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refactor: move peak_finder to registration

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This commit is contained in:
Hannes F. Kuchelmeister
2021-07-19 12:13:43 +02:00
parent f04ff2a296
commit 53261f9ae1
7 changed files with 103 additions and 99 deletions
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2
situr/image/__init__.py
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@@ -1,3 +1,3 @@
from .situ_image import extend_dim, remove_dim
from .situ_image import SituImage, PeakFinderDifferenceOfGaussian
from .situ_image import SituImage
from .situ_tile import Tile

82
situr/image/situ_image.py
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@@ -1,10 +1,6 @@
import abc
from situr.transformation.transformation import Transform
import numpy as np
from PIL import Image, ImageDraw
from skimage import img_as_float
from skimage.feature import blob_dog
from PIL import Image
from typing import List
from situr.transformation import Transform, IdentityTransform
@@ -18,32 +14,6 @@ def extend_dim(array: np.ndarray):
def remove_dim(array: np.ndarray):
return array[:, :-1]
# TODO: move peak finder out of image and reverse relationship (peakfinder know about image not the other way around)
class PeakFinder:
__metaclass__ = abc.ABCMeta
@abc.abstractmethod
def find_peaks(self, img_array: np.ndarray) -> np.ndarray:
"""Finds the peaks in the input image"""
raise NotImplementedError(
self.__class__.__name__ + '.find_peaks')
class PeakFinderDifferenceOfGaussian(PeakFinder):
def __init__(self, min_sigma=0.75, max_sigma=3, threshold=0.1):
self.min_sigma = min_sigma
self.max_sigma = max_sigma
self.threshold = threshold
def find_peaks(self, img_array: np.ndarray) -> np.ndarray:
img = img_as_float(img_array)
peaks = blob_dog(img, min_sigma=self.min_sigma,
max_sigma=self.max_sigma, threshold=self.threshold)
# Swap x and y
peaks = peaks[:, [0, 1]] = peaks[:, [1, 0]]
return peaks
class SituImage:
"""
@@ -62,14 +32,13 @@ class SituImage:
peak_finder :
"""
def __init__(self, file_list: List[List[str]], nucleaus_channel: int = 4, peak_finder: PeakFinder = PeakFinderDifferenceOfGaussian()):
def __init__(self, file_list: List[List[str]], nucleaus_channel: int = 4):
self.files = file_list
self.data = None
self.nucleaus_channel = nucleaus_channel
self.channel_transformations = [
IdentityTransform() for file in file_list
]
self.peak_finder = peak_finder
def get_data(self) -> np.ndarray:
if self.data is None:
@@ -136,58 +105,19 @@ class SituImage:
"""
self.data = None
def show_channel(self, channel: int, focus_level: int = 0) -> Image:
def show_channel(self, channel: int, focus_level: int = 0, img_show=True) -> 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.
img_show (bool, optional): Specifies if img.show is to be called or if just the image should be returned. Defaults to True.
Returns:
Image: The image of the specified focus level and channel
"""
img = Image.fromarray(
self.get_data()[channel, focus_level, :, :].astype(np.uint8))
img.show()
return img
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.
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.
Returns:
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, :, :])
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.
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.
Returns:
Image: The image of the specified focus level and channel with encircled peaks.
"""
peaks = self.get_channel_peaks(
channel, focus_level)
img = Image.fromarray(
self.get_data()[channel, focus_level, :, :].astype(np.uint8)).convert('RGB')
draw = ImageDraw.Draw(img)
width = 3
inner_radius = 5
outer_radius = inner_radius + width
for x, y in zip(peaks[:, 0], peaks[:, 1]):
draw.ellipse((x - inner_radius, y - inner_radius, x + inner_radius, y + inner_radius),
outline='navy', width=width)
draw.ellipse((x - outer_radius, y - outer_radius, x + outer_radius, y + outer_radius),
outline='yellow', width=width)
img.show()
if img_show:
img.show()
return img

1
situr/registration/__init__.py
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@@ -2,3 +2,4 @@ from .registration import Registration, RegistrationFunction, FilterregRegistrat
from .channel_registration import ChannelRegistration
from .round_registration import RoundRegistration
from .tile_registration import CombinedRegistration
from .peak_finder import PeakFinder, PeakFinderDifferenceOfGaussian

14
situr/registration/channel_registration.py
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@@ -1,23 +1,19 @@
from situr.registration.peak_finder import PeakFinder, PeakFinderDifferenceOfGaussian
from situr.image.situ_image import SituImage
from situr.registration import Registration, RegistrationFunction, FilterregRegistrationFunction
class ChannelRegistration(Registration):
def __init__(self, registration_function: RegistrationFunction = FilterregRegistrationFunction()):
"""Initialize channel registration and tell which registration function to use.
Args:
registration_function (RegistrationFunction, optional): Registration function. Defaults to FilterregRegistrationFunction(ScaleRotateTranslateChannelTransform).
"""
super().__init__(registration_function)
def do_channel_registration(self, situ_img: SituImage, reference_channel: int = 0):
# For each channel (except nucleus) compute transform compared to reference_channel
# Add Channel transformation to Channel
reference_peaks = situ_img.get_channel_peaks(reference_channel)
reference_peaks = self.peak_finder.get_channel_peaks(
situ_img, reference_channel)
for channel in range(situ_img.get_channel_count()):
if channel != situ_img.nucleaus_channel and channel != reference_channel:
current_channel_peaks = situ_img.get_channel_peaks(channel)
current_channel_peaks = self.peak_finder.get_channel_peaks(
situ_img, channel)
transformation = self.registration_function.do_registration(
current_channel_peaks, reference_peaks)
situ_img.set_channel_transformation(channel, transformation)

77
situr/registration/peak_finder.py Normal file
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@@ -0,0 +1,77 @@
import abc
from PIL import Image, ImageDraw
from skimage import img_as_float
from skimage.feature import blob_dog
import numpy as np
from situr.image.situ_image import SituImage
class PeakFinder:
__metaclass__ = abc.ABCMeta
@abc.abstractmethod
def find_peaks(self, img_array: np.ndarray) -> np.ndarray:
"""Finds the peaks in the input image"""
raise NotImplementedError(
self.__class__.__name__ + '.find_peaks')
def get_channel_peaks(self, img: SituImage, 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.
Args:
img (SituImage): The image to find the peaks on.
channel (int): The channel that should be used when printing
focus_level (int, optional): The focus level that should be used. Defaults to 0.
Returns:
np.ndarray: np.ndarray: The peaks found by this method as np.array of shape (n, 2)
"""
return self.find_peaks(img.get_data()[channel, focus_level, :, :])
def show_channel_peaks(self, img: SituImage, channel: int, focus_level: int = 0, img_show=True) -> Image:
"""Returns and shows the found peaks drawn onto the image. Uses get_channel_peaks internally.
Args:
img (SituImage): The image to find the peaks on.
channel (int): The channel that should be used when printing
focus_level (int, optional): The focus level that should be used. Defaults to 0.
img_show (bool, optional): Specifies if img.show is to be called or if just the image should be returned. Defaults to True.
Returns:
Image: The image of the specified focus level and channel with encircled peaks.
"""
peaks = self.get_channel_peaks(img, channel, focus_level)
img = img.show_channel(
channel, focus_level=focus_level, img_show=False).convert('RGB')
draw = ImageDraw.Draw(img)
width = 3
inner_radius = 5
outer_radius = inner_radius + width
for x, y in zip(peaks[:, 0], peaks[:, 1]):
draw.ellipse((x - inner_radius, y - inner_radius, x + inner_radius, y + inner_radius),
outline='navy', width=width)
draw.ellipse((x - outer_radius, y - outer_radius, x + outer_radius, y + outer_radius),
outline='yellow', width=width)
if img_show:
img.show()
return img
class PeakFinderDifferenceOfGaussian(PeakFinder):
def __init__(self, min_sigma=0.75, max_sigma=3, threshold=0.1):
self.min_sigma = min_sigma
self.max_sigma = max_sigma
self.threshold = threshold
def find_peaks(self, img_array: np.ndarray) -> np.ndarray:
img = img_as_float(img_array)
peaks = blob_dog(img, min_sigma=self.min_sigma,
max_sigma=self.max_sigma, threshold=self.threshold)
# Swap x and y
peaks = peaks[:, [0, 1]] = peaks[:, [1, 0]]
return peaks

10
situr/registration/registration.py
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@@ -1,4 +1,5 @@
import abc
from situr.registration.peak_finder import PeakFinderDifferenceOfGaussian
import open3d as o3
from probreg import filterreg
import numpy as np
@@ -31,5 +32,12 @@ class FilterregRegistrationFunction(RegistrationFunction):
class Registration:
__metaclass__ = abc.ABCMeta
def __init__(self, registration_function: RegistrationFunction):
def __init__(self, registration_function: RegistrationFunction() = FilterregRegistrationFunction(), peak_finder=PeakFinderDifferenceOfGaussian()):
"""Initialize channel registration and tell which registration function to use.
Args:
registration_function (RegistrationFunction, optional): Registration function. Defaults to FilterregRegistrationFunction(ScaleRotateTranslateChannelTransform).
peak_finder (PeakFinder, optional): The peak finder to be used for the registration. Defaults to PeakFinderDifferenceOfGaussian().
"""
self.registration_function = registration_function
self.peak_finder = peak_finder

16
situr/registration/round_registration.py
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@@ -2,13 +2,6 @@ from situr.registration import Registration, RegistrationFunction, FilterregRegi
class RoundRegistration(Registration):
def __init__(self, registration_function: RegistrationFunction = FilterregRegistrationFunction()):
"""Initialize round registration and tell which registration function to use.
Args:
registration_function (RegistrationFunction[RoundTransform], optional): Registration function. Defaults to FilterregRegistrationFunction(ScaleRotateTranslateChannelTransform).
"""
super().__init__(registration_function)
def do_round_registration(self, situ_tile, reference_round: int = 0, reference_channel: int = 0):
"""This method generates a round registration transformation for a tile and saves it in the tile.
@@ -20,13 +13,12 @@ class RoundRegistration(Registration):
"""
# TODO: instead of one reference channel use all channels (maybe without nucleus channel)
reference_peaks = situ_tile.get_round(
reference_round).get_channel_peaks(reference_channel)
reference_peaks = self.peak_finder.get_channel_peaks(situ_tile.get_round(
reference_round), reference_channel)
for round in range(situ_tile.get_round_count()):
if round != reference_channel:
current_round_peaks = situ_tile.get_round(
round
).get_channel_peaks(reference_channel)
current_round_peaks = self.peak_finder.get_channel_peaks(
situ_tile.get_round(round), reference_channel)
transformation = self.registration_function.do_registration(
current_round_peaks, reference_peaks)
situ_tile.set_round_transformation(round, transformation)