# PyPlaque
Python package for virus plaque analysis based on Plaque2.0
# Installation
See project's PyPi page [https://pypi.org/project/PyPlaque/](https://pypi.org/project/PyPlaque/)
```
pip install PyPlaque
```
# Local devloper installation
- Clone repo
- run `pip install -e .`
# Usage
```
from PyPlaque.specimen import PlaquesImageGray
from PyPlaque.phenotypes import Plaque
```
Here's a test example of how to analyze plaques in an image using PyPlaque. First let's load an example image:
```
from skimage.io import imsave, imread, imshow
img_url = 'https://raw.github.com/plaque2/matlab/master/Sample_B01_s1_w2.tif'
img = imread(img_url)
imshow(img)
```
Now let's detect plaques in this flourescence microscopy image and print out all their coordinmates:
```
plq_img = PlaquesImageGray('Sample_B01_s1_w2',img, threshold=0.25)
for plq in plq_img.get_plaques():
print(plq.centroid)
```
This will output following measurements:
```
(435.85833333333335, 1785.8416666666667)
(653.1464788732394, 1209.9647887323943)
(708.058912386707, 1251.6835347432025)
(728.976492712741, 1599.9073812881993)
(712.3316195372751, 1312.491002570694)
(747.0576576576576, 1174.0234234234233)
(752.5772277227722, 1119.0891089108911)
(782.8965517241379, 1151.510344827586)
...
```
Each Plaque object containes a crop of individual virological plaque, as well as, measurements:
```
imshow(plq.mask)
print(plq.area)
```
___________
## Classes structure concept
## Specific classes
### Sepcimen
___________
**PlaquesMask class** designed to hold binary mask of multiple
plaque phenotypes.
_Arguments_:
name - (str, required) string, image sample name for identification
plaques_mask - (np.array, required) numpy array containing
binary mask of all virological plaque objects.
**get_palques method** returns a list of individual plaques
stored as binary numpy arrays.
_Arguments_:
min_are - (int, optional, default = 100) a cut-off value for plaque area
in px.
___________
**PlaqueImageGray class** designed to hold grayscale image data containing
multiple plaque phenotypes with a respective binary mask. The class inherits
from PlaquesMask.
_Additonal arguments_:
name - (str, required) string, image sample name for identification
image - (np.array, required) numpy array containing 2D grayscale image of
a virological plaque object, respective to the mask. Used, in case of
measuring properties of fluorescent plaque image.
plaques_mask - (np.array, optional, default None) numpy array containing
binary mask of all virological plaque objects.
threshold - (float between 0 and 1, optional, default None) fixed threshold
value for creating the binary mask.
sigma - (int, optional, default = 5) guassian blur sigma in pixels used by
the fixed thresholding approach.
Either mask or fixed threshold must be provided
___________
**PlaquesImageRGB class** designed to hold RGB image data containing
multiple plaque phenotypes with a respective binary mask.The class inherits
from PlaquesMask.
_Additonal arguments_:
name - (str, required) image sample name for identification
image - (np.array, required) 3D (red, green, blue) numpy array
containing image of a virological plaque object, respective to the mask.
Used, e.g. in case of measuring properties of crystal violet plaque image.
plaques_mask - (np.array, required) numpy array containing binary mask of all
virological plaque objects.
___________
**Class PlaquesWell** is aimed to contain a full well of a multititre plate.
_Arguments_:
row - (int or str, required) row id of the well.
column - (int or str, required) column id of the well.
well_image - (np.array, required) numpy array containing image of
the well.
well_mask - (np.array, required) numpy array containing binary mask of
the well.
___________
**PlateImage Class** is aimed to contain a full multititre plate image and
it's respective binary mask.
_Arguments_:
n_rows - (int, required) number of rows in the plate (usually lower than
the number of rows).
n_columns - (int, required) number of columns in the plate (usually higher than
the number of rows).
plate_image - (np.array, required) an image of individual wells of the
plate.
plate_mask - (np.array, required) a binary mask outlining individual wells of the
plate.
**get_wells method** returns a list of individual wells of the plate
stored as binary numpy arrays.
___________
### Phenotypes
___________
**Plaque** class is designed to hold a single virological plaque
phenotype as an object.
_Arguments_:
mask - (required, 2D numpy array) containing binary mask of a single
virological plaque object.
centroid - (float tuple, optional) contains x and y of the centroid of the
plaque object
bbox - (float tuple, optional) contains minr, minc, maxr, maxc of the
plaque object
___________
**FluorescencePlaque** conains plaque obtained from fluorescence image.
Class inherits from Plaque class and is also designed to hold a single
virological plaque phenotype.
_Additonal arguments_:
image - (required) numpy array containing grayscale image of a single
virological plaque object.
___________
**CrystalVioletPlaque** plaque obtained from crystal violet image. Class
inherits from Plaque class and is also designed to hold a single virological
plaque phenotype.
_Additonal arguments_:
image - (required) numpy array containing RGB or a graysacle image of a
single virological plaque object.
Raw data
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First let's load an example image:\n\n```\nfrom skimage.io import imsave, imread, imshow\n\nimg_url = 'https://raw.github.com/plaque2/matlab/master/Sample_B01_s1_w2.tif'\nimg = imread(img_url)\nimshow(img)\n```\n\n\n\nNow let's detect plaques in this flourescence microscopy image and print out all their coordinmates:\n\n```\nplq_img = PlaquesImageGray('Sample_B01_s1_w2',img, threshold=0.25)\nfor plq in plq_img.get_plaques():\n print(plq.centroid)\n```\n\nThis will output following measurements:\n\n```\n(435.85833333333335, 1785.8416666666667)\n(653.1464788732394, 1209.9647887323943)\n(708.058912386707, 1251.6835347432025)\n(728.976492712741, 1599.9073812881993)\n(712.3316195372751, 1312.491002570694)\n(747.0576576576576, 1174.0234234234233)\n(752.5772277227722, 1119.0891089108911)\n(782.8965517241379, 1151.510344827586)\n\n...\n```\n\nEach Plaque object containes a crop of individual virological plaque, as well as, measurements:\n\n```\nimshow(plq.mask)\nprint(plq.area)\n```\n\n\n\n___________\n\n## Classes structure concept\n\n\n\n\n## Specific classes\n\n### Sepcimen\n\n___________\n\n**PlaquesMask class** designed to hold binary mask of multiple\nplaque phenotypes.\n\n_Arguments_:\n\nname - (str, required) string, image sample name for identification\n\nplaques_mask - (np.array, required) numpy array containing\nbinary mask of all virological plaque objects.\n\n**get_palques method** returns a list of individual plaques\nstored as binary numpy arrays.\n\n_Arguments_:\n\nmin_are - (int, optional, default = 100) a cut-off value for plaque area\nin px.\n\n___________\n\n**PlaqueImageGray class** designed to hold grayscale image data containing\nmultiple plaque phenotypes with a respective binary mask. 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