pimsviewer 1.1

# pimsviewer
[](https://anaconda.org/conda-forge/pimsviewer)

A graphical user interface (GUI) for PIMS (screenshot below)

This viewer was based on `skimage.viewer.CollectionViewer` ([docs](http://scikit-image.org/docs/dev/user_guide/viewer.html))
and is able to work with N-dimensional image files that are opened by PIMS.

Also, it exposes a matplotlib plotting area on which images can be (dynamically)
annotated, making use of the `Plugin` infrastructure.

## Installation

Pimsviewer can be installed using conda:

```
conda install -c conda-forge pimsviewer
```

Alternatively, it can also be installed using pip:

```
pip install pimsviewer
```

## Starting the viewer

After installing the viewer, an executable `pimsviewer` is available. Simply run the command via your terminal/command line interface.
It is also possible to specify a reader. `pimsviewer --help` will list all installed readers, for example:

```
$ pimsviewer --help
Usage: pimsviewer [OPTIONS] [FILE]

Options:
--reader-class [ImageSequenceND|NorpixSeq|SpeStack|TiffStack_pil|MoviePyReader|ImageReaderND|ReaderSequence|ImageIOReader|ImageSequence|TiffStack_tifffile|TiffSeries|TiffStack_libtiff|BioformatsReader|PyAVReaderTimed|PyAVReaderIndexed|MM_TiffStack|ImageReader|FramesSequenceND|Cine]
Reader with which to open the file.
--help Show this message and exit.
```

## Using the viewer from Python
You can use the viewer in a Python script as follows:

```
from pimsviewer import Viewer
viewer = Viewer()
viewer.show()
```
Optionally you may include a reader:

```
import pims
from pimsviewer import Viewer
viewer = Viewer(pims.open('path/to/file'))
viewer.show()
```

## Example: evaluating the effect of a processing function
This example adds a processing function that adds an adjustable amount of noise
to an image. The amount of noise is tunable with a slider, which is displayed
on the right of the image window.

```
import numpy as np
import pims
from pimsviewer import Viewer, ProcessPlugin, Slider

reader = pims.open('path/to/file')

def add_noise(img, noise_level):
return img + np.random.random(img.shape) * noise_level / 100 * img.max()

AddNoise = ProcessPlugin(add_noise, 'Add noise', dock='right')
AddNoise += Slider('noise_level', low=0, high=100, value=10,
orientation='vertical')
viewer = Viewer(reader) + AddNoise
viewer.show()
```

## Example: annotating features on a video
This example annotates features that were obtained via trackpy onto a video.

```
import trackpy as tp
from pimsviewer import Viewer, AnnotatePlugin
reader = pims.open('path/to/file')
f = tp.batch(reader, diameter=15)
(Viewer(reader) + AnnotatePlugin(f)).show()
```

## Example: selecting features on a video
This example annotates features on a video, allows to hide and move
features, and returns the adapted dataframe.

```
import trackpy as tp
from pimsviewer import Viewer, SelectionPlugin
reader = pims.open('path/to/file')
f = tp.batch(reader, diameter=15)
f = tp.link_df(f, search_range=10)
viewer = Viewer(reader) + SelectionPlugin(f)
f_result = viewer.show()
```

## Example: designing a custom plotting function
This dynamically shows the effect of `tp.locate`.

```
import trackpy as tp
from pimsviewer import Viewer, Slider, PlottingPlugin

def locate_and_plot(image, radius, minmass, separation, ax):
f = tp.locate(image, diameter=radius * 2 + 1, minmass=minmass,
separation=separation)
if len(f) == 0:
return
return ax.plot(f['x'], f['y'], markersize=15, markeredgewidth=2,
markerfacecolor='none', markeredgecolor='r',
marker='o', linestyle='none')

reader = pims.open('path/to/file')
Locate = PlottingPlugin(locate_and_plot, 'Locate', dock='right')
Locate += Slider('radius', 2, 20, 7, value_type='int', orientation='vertical')
Locate += Slider('separation', 1, 100, 7, value_type='float', orientation='vertical')
Locate += Slider('minmass', 1, 10000, 100, value_type='int', orientation='vertical')
viewer = Viewer(reader) + Locate
viewer.show()
```

## Screenshot