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dense_feature_matching.py
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dense_feature_matching.py
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'''
Author: Xingtong Liu, Yiping Zheng, Benjamin Killeen, Masaru Ishii, Gregory D. Hager, Russell H. Taylor, and Mathias Unberath
Copyright (C) 2020 Johns Hopkins University - All Rights Reserved
You may use, distribute and modify this code under the
terms of the GNU GENERAL PUBLIC LICENSE Version 3 license for non-commercial usage.
You should have received a copy of the GNU GENERAL PUBLIC LICENSE Version 3 license with
this file. If not, please write to: xliu89@jh.edu or unberath@jhu.edu
'''
import cv2
import numpy as np
from pathlib import Path
import argparse
import h5py
import multiprocessing
import tqdm
from skimage.measure import ransac
from skimage.transform import FundamentalMatrixTransform
# Local
import utils
if __name__ == '__main__':
multiprocessing.set_start_method('spawn', force=True)
parser = argparse.ArgumentParser(
description='Dense Descriptor Learning -- dense feature matching',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--image_downsampling', type=float, default=4.0,
help='input image downsampling rate')
parser.add_argument('--network_downsampling', type=int, default=64, help='network bottom layer downsampling')
parser.add_argument('--input_size', nargs='+', type=int, required=True,
help='input size')
parser.add_argument('--batch_size', type=int, default=8, help='batch size for testing')
parser.add_argument('--num_workers', type=int, default=8, help='number of workers for input data loader')
parser.add_argument('--load_intermediate_data', action='store_true', help='whether to load intermediate data')
parser.add_argument('--data_root', type=str, required=True, help='path to the data for '
'feature matching')
parser.add_argument('--sequence_root', type=str, required=True,
help='root of the specific video sequence')
parser.add_argument('--trained_model_path', type=str, required=True, help='path to the trained model')
parser.add_argument('--feature_length', type=int, default=256, help='output channel dimension of network')
parser.add_argument('--filter_growth_rate', type=int, default=10, help='filter growth rate of network')
parser.add_argument('--max_feature_detection', type=int, default=3000,
help='max allowed number of detected features per frame')
parser.add_argument('--cross_check_distance', type=float, default=5.0,
help='max cross check distance for valid matches')
parser.add_argument('--id_range', nargs='+', type=int,
help='range of patient ids')
parser.add_argument('--gpu_id', type=int, default=0, help='gpu id for matching generation')
parser.add_argument('--temporal_range', type=int, default=30, help='range for temporal sampling')
parser.add_argument('--test_keypoint_num', type=int, default=200, help='number of keypoints used for quick '
'spatial testing')
parser.add_argument('--residual_threshold', type=float, default=5.0, help='pixel threshold for ransac estimation')
parser.add_argument('--octave_layers', type=int, default=8)
parser.add_argument('--contrast_threshold', type=float, default=0.00005)
parser.add_argument('--edge_threshold', type=float, default=100)
parser.add_argument('--sigma', type=float, default=1.1)
parser.add_argument('--skip_interval', type=int, default=5,
help="number of skipping frames in searching state")
parser.add_argument('--min_inlier_ratio', type=float, default=0.2,
help="minimum inlier ratio of ransac")
parser.add_argument('--hysterisis_factor', type=float, default=0.7,
help="factor of the inlier ratio in the spatial_range state")
args = parser.parse_args()
# Hyper-parameters
id_range = args.id_range
image_downsampling = args.image_downsampling
height, width = args.input_size
batch_size = args.batch_size
num_workers = args.num_workers
network_downsampling = args.network_downsampling
load_intermediate_data = args.load_intermediate_data
sequence_root = Path(args.sequence_root)
trained_model_path = Path(args.trained_model_path)
data_root = Path(args.data_root)
max_feature_detection = args.max_feature_detection
cross_check_distance = args.cross_check_distance
gpu_id = args.gpu_id
temporal_range = args.temporal_range
test_keypoint_num = args.test_keypoint_num
residual_threshold = args.residual_threshold
feature_length = args.feature_length
filter_growth_rate = args.filter_growth_rate
octave_layers = args.octave_layers
contrast_threshold = args.contrast_threshold
edge_threshold = args.edge_threshold
sigma = args.sigma
skip_interval = args.skip_interval
min_inlier_ratio = args.min_inlier_ratio
hysterisis_factor = args.hysterisis_factor
precompute_root = data_root / "precompute"
if not precompute_root.exists():
precompute_root.mkdir(parents=True)
print("SIFT detector creating...")
sift = cv2.xfeatures2d.SIFT_create(nfeatures=max_feature_detection, nOctaveLayers=octave_layers,
contrastThreshold=contrast_threshold,
edgeThreshold=edge_threshold, sigma=sigma)
colors_list, boundary, feature_maps_list, start_h, start_w = \
utils.gather_feature_matching_data(feature_descriptor_model_path=trained_model_path,
sub_folder=sequence_root,
data_root=data_root, image_downsampling=image_downsampling,
network_downsampling=network_downsampling,
load_intermediate_data=load_intermediate_data,
precompute_root=precompute_root,
batch_size=batch_size, id_range=id_range,
filter_growth_rate=filter_growth_rate,
feature_length=feature_length, gpu_id=gpu_id)
# Erode the boundary to remove near-boundary matches
kernel = np.ones((5, 5), np.uint8)
boundary = cv2.erode(boundary, kernel, iterations=3)
f_matches = None
print("Extracting keypoint locations...")
sift_keypoints_list, sift_keypoint_location_list_1D, sift_keypoint_location_list_2D, sift_descriptions_list = \
utils.extract_keypoints(sift, colors_list, boundary, height, width)
f_matches = h5py.File(str(sequence_root / "feature_matches.hdf5"), 'w')
dataset_matches = f_matches.create_dataset('matches', (0, 4, 1),
maxshape=(None, 4, 1), chunks=(40960, 4, 1),
compression="gzip", compression_opts=9, dtype='int16')
feature_length, height, width = feature_maps_list[0].shape
frame_count_in_total = len(colors_list)
tq = tqdm.tqdm(total=frame_count_in_total * (frame_count_in_total - 1) // 2)
for i in range(frame_count_in_total - 1):
color_1 = colors_list[i]
feature_map_1 = feature_maps_list[i].cuda(gpu_id)
sift_keypoint_1 = sift_keypoints_list[i]
np.random.seed(10086)
random_indexes = list(np.random.choice(range(0, len(sift_keypoint_1)), test_keypoint_num,
replace=False))
sift_keypoint_locations_1D_1 = sift_keypoint_location_list_1D[i]
sift_keypoint_locations_2D_1 = sift_keypoint_location_list_2D[i]
cur_state = "temporal_range"
for j in range(1, len(colors_list) - i):
tq.set_description(cur_state)
if cur_state == "temporal_range":
if j > temporal_range:
cur_state = "searching"
if cur_state == "searching":
if j % skip_interval != 0:
tq.update(1)
continue
if cur_state == "spatial_range":
pass
color_2 = colors_list[i + j]
feature_map_2 = feature_maps_list[i + j].cuda(gpu_id)
if cur_state == "temporal_range" or cur_state == "spatial_range":
x = utils.feature_matching_single_generation(
feature_map_1=feature_map_1,
feature_map_2=feature_map_2,
kps_1D_1=sift_keypoint_locations_1D_1,
cross_check_distance=cross_check_distance,
gpu_id=gpu_id)
elif cur_state == "searching":
x = utils.feature_matching_single_generation(
feature_map_1=feature_map_1,
feature_map_2=feature_map_2,
kps_1D_1=sift_keypoint_locations_1D_1[random_indexes],
cross_check_distance=cross_check_distance,
gpu_id=gpu_id)
if x is None:
tq.update(1)
continue
source_keypoint_indexes, target_keypoint_locations = x
if cur_state == "searching":
source_keypoint_indexes = [random_indexes[source_keypoint_index] for
source_keypoint_index in source_keypoint_indexes]
source_keypoint_locations = sift_keypoint_locations_2D_1[source_keypoint_indexes,
:].reshape((-1, 2))
source_keypoint_locations[:, 0] = image_downsampling * (
source_keypoint_locations[:, 0] + start_w)
source_keypoint_locations[:, 1] = image_downsampling * (
source_keypoint_locations[:, 1] + start_h)
target_keypoint_locations[:, 0] = image_downsampling * (
target_keypoint_locations[:, 0] + start_w)
target_keypoint_locations[:, 1] = image_downsampling * (
target_keypoint_locations[:, 1] + start_h)
try:
model, inliers = ransac((source_keypoint_locations,
target_keypoint_locations),
FundamentalMatrixTransform, min_samples=8,
residual_threshold=residual_threshold, max_trials=5)
except ValueError:
tq.set_postfix(
source_frame_index='{:d}'.format(i),
target_frame_index='{:d}'.format(i + j),
point_num='{:d}'.format(target_keypoint_locations.shape[0]))
tq.update(1)
continue
inlier_ratio = np.sum(inliers) / source_keypoint_locations.shape[0]
if j == 1:
mean_inlier_ratio = inlier_ratio
elif j <= temporal_range:
mean_inlier_ratio = mean_inlier_ratio * ((j - 1) / j) + inlier_ratio * (1 / j)
elif j == temporal_range + 1:
mean_inlier_ratio = max(min_inlier_ratio, mean_inlier_ratio)
if cur_state == "temporal_range":
start_index = dataset_matches.shape[0]
dataset_matches.resize(
(dataset_matches.shape[0] + target_keypoint_locations.shape[0] + 1, 4, 1))
dataset_matches[start_index, :, :] = np.asarray(
[target_keypoint_locations.shape[0], i, i + j, -1]).reshape((4, 1))
dataset_matches[start_index + 1:start_index + 1 + target_keypoint_locations.shape[0],
:] = \
np.concatenate([source_keypoint_locations.reshape((-1, 2)),
target_keypoint_locations.reshape((-1, 2))], axis=1).reshape(
(-1, 4, 1)).astype(np.int16)
tq.set_description(cur_state)
tq.set_postfix(
source_frame_index='{:d}'.format(i),
target_frame_index='{:d}'.format(i + j),
mean_inlier_ratio='{:.3f}'.format(mean_inlier_ratio))
elif cur_state == "searching":
if inlier_ratio >= mean_inlier_ratio:
cur_state = "spatial_range"
# Redo the feature matching with full set of keypoints
x = utils.feature_matching_single_generation(
feature_map_1=feature_map_1,
feature_map_2=feature_map_2,
kps_1D_1=sift_keypoint_locations_1D_1,
cross_check_distance=cross_check_distance,
gpu_id=gpu_id)
if x is None:
tq.update(1)
continue
source_keypoint_indexes, target_keypoint_locations = x
source_keypoint_locations = sift_keypoint_locations_2D_1[source_keypoint_indexes,
:].reshape((-1, 2))
source_keypoint_locations[:, 0] = image_downsampling * (
source_keypoint_locations[:, 0] + start_w)
source_keypoint_locations[:, 1] = image_downsampling * (
source_keypoint_locations[:, 1] + start_h)
target_keypoint_locations[:, 0] = image_downsampling * (
target_keypoint_locations[:, 0] + start_w)
target_keypoint_locations[:, 1] = image_downsampling * (
target_keypoint_locations[:, 1] + start_h)
start_index = dataset_matches.shape[0]
dataset_matches.resize(
(dataset_matches.shape[0] + target_keypoint_locations.shape[0] + 1, 4, 1))
dataset_matches[start_index, :, :] = np.asarray(
[target_keypoint_locations.shape[0], i, i + j, -1]).reshape((4, 1))
dataset_matches[start_index + 1:start_index + 1 + target_keypoint_locations.shape[0],
:] = \
np.concatenate([source_keypoint_locations.reshape((-1, 2)),
target_keypoint_locations.reshape((-1, 2))], axis=1).reshape(
(-1, 4, 1)).astype(np.int16)
tq.set_description(cur_state)
tq.set_postfix(
source_frame_index='{:d}'.format(i),
target_frame_index='{:d}'.format(i + j),
inlier_ratio='{:.3f}'.format(inlier_ratio))
else:
tq.set_description(cur_state)
tq.set_postfix(
source_frame_index='{:d}'.format(i),
target_frame_index='{:d}'.format(i + j))
elif cur_state == "spatial_range":
# Leave a bit of hyterisis space for spatial_range state to allow for more frame matches
if inlier_ratio >= hysterisis_factor * mean_inlier_ratio:
start_index = dataset_matches.shape[0]
dataset_matches.resize(
(dataset_matches.shape[0] + target_keypoint_locations.shape[0] + 1, 4, 1))
dataset_matches[start_index, :, :] = np.asarray(
[target_keypoint_locations.shape[0], i, i + j, -1]).reshape((4, 1))
dataset_matches[start_index + 1:start_index + 1 + target_keypoint_locations.shape[0],
:] = \
np.concatenate([source_keypoint_locations.reshape((-1, 2)),
target_keypoint_locations.reshape((-1, 2))], axis=1).reshape(
(-1, 4, 1)).astype(np.int16)
tq.set_description(cur_state)
tq.set_postfix(
source_frame_index='{:d}'.format(i),
target_frame_index='{:d}'.format(i + j),
inlier_ratio='{:.3f}'.format(inlier_ratio))
else:
cur_state = "searching"
tq.set_description(cur_state)
tq.set_postfix(
source_frame_index='{:d}'.format(i),
target_frame_index='{:d}'.format(i + j))
tq.update(1)
continue
tq.update(1)
tq.close()
if f_matches is not None:
f_matches.close()