def main(self):
# compose bayer image from input image buffer
self.comp_bayer()
# debayer to rgb image
if 'bay' in self.cfg.lfpimg.keys() and len(self._bay_img.shape) == 2:
self.bay2rgb()
# auto white balance
if 'awb' in self.cfg.lfpimg.keys():
self._rgb_img = self.correct_awb(self._rgb_img,
self.cfg.lfpimg['bay'],
gains=self.cfg.lfpimg['awb'])
self._rgb_img = self.desaturate_clipped(
self._rgb_img, gains=self.cfg.lfpimg['awb'])
# perform gamma correction
if 'gam' in self.cfg.lfpimg.keys():
self._rgb_img = self.correct_gamma(self._rgb_img,
gamma=self.cfg.lfpimg['gam'])
# 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)
# convert to uint16
self._rgb_img = misc.uint16_norm(self._rgb_img)
return True
def export_refo_stack(refo_stack, cfg, type='tiff'):
refo_stack = misc.uint16_norm(refo_stack)
patch_len = cfg.params[cfg.ptc_leng]
# create folder
string = ''
if cfg.params[cfg.opt_refo] == 2 or cfg.params[cfg.opt_refi]:
string = 'subpixel_'
elif cfg.params[cfg.opt_refo] == 1 or cfg.params[cfg.opt_view]:
string = ''
folder_path = os.path.join(cfg.params[cfg.lfp_path].split('.')[0], 'refo_' + string + str(patch_len) + 'px')
misc.mkdir_p(folder_path)
for i, refo_img in enumerate(refo_stack):
# get depth plane number for filename
a = range(*cfg.params[cfg.ran_refo])[i]
# account for sub-pixel precise depth value
a = round(float(a)/patch_len, 2) if cfg.params[cfg.opt_refi] else a
# write image file
misc.save_img_file(refo_img, os.path.join(folder_path, str(a)), type=type)
return True
def export_viewpoints(vp_img_arr, cfg, type='tiff'):
ptc_leng = cfg.params[cfg.ptc_leng]
# create folder
folderpath = os.path.join(cfg.params[cfg.lfp_path].split('.')[0], 'viewpoints_' + str(ptc_leng) + 'px')
misc.mkdir_p(folderpath)
# normalize image array to 16-bit unsigned integer
vp_img_arr = misc.uint16_norm(vp_img_arr)
# 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)), type=type)
return True
def _write_lfp_align(self):
# convert to 16bit unsigned integer
self._lfp_out = misc.uint16_norm(self._lfp_out)
out_path = self.cfg.params[self.cfg.lfp_path].split('.')[0]
# create output data folder
misc.mkdir_p(self.cfg.params[self.cfg.lfp_path].split('.')[0],
self.cfg.params[self.cfg.opt_prnt])
# write aligned light field as pickle file to avoid recalculation
with open(os.path.join(out_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(out_path, 'lfp_img_align.tiff'))
def scheimpflug_from_stack(self):
a_start, a_stop = self.cfg.params[self.cfg.ran_refo]
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[2]:
a_map = a_map_x
# diagonal orientation
elif self.cfg.params[self.cfg.opt_pflu] == (c.PFLU_VALS[3] or c.PFLU_VALS[4]):
a_map = np.mean([a_map_x, a_map_y], dtype='int', axis=self.cfg.params[self.cfg.opt_pflu]-3)
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
fp = os.path.splitext(self.cfg.params[self.cfg.lfp_path])[0]
fn = 'scheimpflug_' + str(a_start) + '_' + str(a_stop) + '_' + self.cfg.params[self.cfg.opt_pflu] + '.png'
misc.save_img_file(misc.uint16_norm(scheimpflug_img), os.path.join(fp, fn))
return True
def save_img_file(img, file_path, type=None):
img = place_dnp(img)
ext = os.path.splitext(file_path)[-1][1:]
if not type:
type = ext if ext == 'png' or ext == 'tiff' else 'tiff' if img.dtype == 'uint16' else 'png'
file_path = file_path + '.' + type if ext != type else file_path
if type == 'tiff':
obj = TIFF.open(file_path, mode='w')
obj.write_image(misc.uint16_norm(img),
compression=None,
write_rgb=True)
obj.close()
elif type == 'png' or type == 'bmp':
Image.fromarray(misc.uint8_norm(img)).save(file_path,
type,
optimize=True)
return True
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
fp = self.cfg.params[self.cfg.cfn_path].split('.')[0]
img = misc.uint16_norm(ver_refo)
misc.save_img_file(img, os.path.join(fp, 'scheimpflug_' + str(patch_len) + 'px.tiff'))
return True
def main(self):
if self._lfp_path.endswith(('.lfp', '.lfr', '.raw') + tuple('.c.'+str(num) for num in (0, 1, 2, 3))):
# 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 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)
except FileNotFoundError as e:
# print status
self.sta.status_msg('File {0} not found'.format(self._lfp_path), self.cfg.params[self.cfg.opt_prnt])
self.sta.progress(100, self.cfg.params[self.cfg.opt_prnt])
raise PlenopticamError(e)
else:
try:
# Lytro type decoding
with open(self._lfp_path, mode='rb') as file:
# print status
self.sta.status_msg('Decode Lytro image file', self.cfg.params[self.cfg.opt_prnt])
self.sta.progress(None, self.cfg.params[self.cfg.opt_prnt])
# LFC and raw type decoding
obj = LfpDecoder(file, self.cfg, self.sta)
if self._lfp_path.endswith(('.lfp', '.lfr') + tuple('.c.'+str(num) for num in (0, 1, 2, 3))):
# 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.endswith('.raw'):
# raw type decoding
obj.decode_raw()
self._lfp_img = obj.rgb_img
del obj
# save bayer image as file
misc.save_img_file(misc.uint16_norm(self._lfp_img), fp, type='tiff')
# print status
self.sta.progress(100, self.cfg.params[self.cfg.opt_prnt])
except FileNotFoundError as e:
# print status
self.sta.status_msg('File {0} not found'.format(self._lfp_path), self.cfg.params[self.cfg.opt_prnt])
self.sta.progress(100, self.cfg.params[self.cfg.opt_prnt])
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
