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generate_regist_pairs.py
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generate_regist_pairs.py
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"""
Script for generating registration pairs in two schemas
Sample run::
python create_registration_pairs.py \
-i ../output/synth_dataset/*.jpg \
-l ../output/synth_dataset/*.csv \
-csv ../output/cover.csv --mode each2all
Copyright (C) 2016-2019 Jiri Borovec <jiri.borovec@fel.cvut.cz>
"""
import os
import sys
import glob
import argparse
import logging
import pandas as pd
sys.path += [os.path.abspath('.'), os.path.abspath('..')] # Add path to root
from birl.utilities.data_io import image_sizes
from birl.utilities.experiments import parse_arg_params
from birl.benchmark import ImRegBenchmark
# list of combination options
OPTIONS_COMBINE = ('first2all', 'each2all')
def arg_parse_params():
""" parse the input parameters
:return dict: parameters
"""
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--path_pattern_images', type=str,
help='path to the input image', required=True)
parser.add_argument('-l', '--path_pattern_landmarks', type=str,
help='path to the input landmarks', required=True)
parser.add_argument('-csv', '--path_csv', type=str, required=True,
help='path to coordinate csv file')
parser.add_argument('--mode', type=str, required=False,
help='type of combination of registration pairs',
default=OPTIONS_COMBINE[0], choices=OPTIONS_COMBINE)
args = parse_arg_params(parser, upper_dirs=['path_csv'])
return args
def generate_pairs(path_pattern_imgs, path_pattern_lnds, mode):
""" generate the registration pairs as reference and moving images
:param str path_pattern_imgs: path to the images and image name pattern
:param str path_pattern_lnds: path to the landmarks and its name pattern
:param str mode: one of OPTIONS_COMBINE
:return: DF
"""
list_imgs = sorted(glob.glob(path_pattern_imgs))
list_lnds = sorted(glob.glob(path_pattern_lnds))
assert len(list_imgs) == len(list_lnds), \
'the list of loaded images (%i) and landmarks (%i) ' \
'is different length' % (len(list_imgs), len(list_lnds))
assert len(list_imgs) >= 2, 'the minimum is 2 elements'
logging.info('combining list %i files with "%s"', len(list_imgs), mode)
pairs = [(0, i) for i in range(1, len(list_imgs))]
if mode == 'each2all':
pairs += [(i, j) for i in range(1, len(list_imgs))
for j in range(i + 1, len(list_imgs))]
reg_pairs = []
for i, j in pairs:
rec = dict(zip(ImRegBenchmark.COVER_COLUMNS,
(list_imgs[i], list_imgs[j], list_lnds[i], list_lnds[j])))
img_size, img_diag = image_sizes(rec[ImRegBenchmark.COL_IMAGE_REF])
rec.update({
ImRegBenchmark.COL_IMAGE_SIZE: img_size,
ImRegBenchmark.COL_IMAGE_DIAGONAL: img_diag,
})
reg_pairs.append(rec)
df_overview = pd.DataFrame(reg_pairs)
return df_overview
def main(path_pattern_images, path_pattern_landmarks, path_csv, mode='all2all'):
""" main entry point
:param str path_pattern_images: path to images
:param str path_pattern_landmarks: path to landmarks
:param str path_csv: path output cover table, add new rows if it exists
:param str mode: option first2all or all2all
"""
# if the cover file exist continue in it, otherwise create new
if os.path.isfile(path_csv):
logging.info('loading existing csv file: %s', path_csv)
df_overview = pd.read_csv(path_csv, index_col=0)
else:
logging.info('creating new cover file')
df_overview = pd.DataFrame()
df_ = generate_pairs(path_pattern_images, path_pattern_landmarks, mode)
df_overview = pd.concat((df_overview, df_), axis=0, sort=True)
df_overview = df_overview[list(ImRegBenchmark.COVER_COLUMNS_EXT)].reset_index(drop=True)
logging.info('saving csv file with %i records \n %s', len(df_overview), path_csv)
df_overview.to_csv(path_csv)
if __name__ == '__main__':
logging.basicConfig(level=logging.INFO)
arg_params = arg_parse_params()
logging.info('running...')
main(**arg_params)
logging.info('DONE')