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