def test_lfp(self):
# set config for unit test purposes
cfg = PlenopticamConfig()
cfg.params[cfg.opt_dbug] = True
for fn_lfp, fn_wht in zip(self.fnames_lfp, self.fnames_wht):
# update file paths and calibration data in config
cfg.params[cfg.lfp_path] = os.path.join(self.fp, fn_lfp)
cfg.params[cfg.cal_path] = os.path.join(self.fp, fn_wht)
cfg.params[cfg.cal_meta] = os.path.splitext(
cfg.params[cfg.cal_path])[0] + '.json'
cfg.load_cal_data()
# create folder (if it doesn't already exist)
misc.mkdir_p(os.path.splitext(cfg.params[cfg.lfp_path])[0])
# test light field alignment
lfp_img = misc.load_img_file(cfg.params[cfg.lfp_path])
lfp_obj = LfpAligner(lfp_img=lfp_img, cfg=cfg, sta=None)
ret_val = lfp_obj.main()
lfp_img = lfp_obj.lfp_img
del lfp_obj
# assertion
self.assertEqual(True, ret_val)
# test light field extraction
lfp_obj = LfpExtractor(lfp_img_align=lfp_img, cfg=cfg, sta=None)
ret_val = lfp_obj.main()
del lfp_obj
# assertion
self.assertEqual(True, ret_val)
def test_lfp(self):
# set config for unit test purposes
sta = PlenopticamStatus()
cfg = PlenopticamConfig()
cfg.reset_values()
cfg.params[cfg.opt_dbug] = False
cfg.params[
cfg.
opt_prnt] = False # prevent Travis CI to terminate after reaching 4MB logfile size
for fn_lfp, fn_wht in zip(self.fnames_lfp, self.fnames_wht):
# generate console output to prevent abort in Travis CI
print(fn_lfp)
# update file paths and calibration data in config
cfg.params[cfg.lfp_path] = os.path.join(self.fp, fn_lfp)
cfg.params[cfg.cal_path] = os.path.join(self.fp, fn_wht)
cfg.params[cfg.cal_meta] = os.path.splitext(
cfg.params[cfg.cal_path])[0] + '.json'
cfg.load_cal_data()
# create folder (if it doesn't already exist)
mkdir_p(os.path.splitext(cfg.params[cfg.lfp_path])[0])
# test light field alignment
lfp_img = load_img_file(cfg.params[cfg.lfp_path])
lfp_obj = LfpAligner(lfp_img=lfp_img, cfg=cfg, sta=sta)
ret_val = lfp_obj.main()
lfp_img = lfp_obj.lfp_img
del lfp_obj
# assertion
self.assertEqual(True, ret_val)
# test light field extraction
lfp_obj = LfpExtractor(lfp_img_align=lfp_img, cfg=cfg, sta=sta)
lfp_obj.main()
vp_img_arr = lfp_obj.vp_img_arr
del lfp_obj
lfp_obj = LfpRefocuser(vp_img_arr=vp_img_arr, cfg=cfg, sta=sta)
lfp_obj.main()
del lfp_obj
# assertion
self.assertEqual(True, ret_val)
def test_custom_lfp(self):
for fn_lfp, fn_wht in zip(self.loader.opex_fnames_lfp,
self.loader.opex_fnames_wht):
# generate console output to prevent abort in Travis CI
print(fn_lfp)
# update file paths and calibration data in config
self.cfg.params[self.cfg.lfp_path] = join(self.fp, fn_lfp)
self.cfg.params[self.cfg.cal_path] = join(self.fp, fn_wht)
self.cfg.params[self.cfg.cal_meta] = splitext(
self.cfg.params[self.cfg.cal_path])[0] + '.json'
self.cfg.load_cal_data()
# create folder (if it doesn't already exist)
mkdir_p(splitext(self.cfg.params[self.cfg.lfp_path])[0])
# test light field alignment
lfp_img = load_img_file(self.cfg.params[self.cfg.lfp_path])
lfp_obj = LfpAligner(lfp_img=lfp_img, cfg=self.cfg, sta=self.sta)
ret_val = lfp_obj.main()
lfp_img = lfp_obj.lfp_img
del lfp_obj
# assertion
self.assertEqual(True, ret_val)
# test light field extraction
lfp_obj = LfpExtractor(lfp_img_align=lfp_img,
cfg=self.cfg,
sta=self.sta)
ret_val = lfp_obj.main()
vp_img_arr = lfp_obj.vp_img_arr
del lfp_obj
# assertion
self.assertEqual(True, ret_val)
lfp_obj = LfpRefocuser(vp_img_arr=vp_img_arr,
cfg=self.cfg,
sta=self.sta)
ret_val = lfp_obj.main()
del lfp_obj
# assertion
self.assertEqual(True, ret_val)
def test_illum(self):
# use pre-loaded calibration dataset
wht_list = [
file for file in listdir(self.fp) if file.startswith('caldata')
]
lfp_list = [
file for file in listdir(self.fp) if file.endswith(('lfr', 'lfp'))
]
self.cfg.params[self.cfg.cal_path] = join(self.fp, wht_list[0])
for lfp_file in lfp_list:
self.cfg.params[self.cfg.lfp_path] = join(self.fp, lfp_file)
print('\nCompute image %s' %
basename(self.cfg.params[self.cfg.lfp_path]))
# decode light field image
obj = LfpReader(self.cfg, self.sta)
ret = obj.main()
# use third of original image size (to prevent Travis from stopping due to memory error)
crop_h, crop_w = obj.lfp_img.shape[0] // 3, obj.lfp_img.shape[
1] // 3
crop_h, crop_w = crop_h + crop_h % 2, crop_w + crop_w % 2 # use even number for correct Bayer arrangement
lfp_img = obj.lfp_img[crop_h:-crop_h, crop_w:-crop_w]
del obj
self.assertEqual(True, ret)
# create output data folder
mkdir_p(self.cfg.exp_path, self.cfg.params[self.cfg.opt_prnt])
if not self.cfg.cond_meta_file():
# automatic calibration data selection
obj = CaliFinder(self.cfg, self.sta)
ret = obj.main()
wht_img = obj.wht_bay[
crop_h:-crop_h,
crop_w:-crop_w] if obj.wht_bay is not None else obj.wht_bay
del obj
self.assertEqual(True, ret)
meta_cond = not (
exists(self.cfg.params[self.cfg.cal_meta]) and
self.cfg.params[self.cfg.cal_meta].lower().endswith('json'))
if meta_cond or self.cfg.params[self.cfg.opt_cali]:
# perform centroid calibration
obj = LfpCalibrator(wht_img, self.cfg, self.sta)
ret = obj.main()
self.cfg = obj.cfg
del obj
self.assertEqual(True, ret)
# load calibration data
self.cfg.load_cal_data()
# write centroids as png file
if wht_img is not None:
obj = CentroidDrawer(wht_img,
self.cfg.calibs[self.cfg.mic_list],
self.cfg)
ret = obj.write_centroids_img(fn='testcase_wht_img+mics.png')
del obj
self.assertEqual(True, ret)
# check if light field alignment has been done before
if self.cfg.cond_lfp_align():
# align light field
obj = LfpAligner(lfp_img, self.cfg, self.sta, wht_img)
ret = obj.main()
del obj
self.assertEqual(True, ret)
# load previously computed light field alignment
with open(join(self.cfg.exp_path, 'lfp_img_align.pkl'), 'rb') as f:
lfp_img_align = pickle.load(f)
# extract viewpoint data
CaliFinder(self.cfg).main()
obj = LfpExtractor(lfp_img_align, cfg=self.cfg, sta=self.sta)
ret = obj.main()
vp_img_arr = obj.vp_img_linear
del obj
self.assertEqual(True, ret)
# do refocusing
if self.cfg.params[self.cfg.opt_refo]:
obj = LfpRefocuser(vp_img_arr, cfg=self.cfg, sta=self.sta)
ret = obj.main()
del obj
self.assertEqual(True, ret)
return True
def test_illum(self):
# instantiate config and status objects
cfg = PlenopticamConfig()
cfg.default_values()
sta = PlenopticamStatus()
# skip concole output message (prevent Travis from terminating due to reaching 4MB logfile size)
cfg.params[cfg.opt_prnt] = False
# use pre-loaded calibration dataset
wht_list = [file for file in os.listdir(self.fp) if file.startswith('caldata')]
lfp_list = [file for file in os.listdir(self.fp) if file.endswith(('lfr', 'lfp'))]
cfg.params[cfg.cal_path] = os.path.join(self.fp, wht_list[0])
for lfp_file in lfp_list:
print('Compute image %s' % os.path.basename(cfg.params[cfg.lfp_path]))
cfg.params[cfg.lfp_path] = os.path.join(self.fp, lfp_file)
# decode light field image
lfp_obj = LfpReader(cfg, sta)
ret_val = lfp_obj.main()
lfp_img = lfp_obj.lfp_img
del lfp_obj
self.assertEqual(True, ret_val)
# create output data folder
mkdir_p(cfg.exp_path, cfg.params[cfg.opt_prnt])
if not cfg.cond_meta_file():
# automatic calibration data selection
obj = CaliFinder(cfg, sta)
ret_val = obj.main()
wht_img = obj.wht_bay
del obj
self.assertEqual(True, ret_val)
meta_cond = not (os.path.exists(cfg.params[cfg.cal_meta]) and cfg.params[cfg.cal_meta].lower().endswith('json'))
if meta_cond or cfg.params[cfg.opt_cali]:
# perform centroid calibration
cal_obj = LfpCalibrator(wht_img, cfg, sta)
ret_val = cal_obj.main()
cfg = cal_obj.cfg
del cal_obj
self.assertEqual(True, ret_val)
# load calibration data
cfg.load_cal_data()
# check if light field alignment has been done before
if cfg.cond_lfp_align():
# align light field
lfp_obj = LfpAligner(lfp_img, cfg, sta, wht_img)
ret_val = lfp_obj.main()
lfp_obj = lfp_obj.lfp_img
del lfp_obj
self.assertEqual(True, ret_val)
# load previously computed light field alignment
with open(os.path.join(cfg.exp_path, 'lfp_img_align.pkl'), 'rb') as f:
lfp_img_align = pickle.load(f)
# extract viewpoint data
CaliFinder(cfg).main()
obj = LfpExtractor(lfp_img_align, cfg=cfg, sta=sta)
ret_val = obj.main()
vp_img_arr = obj.vp_img_arr
del obj
self.assertEqual(True, ret_val)
# do refocusing
if cfg.params[cfg.opt_refo]:
obj = LfpRefocuser(vp_img_arr, cfg=cfg, sta=sta)
ret_val = obj.main()
del obj
self.assertEqual(True, ret_val)
return True
def test_illum(self):
# instantiate config and status objects
cfg = PlenopticamConfig()
cfg.default_values()
sta = PlenopticamStatus()
# enable options in config to test more algorithms
cfg.params[cfg.cal_meth] = 'grid-fit'
cfg.params[cfg.opt_vign] = True
cfg.params[cfg.opt_rota] = True
cfg.params[cfg.opt_refi] = True
cfg.params[cfg.opt_pflu] = True
cfg.params[cfg.opt_arti] = True
cfg.params[cfg.opt_lier] = True
cfg.params[cfg.opt_cont] = True
cfg.params[cfg.opt_awb_] = True
cfg.params[cfg.opt_sat_] = True
cfg.params[cfg.ran_refo] = [0, 1]
# compute 3x3 viewpoints only (to reduce computation time)
cfg.params[cfg.ptc_leng] = 3
# skip progress prints (prevent Travis from terminating due to reaching 4MB logfile size)
sta.prog_opt = False
# print current process message (to prevent Travis from stopping after 10 mins)
cfg.params[cfg.opt_prnt] = True
# use pre-loaded calibration dataset
wht_list = [
file for file in os.listdir(self.fp) if file.startswith('caldata')
]
lfp_list = [
file for file in os.listdir(self.fp)
if file.endswith(('lfr', 'lfp'))
]
cfg.params[cfg.cal_path] = os.path.join(self.fp, wht_list[0])
for lfp_file in lfp_list:
cfg.params[cfg.lfp_path] = os.path.join(self.fp, lfp_file)
print('\nCompute image %s' %
os.path.basename(cfg.params[cfg.lfp_path]))
# decode light field image
obj = LfpReader(cfg, sta)
ret = obj.main()
# use third of original image size (to prevent Travis from stopping due to memory error)
crop_h, crop_w = obj.lfp_img.shape[0] // 3, obj.lfp_img.shape[
1] // 3
crop_h, crop_w = crop_h + crop_h % 2, crop_w + crop_w % 2 # use even number for correct Bayer arrangement
lfp_img = obj.lfp_img[crop_h:-crop_h, crop_w:-crop_w]
del obj
self.assertEqual(True, ret)
# create output data folder
mkdir_p(cfg.exp_path, cfg.params[cfg.opt_prnt])
if not cfg.cond_meta_file():
# automatic calibration data selection
obj = CaliFinder(cfg, sta)
ret = obj.main()
wht_img = obj.wht_bay[
crop_h:-crop_h,
crop_w:-crop_w] if obj.wht_bay is not None else obj.wht_bay
del obj
self.assertEqual(True, ret)
meta_cond = not (os.path.exists(cfg.params[cfg.cal_meta]) and
cfg.params[cfg.cal_meta].lower().endswith('json'))
if meta_cond or cfg.params[cfg.opt_cali]:
# perform centroid calibration
obj = LfpCalibrator(wht_img, cfg, sta)
ret = obj.main()
cfg = obj.cfg
del obj
self.assertEqual(True, ret)
# load calibration data
cfg.load_cal_data()
# write centroids as png file
if wht_img is not None:
obj = CentroidDrawer(wht_img, cfg.calibs[cfg.mic_list], cfg)
ret = obj.write_centroids_img(
fn=os.path.join(cfg.exp_path, 'wht_img+mics.png'))
del obj
self.assertEqual(True, ret)
# check if light field alignment has been done before
if cfg.cond_lfp_align():
# align light field
obj = LfpAligner(lfp_img, cfg, sta, wht_img)
ret = obj.main()
del obj
self.assertEqual(True, ret)
# load previously computed light field alignment
with open(os.path.join(cfg.exp_path, 'lfp_img_align.pkl'),
'rb') as f:
lfp_img_align = pickle.load(f)
# extract viewpoint data
CaliFinder(cfg).main()
obj = LfpExtractor(lfp_img_align, cfg=cfg, sta=sta)
ret = obj.main()
vp_img_arr = obj.vp_img_arr
del obj
self.assertEqual(True, ret)
# do refocusing
if cfg.params[cfg.opt_refo]:
obj = LfpRefocuser(vp_img_arr, cfg=cfg, sta=sta)
ret = obj.main()
del obj
self.assertEqual(True, ret)
return True