### Install HistomicsTK from source

Source: https://digitalslidearchive.github.io/HistomicsTK/installation.html

Use this method for non-Linux systems or when using system-level image libraries.

```bash
$ git clone https://github.com/DigitalSlideArchive/HistomicsTK.git
$ cd HistomicsTK
$ pip install -e .
```

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

### Install HistomicsTK via pip

Source: https://digitalslidearchive.github.io/HistomicsTK/installation.html

Use this command on Linux to install HistomicsTK with prebuilt libraries for image format support.

```bash
$ pip install histomicstk --find-links https://girder.github.io/large_image_wheels
```

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

### Import necessary libraries for HistomicsTK workflows

Source: https://digitalslidearchive.github.io/HistomicsTK/examples/workflows.html

Imports essential modules for running workflows, including girder client, data reading, and specific workflow components. Ensure these libraries are installed before use.

```python
import tempfile
import girder_client
# import numpy as np
from pandas import read_csv
from histomicstk.workflows.workflow_runner import Slide_iterator
# from histomicstk.saliency.cellularity_detection import (
#     Cellularity_detector_superpixels)
from histomicstk.saliency.cellularity_detection_thresholding import (
    Cellularity_detector_thresholding)
from histomicstk.workflows.workflow_runner import (
    Workflow_runner, Slide_iterator)
from histomicstk.workflows.specific_workflows import (
    cellularity_detection_workflow)
```

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

### Get Single Tile by Position

Source: https://digitalslidearchive.github.io/HistomicsTK/examples/using_large_image.html

Retrieves a single tile from the slide at a specific position using `getSingleTile`. This is useful when you need a tile at a known linear position. Requires specifying tile size and scale.

```python
pos = 1000

tile_info = ts.getSingleTile(
    tile_size=dict(width=1000, height=1000),
    scale=dict(magnification=20),
    tile_position=pos,
)

plt.imshow(tile_info['tile'])
```

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

### Read Entire Slide at Low Magnification

Source: https://digitalslidearchive.github.io/HistomicsTK/examples/using_large_image.html

Reads the entire slide at a low magnification level. This is useful for getting a low-resolution overview of the whole slide. Ensure the desired magnification is specified.

```python
im_low_res, _ = ts.getRegion(
    scale=dict(magnification=1.25),
    format=large_image.tilesource.TILE_FORMAT_NUMPY,
)

plt.imshow(im_low_res)
```

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

### Get slide metadata

Source: https://digitalslidearchive.github.io/HistomicsTK/examples/using_large_image.html

Retrieve basic slide metadata, including the number of levels, magnification, pixel dimensions, and tile size, using the getMetadata() function of the TileSource object.

```python
ts.getMetadata()
```

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

### Get ROI at Specific Magnification

Source: https://digitalslidearchive.github.io/HistomicsTK/examples/using_large_image.html

Extracts a rectangular region of interest (ROI) from the slide at a specified magnification. Use this when you need a specific area of the image at a particular resolution. The output format can be NumPy array or PIL image.

```python
im_roi, _ = ts.getRegion(
    region=dict(left=10000, top=10000, width=1000, height=1000, units='base_pixels'),
    format=large_image.tilesource.TILE_FORMAT_NUMPY,
)

plt.imshow(im_roi)
```

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

### Print Workflow_runner.__init__.__doc__

Source: https://digitalslidearchive.github.io/HistomicsTK/examples/workflows.html

Displays the documentation string for the Workflow_runner initialization method, outlining its arguments and purpose.

```python
print(Workflow_runner.__init__.__doc__)
```

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

### Print Slide_iterator.__init__.__doc__

Source: https://digitalslidearchive.github.io/HistomicsTK/examples/workflows.html

Displays the documentation string for the Slide_iterator initialization method, detailing its parameters for iterating over slides.

```python
print(Slide_iterator.__init__.__doc__)
```

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

### Configure plotting and import libraries

Source: https://digitalslidearchive.github.io/HistomicsTK/examples/using_large_image.html

Set up default plotting configurations using matplotlib and import essential libraries like os and numpy.

```python
import os

import numpy as np
import matplotlib.pyplot as plt

%matplotlib inline

#Some nice default configuration for plots
plt.rcParams['figure.figsize'] = 10, 10
plt.rcParams['image.cmap'] = 'gray'
```

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

### Workflow_runner Initialization Documentation

Source: https://digitalslidearchive.github.io/HistomicsTK/examples/workflows.html

Provides details on initializing the Workflow_runner object. It requires a Slide_iterator instance, a workflow method, and workflow keyword arguments. Additional keyword arguments can override default settings.

```python
Init Workflow_runner object.

        Arguments
        -----------
        slide_iterator : object
            Slide_iterator object
        workflow : method
            method whose parameters include slide_id and monitorPrefix,
            which is called for each slide
        workflow_kwargs : dict
            keyword arguments for the workflow method
        kwargs : key-value pairs
            The following are already assigned defaults by Base_HTK_Class
            but can be passed here to override defaults
            [verbose, monitorPrefix, logging_savepath, suppress_warnings]




```

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

### Connect to Girder Client and Set Analysis Parameters

Source: https://digitalslidearchive.github.io/HistomicsTK/examples/workflows.html

Establishes a connection to a DSA server using provided API URL and API key. It also defines parameters for a cellularity detection workflow, including GTcodes, magnification, and visualization settings. The logging path is set to a temporary directory.

```python
APIURL = 'http://candygram.neurology.emory.edu:8080/api/v1/'
SAMPLE_SOURCE_FOLDER_ID = '5d5c28c6bd4404c6b1f3d598'
SAMPLE_DESTINATION_FOLDER_ID = '5d9246f6bd4404c6b1faaa89'

# girder client
gc = girder_client.GirderClient(apiUrl=APIURL)
# gc.authenticate(interactive=True)
gc.authenticate(apiKey='kri19nTIGOkWH01TbzRqfohaaDWb6kPecRqGmemb')

# This is where the run logs will be saved
logging_savepath = tempfile.mkdtemp()

# params for cellularity thresholding
cdt_params = {
    'gc': gc,
    'slide_id': '',  # this will be handled by the slide iterator
    'GTcodes': read_csv('../../histomicstk/saliency/tests/saliency_GTcodes.csv'),
    'MAG': 3.0,
    'visualize': True,
    'verbose': 2,
    'logging_savepath': logging_savepath,
}
```

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

### Initialize Cellularity Detector and Workflow Runner

Source: https://digitalslidearchive.github.io/HistomicsTK/examples/workflows.html

Initializes a specific cellularity detector (thresholding-based) and a workflow runner. The runner is configured to process a specific list of slides from a source folder, with results potentially copied to a destination folder. Existing annotations are not kept.

```python
# Init specific workflow (Cellularity_detector_thresholding)
cdt = Cellularity_detector_thresholding(**cdt_params)

# Init workflow runner
workflow_runner = Workflow_runner(
    slide_iterator=Slide_iterator(
        gc, source_folder_id=SAMPLE_SOURCE_FOLDER_ID,
        # keep_slides=None),  # run all slides in girder directory
        keep_slides=[
            'TCGA-A1-A0SK-01Z-00-DX1_POST.svs',
            'TCGA-A2-A04Q-01Z-00-DX1_POST.svs',
        ]),
    workflow=cellularity_detection_workflow,
    workflow_kwargs={
        'gc': gc,
        'cdo': cdt,
        'destination_folder_id': SAMPLE_DESTINATION_FOLDER_ID,
        'keep_existing_annotations': False },
    logging_savepath=cdt.logging_savepath,
    monitorPrefix='test')
```

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

### Import large_image library

Source: https://digitalslidearchive.github.io/HistomicsTK/examples/using_large_image.html

Import the necessary large_image library for image processing.

```python
import large_image
```

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

### Download sample whole-slide image

Source: https://digitalslidearchive.github.io/HistomicsTK/examples/using_large_image.html

Download a sample whole-slide image from a given URL using curl. This code checks if the file already exists before downloading.

```python
wsi_url = 'https://data.kitware.com/api/v1/file/5899dd6d8d777f07219fcb23/download'

wsi_path = 'TCGA-02-0010-01Z-00-DX4.07de2e55-a8fe-40ee-9e98-bcb78050b9f7.svs'

if not os.path.isfile(wsi_path):
    !curl -OJ "$wsi_url"
```

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

### Slide_iterator Initialization Documentation

Source: https://digitalslidearchive.github.io/HistomicsTK/examples/workflows.html

Details the initialization of the Slide_iterator object. It requires a girder client object, the source folder ID, and optional lists for keeping or discarding specific slides. Other keyword arguments can also be passed.

```python
Init Slide_iterator object.

        Arguments
        -----------
        gc : object
            girder client object
        source_folder_id : str
            girder ID of folder in which slides are located
        keep_slides : list
            List of slide names to keep. If None, all are kept.
        discard_slides : list
            List of slide names to discard.
        kwargs : key-value pairs
            The following are already assigned defaults by Base_HTK_Class
            but can be passed here to override defaults
            [verbose, monitorPrefix, logger, logging_savepath,
            suppress_warnings]




```

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

### Print Cellularity Detection Workflow Docstring

Source: https://digitalslidearchive.github.io/HistomicsTK/examples/workflows.html

Prints the docstring for the cellularity detection workflow, outlining its arguments and behavior. This is useful for understanding the workflow's parameters before execution.

```python
print(cellularity_detection_workflow.__doc__)
```

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

### HistomicsTK Workflows

Source: https://digitalslidearchive.github.io/HistomicsTK/histomicstk.html

Tools for running predefined workflows and orchestrating complex image analysis pipelines.

```APIDOC
## histomicstk.workflows

### Description
Tools for running image analysis workflows.

### Capabilities
- Workflow runner
- Specific workflows
```

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

### getSingleTile()

Source: https://digitalslidearchive.github.io/HistomicsTK/examples/using_large_image.html

Retrieves a single tile at a specific linear position within the tile iterator.

```APIDOC
## getSingleTile()

### Description
Directly retrieves the tile at a specific position of the tile iterator.

### Parameters
#### Query Parameters
- **tile_size** (dict) - Optional - Size of the tile.
- **scale** (dict) - Optional - Magnification or resolution.
- **tile_position** (int) - Required - The linear position of the tile of interest.
```

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

### Load whole-slide image using getTileSource

Source: https://digitalslidearchive.github.io/HistomicsTK/examples/using_large_image.html

Load a whole-slide image using the large_image.getTileSource function. This function automatically detects and abstracts differences between various image file formats.

```python
ts = large_image.getTileSource(wsi_path)
```

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

### Retrieve image region at scale with getRegionAtAnotherScale

Source: https://digitalslidearchive.github.io/HistomicsTK/examples/using_large_image.html

Retrieves an image region at a specified target magnification. Requires the source region definition and the desired output format.

```python
# get a large region defined at base resolution at a much lower scale
im_roi, _ = ts.getRegionAtAnotherScale(
    sourceRegion=dict(left=5000, top=5000, width=10000, height=10000,
                      units='base_pixels'),
    targetScale=dict(magnification=1.25),
    format=large_image.tilesource.TILE_FORMAT_NUMPY)

print im_roi.shape
```

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

### Iterate Through ROI with Specific Tile Size and Resolution

Source: https://digitalslidearchive.github.io/HistomicsTK/examples/using_large_image.html

Iterates through a specified region of interest (ROI) with custom tile size and resolution. It calculates the mean RGB color for each tile and the overall slide. Use this for processing large images tile by tile.

```python
num_tiles = 0
tile_means = []
tile_areas = []

for tile_info in ts.tileIterator(
    region=dict(left=5000, top=5000, width=20000, height=20000, units='base_pixels'),
    scale=dict(magnification=20),
    tile_size=dict(width=1000, height=1000),
    tile_overlap=dict(x=50, y=50),
    format=large_image.tilesource.TILE_FORMAT_PIL,
):

    if num_tiles == 100:
        print('Tile-{} = '.format(num_tiles))
        display(tile_info)

    im_tile = np.array(tile_info['tile'])
    tile_mean_rgb = np.mean(im_tile[:, :, :3], axis=(0, 1))

    tile_means.append( tile_mean_rgb )
    tile_areas.append( tile_info['width'] * tile_info['height'] )

    num_tiles += 1

slide_mean_rgb = np.average(tile_means, axis=0, weights=tile_areas)

print('Number of tiles = {}'.format(num_tiles))
print('Slide mean color = {}'.format(slide_mean_rgb))
```

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

### getMagnificationForLevel()

Source: https://digitalslidearchive.github.io/HistomicsTK/examples/using_large_image.html

Retrieves magnification and physical pixel size for a specified level in the image pyramid.

```APIDOC
## getMagnificationForLevel(level)

### Description
The `getMagnificationForLevel()` function of the TileSource class returns a python dict containing the magnification and physical size of a pixel for a specified level in the image pyramid.

### Method
GET (or equivalent for object method call)

### Endpoint
Not applicable (object method)

### Parameters
#### Path Parameters
None

#### Query Parameters
- **level** (int) - Required - The image pyramid level for which to retrieve magnification information.

### Request Example
```python
# Get the magnification associated with Level 0
ts.getMagnificationForLevel(level=0)

# Get the magnification associated with all levels of the image pyramid
for i in range(ts.levels):
    print('Level-{} : {}'.format(i, ts.getMagnificationForLevel(level=i)))
```

### Response
#### Success Response (200)
- **level** (int) - The image pyramid level.
- **magnification** (float) - The magnification factor for the specified level.
- **mm_x** (float) - The physical width of a pixel in millimeters for the specified level.
- **mm_y** (float) - The physical height of a pixel in millimeters for the specified level.
- **scale** (float) - The scale factor for the specified level.

#### Response Example
```json
{
  "level": 0,
  "magnification": 0.078125,
  "mm_x": 0.128384,
  "mm_y": 0.128384,
  "scale": 256.0
}
```
```

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

### HistomicsTK Utilities

Source: https://digitalslidearchive.github.io/HistomicsTK/histomicstk.html

Utility functions for image and matrix manipulation, numerical computations, and pixel sampling.

```APIDOC
## histomicstk.utils

### Description
Provides utility functions for image analysis tasks.

### Functions
- `compute_tile_foreground_fraction()`
- `convert_image_to_matrix()`
- `convert_matrix_to_image()`
- `del2()`
- `eigen()`
- `exclude_nonfinite()`
- `fit_poisson_mixture()`
- `gradient_diffusion()`
- `hessian()`
- `merge_colinear()`
- `sample_pixels()`
- `simple_mask()`
```

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

### tileIterator()

Source: https://digitalslidearchive.github.io/HistomicsTK/examples/using_large_image.html

Provides an iterator for sequentially processing a slide or ROI in a tile-wise fashion at a specified resolution.

```APIDOC
## tileIterator()

### Description
Provides an iterator for sequentially iterating through the entire slide or a region of interest (ROI) within the slide at any desired resolution in a tile-wise fashion.

### Parameters
#### Query Parameters
- **region** (dict) - Optional - ROI within the slide (left, top, width, height, units).
- **scale** (dict) - Optional - Desired magnification or resolution.
- **tile_size** (dict) - Optional - Size of the tile (width, height).
- **tile_overlap** (dict) - Optional - Overlap between adjacent tiles.
- **format** (str) - Optional - Format of the tile image (numpy array or PIL image).

### Response
- **tile** (object) - Cropped tile image.
- **format** (str) - Format of the tile.
- **x, y** (int) - (left, top) coordinates in current magnification pixels.
- **width, height** (int) - Size of current tile in current magnification pixels.
- **level** (int) - Level of the current tile.
- **magnification** (float) - Magnification of the current tile.
- **mm_x, mm_y** (float) - Size of the current tile pixel in millimeters.
- **gx, gy** (int) - (left, top) coordinate in base/maximum resolution pixels.
- **gwidth, gheight** (int) - Size of the current tile in base/maximum resolution pixels.
```

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

### Convert region scale with convertRegionScale

Source: https://digitalslidearchive.github.io/HistomicsTK/examples/using_large_image.html

Converts a defined region from one magnification scale to another. The output provides the new coordinates and dimensions in the target units.

```python
tr = ts.convertRegionScale(
    sourceRegion=dict(left=5000, top=5000, width=1000, height=1000,
                      units='mag_pixels'),
    sourceScale=dict(magnification=20),
    targetScale=dict(magnification=10),
    targetUnits='mag_pixels',
)

display(tr)
```

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

### HistomicsTK Modules

Source: https://digitalslidearchive.github.io/HistomicsTK/genindex.html

Lists the available modules within the HistomicsTK library.

```APIDOC
## HistomicsTK Modules

This section lists the primary modules available in the HistomicsTK library.

### Modules
- histomicstk.annotations_and_masks
- histomicstk.annotations_and_masks.annotation_and_mask_utils
- histomicstk.annotations_and_masks.annotation_database_parser
- histomicstk.annotations_and_masks.annotations_to_masks_handler
- histomicstk.annotations_and_masks.annotations_to_object_mask_handler
- histomicstk.annotations_and_masks.masks_to_annotations_handler
- histomicstk.annotations_and_masks.polygon_merger
- histomicstk.annotations_and_masks.polygon_merger_v2
- histomicstk.annotations_and_masks.review_gallery
- histomicstk.features
- histomicstk.filters
- histomicstk.filters.edge
- histomicstk.filters.shape
- histomicstk.preprocessing
- histomicstk.preprocessing.augmentation
- histomicstk.preprocessing.color_conversion
- histomicstk.preprocessing.color_deconvolution
- histomicstk.preprocessing.color_normalization
- histomicstk.saliency
- histomicstk.saliency.cellularity_detection_superpixels
- histomicstk.saliency.cellularity_detection_thresholding
- histomicstk.saliency.tissue_detection
- histomicstk.segmentation
- histomicstk.segmentation.label
- histomicstk.segmentation.level_set
- histomicstk.segmentation.nuclear
- histomicstk.segmentation.positive_pixel_count
- histomicstk.utils
- histomicstk.workflows
- histomicstk.workflows.specific_workflows
- histomicstk.workflows.workflow_runner
```

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

### Retrieve Magnification for Pyramid Level

Source: https://digitalslidearchive.github.io/HistomicsTK/examples/using_large_image.html

Returns the magnification and physical pixel size for a specific level in the image pyramid.

```python
# Get the magnification associated with Level 0
ts.getMagnificationForLevel(level=0)
```

```python
# Get the magnification associated with all levels of the image pyramid
for i in range(ts.levels):
    print('Level-{} : {}'.format(
        i, ts.getMagnificationForLevel(level=i)))
```

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

### Retrieve Native Magnification

Source: https://digitalslidearchive.github.io/HistomicsTK/examples/using_large_image.html

Returns the base magnification and physical pixel size of the scanned slide.

```python
ts.getNativeMagnification()
```

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

### convertRegionScale()

Source: https://digitalslidearchive.github.io/HistomicsTK/examples/using_large_image.html

Converts a defined region from one scale or magnification to another.

```APIDOC
## convertRegionScale()

### Description
Converts a region from one scale/magnification to another.

### Parameters
- **sourceRegion** (dict) - Required - Dictionary containing left, top, width, height, and units.
- **sourceScale** (dict) - Required - Dictionary containing the source magnification.
- **targetScale** (dict) - Required - Dictionary containing the target magnification.
- **targetUnits** (string) - Required - The units for the target region.

### Request Example
```python
tr = ts.convertRegionScale(
    sourceRegion=dict(left=5000, top=5000, width=1000, height=1000, units='mag_pixels'),
    sourceScale=dict(magnification=20),
    targetScale=dict(magnification=10),
    targetUnits='mag_pixels',
)
```

### Response
- **left** (float) - The converted left coordinate.
- **top** (float) - The converted top coordinate.
- **width** (float) - The converted width.
- **height** (float) - The converted height.
- **units** (string) - The units of the result.
```

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

### Retrieve Pyramid Level for Magnification

Source: https://digitalslidearchive.github.io/HistomicsTK/examples/using_large_image.html

Finds the pyramid level that most closely matches a specified magnification or physical pixel width.

```python
# get level whose magnification is closest to 10x
print('Level with magnification closest to 10x = {}'.format(
    ts.getLevelForMagnification(10)))
```

```python
# get level whose pixel width is closest to 0.0005 mm
print('Level with pixel width closest to 0.0005mm = {}'.format(
    ts.getLevelForMagnification(mm_x=0.0005)))
```

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

### HistomicsTK Annotations and Masks

Source: https://digitalslidearchive.github.io/HistomicsTK/histomicstk.html

Tools for managing and converting between annotations (e.g., polygons) and image masks, including database parsing and utilities.

```APIDOC
## histomicstk.annotations_and_masks

### Description
Tools for handling annotations and masks.

### Capabilities
- Annotation database backup and sqlite parser
- Mosaic review gallery
- Annotations to semantic segmentation masks
- Annotations to object segmentation masks
- Masks to annotations
- Annotation and mask utilities
- Polygon merger
```

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

### getLevelForMagnification()

Source: https://digitalslidearchive.github.io/HistomicsTK/examples/using_large_image.html

Finds the image pyramid level corresponding to a given magnification or pixel size.

```APIDOC
## getLevelForMagnification(magnification=None, mm_x=None, mm_y=None)

### Description
The `getLevelForMagnification()` function of the TileSource class returns the level of the image pyramid associated with a specific magnification or pixel size in millimeters.

### Method
GET (or equivalent for object method call)

### Endpoint
Not applicable (object method)

### Parameters
#### Query Parameters
- **magnification** (float) - Optional - The target magnification factor.
- **mm_x** (float) - Optional - The target pixel width in millimeters.
- **mm_y** (float) - Optional - The target pixel height in millimeters.

*Note: At least one of `magnification`, `mm_x`, or `mm_y` must be provided.*

### Request Example
```python
# Get level whose magnification is closest to 10x
ts.getLevelForMagnification(10)

# Get level whose pixel width is closest to 0.0005 mm
ts.getLevelForMagnification(mm_x=0.0005)
```

### Response
#### Success Response (200)
- **level** (int) - The image pyramid level that best matches the provided criteria.

#### Response Example
```json
{
  "level": 7
}
```
```

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

### Utility Functions and Attributes

Source: https://digitalslidearchive.github.io/HistomicsTK/genindex.html

Details specific functions and attributes available within HistomicsTK modules.

```APIDOC
## Utility Functions and Attributes

This section details specific functions and attributes found within the HistomicsTK library.

### hessian()
- **Module**: histomicstk.utils
- **Description**: Computes the Hessian matrix.

### initialize_labeled_mask()
- **Module**: histomicstk.saliency.cellularity_detection_thresholding
- **Method**: CDT_single_tissue_piece
- **Description**: Initializes a labeled mask.

### lab_mean_std()
- **Module**: histomicstk.preprocessing.color_conversion
- **Description**: Calculates the mean and standard deviation in LAB color space.

### lab_to_rgb()
- **Module**: histomicstk.preprocessing.color_conversion
- **Description**: Converts an image from LAB color space to RGB.

### max_clustering()
- **Module**: histomicstk.segmentation.nuclear
- **Description**: Performs maximum clustering.

### merge_colinear()
- **Module**: histomicstk.utils
- **Description**: Merges colinear points or segments.

### min_model()
- **Module**: histomicstk.segmentation.nuclear
- **Description**: Finds the minimum model.

### hue_value (Attribute)
- **Module**: histomicstk.segmentation.positive_pixel_count.Parameters
- **Description**: Represents the hue value parameter.

### hue_width (Attribute)
- **Module**: histomicstk.segmentation.positive_pixel_count.Parameters
- **Description**: Represents the hue width parameter.

### intensity_lower_limit (Attribute)
- **Module**: histomicstk.segmentation.positive_pixel_count.Parameters
- **Description**: Represents the lower intensity limit parameter.

### intensity_strong_threshold (Attribute)
- **Module**: histomicstk.segmentation.positive_pixel_count.Parameters
- **Description**: Represents the strong intensity threshold parameter.

### intensity_upper_limit (Attribute)
- **Module**: histomicstk.segmentation.positive_pixel_count.Parameters
- **Description**: Represents the upper intensity limit parameter.

### intensity_weak_threshold (Attribute)
- **Module**: histomicstk.segmentation.positive_pixel_count.Parameters
- **Description**: Represents the weak intensity threshold parameter.

### IntensityAverage (Output Attribute)
- **Module**: histomicstk.segmentation.positive_pixel_count.Output
- **Description**: Represents the average intensity output.

### IntensityAverageWeakAndPositive (Output Attribute)
- **Module**: histomicstk.segmentation.positive_pixel_count.Output
- **Description**: Represents the average weak and positive intensity output.

### IntensitySumPositive (Output Attribute)
- **Module**: histomicstk.segmentation.positive_pixel_count.Output
- **Description**: Represents the sum of positive intensity output.

### IntensitySumStrongPositive (Output Attribute)
- **Module**: histomicstk.segmentation.positive_pixel_count.Output
- **Description**: Represents the sum of strong positive intensity output.

### IntensitySumWeakPositive (Output Attribute)
- **Module**: histomicstk.segmentation.positive_pixel_count.Output
- **Description**: Represents the sum of weak positive intensity output.
```

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

### HistomicsTK Preprocessing

Source: https://digitalslidearchive.github.io/HistomicsTK/histomicstk.html

Algorithms for preprocessing histopathology images, including color space conversion, deconvolution, normalization, and data augmentation.

```APIDOC
## histomicstk.preprocessing

### Description
Algorithms for preprocessing histopathology images.

### Capabilities
- Color space conversion
- Color deconvolution
- Color normalization
- Data augmentation
```

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

### getRegion()

Source: https://digitalslidearchive.github.io/HistomicsTK/examples/using_large_image.html

Extracts a rectangular region of interest (ROI) from the slide at a specified scale.

```APIDOC
## getRegion()

### Description
Reads a rectangular region of interest (ROI) within the slide at any scale or magnification.

### Parameters
#### Query Parameters
- **region** (dict) - Optional - Dictionary containing (left, top, width, height, units) of the ROI.
- **scale** (dict) - Optional - Dictionary containing magnification or physical pixel size (mm_x, mm_y).
- **format** (str) - Optional - Format of the returned image.
```

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

### getNativeMagnification()

Source: https://digitalslidearchive.github.io/HistomicsTK/examples/using_large_image.html

Retrieves the magnification and physical pixel size at the base resolution level of the scanned slide.

```APIDOC
## getNativeMagnification()

### Description
The `getNativeMagnification()` function of the TileSource class returns a python dict containing the magnification and physical size of a pixel in millimeters at the base or highest resolution level at which the slide was scanned.

### Method
GET (or equivalent for object method call)

### Endpoint
Not applicable (object method)

### Parameters
None

### Request Example
```python
ts.getNativeMagnification()
```

### Response
#### Success Response (200)
- **magnification** (float) - The magnification factor.
- **mm_x** (float) - The physical width of a pixel in millimeters.
- **mm_y** (float) - The physical height of a pixel in millimeters.

#### Response Example
```json
{
  "magnification": 20.0,
  "mm_x": 0.0005015,
  "mm_y": 0.0005015
}
```
```

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

### HistomicsTK Saliency (Tissue/Cellularity Detection)

Source: https://digitalslidearchive.github.io/HistomicsTK/histomicstk.html

Algorithms for detecting tissue regions and cellularity within histopathology images.

```APIDOC
## histomicstk.saliency

### Description
Algorithms for tissue and cellularity detection.

### Detection Methods
- Tissue detection
- Cellularity detection (using thresholding)
- Cellularity detection (using superpixels)
```

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

### getRegionAtAnotherScale()

Source: https://digitalslidearchive.github.io/HistomicsTK/examples/using_large_image.html

Retrieves an image of a region at a specified target scale.

```APIDOC
## getRegionAtAnotherScale()

### Description
Retrieves an image of a region defined at one scale at a different target scale.

### Parameters
- **sourceRegion** (dict) - Required - Dictionary defining the region (left, top, width, height, units).
- **targetScale** (dict) - Required - Dictionary containing the target magnification.
- **format** (constant) - Required - The output format (e.g., TILE_FORMAT_NUMPY).

### Request Example
```python
im_roi, _ = ts.getRegionAtAnotherScale(
    sourceRegion=dict(left=5000, top=5000, width=10000, height=10000, units='base_pixels'),
    targetScale=dict(magnification=1.25),
    format=large_image.tilesource.TILE_FORMAT_NUMPY)
```

### Response
- **im_roi** (numpy.ndarray) - The image data of the requested region.
```

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

### HistomicsTK Segmentation

Source: https://digitalslidearchive.github.io/HistomicsTK/histomicstk.html

Algorithms for segmenting various components within histopathology images, such as cells, nuclei, and boundaries.

```APIDOC
## histomicstk.segmentation

### Description
Algorithms for segmenting image components.

### Functions
- `embed_boundaries()`
- `rag()`
- `rag_add_layer()`
- `rag_color()`
- `simple_mask()`

### Capabilities
- Label mask processing
- Level-set segmentation
- Nuclei segmentation
- Positive pixel counting
```

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

### HistomicsTK Features

Source: https://digitalslidearchive.github.io/HistomicsTK/histomicstk.html

Functions for extracting a wide range of features from histopathology images, including cell morphology, intensity, and texture.

```APIDOC
## histomicstk.features

### Description
Functions for extracting image features.

### Feature Extraction Functions
- `compute_fsd_features()`
- `compute_global_cell_graph_features()`
- `compute_gradient_features()`
- `compute_haralick_features()`
- `compute_intensity_features()`
- `compute_morphometry_features()`
- `compute_nuclei_features()`
- `graycomatrixext()`
```

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

### HistomicsTK Filters

Source: https://digitalslidearchive.github.io/HistomicsTK/histomicstk.html

Image filtering techniques for enhancing specific structures or features within histopathology images.

```APIDOC
## histomicstk.filters

### Description
Image filtering techniques for enhancing structures.

### Filter Types
- Edge/gradient filters
- Shape filters
```

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