def load_multiples(cls, image_file_list, method='mean', stretch=True):
"""Combine multiple image files into one superimposed image.
.. versionadded:: 0.5.1
"""
# load images
img_list = [cls.load(path) for path in image_file_list]
first_img = img_list[0]
# check that all images are the same size and stretch if need be
for img in img_list:
if img.shape != first_img.shape:
raise ValueError("Images were not the same shape")
if stretch:
img.array = stretcharray(img.array)
# stack and combine arrays
new_array = np.dstack(tuple(img.array for img in img_list))
if method == 'mean':
combined_arr = np.mean(new_array, axis=2)
elif method == 'max':
combined_arr = np.max(new_array, axis=2)
elif method == 'sum':
combined_arr = np.sum(new_array, axis=2)
# replace array of first object and return
first_img.array = combined_arr
first_img.check_inversion()
return first_img
def load_multiples(cls, image_file_list, method='mean', stretch=True):
"""Combine multiple image files into one superimposed image.
.. versionadded:: 0.5.1
"""
# load images
img_list = [cls.load(path) for path in image_file_list]
first_img = img_list[0]
# check that all images are the same size and stretch if need be
for img in img_list:
if img.shape != first_img.shape:
raise ValueError("Images were not the same shape")
if stretch:
img.array = stretcharray(img.array)
# stack and combine arrays
new_array = np.dstack(tuple(img.array for img in img_list))
if method == 'mean':
combined_arr = np.mean(new_array, axis=2)
elif method == 'max':
combined_arr = np.max(new_array, axis=2)
elif method == 'sum':
combined_arr = np.sum(new_array, axis=2)
# replace array of first object and return
first_img.array = combined_arr
first_img.check_inversion()
return first_img
def from_multiples(cls, image_file_list, method='mean', stretch=True):
"""Combine multiple image files into one superimposed image.
.. versionadded:: 0.5.1
"""
# open first one to get initial settings
init_obj = cls(image_file_list[0])
# init_obj.check_inversion()
if stretch:
array = stretcharray(init_obj.array)
else:
array = init_obj.array
concat_arr = array
initial_shape = init_obj.shape
# open each image and append each array
for img_file in image_file_list[1:]:
obj = cls(img_file)
if obj.shape != initial_shape:
raise AttributeError("Images must be the same size when combining.")
# obj.check_inversion()
if stretch:
obj.array = stretcharray(obj.array)
concat_arr = np.dstack((concat_arr, obj.array))
# create new array
if method == 'mean':
combined_arr = np.mean(concat_arr, axis=2)
elif method == 'max':
combined_arr = np.max(concat_arr, axis=2)
elif method == 'sum':
combined_arr = np.sum(concat_arr, axis=2)
# use the initial Image object and replace its array, thus keeping all the other properties
init_obj.array = combined_arr
init_obj.check_inversion()
return init_obj
