Point Cloud Data

Description

Point cloud data structure hold points. It’s a data frame which contains points and their properties.
The corresponding class in gnomon is gnomonPointCloudDataPandas.
It gives possibility to access information like:

• Number of points

• Points coordinate

Default reader plugin

The default reader of point cloud form is gnomonPointCloudReaderDataFrame. It loads a CSV file as 3D point cloud.
This reader supports one extension csv.

Default writer plugin

The default writer of point cloud form is pointCloudWriterDataFrame

Gnomon Point Cloud Example

Plugins which take Image as input

Here is a non exhaustive list of some algorithms which take this form as input.

• seededWatershedSegmentationTimagetk

• nucleiSignalQuantificationTimagetk

Plugins which produce Image as output

Here is a non exhaustive list of some algorithms which produce this form as output.

• nucleiSignalQuantificationTimagetk

• nucleiDetectionTimagetk

A Use Case, Signal Quantification

from copy import deepcopy

import numpy as np
import pandas as pd

from dtkcore import d_inliststringlist
from dtkcore import d_real

from gnomon.utils import algorithmPlugin
from gnomon.utils.decorators import dataFrameOutput
from gnomon.utils.decorators import imageInput
from gnomon.utils.decorators import pointCloudInput
from gnomon.utils.decorators import pointCloudOutput

from gnomon.core import gnomonAbstractPointCloudQuantification

from timagetk.algorithms.signal_quantification import quantify_nuclei_signal_intensity


@algorithmPlugin(version="0.3.1", coreversion="0.81.1")
@imageInput('img', data_plugin='gnomonImageDataMultiChannelImage')
@pointCloudInput("df", data_plugin="gnomonPointCloudDataPandas")
@pointCloudOutput('out_df', data_plugin="gnomonPointCloudDataPandas")
@dataFrameOutput('data_df', data_plugin="gnomonDataFrameDataPandas")
class nucleiSignalQuantificationTimagetk(gnomonAbstractPointCloudQuantification):
    """Quantify image signal intensities on a 3D nuclei point cloud.
    """

    def __init__(self):
        super().__init__()

        self.img = {}
        self.df = {}
        self.out_df = {}
        self.data_df = {}

        self._parameters = {}
        self._parameters['signal_channels'] = d_inliststringlist("Channels", [""], [""], "Channels on which to compute the signal intensity")
        self._parameters['gaussian_sigma'] = d_real("Sigma", 0.5, 0., 50., 2, "Standard deviation of the Gaussian kernel used to smooth signal")

    def run(self):
        self.data_df = {}
        self.out_df = {}

        for time in self.df.keys():
            df = self.df[time]
            out_df = deepcopy(df)

            nuclei_points = df[['center_'+dim for dim in 'xyz']].values

            assert (time in self.img) and (self.img[time] is not None)

            img = self.img[time]

            for signal_name in self['signal_channels']:
                signal_img = img[signal_name]
                out_df[signal_name] = quantify_nuclei_signal_intensity(signal_img, nuclei_points, nuclei_sigma=self['gaussian_sigma'])

            self.out_df[time] = out_df
            
            self.data_df[time] = deepcopy(out_df)
            self.data_df[time]['time'] = time

        if len(self.data_df)>0:
            self.data_df = {0:pd.concat([self.data_df[time] for time in self.data_df.keys()])}