### Complete Image Processing Pipeline

Source: https://context7.com/scifycodes/neutrosophic-image-processing/llms.txt

This example outlines a comprehensive neutrosophic image processing workflow, including image loading, preprocessing, computation of neutrosophic membership values (truth, indeterminacy, falsity), threshold-based segmentation, and creation of uncertainty masks.

```APIDOC
## Complete Image Processing Pipeline

### Description
Demonstrates a full workflow from image loading to segmentation using neutrosophic image processing techniques.

### Method
N/A (Script execution)

### Endpoint
N/A (Local computation)

### Parameters
- **img_path** (str) - Path to the input image.
- **h** (int) - Neighborhood window size for processing.
- **sz** (int) - Target dimension for resizing the image.
- **threshold** (float) - Threshold value for binary segmentation based on truth membership.

### Request Example
```python
import os
import cv2 as cv
import numpy as np
import matplotlib.pyplot as plt

from NeutrosophicSet import Neutrosophic_set, Image_stat
from SVNeutrosophicSet import SV_Neutrosophic_set
from Visualization import NS_plots

# Setup
output_dir = "./Results/"
os.makedirs(output_dir, exist_ok=True)

# Load and preprocess image
img_path = "./samples/c_man.png"
img = cv.imread(img_path, 0)  # Load as grayscale
print(f"Original image shape: {img.shape}")

# Parameters for neutrosophic processing
h = 3      # 3x3 neighborhood window
sz = 250   # Resize to 250x250

# Step 1: Compute neutrosophic membership values
ns = Neutrosophic_set(img, h, sz)
truth = ns.truth_mem()
indeterminacy = ns.indeter_mem()
falsity = ns.false_mem()

print(f"Processed image shape: {truth.shape}")
print(f"Truth stats: mean={truth.mean():.4f}, std={truth.std():.4f}")
print(f"Indeterminacy stats: mean={indeterminacy.mean():.4f}, std={indeterminacy.std():.4f}")
print(f"Falsity stats: mean={falsity.mean():.4f}, std={falsity.std():.4f}")

# Step 2: Perform threshold-based segmentation
threshold = 0.5
segmented = (truth > threshold).astype(np.uint8) * 255
cv.imwrite(output_dir + "segmented_binary.png", segmented)

# Step 3: Create uncertainty mask
high_uncertainty = (indeterminacy > 0.7).astype(np.uint8) * 255
cv.imwrite(output_dir + "high_uncertainty_regions.png", high_uncertainty)
```

### Response
#### Success Response (N/A)
Generates segmented and uncertainty mask images.

#### Response Example
Output files:
- `segmented_binary.png`: Binary image showing segmented regions.
- `high_uncertainty_regions.png`: Image highlighting regions with high indeterminacy.
```

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

### Compute Neutrosophic Membership Values with Neutrosophic_set

Source: https://context7.com/scifycodes/neutrosophic-image-processing/llms.txt

This snippet demonstrates how to use the `Neutrosophic_set` class to compute truth, indeterminacy, and falsity membership values for each pixel in a grayscale image. It involves initializing the class with an image and parameters like neighborhood block size and resize dimensions, then calling methods to get the membership values. Intermediate computations like local neighborhood mean (`g_bar`) and deviation from mean (`delta`) can also be accessed. The output membership values are typically in the range [0, 1].

```python
import cv2 as cv
import numpy as np
from NeutrosophicSet import Neutrosophic_set

# Load a grayscale image
img = cv.imread("./samples/lena.png", 0)

# Initialize Neutrosophic set with:
# - img: input grayscale image
# - h: neighborhood block size (3x3 window)
# - sz: resize dimension (150x150 pixels)
# - z_pd: use zero padding (default True)
h = 3
sz = 150

ns = Neutrosophic_set(img, h, sz, z_pd=True)

# Compute truth membership - measures pixel intensity relative to local mean
# Returns values in [0, 1] where higher values indicate stronger membership
truth = ns.truth_mem()
print(f"Truth membership shape: {truth.shape}")
print(f"Truth value range: [{truth.min():.4f}, {truth.max():.4f}]")

# Compute indeterminacy membership - measures uncertainty based on local variance
# Higher values indicate greater uncertainty/ambiguity
indeterminacy = ns.indeter_mem()
print(f"Indeterminacy value range: [{indeterminacy.min():.4f}, {indeterminacy.max():.4f}]")

# Compute falsity membership - complement of truth (1 - truth)
falsity = ns.false_mem()
print(f"Falsity value range: [{falsity.min():.4f}, {falsity.max():.4f}]")

# Access intermediate computations
g_bar = ns.g_bar()  # Local neighborhood mean for each pixel
delta = ns.delta()  # Absolute difference from neighborhood mean

# Example output:
# Truth membership shape: (150, 150)
# Truth value range: [0.0000, 1.0000]
# Indeterminacy value range: [0.0000, 1.0000]
# Falsity value range: [0.0000, 1.0000]
```

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

### Compute and Visualize Neutrosophic Sets in Python

Source: https://github.com/scifycodes/neutrosophic-image-processing/blob/main/README.md

This snippet demonstrates how to initialize a Neutrosophic set from an image, compute its truth, indeterminacy, and falsity membership functions, and generate various visualizations including intensity plots and kernel density estimations.

```python
h = 3
sz = 53
x = cv.imread("./samples/c_man.png", 0)
tru = Neutrosophic_set(x, h, sz).truth_mem()
ind = Neutrosophic_set(x, h, sz).indeter_mem()
fal = Neutrosophic_set(x, h, sz).false_mem()

NS_plots(x, h, sz).tru_viz(folder_path+"tru.png")
NS_plots(x, h, sz).ind_viz(folder_path+"ind.png")
NS_plots(x, h, sz).fal_viz(folder_path+"fal.png")

NS_plots(x, h ,sz).tru_intensity(folder_path+"tru_int.png")
NS_plots(x, h ,sz).ind_intensity(folder_path+"ind_int.png")
NS_plots(x, h ,sz).fal_intensity(folder_path+"fal_int.png")

NS_plots(x, h ,sz).ns_kde(folder_path+"ns_kde.png")
```

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

### Visualize Neutrosophic Membership Functions

Source: https://context7.com/scifycodes/neutrosophic-image-processing/llms.txt

Demonstrates how to use the NS_plots class to generate 2D grayscale maps, 3D surface plots, and KDE distribution comparisons for neutrosophic membership values.

```python
plotter = NS_plots(img, h, sz)
plotter.tru_viz("truth_membership.png")
plotter.ind_viz("indeterminacy_membership.png")
plotter.fal_viz("falsity_membership.png")
plotter.tru_intensity("truth_3d_surface.png")
plotter.ns_kde("membership_kde_comparison.png")
```

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

### Compute Image Neighborhood Statistics with Image_stat

Source: https://context7.com/scifycodes/neutrosophic-image-processing/llms.txt

This snippet shows how to utilize the `Image_stat` class for calculating local neighborhood statistics on an image. It computes essential metrics such as mean, standard deviation, maximum, and minimum values within sliding windows. These statistics are valuable for image preprocessing and analysis, often complementing neutrosophic processing techniques.

```python
import cv2 as cv
import numpy as np
from NeutrosophicSet import Image_stat

# Load image
img = cv.imread("./samples/lena.png", 0)

# The Image_stat class is intended for computing neighborhood statistics.
# Example usage would involve initializing Image_stat with the image
# and then calling methods like mean(), std_dev(), max_val(), min_val()
# For brevity, the full implementation details are omitted here, 
# but it would typically involve a sliding window approach.

# Placeholder for demonstration:
# stat_computer = Image_stat(img, h=3) # Assuming h is a parameter
# mean_map = stat_computer.mean()
# std_dev_map = stat_computer.std_dev()

print("Image_stat class is used for neighborhood statistics computation.")

```

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

### Generate Neutrosophic Image Visualizations using NS_plots

Source: https://context7.com/scifycodes/neutrosophic-image-processing/llms.txt

This code snippet demonstrates how to generate standard visualizations for neutrosophic image components. It utilizes the NS_plots class to create and save PNG images for truth, indeterminacy, and falsity visualizations, as well as their corresponding intensity plots. Dependencies include the NS_plots class and an output directory path.

```Python
from neutrosophic_image_processing import NS_plots

# Assuming img, h, sz, and output_dir are defined previously
plotter = NS_plots(img, h, sz)
plotter.tru_viz(output_dir + "tru.png")
plotter.ind_viz(output_dir + "ind.png")
plotter.fal_viz(output_dir + "fal.png")
plotter.tru_intensity(output_dir + "tru_int.png")
plotter.ind_intensity(output_dir + "ind_int.png")
plotter.fal_intensity(output_dir + "fal_int.png")
plotter.ns_kde(output_dir + "ns_kde.png")
```

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

### Image Statistics Calculation

Source: https://context7.com/scifycodes/neutrosophic-image-processing/llms.txt

This section demonstrates how to compute various statistical measures of an image using the Image_stat class, including local mean, standard deviation, maximum, and minimum values within specified neighborhood blocks. These statistics can be used for tasks like edge detection and texture analysis.

```APIDOC
## Image Statistics Calculation

### Description
Computes statistical properties of an image within local neighborhoods.

### Method
N/A (Class usage)

### Endpoint
N/A (Local computation)

### Parameters
- **img** (numpy.ndarray) - Input grayscale image.
- **h** (int) - Size of the neighborhood block (e.g., 5 for 5x5).
- **sz** (int) - Dimension to resize the image to.
- **z_pd** (bool) - Whether to perform zero-padding.

### Request Example
```python
from NeutrosophicSet import Image_stat

# Load image (replace with actual image loading)
# img = cv.imread("path/to/your/image.png", 0)

h = 5
sz = 100
stats = Image_stat(img, h, sz, z_pd=True)

local_mean = stats.img_mean()
local_std = stats.img_std()
local_max = stats.img_max()
local_min = stats.img_min()

edge_indicator = local_max - local_min
texture_measure = local_std / (local_mean + 1e-6)
```

### Response
#### Success Response (N/A)
Returns computed statistical measures as numpy arrays.

#### Response Example
```python
# Example output for print statements:
# Local mean range: [10.50, 200.25]
# Local std range: [5.10, 50.75]
# Local max range: [20.00, 250.00]
```
```

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

### Compute Bounded Neutrosophic Membership with SV_Neutrosophic_set

Source: https://context7.com/scifycodes/neutrosophic-image-processing/llms.txt

This code snippet illustrates the use of the `SV_Neutrosophic_set` class, which provides a Single-Valued Neutrosophic Set implementation. It ensures that all computed membership values (truth, indeterminacy, falsity) are strictly within the [0, 1] range by clipping them. This is useful for applications that require guaranteed bounded outputs. The class takes similar parameters to `Neutrosophic_set` and provides access to the processed image dimensions.

```python
import cv2 as cv
from SVNeutrosophicSet import SV_Neutrosophic_set

# Load grayscale image
img = cv.imread("./samples/c_man.png", 0)

# Create Single-Valued Neutrosophic Set
# Parameters same as Neutrosophic_set: image, block size, resize, padding mode
h = 3
sz = 100

svns = SV_Neutrosophic_set(img, h, sz, z_pd=True)

# Get clipped membership values guaranteed in [0, 1]
truth = svns.truth_mem()
indeterminacy = svns.indeter_mem()
falsity = svns.false_mem()

# Access the computed image dimensions
rows, cols = svns.r, svns.c
print(f"Processed image dimensions: {rows} x {cols}")

# Verify all values are properly bounded
assert truth.min() >= 0 and truth.max() <= 1
assert indeterminacy.min() >= 0 and indeterminacy.max() <= 1
assert falsity.min() >= 0 and falsity.max() <= 1

print("All membership values properly bounded in [0, 1]")
```

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

### Compute Local Neighborhood Statistics

Source: https://context7.com/scifycodes/neutrosophic-image-processing/llms.txt

Calculates local mean, standard deviation, maximum, and minimum values for an image using a specified block size. These statistics are essential for edge detection and texture analysis.

```python
h = 5
sz = 100
stats = Image_stat(img, h, sz, z_pd=True)
local_mean = stats.img_mean()
local_std = stats.img_std()
local_max = stats.img_max()
local_min = stats.img_min()
edge_indicator = local_max - local_min
texture_measure = local_std / (local_mean + 1e-6)
```

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

### Analyze Neutrosophic Membership Constraint (T+I+F Sum)

Source: https://context7.com/scifycodes/neutrosophic-image-processing/llms.txt

This code snippet analyzes the neutrosophic constraint by calculating the sum of truth (T), indeterminacy (I), and falsity (F) membership values. It prints the minimum and maximum ranges of this sum, which should ideally be approximately 3 for complement-based systems. This analysis is crucial for validating the consistency of the neutrosophic representation. Dependencies include pre-calculated truth, indeterminacy, and falsity values (assumed to be NumPy arrays).

```Python
# Assuming truth, indeterminacy, and falsity are defined NumPy arrays
constraint_sum = truth + indeterminacy + falsity
print(f"T+I+F sum range: [{constraint_sum.min():.4f}, {constraint_sum.max():.4f}]")
```

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

### Perform Neutrosophic Image Segmentation

Source: https://context7.com/scifycodes/neutrosophic-image-processing/llms.txt

A complete pipeline showing how to compute neutrosophic membership sets and perform threshold-based segmentation and uncertainty masking.

```python
ns = Neutrosophic_set(img, h, sz)
truth = ns.truth_mem()
indeterminacy = ns.indeter_mem()
segmented = (truth > 0.5).astype(np.uint8) * 255
high_uncertainty = (indeterminacy > 0.7).astype(np.uint8) * 255
```

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

### NS_plots Class - Visualization and Plotting

Source: https://context7.com/scifycodes/neutrosophic-image-processing/llms.txt

The NS_plots class facilitates the visualization of neutrosophic membership functions. It generates 2D grayscale membership maps, 3D surface plots, and Kernel Density Estimation (KDE) plots, saving all visualizations as high-resolution PNG files.

```APIDOC
## NS_plots Class - Visualization and Plotting

### Description
Provides visualization capabilities for neutrosophic membership functions, including 2D maps, 3D surfaces, and KDE plots.

### Method
N/A (Class usage)

### Endpoint
N/A (Local computation)

### Parameters
- **img** (numpy.ndarray) - Input grayscale image.
- **h** (int) - Neighborhood block size.
- **sz** (int) - Dimension to resize the image to.

### Usage
```python
import os
import cv2 as cv
from Visualization import NS_plots

output_dir = "./Results/"
os.makedirs(output_dir, exist_ok=True)

img = cv.imread("./samples/c_man.png", 0)
h = 3
sz = 250

plotter = NS_plots(img, h, sz)

# 2D Grayscale Membership Maps
plotter.tru_viz(output_dir + "truth_membership.png")
plotter.ind_viz(output_dir + "indeterminacy_membership.png")
plotter.fal_viz(output_dir + "falsity_membership.png")

# 3D Surface Plots
plotter.tru_intensity(output_dir + "truth_3d_surface.png")
plotter.ind_intensity(output_dir + "indeterminacy_3d_surface.png")
plotter.fal_intensity(output_dir + "falsity_3d_surface.png")

# KDE Plot
plotter.ns_kde(output_dir + "membership_kde_comparison.png")

print(f"All visualizations saved to {output_dir}")
```

### Response
#### Success Response (N/A)
Generates and saves PNG image files for each visualization.

#### Response Example
Output files include:
- `truth_membership.png`
- `indeterminacy_membership.png`
- `falsity_membership.png`
- `truth_3d_surface.png`
- `indeterminacy_3d_surface.png`
- `falsity_3d_surface.png`
- `membership_kde_comparison.png`
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.