def save_img_file(img, file_path, file_type=None):
img = place_dnp(img)
ext = os.path.splitext(file_path)[-1][1:]
if not file_type:
file_type = ext if ext == 'png' or ext == 'tiff' else 'tiff' if img.dtype == 'uint16' else 'png'
# try libtiff import or use png instead if import fails
TIFF, file_type = try_tiff_import(file_type)
# compose new file path string if extension type changed
file_path = os.path.splitext(file_path)[-2] if file_path.endswith(
('.tiff', '.png', '.bmp')) else file_path
file_path = file_path + '.' + file_type
if file_type == 'tiff':
obj = TIFF.open(file_path, mode='w')
obj.write_image(misc.Normalizer(img).uint16_norm(),
compression=None,
write_rgb=True)
obj.close()
elif file_type == 'png' or file_type == 'bmp':
Image.fromarray(misc.Normalizer(img).uint8_norm()).save(file_path,
file_type,
optimize=True)
return True
def main(self):
# compose bayer image from input image buffer
self.comp_bayer()
# auto white balance
if 'awb' in self.cfg.lfpimg.keys():
self._bay_img = self.correct_awb(self._bay_img,
self.cfg.lfpimg['bay'],
gains=self.cfg.lfpimg['awb'])
self._reshape_bayer()
self._bay_img = self.desaturate_clipped(
self._bay_img, gains=self.cfg.lfpimg['awb'])
self._reshape_bayer()
# debayer to rgb image
if 'bay' in self.cfg.lfpimg.keys() and len(self._bay_img.shape) == 2:
self.bay2rgb()
# color matrix correction
if 'ccm' in self.cfg.lfpimg.keys():
self._rgb_img = self.correct_color(self._rgb_img,
ccm_mat=np.reshape(
self.cfg.lfpimg['ccm'],
(3, 3)).T)
# perform gamma correction
if 'gam' in self.cfg.lfpimg.keys():
self._rgb_img = self.correct_gamma(self._rgb_img,
gamma=self.cfg.lfpimg['gam'])
# convert to uint16
self._rgb_img = misc.Normalizer(self._rgb_img).uint16_norm()
return True
def wht_bal(self, method=None, msg_opt=True):
# status update
if msg_opt:
msg = 'Auto white balance' if self.p_hi != 1 else 'Color adjustment'
self.sta.status_msg(msg=msg, opt=self.cfg.params[self.cfg.opt_prnt])
self.sta.progress(0, opt=self.cfg.params[self.cfg.opt_prnt])
ch_num = self.vp_img_arr.shape[-1] if len(self.vp_img_arr.shape) > 4 else 3
for i in range(ch_num):
if method is None:
# channel selection
ref_ch = self.ref_img[..., i]
img_ch = self.vp_img_arr[..., i]
# define level limits
min = np.percentile(ref_ch, self.p_lo*100)
max = np.percentile(ref_ch, self.p_hi*100)
# normalization of color channel
self.vp_img_arr[..., i] = misc.Normalizer(img_ch, min=min, max=max).uint16_norm()
else:
# brightness and contrast method
self.set_stretch(ref_ch=self.ref_img[..., i])
self.apply_stretch(ch=i)
# status update
if msg_opt:
self.sta.progress((i+1)/ch_num*100, opt=self.cfg.params[self.cfg.opt_prnt])
return True
def plot_hist(data, dtype=None):
data = misc.Normalizer(data).uint16_norm()
dtype = str(
data.dtype) if dtype is None and type(dtype) is np.ndarray else str(
dtype)
if dtype.startswith(('int', 'uint')):
bins = np.iinfo(np.dtype(dtype)).max
else:
bins = int(data.max() - data.min())
h = np.histogram(data, bins=bins)
import matplotlib.pyplot as plt
plt.switch_backend('Agg')
plt.figure()
plt.plot(h[1][:-1], h[0])
plt.savefig('iwas.png')
try:
plt.show()
except:
pass
return True
def auto_wht_bal(self, method=None):
# status update
self.sta.status_msg(msg='Auto white balance',
opt=self.cfg.params[self.cfg.opt_prnt])
self.sta.progress(None, opt=self.cfg.params[self.cfg.opt_prnt])
ch_num = self.vp_img_arr.shape[-1] if len(
self.vp_img_arr.shape) > 4 else 3
for i in range(ch_num):
if method is None:
# channel selection
ref_ch = self.ref_img[..., i]
img_ch = self.vp_img_arr[..., i]
# normalization of color channel
self.vp_img_arr[..., i] = misc.Normalizer(
img=img_ch,
min=np.percentile(ref_ch, self.p_lo * 100),
max=np.percentile(ref_ch, self.p_hi * 100)).uint16_norm()
else:
# brightness and contrast method
self.set_stretch(ref_ch=self.ref_img[..., i])
self.apply_stretch(ch=i)
# status update
self.sta.progress((i + 1) / ch_num * 100,
opt=self.cfg.params[self.cfg.opt_prnt])
return True
def channel_bal(self):
# status update
self.sta.status_msg(msg='Contrast balance',
opt=self.cfg.params[self.cfg.opt_prnt])
self.sta.progress(None, opt=self.cfg.params[self.cfg.opt_prnt])
ch_num = self.vp_img_arr.shape[-1] if len(
self.vp_img_arr.shape) > 4 else 3
min = float('Inf')
max = 0.
for i in range(ch_num):
min = np.min(
[min,
np.percentile(self.ref_img[..., i], self.p_lo * 100)])
max = np.max(
[max,
np.percentile(self.ref_img[..., i], self.p_hi * 100)])
# normalization of color channel
self.vp_img_arr = misc.Normalizer(self.vp_img_arr, min=min,
max=max).uint16_norm()
# status update
self.sta.progress(100, opt=self.cfg.params[self.cfg.opt_prnt])
return True
def bay2rgb(self, method=2):
# print status
self.sta.status_msg('Debayering', self.cfg.params[self.cfg.opt_prnt])
self.sta.progress(None, self.cfg.params[self.cfg.opt_prnt])
# Bayer to RGB conversion
if method == 0:
self._rgb_img = demosaicing_CFA_Bayer_bilinear(
self._bay_img, self.cfg.lfpimg['bay'])
elif method == 1:
self._rgb_img = demosaicing_CFA_Bayer_Malvar2004(
self._bay_img, self.cfg.lfpimg['bay'])
else:
self._rgb_img = demosaicing_CFA_Bayer_Menon2007(
self._bay_img, self.cfg.lfpimg['bay'])
# normalize image
min = np.percentile(self._rgb_img, 0.05)
max = np.max(self.rgb_img)
self._rgb_img = misc.Normalizer(self._rgb_img, min=min,
max=max).type_norm()
# update status message
self.sta.progress(100, self.cfg.params[self.cfg.opt_prnt])
return True
def export_vp_stack(self, type='png', downscale=None):
# print status
self.sta.status_msg('Write viewpoint image stack',
self.cfg.params[self.cfg.opt_prnt])
self.sta.progress(None, self.cfg.params[self.cfg.opt_prnt])
# downscale image
downscale = True if downscale is None else downscale
views_stacked_img = misc.img_resize(self.views_stacked_img.copy(), 1 / self._M) \
if downscale else self.views_stacked_img.copy()
# normalization
p_lo = np.percentile(misc.rgb2gray(self.central_view), 0.05)
p_hi = np.percentile(misc.rgb2gray(self.central_view), 99.995)
views_stacked_img = misc.Normalizer(views_stacked_img,
min=p_lo,
max=p_hi).uint8_norm()
# export all viewpoints in single image
views_stacked_path = os.path.join(
self.cfg.exp_path, 'views_stacked_img_' + str(self._M) + 'px')
misc.save_img_file(views_stacked_img,
file_path=views_stacked_path,
file_type=type)
self.sta.progress(100, self.cfg.params[self.cfg.opt_prnt])
return True
def export_refo_stack(self, file_type=None):
# print status
self.sta.status_msg('Write refocused images',
self.cfg.params[self.cfg.opt_prnt])
self.sta.progress(None, self.cfg.params[self.cfg.opt_prnt])
refo_stack = misc.Normalizer(self.refo_stack).uint16_norm()
if self.cfg.params[self.cfg.opt_refi]:
a_list = np.arange(*np.array(self.cfg.params[self.cfg.ran_refo]) *
self.cfg.params[self.cfg.ptc_leng])
a_list = np.round(a_list / self.cfg.params[self.cfg.ptc_leng], 2)
else:
a_list = range(*self.cfg.params[self.cfg.ran_refo])
# create folder
string = 'subpixel_' if self.cfg.params[self.cfg.opt_refi] else ''
folder_path = os.path.join(self.cfg.exp_path,
'refo_' + string + str(self._M) + 'px')
misc.mkdir_p(folder_path)
for i, refo_img in enumerate(refo_stack):
# get depth plane number for filename
a = a_list[i]
self.save_refo_slice(a, refo_img, folder_path, file_type=file_type)
# print status
percentage = ((i + 1) / len(refo_stack)) * 100
self.sta.progress(percentage, self.cfg.params[self.cfg.opt_prnt])
return True
def gif_refo(self):
# export gif animation
fn = 'refocus_animation_' + str(self.cfg.params[self.cfg.ptc_leng]) + 'px'
refo_stack = misc.Normalizer(np.asarray(self._refo_stack)).uint8_norm()
refo_stack = np.concatenate((refo_stack, refo_stack[::-1]), axis=0) # play forward and backwards
misc.save_gif(refo_stack, duration=.5, fp=self.cfg.exp_path, fn=fn)
return True
def main(self):
if self.cfg.calibs[self.cfg.pat_type] == 'hex':
self.proc_vp_arr(self.ver_hex_bulge,
msg='Hexagonal artifact removal')
self.vp_img_arr = misc.Normalizer(self.vp_img_arr).uint16_norm()
return True
def scheimpflug_from_stack(self):
a_start, a_stop = (0, len(self.refo_stack))
if len(self.refo_stack[0].shape) == 3:
m, n, p = self.refo_stack[0].shape
else:
m, n, p = (self.refo_stack[0].shape[0],
self.refo_stack[0].shape[1], 1)
scheimpflug_img = np.zeros([m, n, p])
# map generation
a_x = np.linspace(
0, a_stop - a_start, n + 2,
dtype='int')[1:-1] # flooring via integer type while excluding
a_y = np.linspace(0, a_stop - a_start, m + 2, dtype='int')[1:-1]
a_map_x = np.outer(np.ones(m, dtype='int'), a_x)
a_map_y = np.outer(a_y, np.ones(n, dtype='int'))
# vertical orientation (default)
a_map = a_map_y
# horizontal orientation
if self.cfg.params[self.cfg.opt_pflu] == c.PFLU_VALS[1]:
a_map = a_map_x
# diagonal orientation
elif self.cfg.params[self.cfg.opt_pflu] == (c.PFLU_VALS[2]
or c.PFLU_VALS[3]):
# swap refocusing directions if option set
if self.cfg.params[self.cfg.opt_pflu] == c.PFLU_VALS[3]:
a_map_x, a_map_y = a_map_x[::-1], a_map_y[::-1]
a_map = np.mean(np.stack([a_map_x, a_map_y]), dtype='int', axis=0)
for y in range(m):
for x in range(n):
scheimpflug_img[y, x] = self.refo_stack[a_map[y, x]][y, x]
# check interrupt status
if self.sta.interrupt:
return False
# print status
percentage = (((y * n + x + 1) / (m * n)) * 100)
self.sta.progress(percentage,
self.cfg.params[self.cfg.opt_prnt])
# write image file to hard drive
a_ran = self.cfg.params[self.cfg.ran_refo]
fn = 'scheimpflug_' + str(a_ran[0]) + '_' + str(
a_ran[-1]) + '_' + self.cfg.params[self.cfg.opt_pflu] + '.png'
misc.save_img_file(
misc.Normalizer(scheimpflug_img).uint16_norm(),
os.path.join(self.fp, fn))
return True
def export_thumbnail(self, type='tiff'):
thumb = misc.Normalizer(self.central_view.copy()).type_norm()
# export central viewpoint as thumbnail image
fp = os.path.join(self.cfg.exp_path, 'thumbnail')
misc.save_img_file(thumb,
file_path=fp,
file_type=type,
tag=self.cfg.params[self.cfg.opt_dbug])
return True
def thresh_hist_stretch(self, th=2e-10, bins=2**16-1):
h = np.histogram(self.central_view, bins=bins)
hn = h[0] / h[0].sum()
x_vals = np.where(hn / len(hn) > th)[0] / bins
hs = np.diff(h[0][::128] / h[0][::128].sum())
s_vals = np.where(hs > 1.5e-4)[0] / (bins / 128)
#img = misc.Normalizer(self.central_view.copy(), min=x_vals[1], max=self.central_view.max()).type_norm()
self.proc_vp_arr(misc.Normalizer().type_norm, msg='Histogram crop', min=x_vals[1], max=1)
return True
def post_lum(self, ch=None):
self.vp_img_arr = misc.Normalizer(self.vp_img_arr).uint16_norm()
# channel selection
ch = ch if ch is not None else 0
ref_ch = misc.clr_spc_conv.yuv_conv(self.central_view)[..., ch]
# define level limits
self._min = np.percentile(ref_ch, self.p_lo * 100)
self._max = np.percentile(ref_ch, self.p_hi * 100)
self.proc_vp_arr(self.lum_norm, msg='Luminance normalization')
def main(self):
# apply auto white balance gains while considering image highlights
self.safe_bayer_awb()
# debayer to rgb image
if 'bay' in self.cfg.lfpimg.keys() and len(self._bay_img.shape) == 2:
self.bay2rgb(2)
# convert to uint16
self._rgb_img = misc.Normalizer(self._rgb_img).uint16_norm()
return True
def gif_refo(self):
# image normalization
refo_stack = misc.Normalizer(self.refo_stack).uint8_norm()
# append reversed array copy to play forward and backwards
refo_stack = np.concatenate((refo_stack, refo_stack[::-1]), axis=0)
# export gif animation
fn = 'refocus_animation_' + str(
self.cfg.params[self.cfg.ptc_leng]) + 'px'
misc.save_gif(refo_stack, duration=.5, fp=self.cfg.exp_path, fn=fn)
return True
def _write_lfp_align(self):
# convert to 16bit unsigned integer
self._lfp_out = misc.Normalizer(self._lfp_out).uint16_norm()
# create output data folder
misc.mkdir_p(self.cfg.exp_path, self.cfg.params[self.cfg.opt_prnt])
# write aligned light field as pickle file to avoid recalculation
with open(os.path.join(self.cfg.exp_path, 'lfp_img_align.pkl'), 'wb') as f:
pickle.dump(self._lfp_out, f)
if self.cfg.params[self.cfg.opt_dbug]:
misc.save_img_file(self._lfp_out, os.path.join(self.cfg.exp_path, 'lfp_img_align.tiff'))
def main(self):
# check interrupt status
if self.sta.interrupt:
return False
# remove hexagonal artifact
if self.cfg.calibs[self.cfg.pat_type] == 'hex':
self.proc_vp_arr(self.ver_hex_bulge, msg='Hexagonal artifact removal')
# normalize light-field
self.vp_img_arr = misc.Normalizer(self.vp_img_arr).uint16_norm()
return True
def auto_hist_align(img, ref_img, opt=None):
if opt:
p_lo, p_hi = (0.005, 99.9) #(0.001, 99.999)
min_perc = np.percentile(misc.rgb2gray(ref_img), p_lo)
max_perc = np.percentile(ref_img, p_hi)
else:
p_lo, p_hi = (0.5, 99.9)
min_perc = np.percentile(ref_img, p_lo)
max_perc = np.percentile(ref_img, p_hi)
img = misc.Normalizer(img, min=min_perc, max=max_perc).type_norm()
return img
def lum_norm(self, img, ch=None):
# set default channel
ch = ch if ch is not None else 0
# RGB to YUV conversion
img = misc.clr_spc_conv.yuv_conv(img)
# normalization of Y (luminance channel)
img = misc.Normalizer(img, min=self._min, max=self._max).uint16_norm()
# YUV to RGB conversion
img = misc.clr_spc_conv.yuv_conv(img, inverse=True)
return img
def robust_awb(img, t=0.3, max_iter=1000):
''' inspired by Jun-yan Huo et al. and http://web.stanford.edu/~sujason/ColorBalancing/Code/robustAWB.m '''
img = misc.Normalizer(img).type_norm(lim_min=0, lim_max=1.0)
ref_pixel = img[0, 0, :].copy()
u = .01 # gain step size
a = .8 # double step threshold
b = .001 # convergence threshold
gains_adj = np.array([1., 1., 1.])
for i in range(max_iter):
img_yuv = plenopticam.misc.clr_spc_conv.yuv_conv(img)
f = (abs(img_yuv[..., 1]) + abs(img_yuv[..., 2])) / img_yuv[..., 0]
grays = np.zeros(img_yuv.shape)
grays[f < t] = img_yuv[f < t]
if np.sum(f < t) == 0:
print('No valid gray pixels found.')
break
u_bar = np.mean(grays[..., 1]) # estimate
v_bar = np.mean(grays[..., 2]) # estimate
# rgb_est = misc.yuv_conv(np.array([100, u_bar, v_bar]), inverse=True) # convert average gray from YUV to RGB
# U > V: blue needs adjustment otherwise red is treated
err, ch = (u_bar, 2) if abs(u_bar) > abs(v_bar) else (v_bar, 0)
if abs(err) >= a:
delta = 2 * np.sign(
err) * u # accelerate gain adjustment if far off
elif abs(err) < b: # converged when u_bar and v_bar < b
# delta = 0
#self.sta.status_msg('AWB convergence reached', self.cfg.params[self.cfg.opt_prnt])
break
else:
delta = err * u
# negative feedback loop
gains_adj[ch] -= delta
img = np.dot(img, np.diag(gains_adj))
# take gains only if result is obtained by convergence
gains = img[0, 0, :] / ref_pixel if i != max_iter - 1 else (1, 1, 1)
return img, gains
def gif_vp_img(self, duration, pattern='circle'):
# micro image size estimate
M = max(self.cfg.calibs[self.cfg.ptc_mean]
) if self.cfg.calibs else self.cfg.params[self.cfg.ptc_leng]
# filter images forming a pattern
lf_radius = min(int((M + 1) // 4), self._C)
img_set = self.reorder_vp_arr(pattern=pattern, lf_radius=lf_radius)
# image normalization
img_set = misc.Normalizer(img_set).uint8_norm()
# export gif animation
fn = 'view_animation_' + str(lf_radius * 2 + 1) + 'px'
misc.save_gif(img_set, duration=duration, fp=self.cfg.exp_path, fn=fn)
return True
def con_bal(self):
# find extrema from reference image
self.ref_img = yuv_conv(self.central_view)[..., 0]
max = self.ref_img.max()
min = self.ref_img.min()
# convert to yuv space
self.proc_vp_arr(yuv_conv, msg='Convert to YUV')
# boost luminance channel
self.sta.status_msg(msg='Contrast balance', opt=self.cfg.params[self.cfg.opt_prnt])
self._vp_img_arr[..., 0] = misc.Normalizer(self._vp_img_arr[..., 0]).type_norm(max=max, min=min)
self.sta.progress(100, opt=self.cfg.params[self.cfg.opt_prnt])
# convert to rgb space
self.proc_vp_arr(yuv_conv, inverse=True, msg='Convert to RGB')
return True
def lum_norm(self, img, ch=None, dtype=None):
# set default channel
ch = ch if ch is not None else 0
# set default data type
dtype = img.dtype if dtype is None else dtype
# RGB to YUV conversion
img = misc.clr_spc_conv.yuv_conv(img)
# normalization of Y (luminance channel) for given data type
img[...,
ch] = misc.Normalizer(img=img[..., ch],
min=np.percentile(img[..., ch],
self.p_lo * 100),
max=np.percentile(img[..., ch],
self.p_hi * 100),
dtype=dtype).type_norm()
# YUV to RGB conversion
img = misc.clr_spc_conv.yuv_conv(img, inverse=True)
return img
def decode_lytro_file(self):
# Lytro type decoding
with open(self._lfp_path, mode='rb') as file:
# LFC and raw type decoding
obj = LfpDecoder(file, self.cfg, self.sta, lfp_path=self._lfp_path)
obj.main()
self._lfp_img = obj.bay_img
self._json_dict = obj.json_dict
del obj
# save bayer image as file (if not already present)
if not os.path.exists(self.fp) and not self.sta.interrupt:
self.sta.status_msg(msg='Save raw image',
opt=self.cfg.params[self.cfg.opt_prnt])
self.sta.progress(None, self.cfg.params[self.cfg.opt_prnt])
misc.save_img_file(misc.Normalizer(
self._lfp_img).uint16_norm(),
self.fp,
file_type='tiff')
self.sta.progress(100, self.cfg.params[self.cfg.opt_prnt])
return True
def export_viewpoints(self, type='tiff'):
# print status
self.sta.status_msg('Write viewpoint images',
self.cfg.params[self.cfg.opt_prnt])
self.sta.progress(None, self.cfg.params[self.cfg.opt_prnt])
ptc_leng = self.cfg.params[self.cfg.ptc_leng]
# create folder
folderpath = os.path.join(self.cfg.exp_path,
'viewpoints_' + str(ptc_leng) + 'px')
misc.mkdir_p(folderpath)
# normalize image array to 16-bit unsigned integer
vp_img_arr = misc.Normalizer(self.vp_img_arr).uint16_norm()
# export viewpoint images as image files
for j in range(ptc_leng):
for i in range(ptc_leng):
misc.save_img_file(vp_img_arr[j, i],
os.path.join(folderpath,
str(j) + '_' + str(i)),
file_type=type,
tag=self.cfg.params[self.cfg.opt_dbug])
# print status
percentage = (((j * self._M + i + 1) / self._M**2) * 100)
self.sta.progress(percentage,
self.cfg.params[self.cfg.opt_prnt])
if self.sta.interrupt:
return False
return True
# iterate through directories
for set in [(fp_lytro, coords_lists_lytro), (fp_ours, coords_lists_pcam)]:
fp, coords_lists = set
img_tiles, slice_fns = crop_imgs(fp, coords_lists)
# iterate through file names in directory
for img_tile, slice_fn in zip(img_tiles, slice_fns):
# leave out alpha channel if present
img_tile = img_tile[..., :3]
# rescale tile for fair comparison
img_tile = misc.img_resize(
img_tile, scale_comp_x,
scale_comp_y) if fp.__contains__('refo_lytro') else img_tile
img_tile = misc.Normalizer(img_tile).uint8_norm()
# store results
s_list.append(
(slice_fn, blur_metric(img_tile), michelson_contrast(img_tile),
brisque_metric(img_tile)))
for s in s_list:
print(s)
s_arr = np.asarray(s_list)
np.save('refo_metrics', s_list)
def scheimpflug_from_scratch(self):
patch_len = self.cfg.params[self.cfg.ptc_leng]
a_start, a_stop = self.cfg.params[self.cfg.ran_refo]
img = self.lfp_img.astype('float') / patch_len
m, n, P = self.lfp_img.shape
overlap = int(abs(a_stop) * (patch_len - 1))
#tilt_vec_y = np.linspace(a_start, planes, m).astype('uint')
a_x = np.linspace(a_start, a_stop, n + overlap).astype(
'int'
) # flooring values instead of rounding => 1st round, then int()
a_y = np.zeros(m).astype(
'int') #np.linspace(a_start, planes, m).astype('uint')
a_xy = np.ones([m, n + overlap]).astype('int') * a_x
# initialize matrices for intermediate and plane results
hor_refo = np.zeros([m, n + overlap, P], dtype='float')
ver_refo = np.zeros([m + overlap, n + overlap, P], dtype='float')
fraction_vec = np.zeros([patch_len, P], dtype='float')
# horizontal scheimpflug shift and integration
for y in range(m):
for x in range(n):
# prevent from taking adjacent pixel being beyond image border
if x + patch_len < n:
adj_idx = x + patch_len
else:
adj_idx = x
for p in range(P):
fraction_vec[:, p] = np.linspace(img[y, x, p],
img[y, adj_idx,
p], patch_len)
# load refocus value at x,y
a = a_xy[y, x]
# consider negative shift
negative_a = int((patch_len - 1) * abs(a)) if a < 0 else 0
# centralize row
row_shift = int((a_y.max() - a) * (patch_len - 1) / 2)
newX = int(x + np.mod(x, patch_len) *
(a - 1) + negative_a) + row_shift
hor_refo[y, newX:newX +
patch_len, :] = hor_refo[y, newX:newX +
patch_len, :] + fraction_vec
# check interrupt status
if self.sta.interrupt:
return False
# print progress status
self.sta.progress((y + 1) / m * 50,
self.cfg.params[self.cfg.opt_prnt])
# vertical scheimpflug shift and integration
new_n = n + int(abs(a_x.max()) * (patch_len - 1))
for x in range(new_n):
for y in range(m):
if y + patch_len < m:
adj_idx = y + patch_len
else:
adj_idx = y
frac_vec = np.zeros(
[patch_len,
int(n + abs(a_x.max()) * (patch_len - 1)), P],
dtype='float')
for p in range(P):
frac_vec[:, x, p] = np.linspace(hor_refo[y, x, p],
hor_refo[adj_idx, x,
p], patch_len)
# load refocus value at x,y
a = a_xy[y, x]
# consider negative shift
negative_a = int((patch_len - 1) * abs(a)) if a < 0 else 0
# centralize column
column_shift = int((a_x.max() - a) * (patch_len - 1) / 2)
# put interpolated vector to refocusing plane
newY = int(y + np.mod(y, patch_len) *
(a - 1) + negative_a) + column_shift
ver_refo[newY:newY + patch_len, :, :] = ver_refo[
newY:newY + patch_len, :, :] + frac_vec
# print progress status
self.sta.progress(((x + 1) / new_n + .5) * 100,
self.cfg.params[self.cfg.opt_prnt])
# write image file to hard drive
img = misc.Normalizer(ver_refo).uint16_norm()
misc.save_img_file(
img,
os.path.join(self.fp, 'scheimpflug_' + str(patch_len) + 'px.tiff'))
return True
def main(self):
if self._lfp_path.lower().endswith(SUPP_FILE_EXT):
# filename and file path from previously decoded data
dp = os.path.splitext(self._lfp_path)[0]
fn = os.path.basename(dp) + '.tiff'
fp = os.path.join(dp, fn)
# load previously generated tiff if present
if os.path.exists(fp):
try:
self._lfp_img = misc.load_img_file(fp)
except FileNotFoundError:
# print status
self.sta.status_msg(
'{0} not found'.format(os.path.basename(
self._lfp_path)),
self.cfg.params[self.cfg.opt_prnt])
self.sta.progress(100, self.cfg.params[self.cfg.opt_prnt])
self.sta.error = True
except TypeError as e:
self.sta.status_msg(e, self.cfg.params[self.cfg.opt_prnt])
self.sta.progress(100, self.cfg.params[self.cfg.opt_prnt])
raise LfpTypeError(e)
else:
try:
# Lytro type decoding
with open(self._lfp_path, mode='rb') as file:
# LFC and raw type decoding
obj = LfpDecoder(file, self.cfg, self.sta)
if self._lfp_path.lower().endswith(SUPP_FILE_EXT[1:]):
# LFC type decoding
obj.decode_lfc()
self.cfg.save_json(os.path.join(
dp,
os.path.basename(dp) + '.json'),
json_dict=obj.json_dict)
elif self._lfp_path.lower().endswith(SUPP_FILE_EXT[0]):
# raw type decoding
obj.decode_raw()
self._lfp_img = obj.rgb_img
del obj
# save bayer image as file
self.sta.status_msg(
msg='Save raw image',
opt=self.cfg.params[self.cfg.opt_prnt])
self.sta.progress(None,
self.cfg.params[self.cfg.opt_prnt])
misc.save_img_file(misc.Normalizer(
self._lfp_img).uint16_norm(),
fp,
file_type='tiff')
self.sta.progress(100,
self.cfg.params[self.cfg.opt_prnt])
except FileNotFoundError:
# print status
self.sta.status_msg(
'{0} not found'.format(os.path.basename(
self._lfp_path)),
self.cfg.params[self.cfg.opt_prnt])
self.sta.progress(100, self.cfg.params[self.cfg.opt_prnt])
self.sta.error = True
except Exception as e:
# unrecognized LFP file type
if not obj.json_dict:
raise LfpTypeError(e)
else:
raise PlenopticamError(e)
else:
try:
# read and decode generic image file type
self._lfp_img = misc.load_img_file(self._lfp_path)
except TypeError as e:
raise LfpTypeError(e)
try:
# try to load json file (if present)
json_dict = self.cfg.load_json(self._lfp_path)
self.cfg.lfpimg = LfpDecoder.filter_json(json_dict)
except:
pass
# write json file
self.cfg.save_params()
return True
