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autoformat code and add missing imports

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This commit is contained in:
Hannes F. Kuchelmeister
2021-07-12 15:54:50 +02:00
parent 85e2edb8a5
commit 93c84e731d
4 changed files with 47 additions and 24 deletions
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30
situr/image/situ_image.py
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@@ -8,8 +8,9 @@ def extend_dim(array):
ones = np.ones((array.shape[0], 1))
return np.append(array, ones, axis=1)
def remove_dim(array):
return array[:,:-1]
return array[:, :-1]
class SituImage:
@@ -32,7 +33,7 @@ class SituImage:
self.files = file_list
self.data = None
self.nucleaus_channel = nucleaus_channel
def get_data(self):
if self.data is None:
self._load_image()
@@ -45,8 +46,8 @@ class SituImage:
Returns:
numpy.array: The loaded image of shape (focus_level, width, height)
'''
return self.get_data()[channel,:,:,:]
return self.get_data()[channel, :, :, :]
def _load_image(self):
'''
Loads the channels of an image from seperate files and returns them as numpy array.
@@ -64,7 +65,7 @@ class SituImage:
channels.append(np.array(Image.open(file)))
image_list.append(channels)
self.data = np.array(image_list)
def unload_image(self):
'''
Unloads the image data to free up memory
@@ -83,7 +84,7 @@ class SituImage:
Returns:
image: The image of the specified focus level and channel
'''
img = Image.fromarray(self.get_data()[0,0,:,:])
img = Image.fromarray(self.get_data()[0, 0, :, :])
img.show()
return img
@@ -107,9 +108,10 @@ class SituImage:
np.array: The peaks found by this method as np.array of shape (n, 2)
'''
img = img_as_float(self.get_data()[channel, focus_level, :, :])
peaks = blob_dog(img, min_sigma=min_sigma, max_sigma=max_sigma, threshold=threshold)
peaks = blob_dog(img, min_sigma=min_sigma,
max_sigma=max_sigma, threshold=threshold)
return peaks[:, 0:2]
def show_channel_peaks(self, channel, focus_level=0, min_sigma=0.75, max_sigma=3, threshold=0.1):
'''
Returns and shows the found. Uses get_channel_peaks internally.
@@ -128,11 +130,13 @@ class SituImage:
Returns:
image: The image of the specified focus level and channel with encircled peaks.
'''
peaks = self.get_channel_peaks(channel, focus_level, min_sigma, max_sigma, threshold)
peaks = self.get_channel_peaks(
channel, focus_level, min_sigma, max_sigma, threshold)
img = Image.fromarray(self.get_data()[channel, focus_level, :, :])
draw = ImageDraw.Draw(img)
for x, y in zip(peaks[:,0], peaks[:,1]):
draw.ellipse((x - 5, y - 5, x + 5, y + 5), outline ='white', width = 3)
for x, y in zip(peaks[:, 0], peaks[:, 1]):
draw.ellipse((x - 5, y - 5, x + 5, y + 5),
outline='white', width=3)
img.show()
return img
return img

17
situr/registration/channel_registration.py
View File

@@ -1,14 +1,25 @@
import abc
import open3d as o3
from probreg import filterreg
from situr.image import extend_dim
from situr.transformation import ScaleRotateTranslateChannelTransform
class ChannelRegistration:
__metaclass__ = abc.ABCMeta
def do_registration(self, situ_img , reference_channel=0):
def do_registration(self, situ_img, reference_channel=0):
# For each channel (except nucleus) compute transform compared to reference_channel
# Add Channel transformation to Channel
pass
@abc.abstractmethod
def register_single_channel(self, peaks_data, reference_peaks):
"""Performs the channel registration on an image. Expects the peaks in each image as input."""
raise NotImplementedError(self.__class__.__name__ + '.register_single_channel')
raise NotImplementedError(
self.__class__.__name__ + '.register_single_channel')
class FilterregChannelRegistration(ChannelRegistration):
def register_single_channel(self, data_peaks, reference_peaks):
@@ -17,7 +28,7 @@ class FilterregChannelRegistration(ChannelRegistration):
target = o3.geometry.PointCloud()
target.points = o3.utility.Vector3dVector(extend_dim(reference_peaks))
registration_method=filterreg.registration_filterreg
registration_method = filterreg.registration_filterreg
tf_param, _, _ = filterreg.registration_filterreg(source, target)
return ScaleRotateTranslateChannelTransform(transform_matrix=tf_param.rot[0:2, 0:2], scale=tf_param.scale, offset=tf_param.t[0:2])

1
situr/transformation/__init__.py
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@@ -0,0 +1 @@
from .channel_transformation import ChannelTransform, ScaleRotateTranslateChannelTransform

23
situr/transformation/channel_transformation.py
View File

@@ -1,28 +1,35 @@
import abc
import numpy as np
import scipy
class ChannelTransform:
__metaclass__ = abc.ABCMeta
@abc.abstractmethod
def apply_transformation(self, situ_img , channel):
def apply_transformation(self, situ_img, channel):
"""Performs a transformation on one channel, all focus_levels are transformed the same way"""
raise NotImplementedError(self.__class__.__name__ + '.apply_transformation')
raise NotImplementedError(
self.__class__.__name__ + '.apply_transformation')
class ScaleRotateTranslateChannelTransform(ChannelTransform):
def __init__(self, transform_matrix, scale=1, offset=np.array([0, 0])):
# TODO: check
# TODO: check
# * transform matrix is 2x2
# * offset is array (2,)
self.transform_matrix = transform_matrix
self.offset = offset
self.scale = scale
def apply_tranformation(self, situ_img , channel):
def apply_tranformation(self, situ_img, channel):
channel_img = situ_img.get_channel(channel)
focus_levels = channel_img.shape[0]
for focus_level in range(focus_levels):
img = channel_img [focus_level, :, :]
img = channel_img[focus_level, :, :]
img [:, :] = scipy.ndimage.affine_transform(img, self.transform_matrix)
img [:, :] = scipy.ndimage.zoom(img, self.scale)
img [:, :] = scipy.ndimage.shift(img, self.offset)
img[:, :] = scipy.ndimage.affine_transform(
img, self.transform_matrix)
img[:, :] = scipy.ndimage.zoom(img, self.scale)
img[:, :] = scipy.ndimage.shift(img, self.offset)