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add typehint to situ image and update docstring

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Hannes F. Kuchelmeister
2021-07-14 11:24:55 +02:00
parent f1e801d07e
commit c2b3b46fc4
1 changed files with 55 additions and 64 deletions
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115
situr/image/situ_image.py
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@@ -3,15 +3,17 @@ from PIL import Image, ImageDraw
from skimage import img_as_float from skimage import img_as_float
from skimage.feature import blob_dog from skimage.feature import blob_dog
from situr.transformation.channel_transformation import IdentityChannelTransform from typing import List
from situr.transformation.channel_transformation import ChannelTransform, IdentityChannelTransform
def extend_dim(array): def extend_dim(array: np.ndarray):
ones = np.ones((array.shape[0], 1)) ones = np.ones((array.shape[0], 1))
return np.append(array, ones, axis=1) return np.append(array, ones, axis=1)
def remove_dim(array): def remove_dim(array: np.ndarray):
return array[:, :-1] return array[:, :-1]
@@ -21,7 +23,7 @@ class PeakFinderDifferenceOfGaussian:
self.max_sigma = max_sigma self.max_sigma = max_sigma
self.threshold = threshold self.threshold = threshold
def find_peaks(self, img_array): def find_peaks(self, img_array: np.ndarray) -> np.ndarray:
img = img_as_float(img_array) img = img_as_float(img_array)
peaks = blob_dog(img, min_sigma=self.min_sigma, peaks = blob_dog(img, min_sigma=self.min_sigma,
max_sigma=self.max_sigma, threshold=self.threshold) max_sigma=self.max_sigma, threshold=self.threshold)
@@ -38,13 +40,13 @@ class SituImage:
---------- ----------
data : numpy.array data : numpy.array
the image data containing all the channels of shape (channels, focus_levels, image_size_y, image_size_x) the image data containing all the channels of shape (channels, focus_levels, image_size_y, image_size_x)
files: (list(list(str))) files : List[List[str]]
A list of lists. Each inner list corresponds to one focus level. Its contents correspons to a file for each channel. A list of lists. Each inner list corresponds to one focus level. Its contents correspons to a file for each channel.
nucleaus_channel : int nucleaus_channel : int
tells which channel is used for showing where the cell nucleuses are. tells which channel is used for showing where the cell nucleuses are.
""" """
def __init__(self, file_list, nucleaus_channel=4): def __init__(self, file_list: List[List[str]], nucleaus_channel: int = 4):
self.files = file_list self.files = file_list
self.data = None self.data = None
self.nucleaus_channel = nucleaus_channel self.nucleaus_channel = nucleaus_channel
@@ -52,56 +54,53 @@ class SituImage:
IdentityChannelTransform() for file in file_list IdentityChannelTransform() for file in file_list
] ]
self.peak_finder = PeakFinderDifferenceOfGaussian() self.peak_finder = PeakFinderDifferenceOfGaussian()
# TODO: make peak finder a constructor argument
def get_data(self): def get_data(self) -> np.ndarray:
if self.data is None: if self.data is None:
self._load_image() self._load_image()
# TODO: apply transformations # TODO: apply transformations
return self.data return self.data
def apply_transformations(): def apply_transformations(self):
# TODO: implement # TODO: implement
pass pass
def set_channel_transformation(self, channel, transformation): def set_channel_transformation(self, channel: int, transformation: ChannelTransform):
self.channel_transformations[channel] = transformation self.channel_transformations[channel] = transformation
def get_channel_count(self): def get_channel_count(self) -> int:
return self.get_data().shape[0] return self.get_data().shape[0]
def get_focus_level_count(self): def get_focus_level_count(self) -> int:
return self.get_data().shape[1] return self.get_data().shape[1]
def get_focus_level(self, channel, focus_level): def get_focus_level(self, channel: int, focus_level: int) -> np.ndarray:
"""Loads channel and focus level of an image. """Loads channel and focus level of an image.
Args: Args:
channel (int): The channel to be used channel (int): The channel to be used
focus_level (int): The focus level to be used focus_level (int): The focus level to be used
Returns: Returns:
numpy.array: The loaded image of shape (width, height) np.ndarray: The loaded image of shape (width, height)
""" """
return self.get_data()[channel, focus_level, :, :] return self.get_data()[channel, focus_level, :, :]
def get_channel(self, channel): def get_channel(self, channel: int) -> np.ndarray:
''' """Loads and returns the specified channel for all focus_levels.
Loads and returns the specified channel for all focus_levels.
Args:
channel (int): The channel to be returned
Returns: Returns:
numpy.array: The loaded image of shape (focus_level, width, height) np.ndarray: The loaded image of shape (focus_level, width, height)
''' """
return self.get_data()[channel, :, :, :] return self.get_data()[channel, :, :, :]
def _load_image(self): def _load_image(self):
''' """Loads the whole image from files
Loads the channels of an image from seperate files and returns them as numpy array. """
Parameters:
channel (int):
The channel that should be used
Returns:
numpy.array: The loaded image of shape (channels, focus_level, width, height)
'''
image_list = [] image_list = []
for focus_level_list in self.files: for focus_level_list in self.files:
channels = [] channels = []
@@ -111,54 +110,46 @@ class SituImage:
self.data = np.array(image_list) self.data = np.array(image_list)
def unload_image(self): def unload_image(self):
''' """Unloads the image data to free up memory
Unloads the image data to free up memory """
'''
self.data = None self.data = None
def show_channel(self, channel, focus_level=0): def show_channel(self, channel: int, focus_level: int = 0) -> Image:
''' """Prints and returns the specified channel and focus_level of the image.
Prints and returns the specified channel and focus_level of the image.
Args:
channel (int): The channel that should be used when printing
focus_level (int, optional): The focus level that should be used. Defaults to 0.
Parameters:
channel (int):
The channel that should be used when printing
focus_level (int) default: 0:
The focus level that should be used
Returns: Returns:
image: The image of the specified focus level and channel Image: The image of the specified focus level and channel
''' """
img = Image.fromarray(self.get_data()[0, 0, :, :]) img = Image.fromarray(self.get_data()[channel, focus_level, :, :])
img.show() img.show()
return img return img
def get_channel_peaks(self, channel, focus_level=0, min_sigma=0.75, max_sigma=3, threshold=0.1): def get_channel_peaks(self, channel: int, focus_level: int = 0) -> np.ndarray:
''' """Returns the coordinates of peaks (local maxima) in the specified channel and focus_level. It uses the self.
Returns the coordinates of peaks (local maxima) in the specified channel and focus_level.
This method uses skimage blob_dog, therefore using difference of gaussian. Args:
channel (int): The channel that should be used when printing
focus_level (int, optional): The focus level that should be used. Defaults to 0.
Parameters:
channel (int):
The channel that should be used when printing
focus_level (int) default: 0:
The focus level that should be used
Returns: Returns:
np.array: The peaks found by this method as np.array of shape (n, 2) np.ndarray: The peaks found by this method as np.array of shape (n, 2)
''' """
return self.peak_finder.find_peaks(self.get_data()[channel, focus_level, :, :]) return self.peak_finder.find_peaks(self.get_data()[channel, focus_level, :, :])
def show_channel_peaks(self, channel, focus_level=0): def show_channel_peaks(self, channel: int, focus_level: int = 0) -> Image:
''' """Returns and shows the found peaks drawn onto the image. Uses get_channel_peaks internally.
Returns and shows the found. Uses get_channel_peaks internally.
Args:
channel (int): The channel that should be used when printing
focus_level (int, optional): The focus level that should be used. Defaults to 0.
Parameters:
channel (int):
The channel that should be used when printing
focus_level (int) default: 0:
The focus level that should be used
Returns: Returns:
image: The image of the specified focus level and channel with encircled peaks. Image: The image of the specified focus level and channel with encircled peaks.
''' """
peaks = self.get_channel_peaks( peaks = self.get_channel_peaks(
channel, focus_level) channel, focus_level)