### Install histolab via pip

Source: https://histolab.readthedocs.io/en/latest/quickstart.html

Install the histolab package using pip.

```bash
pip install histolab
```

--------------------------------

### Install histolab via conda

Source: https://histolab.readthedocs.io/en/latest/quickstart.html

Install the histolab package using conda.

```bash
conda install -c conda-forge histolab
```

--------------------------------

### Import example data

Source: https://histolab.readthedocs.io/en/latest/quickstart.html

Import the prostate and ovarian tissue slides from the histolab data module.

```python
from histolab.data import prostate_tissue, ovarian_tissue
```

--------------------------------

### Local environment setup

Source: https://histolab.readthedocs.io/en/latest/contributing.html

Commands to set up the local development environment, including installing dependencies and pre-commit hooks.

```bash
pip install -e .[testing]
pip install -r requirements-dev.txt
pre-commit install
pytest tests/
```

--------------------------------

### Prostate slide info output

Source: https://histolab.readthedocs.io/en/latest/quickstart.html

Example output for prostate slide information.

```text
Slide name: 6b725022-f1d5-4672-8c6c-de8140345210
Levels: [0, 1, 2]
Dimensions at level 0: (16000, 15316)
Dimensions at level 1: (4000, 3829)
Dimensions at level 2: (2000, 1914)
```

--------------------------------

### Magnification factor output

Source: https://histolab.readthedocs.io/en/latest/quickstart.html

Example output for magnification factors.

```text
Native magnification factor: 20X
Magnification factor corresponding to level 1: 5.0X
```

--------------------------------

### Ovarian slide info output

Source: https://histolab.readthedocs.io/en/latest/quickstart.html

Example output for ovarian slide information.

```text
Slide name: b777ec99-2811-4aa4-9568-13f68e380c86
Levels: [0, 1, 2]
Dimensions at level 0: (30001, 33987)
Dimensions at level 1: (7500, 8496)
Dimensions at level 2: (1875, 2124)
```

--------------------------------

### Download and get slide paths

Source: https://histolab.readthedocs.io/en/latest/quickstart.html

Download the WSI from the corresponding repository and save the slide in a cached directory. Each function outputs the corresponding slide and the path where the slide has been saved.

```python
prostate_svs, prostate_path = prostate_tissue()
ovarian_svs, ovarian_path = ovarian_tissue()
```

--------------------------------

### Install Pixman 0.40 on Windows (pacman)

Source: https://histolab.readthedocs.io/en/latest/installation.html

Installs Pixman version 0.40 using pacman, the package manager for MSYS2.

```bash
pacman -S mingw-w64-x86_64-pixman

```

--------------------------------

### Build Pixman from Source

Source: https://histolab.readthedocs.io/en/latest/installation.html

Downloads, extracts, configures, builds, and installs Pixman from source.

```bash
wget https://cairographics.org/releases/pixman-0.40.0.tar.gz
tar -xvf pixman-0.40.0.tar.gz
cd pixman-0.40.0
./configure
make
sudo make install

```

--------------------------------

### Verify Pixman Installation on macOS

Source: https://histolab.readthedocs.io/en/latest/installation.html

Lists installed Pixman versions on macOS using brew.

```bash
brew list --versions pixman

```

--------------------------------

### Install histolab via pip

Source: https://histolab.readthedocs.io/en/latest/readme.html

The histolab package can be installed via pip.

```bash
pip install histolab

```

--------------------------------

### Verify Pixman Installation on Ubuntu

Source: https://histolab.readthedocs.io/en/latest/installation.html

Checks the installed Pixman library versions on Ubuntu using ldconfig.

```bash
ldconfig -v | grep libpixman

```

--------------------------------

### BinaryOpening Example

Source: https://histolab.readthedocs.io/en/latest/api/filters/morphological_filters.html

Example demonstrating the use of BinaryOpening filter.

```python
from histolab.filters.morphological_filters import BinaryOpening
from histolab.filters.image_filters import RgbToGrayscale, OtsuThreshold
from PIL import Image

image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png")
rgb_to_grayscale = RgbToGrayscale()
uts_threshold = OtsuThreshold()
binary_opening = BinaryOpening()

image_gray = rgb_to_grayscale(image_rgb)
binary_image = otsu_threshold(image_gray)
image_opened = binary_opening(binary_image)
```

--------------------------------

### Install Pixman 0.40 on Ubuntu (Conda)

Source: https://histolab.readthedocs.io/en/latest/installation.html

Installs Pixman version 0.40 using conda for Ubuntu environments.

```bash
conda install -c anaconda pixman==0.40

```

--------------------------------

### WhiteTopHat Example

Source: https://histolab.readthedocs.io/en/latest/api/filters/morphological_filters.html

Example demonstrating the use of WhiteTopHat filter.

```python
from PIL import Image
import numpy as np
from histolab.filters.image_filters import RgbToGrayscale, OtsuThreshold
from histolab.filters.morphological_filters import WhiteTopHat

image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png")
rgb_to_grayscale = RgbToGrayscale()
uts_threshold = OtsuThreshold()
white_that = WhiteTopHat(np.ones((5,5)))

image_gray = rgb_to_grayscale(image_rgb)
binary_image = otsu_threshold(image_gray)
image_out = white_that(binary_image)
```

--------------------------------

### Install Pooch

Source: https://histolab.readthedocs.io/en/latest/api/data.html

Pooch is an optional requirement for `histolab` and needs to be installed separately.

```bash
pip install pooch

```

--------------------------------

### Install histolab via conda

Source: https://histolab.readthedocs.io/en/latest/readme.html

The histolab package can be installed via conda.

```bash
conda install -c conda-forge histolab

```

--------------------------------

### Grid Extraction

Source: https://histolab.readthedocs.io/en/latest/quickstart.html

To retrieve all available tiles using a grid structure, use the GridTiler extractor. This example demonstrates initializing GridTiler with parameters for tile size, extraction level, and pixel overlap.

```python
from histolab.tiler import GridTiler
```

```python
grid_tiles_extractor = GridTiler(
   tile_size=(512, 512),
   level=0,
   check_tissue=True, # default
   pixel_overlap=0, # default
   prefix="grid/", # save tiles in the "grid" subdirectory of slide's processed_path
   suffix=".png" # default
)
```

```python
grid_tiles_extractor.locate_tiles(
    slide=ovarian_slide,
    scale_factor=64,
    alpha=64,
    outline="#046C4C",
)
```

```python
grid_tiles_extractor.extract(ovarian_slide)
```

--------------------------------

### RgbToHsv Example

Source: https://histolab.readthedocs.io/en/latest/api/filters/image_filters.html

Example of converting RGB channels to HSV channels.

```python
>>> from PIL import Image
>>> from histolab.filters.image_filters import RgbToHsv
>>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png")
>>> rgb_to_hsv = RgbToHsv()
>>> image_hsv = rgb_to_hsv(image_rgb)
```

--------------------------------

### AdaptiveEqualization Example

Source: https://histolab.readthedocs.io/en/latest/api/filters/image_filters.html

Example demonstrating how to use the AdaptiveEqualization filter to enhance image contrast.

```python
>>> from PIL import Image
>>> from histolab.filters.image_filters import AdaptiveEqualization, RgbToGrayscale
>>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png")
>>> rgb_to_grayscale = RgbToGrayscale()
>>> adaptive_equalization = AdaptiveEqualization()
>>> image_gray = rgb_to_grayscale(image_rgb)
>>> image_clahe = adaptive_equalization(image_gray)

```

--------------------------------

### BinaryDilation Example

Source: https://histolab.readthedocs.io/en/latest/api/filters/morphological_filters.html

Example demonstrating the use of BinaryDilation to dilate a binary mask.

```python
>>> from PIL import Image
>>> from histolab.filters.image_filters import RgbToGrayscale, OtsuThreshold
>>> from histolab.filters.morphological_filters import BinaryDilation
>>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png")
>>> rgb_to_grayscale = RgbToGrayscale()
>>> otsu_threshold = OtsuThreshold()
>>> binary_dilation = BinaryDilation()
>>> image_gray = rgb_to_grayscale(image_rgb)
>>> binary_image = otsu_threshold(image_gray)
>>> image_dilated = binary_dilation(binary_image)

```

--------------------------------

### FiltersComposition Example

Source: https://histolab.readthedocs.io/en/latest/_modules/histolab/filters/compositions.html

Example of how to use FiltersComposition with Slide and Tile classes.

```python
from histolab.filters.compositions import FiltersComposition
from histolab.slide import Slide
from histolab.tile import Tile

filters_slide = FiltersComposition(Slide).tissue_mask_filters
filters_tile = FiltersComposition(Tile).tissue_mask_filters
```

--------------------------------

### BinaryErosion Example

Source: https://histolab.readthedocs.io/en/latest/api/filters/morphological_filters.html

Example demonstrating the use of BinaryErosion to erode a binary mask.

```python
>>> from PIL import Image
>>> from histolab.filters.image_filters import RgbToGrayscale, OtsuThreshold
>>> from histolab.filters.morphological_filters import BinaryErosion
>>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png")
>>> rgb_to_grayscale = RgbToGrayscale()
>>> otsu_threshold = OtsuThreshold()
>>> binary_erosion = BinaryErosion(disk_size=6)
>>> image_gray = rgb_to_grayscale(image_rgb)
>>> binary_image = otsu_threshold(image_gray)
>>> image_eroded = binary_erosion(binary_image)

```

--------------------------------

### HysteresisThreshold Example

Source: https://histolab.readthedocs.io/en/latest/_modules/histolab/filters/image_filters.html

Example of how to use the HysteresisThreshold filter.

```python
from PIL import Image
from histolab.filters.image_filters import HysteresisThreshold, RgbToGrayscale
image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png")
rgb_to_grayscale = RgbToGrayscale()
hyst_threshold = HysteresisThreshold(low=200, high=250)
image_gray = rgb_to_grayscale(image_rgb)
image_thresholded = hyst_threshold(image_gray)
```

--------------------------------

### WatershedSegmentation Example

Source: https://histolab.readthedocs.io/en/latest/api/filters/morphological_filters.html

Example demonstrating the use of WatershedSegmentation filter.

```python
import numpy as np
from histolab.filters.morphological_filters import WatershedSegmentation

mask = np.array([[0,1],[1,0]]) # or np.load("/path/my_array_mask.npy")
watershed_segmentation = WatershedSegmentation()
mask_segmented = watershed_segmentation(mask)
```

--------------------------------

### StretchContrast Example

Source: https://histolab.readthedocs.io/en/latest/_modules/histolab/filters/image_filters.html

Example of how to use the StretchContrast filter to increase image contrast.

```python
from PIL import Image
from histolab.filters.image_filters import RgbToGrayscale, StretchContrast
image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png")
rgb_to_grayscale = RgbToGrayscale()
stretch_contrast = StretchContrast()
image_gray = rgb_to_grayscale(image_rgb)
image_stretched = stretch_contrast(image_gray)
```

--------------------------------

### LocalOtsuThreshold Example

Source: https://histolab.readthedocs.io/en/latest/_modules/histolab/filters/image_filters.html

Example of how to use the LocalOtsuThreshold filter.

```python
from PIL import Image
from histolab.filters.image_filters import LocalOtsuThreshold, RgbToGrayscale
image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png")
rgb_to_grayscale = RgbToGrayscale()
local_otsu = LocalOtsuThreshold()
image_gray = rgb_to_grayscale(image_rgb)
image_thresholded_locally = local_otsu(image_gray)
```

--------------------------------

### Verify Pixman Installation on Windows (PowerShell)

Source: https://histolab.readthedocs.io/en/latest/installation.html

Retrieves the version information of the libpixman-1-0.dll file on Windows using PowerShell.

```powershell
(Get-Item "C:\OpenSlide\bin\libpixman-1-0.dll").VersionInfo | format-list

```

--------------------------------

### BinaryFillHoles Example

Source: https://histolab.readthedocs.io/en/latest/api/filters/morphological_filters.html

Example demonstrating the use of BinaryFillHoles to fill holes in binary objects.

```python
>>> from PIL import Image
>>> from histolab.filters.image_filters import RgbToGrayscale, OtsuThreshold
>>> from histolab.filters.morphological_filters import BinaryFillHoles
>>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png")
>>> rgb_to_grayscale = RgbToGrayscale()
>>> otsu_threshold = OtsuThreshold()
>>> binary_fill_holes = BinaryFillHoles()
>>> image_gray = rgb_to_grayscale(image_rgb)
>>> binary_image = otsu_threshold(image_gray)
>>> image_filled_holes = binary_fill_holes(binary_image)

```

--------------------------------

### RemoveSmallObjects Example

Source: https://histolab.readthedocs.io/en/latest/api/filters/morphological_filters.html

Example demonstrating the use of RemoveSmallObjects filter.

```python
from PIL import Image
from histolab.filters.image_filters import RgbToGrayscale, OtsuThreshold
from histolab.filters.morphological_filters import RemoveSmallObjects

image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png")
rgb_to_grayscale = RgbToGrayscale()
uts_threshold = OtsuThreshold()
remove_small_objects = RemoveSmallObjects()

image_gray = rgb_to_grayscale(image_rgb)
binary_image = otsu_threshold(image_gray)
image_no_small_objects = remove_small_objects(binary_image)
```

--------------------------------

### BlueFilter Example

Source: https://histolab.readthedocs.io/en/latest/api/filters/image_filters.html

Example demonstrating how to use the BlueFilter to filter out blueish colors from an RGB image.

```python
>>> from PIL import Image
>>> from histolab.filters.image_filters import BlueFilter
>>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/wsi-blue-pen.png")
>>> blue_filter = BlueFilter(30, 20, 105)
>>> mask_filtered = blue_filter(image_rgb)

```

--------------------------------

### K-means Segmentation Example

Source: https://histolab.readthedocs.io/en/latest/api/filters/image_filters.html

Demonstrates how to use the KmeansSegmentation filter to segment an RGB image.

```python
from PIL import Image
from histolab.filters.image_filters import KmeansSegmentation
image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png")
kmeans_segmentation = KmeansSegmentation()
kmeans_segmented_image = kmeans_segmentation(image_rgb)
```

--------------------------------

### BlueFilter Example

Source: https://histolab.readthedocs.io/en/latest/_modules/histolab/filters/image_filters.html

Example of how to use the BlueFilter to filter out blueish colors in an RGB image.

```python
from PIL import Image
from histolab.filters.image_filters import BlueFilter
image_rgb = Image.open("tests/fixtures/pil-images-rgb/wsi-blue-pen.png")
blue_filter = BlueFilter(30, 20, 105)
mask_filtered = blue_filter(image_rgb)
```

--------------------------------

### Local Equalization Example

Source: https://histolab.readthedocs.io/en/latest/api/filters/image_filters.html

Shows how to apply local equalization to a grayscale image to enhance contrast.

```python
from PIL import Image
from histolab.filters.image_filters import LocalEqualization
image_rgb = Image.open("tests/fixtures/pil-images-gs/diagnostic-slide-thumb-gs.png")
local_equ = LocalEqualization()
local_equ_image = local_equ(image_rgb)
```

--------------------------------

### Otsu Threshold Example

Source: https://histolab.readthedocs.io/en/latest/api/filters/image_filters.html

Example of applying Otsu's thresholding to a grayscale image.

```python
>>> otsu_threshold = OtsuThreshold()
>>> image_gray = rgb_to_grayscale(image_rgb)
>>> image_thresholded_array = otsu_threshold(image_gray)
```

--------------------------------

### RgbToGrayscale Example

Source: https://histolab.readthedocs.io/en/latest/api/filters/image_filters.html

Example of converting an RGB image to grayscale using RgbToGrayscale.

```python
>>> from PIL import Image
>>> from histolab.filters.image_filters import RgbToGrayscale
>>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png")
>>> rgb_to_grayscale = RgbToGrayscale()
>>> image_gray = rgb_to_grayscale(image_rgb)
```

--------------------------------

### Hysteresis Threshold Example

Source: https://histolab.readthedocs.io/en/latest/api/filters/image_filters.html

Shows how to apply hysteresis thresholding to an RGB image after converting it to grayscale.

```python
>>> from PIL import Image
>>> from histolab.filters.image_filters import HysteresisThreshold, RgbToGrayscale
>>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png")
>>> rgb_to_grayscale = RgbToGrayscale()
>>> hyst_threshold = HysteresisThreshold(low=200, high=250)
>>> image_gray = rgb_to_grayscale(image_rgb)
>>> image_thresholded = hyst_threshold(image_gray)

```

--------------------------------

### BluePenFilter Example

Source: https://histolab.readthedocs.io/en/latest/api/filters/image_filters.html

Example demonstrating how to use the BluePenFilter to filter out blue pen marks from a diagnostic slide.

```python
>>> from PIL import Image
>>> from histolab.filters.image_filters import BluePenFilter
>>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/wsi-blue-pen.png")
>>> blue_pen_filter = BluePenFilter()
>>> image_no_blue = blue_pen_filter(image_rgb)

```

--------------------------------

### BinaryClosing Example

Source: https://histolab.readthedocs.io/en/latest/api/filters/morphological_filters.html

Example demonstrating the use of BinaryClosing to close a binary mask, which can remove small holes.

```python
>>> from PIL import Image
>>> from histolab.filters.image_filters import RgbToGrayscale, OtsuThreshold
>>> from histolab.filters.morphological_filters import BinaryClosing
>>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png")
>>> rgb_to_grayscale = RgbToGrayscale()
>>> otsu_threshold = OtsuThreshold()
>>> binary_closing = BinaryClosing()
>>> image_gray = rgb_to_grayscale(image_rgb)
>>> binary_image = otsu_threshold(image_gray)
>>> image_closed = binary_closing(binary_image)

```

--------------------------------

### BinaryMask Example

Source: https://histolab.readthedocs.io/en/latest/api/masks.html

This example demonstrates how to create a custom binary mask by subclassing BinaryMask and implementing the _mask method.

```python
>>> from histolab.slide import Slide
>>> class MyCustomMask(BinaryMask):
...     def _mask(self, slide):
...         my_mask = np.array([0,1])
...         return my_mask
>>> binary_mask = MyCustomMask()
>>> slide = Slide("path/to/slide") 
>>> binary_mask(slide) 

```

--------------------------------

### Otsu Threshold Example

Source: https://histolab.readthedocs.io/en/latest/api/filters/image_filters.html

Illustrates computing an Otsu threshold on a grayscale image to create a boolean mask.

```python
from PIL import Image
from histolab.filters.image_filters import OtsuThreshold, RgbToGrayscale
image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png")
rgb_to_grayscale = RgbToGrayscale()
```

--------------------------------

### Lazy Property Example

Source: https://histolab.readthedocs.io/en/latest/_modules/histolab/util.html

Example usage of a lazy property, demonstrating its getter behavior and immutability.

```python
class Obj(object):

    @lazyproperty
    def fget(self):
        return 'some result'

obj = Obj()
```

--------------------------------

### Hysteresis Threshold Mask Example

Source: https://histolab.readthedocs.io/en/latest/api/filters/image_filters.html

Demonstrates creating a boolean mask using hysteresis thresholding on a grayscale image.

```python
>>> from PIL import Image
>>> from histolab.filters.image_filters import HysteresisThresholdMask, RgbToGrayscale
>>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png")
>>> rgb_to_grayscale = RgbToGrayscale()
>>> hyst_threshold_mask = HysteresisThresholdMask()
>>> image_gray = rgb_to_grayscale(image_rgb)
>>> image_thresholded_array = hyst_threshold_mask(image_gray)

```

--------------------------------

### GreenChannelFilter Example

Source: https://histolab.readthedocs.io/en/latest/api/filters/image_filters.html

Example usage of the GreenChannelFilter to create a binary mask based on the green channel threshold.

```python
>>> from PIL import Image
>>> from histolab.filters.image_filters import GreenChannelFilter
>>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png")
>>> g_channel_filter = GreenChannelFilter(avoid_overmask=True, overmask_thresh=90)
>>> image_thresholded_array = g_channel_filter(image_rgb)
```

--------------------------------

### DABChannel Example

Source: https://histolab.readthedocs.io/en/latest/_modules/histolab/filters/image_filters.html

Example of how to use the DABChannel filter to extract the DAB channel from an RGB image.

```python
from PIL import Image
from histolab.filters.image_filters import DABChannel
image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png")
dab_channel = DABChannel()
image_d = dab_channel(image_rgb)
```

--------------------------------

### BinaryOpening Example

Source: https://histolab.readthedocs.io/en/latest/_modules/histolab/filters/morphological_filters.html

Example of using BinaryOpening to remove small objects from a binary mask.

```python
from PIL import Image
from histolab.filters.image_filters import RgbToGrayscale, OtsuThreshold
from histolab.filters.morphological_filters import BinaryOpening
image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png")
rgb_to_grayscale = RgbToGrayscale()
otşu_threshold = OtsuThreshold()
binary_opening = BinaryOpening()
image_gray = rgb_to_grayscale(image_rgb)
binary_image = otsu_threshold(image_gray)
image_opened = binary_opening(binary_image)
```

--------------------------------

### Histogram Equalization Example

Source: https://histolab.readthedocs.io/en/latest/api/filters/image_filters.html

Demonstrates how to apply histogram equalization to an RGB image after converting it to grayscale.

```python
>>> from PIL import Image
>>> from histolab.filters.image_filters import HistogramEqualization, RgbToGrayscale
>>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png")
>>> rgb_to_grayscale = RgbToGrayscale()
>>> histogram_equalization = HistogramEqualization()
>>> image_gray = rgb_to_grayscale(image_rgb)
>>> image_he = histogram_equalization(image_gray)

```

--------------------------------

### RedFilter Example

Source: https://histolab.readthedocs.io/en/latest/api/filters/image_filters.html

Example of using RedFilter to mask reddish colors in an RGB image.

```python
>>> from PIL import Image
>>> from histolab.filters.image_filters import RedFilter
>>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-red-pen.png")
>>> red_filter = RedFilter(10, 30, 25)
>>> mask_filtered = red_filter(image_rgb)
```

--------------------------------

### Lab to RGB Conversion Example

Source: https://histolab.readthedocs.io/en/latest/api/filters/image_filters.html

Illustrates the conversion of an image from Lab color space to RGB using the LabToRgb filter.

```python
import numpy as np
from histolab.filters.image_filters import LabToRgb
arr_lab = np.load("tests/fixtures/arrays/diagnostic-slide-thumb-lab.npy")
lab_to_rgb = LabToRgb()
image_rgb = lab_to_rgb(arr_lab)
```

--------------------------------

### YenThreshold Example

Source: https://histolab.readthedocs.io/en/latest/api/filters/image_filters.html

Apply Yen thresholding to create a boolean mask based on pixel values using the YenThreshold filter.

```python
>>> from PIL import Image
>>> from histolab.filters.image_filters import RgbToGrayscale, YenThreshold
>>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png")
>>> rgb_to_grayscale = RgbToGrayscale()
>>> yen_threshold = YenThreshold()
>>> image_gray = rgb_to_grayscale(image_rgb)
>>> image_thresholded_array = yen_threshold(image_gray)
```

--------------------------------

### YenThreshold Example

Source: https://histolab.readthedocs.io/en/latest/_modules/histolab/filters/image_filters.html

Example of how to use YenThreshold to mask an image based on pixel values relative to the Yen threshold.

```python
from PIL import Image
from histolab.filters.image_filters import RgbToGrayscale, YenThreshold
image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png")
rgb_to_grayscale = RgbToGrayscale()
yen_threshold = YenThreshold()
image_gray = rgb_to_grayscale(image_rgb)
image_thresholded_array = yen_threshold(image_gray)
```

--------------------------------

### RedPenFilter Example

Source: https://histolab.readthedocs.io/en/latest/api/filters/image_filters.html

Example of using RedPenFilter to filter out red pen marks on diagnostic slides.

```python
>>> from PIL import Image
>>> from histolab.filters.image_filters import RedPenFilter
>>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-red-pen.png")
>>> red_pen_filter = RedPenFilter()
>>> image_no_red = red_pen_filter(image_rgb)
```

--------------------------------

### GreenPenFilter Example

Source: https://histolab.readthedocs.io/en/latest/api/filters/image_filters.html

Example of using GreenPenFilter to filter out green pen marks from a diagnostic slide.

```python
>>> from PIL import Image
>>> from histolab.filters.image_filters import GreenPenFilter
>>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-green-pen.png")
>>> green_pen_filter = GreenPenFilter()
>>> image_no_green = green_pen_filter(image_rgb)
```