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move peak finder parameters into separate class

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This commit is contained in:
Hannes F. Kuchelmeister
2021-07-13 11:38:13 +02:00
parent 5e1875db40
commit c2c39dc462
2 changed files with 18 additions and 21 deletions
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2
situr/image/__init__.py
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@@ -1,2 +1,2 @@
from .situ_image import extend_dim, remove_dim
from .situ_image import SituImage
from .situ_image import SituImage, PeakFinderDifferenceOfGaussian

37
situr/image/situ_image.py
View File

@@ -15,6 +15,19 @@ def remove_dim(array):
return array[:, :-1]
class PeakFinderDifferenceOfGaussian:
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):
img = img_as_float(img_array)
peaks = blob_dog(img, min_sigma=self.min_sigma,
max_sigma=self.max_sigma, threshold=self.threshold)
return peaks[:, 0:2]
class SituImage:
"""
A class to representing one situ image with different focus levels.
@@ -38,6 +51,7 @@ class SituImage:
self.channel_transformations = [
IdentityChannelTransform() for file in file_list
]
self.peak_finder = PeakFinderDifferenceOfGaussian()
def get_data(self):
if self.data is None:
@@ -114,23 +128,12 @@ class SituImage:
The channel that should be used when printing
focus_level (int) default: 0:
The focus level that should be used
min_sigma (float) default: 0.75:
The minimum standard deviation for Gaussian kernel. Keep this low to detect smaller blobs. The standard deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes.
max_sigma (float) default: 3:
The maximum standard deviation for Gaussian kernel. Keep this high to detect larger blobs. The standard deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes.
threshold (float) default: 0.1:
The absolute lower bound for scale space maxima. Local maxima smaller than thresh are ignored. Reduce this to detect blobs with less intensities.
Returns:
np.array: The peaks found by this method as np.array of shape (n, 2)
'''
# TODO: think of a better way to declare peak finding parameters (so they don't need to be passedaround as much)
return self.peak_finder.find_peaks(self.get_data()[channel, focus_level, :, :])
img = img_as_float(self.get_data()[channel, focus_level, :, :])
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):
def show_channel_peaks(self, channel, focus_level=0):
'''
Returns and shows the found. Uses get_channel_peaks internally.
@@ -139,17 +142,11 @@ class SituImage:
The channel that should be used when printing
focus_level (int) default: 0:
The focus level that should be used
min_sigma (float) default: 0.75:
The minimum standard deviation for Gaussian kernel. Keep this low to detect smaller blobs. The standard deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes.
max_sigma (float) default: 3:
The maximum standard deviation for Gaussian kernel. Keep this high to detect larger blobs. The standard deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes.
threshold (float) default: 0.1:
The absolute lower bound for scale space maxima. Local maxima smaller than thresh are ignored. Reduce this to detect blobs with less intensities.
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)
channel, focus_level)
img = Image.fromarray(self.get_data()[channel, focus_level, :, :])
draw = ImageDraw.Draw(img)