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viz_stereo.py
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viz_stereo.py
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# Copyright 2020 Google LLC, University of Victoria, Czech Technical University
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import random
from time import time
from config import get_config, print_usage
from utils.io_helper import load_h5, build_composite_image, load_json
from utils.path_helper import (get_data_path, get_kp_file, get_match_file,
get_stereo_epipolar_final_match_file,
get_stereo_viz_folder, get_geom_file,
get_geom_inl_file, get_pairs_per_threshold)
from utils.load_helper import load_depth, load_calib, load_h5_valid_image
from utils.stereo_helper import (np_skew_symmetric, get_projected_kp,
normalize_keypoints, unnormalize_keypoints,
get_truesym, get_episym)
def main(cfg):
'''Visualization of stereo keypoints and matches.
Parameters
----------
cfg: Namespace
Configurations for running this part of the code.
'''
try:
pairwise_keypoints = cfg.method_dict['config_common']['pairwise_keypoints']
except:
pairwise_keypoints = False
# Files should not be named to prevent (easy) abuse
# Instead we use 0, ..., cfg.num_viz_stereo_pairs
viz_folder_hq, viz_folder_lq = get_stereo_viz_folder(cfg)
print(' -- Visualizations, stereo: "{}/{}"'.format(cfg.dataset, cfg.scene))
t_start = time()
# Load deprecated images list
deprecated_images_all = load_json(cfg.json_deprecated_images)
if cfg.dataset in deprecated_images_all and cfg.scene in deprecated_images_all[
cfg.dataset]:
deprecated_images = deprecated_images_all[cfg.dataset][cfg.scene]
else:
deprecated_images = []
# Load keypoints, matches and errors
keypoints_dict = load_h5_valid_image(get_kp_file(cfg), deprecated_images)
matches_dict = load_h5_valid_image(get_match_file(cfg), deprecated_images)
ransac_inl_dict = load_h5_valid_image(get_geom_inl_file(cfg), deprecated_images)
# Hacky: We need to recompute the errors, loading only for the keys
data_dir = get_data_path(cfg)
pairs_all = get_pairs_per_threshold(data_dir)['0.1']
pairs = []
for pair in pairs_all:
if all([key not in deprecated_images for key in pair.split('-')]):
pairs += [pair]
# Create results folder if it does not exist
if not os.path.exists(viz_folder_hq):
os.makedirs(viz_folder_hq)
if not os.path.exists(viz_folder_lq):
os.makedirs(viz_folder_lq)
# Sort alphabetically and pick different images
sorted_keys = sorted(pairs)
picked = []
pairs = []
for pair in sorted_keys:
fn1, fn2 = pair.split('-')
if fn1 not in picked and fn2 not in picked:
picked += [fn1, fn2]
pairs += [pair]
if len(pairs) == cfg.num_viz_stereo_pairs:
break
# Load depth maps
depth = {}
if cfg.dataset != 'googleurban':
for pair in pairs:
files = pair.split('-')
for f in files:
if f not in depth:
depth[f] = load_depth(
os.path.join(data_dir, 'depth_maps',
'{}.h5'.format(f)))
# Generate and save the images
for i, pair in enumerate(pairs):
# load metadata
fn1, fn2 = pair.split('-')
calib_dict = load_calib([
os.path.join(data_dir, 'calibration',
'calibration_{}.h5'.format(fn1)),
os.path.join(data_dir, 'calibration',
'calibration_{}.h5'.format(fn2))
])
calc1 = calib_dict[fn1]
calc2 = calib_dict[fn2]
inl = ransac_inl_dict[pair]
# Get depth for keypoints
if pairwise_keypoints:
kp1 = keypoints_dict[f'{fn1}-{fn2}']
kp2 = keypoints_dict[f'{fn2}-{fn1}']
else:
kp1 = keypoints_dict[fn1]
kp2 = keypoints_dict[fn2]
# Normalize keypoints
kp1n = normalize_keypoints(kp1, calc1['K'])
kp2n = normalize_keypoints(kp2, calc2['K'])
# Get {R, t} from calibration information
R_1, t_1 = calc1['R'], calc1['T'].reshape((3, 1))
R_2, t_2 = calc2['R'], calc2['T'].reshape((3, 1))
# Compute dR, dt
dR = np.dot(R_2, R_1.T)
dT = t_2 - np.dot(dR, t_1)
if cfg.dataset == 'phototourism':
kp1_int = np.round(kp1).astype(int)
kp2_int = np.round(kp2).astype(int)
kp1_int[:, 1] = np.clip(kp1_int[:, 1], 0, depth[fn1].shape[0] - 1)
kp1_int[:, 0] = np.clip(kp1_int[:, 0], 0, depth[fn1].shape[1] - 1)
kp2_int[:, 1] = np.clip(kp2_int[:, 1], 0, depth[fn2].shape[0] - 1)
kp2_int[:, 0] = np.clip(kp2_int[:, 0], 0, depth[fn2].shape[1] - 1)
d1 = np.expand_dims(depth[fn1][kp1_int[:, 1], kp1_int[:, 0]],
axis=-1)
d2 = np.expand_dims(depth[fn2][kp2_int[:, 1], kp2_int[:, 0]],
axis=-1)
# Project with depth
kp1n_p, kp2n_p = get_projected_kp(kp1n, kp2n, d1, d2, dR, dT)
kp1_p = unnormalize_keypoints(kp1n_p, calc2['K'])
kp2_p = unnormalize_keypoints(kp2n_p, calc1['K'])
# Re-index keypoints from matches
kp1_inl = kp1[inl[0]]
kp2_inl = kp2[inl[1]]
kp1_p_inl = kp1_p[inl[0]]
kp2_p_inl = kp2_p[inl[1]]
kp1n_inl = kp1n[inl[0]]
kp2n_inl = kp2n[inl[1]]
kp1n_p_inl = kp1n_p[inl[0]]
kp2n_p_inl = kp2n_p[inl[1]]
d1_inl = d1[inl[0]]
d2_inl = d2[inl[1]]
# Filter out keypoints with invalid depth
nonzero_index = np.nonzero(np.squeeze(d1_inl * d2_inl))
zero_index = np.where(np.squeeze(d1_inl * d2_inl) == 0)[0]
kp1_inl_nonzero = kp1_inl[nonzero_index]
kp2_inl_nonzero = kp2_inl[nonzero_index]
kp1_p_inl_nonzero = kp1_p_inl[nonzero_index]
kp2_p_inl_nonzero = kp2_p_inl[nonzero_index]
kp1n_inl_nonzero = kp1n_inl[nonzero_index]
kp2n_inl_nonzero = kp2n_inl[nonzero_index]
kp1n_p_inl_nonzero = kp1n_p_inl[nonzero_index]
kp2n_p_inl_nonzero = kp2n_p_inl[nonzero_index]
# Compute symmetric distance using the depth image
d = get_truesym(kp1_inl_nonzero, kp2_inl_nonzero,
kp1_p_inl_nonzero, kp2_p_inl_nonzero)
else:
# All points are valid for computing the epipolar distance.
zero_index = []
# Compute symmetric epipolar distance for every match.
kp1_inl_nonzero = kp1[inl[0]]
kp2_inl_nonzero = kp2[inl[1]]
kp1n_inl_nonzero = kp1n[inl[0]]
kp2n_inl_nonzero = kp2n[inl[1]]
# d = np.zeros(inl.shape[1])
d = get_episym(kp1n_inl_nonzero, kp2n_inl_nonzero, dR, dT)
# canvas
im, v_offset, h_offset = build_composite_image(
os.path.join(
data_dir, 'images',
fn1 + ('.png' if cfg.dataset == 'googleurban' else '.jpg')),
os.path.join(
data_dir, 'images',
fn2 + ('.png' if cfg.dataset == 'googleurban' else '.jpg')),
margin=5,
axis=1 if (not cfg.viz_composite_vert
or cfg.dataset == 'googleurban' or cfg.dataset=='pragueparks') else 0)
plt.figure(figsize=(10, 10))
plt.imshow(im)
linewidth = 2
# Plot matches on points without depth
for idx in range(len(zero_index)):
plt.plot(
(kp1_inl[idx, 0] + h_offset[0], kp2_inl[idx, 0] + h_offset[1]),
(kp1_inl[idx, 1] + v_offset[0], kp2_inl[idx, 1] + v_offset[1]),
color='b',
linewidth=linewidth)
# Plot matches
# Points are normalized by the focals, which are on average ~670.
max_dist = 5
if cfg.dataset == 'googleurban':
max_dist = 2e-4
if cfg.dataset == 'pragueparks':
max_dist = 2e-4
cmap = matplotlib.cm.get_cmap('summer')
order = list(range(len(d)))
random.shuffle(order)
for idx in order:
if d[idx] <= max_dist:
min_val = 0
max_val = 255 - min_val
col = cmap(
int(max_val * (1 - (max_dist - d[idx]) / max_dist) +
min_val))
# col = cmap(255 * (max_dist - d[idx]) / max_dist)
else:
col = 'r'
plt.plot((kp1_inl_nonzero[idx, 0] + h_offset[0],
kp2_inl_nonzero[idx, 0] + h_offset[1]),
(kp1_inl_nonzero[idx, 1] + v_offset[0],
kp2_inl_nonzero[idx, 1] + v_offset[1]),
color=col,
linewidth=linewidth)
plt.tight_layout()
plt.axis('off')
viz_file_hq = os.path.join(viz_folder_hq, '{:05d}.png'.format(i))
viz_file_lq = os.path.join(viz_folder_lq, '{:05d}.jpg'.format(i))
plt.savefig(viz_file_hq, bbox_inches='tight')
# Convert with imagemagick
os.system('convert -quality 75 -resize \"500>\" {} {}'.format(
viz_file_hq, viz_file_lq))
plt.close()
print('Done [{:.02f} s.]'.format(time() - t_start))
if __name__ == '__main__':
cfg, unparsed = get_config()
# If we have unparsed arguments, print usage and exit
if len(unparsed) > 0:
print_usage()
exit(1)
main(cfg)