def test_match_method(self, method=None, save=False):
# skip if no method present
if method is None:
self.skipTest('Type \'None\' was passed and skipped')
# load images
plain = load_img_file(os.path.join(self.dat_path, 'scotland_plain.png'))
house = load_img_file(os.path.join(self.dat_path, 'scotland_house.png'))
refer = load_img_file(os.path.join(self.dat_path, 'scotland_pitie.png'))
# create color match object
match = ColorMatcher(src=house, ref=plain, method=method).main()
# assess quality
refer_val = self.avg_hist_dist(plain, refer)
match_val = self.avg_hist_dist(plain, match)
print('\nAvg. histogram distance of original %s vs. %s %s' % (round(refer_val, 3), method, round(match_val, 3)))
obj = ColorMatcher(src=house, ref=plain, method='mvgd')
mu_house, mu_plain, cov_house, cov_plain = obj.mu_r, obj.mu_z, obj.cov_r, obj.cov_z
obj = ColorMatcher(src=house, ref=match, method='mvgd')
mu_match, cov_match = obj.mu_z, obj.cov_z
refer_w2 = ColorMatcher.w2_dist(mu_a=mu_house, mu_b=mu_plain, cov_a=cov_house, cov_b=cov_plain)
match_w2 = ColorMatcher.w2_dist(mu_a=mu_match, mu_b=mu_plain, cov_a=cov_match, cov_b=cov_plain)
print('Wasserstein-2 distance of original %s vs. %s %s' % (round(refer_w2, 3), method, round(match_w2, 3)))
# assertion
self.assertEqual(True, refer_val > match_val)
# write images to tests data directory (if option set)
if save:
save_img_file(match, file_path=os.path.join(self.dat_path, 'scotland_'+method), file_type='png')
def test_img_dims(self, src_img, img_ref, exp_val):
try:
obj = ColorMatcher(src=src_img, ref=img_ref)
res = obj.main()
ret = res.mean().astype('bool')
msg = ''
except BaseException as e:
ret = False
msg = e
self.assertEqual(exp_val, ret, msg=msg)
def test_match_method_imageio(self):
# choose method
method = METHODS[0]
# get tests data from imageio lib
fn_img1 = 'chelsea'
fn_img2 = 'astronaut'
img1 = imageio.imread('imageio:' + fn_img1 + '.png')
img2 = imageio.imread('imageio:' + fn_img2 + '.png')
# create color match object (without using keyword arguments)
match = ColorMatcher(img1, img2, method=method).main()
# assess quality
refer_val = self.avg_hist_dist(img1, img2)
match_val = self.avg_hist_dist(img1, match)
print('\nAvg. histogram distance of original %s vs. %s' %
(round(refer_val, 3), round(match_val, 3)))
# save result
output_filename = os.path.join(
self.dat_path,
fn_img1.split('.')[0] + '_from_' + fn_img2 + '_' + method)
save_img_file(img1, file_path=os.path.join(self.dat_path, fn_img1))
save_img_file(img2, file_path=os.path.join(self.dat_path, fn_img2))
save_img_file(match, file_path=output_filename)
# assertion
self.assertEqual(True, refer_val > match_val)
def test_hist_matcher(self):
# get test data from imageio lib
import imageio
fn_img1 = 'chelsea'
fn_img2 = 'coffee'
img1 = imageio.imread('imageio:' + fn_img1 + '.png')
img2 = imageio.imread('imageio:' + fn_img2 + '.png')
# create color match object
match = ColorMatcher(img1, img2, method='hm').main()
# assess quality
match_val = self.avg_hist_dist(match, img2)
print('Avg. histogram distances %s vs %s' % (float('inf'), match_val))
# save result
loc_path = './test/data'
output_filename = os.path.join(
loc_path,
fn_img1.split('.')[0] + '_from_' + fn_img2)
save_img_file(img1, file_path=os.path.join(loc_path, fn_img1))
save_img_file(img2, file_path=os.path.join(loc_path, fn_img2))
save_img_file(match, file_path=output_filename)
# assertion
self.assertEqual(True, float('inf') > match_val)
def test_kodak_images(self):
# prepare data
url = 'https://www.math.purdue.edu/~lucier/PHOTO_CD/BMP_IMAGES/'
self.fnames = ['IMG' + str(i + 1).zfill(4) + '.bmp' for i in range(24)]
loc_path = os.path.join(self.dat_path, 'kodak')
try:
os.makedirs(loc_path, 0o755)
os.makedirs(os.path.join(loc_path, 'results'), 0o755)
except OSError:
pass
if not os.path.exists(loc_path):
download_stack(url, loc_path)
for fn_img1 in self.fnames:
for fn_img2 in self.fnames:
# load images
img1 = load_img_file(os.path.join(loc_path, fn_img1))
img2 = load_img_file(os.path.join(loc_path, fn_img2))
# create color match object
res = ColorMatcher(img1, img2, method='hm-mkl-hm').main()
# assess quality
val = MatchMethodTester.avg_hist_dist(res, img2)
print('Avg. histogram distance %s' % val)
# save result
output_filename = os.path.join(loc_path, 'results', fn_img1.split('.')[0] + '_from_' + fn_img2)
save_img_file(res, file_path=output_filename)
# assertion
self.assertEqual(True, val != 0)
def main():
# program info
print("\ncolor-matcher v%s \n" % __version__)
# parse options
cfg = parse_options(sys.argv[1:])
# select files from window (if option set)
if cfg['win']:
cfg['src_path'] = select_file('.', 'Select source image')
cfg['ref_path'] = select_file(cfg['src_path'],
'Select reference image')
# cancel if file paths not provided
if not cfg['src_path'] or not cfg['ref_path']:
usage()
print('Canceled due to missing image file path\n')
sys.exit()
# select light field image(s) considering provided folder or file
if os.path.isdir(cfg['src_path']):
filenames = [
f for f in os.listdir(cfg['src_path'])
if f.lower().endswith(FILE_EXTS)
]
elif not os.path.isfile(cfg['src_path']) or not os.path.isfile(
cfg['ref_path']):
print('File(s) not found \n')
sys.exit()
else:
filenames = [cfg['src_path']]
# method handling
cfg['method'] = cfg['method'] if cfg['method'] in METHODS else 'mvgd'
# file handling
ref = load_img_file(cfg['ref_path'])
output_path = os.path.dirname(cfg['src_path'])
# process the images
for f in filenames:
src = load_img_file(f)
res = ColorMatcher(src=src, ref=ref, method=cfg['method']).main()
filename = os.path.splitext(os.path.basename(
cfg['src_path']))[0] + '_' + cfg['method']
file_ext = os.path.splitext(cfg['src_path'])[-1]
save_img_file(res,
file_path=os.path.join(output_path, filename),
file_type=file_ext[1:])
return True
def test_mvgd_matcher(self):
# load images
plain = load_img_file(os.path.join(self.dat_path,
'scotland_house.png'))
house = load_img_file(os.path.join(self.dat_path,
'scotland_plain.png'))
refer = load_img_file(os.path.join(self.dat_path,
'scotland_pitie.png'))
# create color match object
match = ColorMatcher(plain, house, method='mvgd').main()
# assess quality
refer_val = self.avg_hist_dist(refer, house)
match_val = self.avg_hist_dist(match, house)
print('Avg. histogram distances %s vs %s' % (refer_val, match_val))
# assertion
self.assertEqual(True, refer_val > match_val)
