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PyHistopathology-0.1
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مشاهده بیشترتوضیحات
A WSI Image processing application
| ویژگی | مقدار |
|---|---|
| سیستم عامل | OS Independent |
| نام فایل | PyHistopathology-0.1 |
| نام | PyHistopathology |
| نسخه کتابخانه | 0.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Sai Chandra |
| ایمیل نویسنده | deepak.kumar.iet@gmail.com |
| آدرس صفحه اصلی | https://github.com/saichandra1/PyHistopathology |
| آدرس اینترنتی | https://pypi.org/project/PyHistopathology/ |
| مجوز | - |
# Python Package: PyHistopathology
Read our documentation at https://pyhistopathology.readthedocs.io/en/latest/
## Command line tool:
python3 WSI_PATCH_Extraction.py -args
Mandatory args
- -i: input svs file path
- -o: output folder path
- -f: input folder for path
- Note: you should use either -i or -f, cannot use both.
Additional args
- -c: criteria
- criteria: Random or None, Default is None
- -s: patch size
- Size of the patch to extract, default is (256,256)
- -n: number of patches
- Only should be given for -c Random. Default value is 2000.
- -a: input xml
- if annotations are provided annotations file path should be given. Otherwise don't use this arg.
# Package Usage:
## Reading WSI
**Description**
- use WSI_Scanning.readWSI() to read an WSI Image
- Input: WSI path or directory
- Output: functioning numpy array of WSI Image with dtype int32
**Function**
- readWSI(WSI_path, magnification_level, annotation_file, annonated_level)
Arguments
- WSI_path: Directory of WSI
- - magnification_level: level of zoom, example (40x,20x,10x,5x). Default magnification is **“20x”**
- Note if magnification 40x for max zoom level of 20x image an error will be raised.
- annotation_file: Default annotation = None. if annotation are available in xml formats. use annotation = inputxml file path.
- annonated_level= if annotation is not giving no need to consider this variable. if annotation is given then mention z-axis of annotations. Default annotatedlevel =0
```
###Reading image example
from WSI_Preprocessing.Preprocessing import WSI_Scanning
import cv2
img,slide_dim = WSI_Scanning.readWSI("example.svs")
cv2.imwrite("example.png",img)
```
## Denoising WSI
Description
use Denoising.denoising() to remove stains, folds and other background noise in WSI
- input: WSI Path or directory
- Output: functioning numpy array of WSI Image (After denoising) with dtype int32.
Function
denoising(inputsvs, magnification, filtering, patch_size, upperlimit, lowerlimit, red_value, green_value, blue_value)
Arguments
- inputsvs: path or location of WSI.
- magnification: level of zoom, example (40x,20x,10x,5x). Default magnification is **“20x”**
- Note if magnification 40x for max zoom level of 20x image an error will be raised.
- filtering: GuassianBlur, RGBThersholding, None
- GuassianBlur: Homogeneity calculations based on image smoothing and Gaussian blur equations.
We compute sum of square differences between two consecutive Gaussian blurred images as score for homogeneity
- Upper limit: upper threshold of homogeneity score. default value is 9500 with kernel size of 11*11
- lower limit: lower threshold of homogeneity score. default value is 1500 with kernel size of 11*11
- Patch size: Not significant parameters for GuassianBlur filtering
- RGBThersholding:
Validated patches based on RGB values of patches
- red_value, green-value, blue_values are threshold for RGB
- - None:
Only removes Background
- - Note that our default is GuassianBlur technique. GuassianBlur is highly effective and requires more computational power (RAM). RGBThersholding is less effective which needs less computational power
~~~from WSI_Preprocessing.Preprocessing import Denoising
import cv2
# Here mandatory options are example.svs and magnification
img = Denoising.denoising("example.svs", "20x" )
cv2.imwrite("example.png",img)
~~~
# Extracting Patches
Description
use Extractingpatches.extractingPatches() to extract patches from WSI.
- input: WSI Path or directory
- output: patches from WSI.
Function:
extractingPatches(inputsvs, outputpath, magnification, patch_extraction_creatia, number_of_patches, filtering, patch_size, upperlimit, lowerlimit, red_value, green_value, blue_value, Annotation, Annotationlevel, Requiredlevel, reconstructionimagepath)
Arguments
- inputsvs, magnification, patch_extraction_creatia, filtering, patch_size, upperlimit, lowerlimit, red_value, green_value, blue_value, Annotation, Annotationlevel, Requiredlevel, arguments is same as denosing module.
- patch_extraction_creatia: random, None
- - Default is None.
For extracting a fixed number of patches for WSI we can use random.
- Default number of patches is 2000
- outputpath: folder to store the extracted patches
- reconstructionimagepath: we you want to compare the patches with WSI we can mention the reconstructionimagepath.
- Default is None
- - Note: it only works with patch_extraction_creatia = None.
- - Note: For WSI number of patches can exceed 20k.
```##patch extarction and reconstruction example
from WSI_Preprocessing.Preprocessing import Extarctingpatches
import cv2
img = Extarctingpatches.extractingPatches("example.svs","temp" ,"20x" )
cv2.imwrite("exampler.png",img)
# Here mandatory options are example.svs and magnification, and outputpath
Extractingpatches.extractingPatches(example.svs, outputpath, magnification)
```
نحوه نصب
نصب پکیج whl PyHistopathology-0.1:
pip install PyHistopathology-0.1.whl
نصب پکیج tar.gz PyHistopathology-0.1:
pip install PyHistopathology-0.1.tar.gz