def save_img_file(img, file_path=None, file_type=None):
file_path = os.getcwd() if file_path is None else file_path
ext = os.path.splitext(file_path)[-1][1:]
img = place_dnp(img)
if not file_type:
file_type = ext if ext == 'png' or ext == 'tiff' else 'tiff' if img.dtype == 'uint16' else 'png'
# try imageio import or use png instead if import fails
imageio, 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_type = 'png' if file_type is None else file_type
file_path += '.' + file_type
if file_type == 'tiff':
imageio.imwrite(uri=file_path, im=Normalizer(img).uint16_norm())
elif file_type == 'png' or file_type == 'bmp':
Image.fromarray(Normalizer(img).uint8_norm()).save(file_path,
file_type,
optimize=True)
return True
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(Normalizer(img).uint16_norm(),
compression=None,
write_rgb=True)
obj.close()
elif file_type == 'png' or file_type == 'bmp':
Image.fromarray(Normalizer(img).uint8_norm()).save(file_path,
file_type,
optimize=True)
return True
def load_img_file(file_path):
file_type = file_path.split('.')[-1]
img = None
# try libtiff import or use png instead if import fails
imageio, file_type = try_tiff_import(file_type)
if file_type.__contains__('tif'):
suppress_user_warning(True, category=UserWarning)
img = imageio.imread(uri=file_path)
suppress_user_warning(False, category=UserWarning)
elif any(file_type in ext for ext in ('bmp', 'png', 'jpeg', 'jpg')):
try:
img = Image.open(file_path)
#imageio.imread(uri=file_path, format=file_type)
except OSError:
# support load of truncated images
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
img = Image.open(file_path)
except AttributeError:
raise TypeError()
elif not any(file_type in ext
for ext in ('bmp', 'png', 'tiff', 'jpeg', 'jpg')):
raise TypeError('Filetype %s not recognized' % file_type)
# normalize (convert to numpy array)
img = Normalizer(np.asarray(img)).type_norm()
return img
def get_img_list(img_dir, vp=1):
""" obtain list of images from provided directory path """
dir_list = listdir(img_dir)
dir_list.sort()
img_list = []
for i in dir_list:
img_path = join(img_dir, i)
ext = img_path.split('.')[::-1][0].lower()
if ext in GENERIC_EXTS:
# load image
img = load_img_file(img_path)
# convert to uint8 if necessary
img = Normalizer(img).uint8_norm() if str(
img.dtype) != 'uint8' else img
# append to image list
img_list.append((i, img))
# sort image list by indices in file names
img_tuples = sorted(img_list,
key=lambda k: idx_str_sort(k[0], 1 if vp else 0))
_, img_list = zip(*img_tuples)
if vp:
vp_dim = int(np.sqrt(len(img_list)))
img_list = np.reshape(img_list,
newshape=(vp_dim, vp_dim) + img_list[0].shape,
order='C')
return img_list
def save_img_file(img, file_path=None, file_type=None, gamma=None, tag=None):
# do gamma correction
img = img**gamma if gamma is not None else img
file_path = os.getcwd() if file_path is None else file_path
try:
img = place_dnp(img) if not tag else img
except ValueError:
pass
# amend write privileges of (potentially existing) config file
if os.path.exists(file_path):
st = os.stat(file_path)
os.chmod(file_path, st.st_mode | 0o111)
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 imageio import or use png instead if import fails
imageio, 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_type = 'png' if file_type is None else file_type
file_path += '.' + file_type
if file_type.__contains__('tif'):
suppress_user_warning(True, category=UserWarning)
imageio.imwrite(uri=file_path, im=Normalizer(img).uint16_norm())
suppress_user_warning(False, category=UserWarning)
elif file_type == 'png' or file_type == 'bmp':
try:
#Image.fromarray(Normalizer(img).uint8_norm()).save(file_path, file_type, optimize=True)
imageio.imwrite(uri=file_path, im=Normalizer(img).uint8_norm())
except PermissionError as e:
raise Exception(e)
return True
def img_resize(img, x_scale=1, y_scale=None, method=None, new_shape=None):
""" perform image interpolation based on scipy lib """
if not y_scale:
y_scale = x_scale
if x_scale == 1 and y_scale == 1 and new_shape is None or x_scale <= 0 or y_scale <= 0:
return img
method = 'cubic' if method is None else method
dtype = img.dtype
if len(img.shape) == 3:
n, m, p = img.shape
elif len(img.shape) == 2:
n, m, p = img.shape + (1, )
img = img[..., np.newaxis]
else:
raise NotImplementedError
# construct new 2-D shape
y_len, x_len = (int(round(n * y_scale)), int(round(
m * x_scale))) if x_scale != 1 or y_scale != 1 else new_shape
# interpolate
new_img = np.zeros([y_len, x_len, p])
for p in range(p):
f = interp2d(range(m), range(n), img[:, :, p], kind=method)
new_img[:, :, p] = f(np.linspace(0, m - 1, x_len),
np.linspace(0, n - 1, y_len))
# normalize to the 0-1 range
new_img = Normalizer(new_img).type_norm(new_min=0, new_max=1)
# remove added third axes in monochromatic image
new_img = new_img[..., 0] if new_img.shape[-1] == 1 else new_img
return new_img.astype(dtype)
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 = Normalizer(img).type_norm(new_min=0, new_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 = 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 = 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