save_name + '/raw')
os.mkdir(save_name + '/cmap')
os.mkdir(save_name + '/rgb')
os.mkdir(save_name + '/gt')
os.mkdir(save_name + '/cloud')
except OSError as e:
if e.errno != errno.EEXIST:
raise
for s in tqdm(range(num_test_samples)):
if args.dataset == 'kitti':
date_drive = lines[s].split('/')[1]
filename_pred_png = save_name + '/raw/' + date_drive + '_' + lines[s].split()[0].split('/')[-1].replace(
'.jpg', '.png')
filename_pred_ply = save_name + '/cloud/' + date_drive + '_' + lines[s].split()[0].split('/')[-1][:-4] + '_' + 'iebins' + '.ply'
filename_cmap_png = save_name + '/cmap/' + date_drive + '_' + lines[s].split()[0].split('/')[
-1].replace('.jpg', '.png')
filename_image_png = save_name + '/rgb/' + date_drive + '_' + lines[s].split()[0].split('/')[-1]
elif args.dataset == 'kittipred':
filename_pred_png = save_name + '/raw/' + lines[s].split()[0].split('/')[-1].replace('.jpg', '.png')
filename_cmap_png = save_name + '/cmap/' + lines[s].split()[0].split('/')[-1].replace('.jpg', '.png')
filename_image_png = save_name + '/rgb/' + lines[s].split()[0].split('/')[-1]
else:
scene_name = lines[s].split()[0].split('/')[0]
filename_pred_png = save_name + '/raw/' + scene_name + '_' + lines[s].split()[0].split('/')[1].replace(
'.jpg', '.png')
filename_pred_ply = save_name + '/cloud/' + scene_name + '_' + lines[s].split()[0].split('/')[1][:-4] + '_' + 'iebins' + '.ply'
filename_cmap_png = save_name + '/cmap/' + scene_name + '_' + lines[s].split()[0].split('/rgb_')[1].replace(
'.jpg', '.png')
filename_gt_png = save_name + '/gt/' + scene_name + '_' + lines[s].split()[0].split('/rgb_')[1].replace(
'.jpg', '_gt.png')
filename_image_png = save_name + '/rgb/' + scene_name + '_' + lines[s].split()[0].split('/rgb_')[1]
rgb_path = os.path.join(args.data_path, './' + lines[s].split()[0])
image = cv2.imread(rgb_path)
if args.dataset == 'nyu':
gt_path = os.path.join(args.data_path, './' + lines[s].split()[1])
gt = cv2.imread(gt_path, -1).astype(np.float32) / 1000.0 # Visualization purpose only
gt[gt == 0] = np.amax(gt)
pred_depth = pred_depths[s]
if args.dataset == 'kitti' or args.dataset == 'kittipred':
pred_depth_scaled = pred_depth * 256.0
else:
pred_depth_scaled = pred_depth * 1000.0
pred_depth_scaled = pred_depth_scaled.astype(np.uint16)
cv2.imwrite(filename_pred_png, pred_depth_scaled, [cv2.IMWRITE_PNG_COMPRESSION, 0])
if args.save_viz:
cv2.imwrite(filename_image_png, image[10:-1 - 9, 10:-1 - 9, :])
if args.dataset == 'nyu':
plt.imsave(filename_gt_png, (10 - gt) / 10, cmap='jet')
pred_depth_cropped = pred_depth[10:-1 - 9, 10:-1 - 9]
plt.imsave(filename_cmap_png, (10 - pred_depth) / 10, cmap='jet')
else:
plt.imsave(filename_cmap_png, np.log10(pred_depth), cmap='magma')
if args.pred_clouds:
pred_cloud = pred_clouds[s]
o3d.io.write_point_cloud(filename_pred_ply, pred_cloud)
return
if __name__ == '__main__':
test(args)
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