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Description:

locatecolorcluster 0.10

Lightning-fast image color clustering with C-based RGB localization/euclidean distance calculation. Supports DBSCAN/HDBSCAN, Shapely geometry.
pip install locatecolorcluster
Tested against Python 3.11 / Windows 10
Important: A C/C++ compiler is necessary!
Advantages:
Flexibility:
The module supports both DBSCAN and HDBSCAN clustering algorithms, allowing users to choose the method that best fits their needs.
Parallelization:
The code leverages parallelization capabilities in clustering algorithms, specifically in DBSCAN, HDBSCAN and cythoneuclideandistance, by allowing users to specify the number of parallel jobs.
Speed Optimization:
The module exhibits exceptional speed, outperforming standard libraries like SciPy in Euclidean distance calculations by a factor of four. This performance boost is crucial for large-scale image processing tasks.
RGB Localization with C:
The module includes efficient C implementations for RGB localization (300x faster than Python - search_colors method), enhancing the speed and accuracy of color identification in images.
Visualization:
The module includes methods (draw_results and draw_results_real_size) for visualizing the clustered results, making it easier for users to interpret and analyze the outcomes.
Shapely Geometry:
The module provides Shapely geometry information for each cluster, offering additional insights into the spatial characteristics of the clusters.

Dependencies:
- Cython and a C/C++ compiler!
- os.path
- sys
- cv2
- scipy.spatial.distance.pdist, squareform
- cythoneuclideandistance
- a_cv_imwrite_imread_plus
- locate_pixelcolor_c
- numpy
- shapely.geometry
- sklearn.cluster.DBSCAN, HDBSCAN
- numexpr
- a_cv2_easy_resize

Usage:
- Create an instance of ColorCluster by providing an image path, optional parameters for resizing,
and interpolation method.
- Use the find_colors method to search for specific colors in the image.
- Calculate the Euclidean distance matrix using the calculate_euclidean_matrix method with the desired backend.
- Apply clustering algorithms (DBSCAN or HDBSCAN) using the get_dbscan_labels or get_hdbscan_labels methods.
- Extract clusters and visualize results using get_clusters, draw_results, and draw_results_real_size methods.
- Obtain Shapely geometry information for each cluster using get_shapely method.

Example:

import cv2

from locatecolorcluster import ColorCluster, get_range_of_colors
# Some valid color inputs
colors = get_range_of_colors(
start=(0, 0, 0),
end=(10, 10, 10),
rgb_or_bgr="bgr",
min_r=0,
max_r=10,
min_g=0,
max_g=10,
min_b=0,
max_b=10,
)
colors = ((0, 0, 0),)
colors = get_range_of_colors(
start=(100, 0, 0),
end=(255, 0, 0),
rgb_or_bgr="bgr",
min_r=100,
max_r=255,
min_g=0,
max_g=10,
min_b=0,
max_b=10,
)
cbackend = (
ColorCluster(
img=r'https://avatars.githubusercontent.com/u/77182807?s=400&u=b3398787384abf38d62c6f080195550df64f3990&v=4',
max_width=200,
max_height=200,
interpolation=cv2.INTER_NEAREST,
)
.find_colors(colors=colors, reverse_colors=False)
.calculate_euclidean_matrix(backend="C", memorylimit_mb=10000) # Define a memory limit for the C backend - It's 4x faster than scipy, but if the array is too big for the memory, the process crashes with 0xc0000005
.get_dbscan_labels(eps=3, min_samples=10, algorithm="auto", leaf_size=30, n_jobs=5)
.get_clusters()
.draw_results(folder=r"C:\myimageresults\1", color=(255, 0, 255))
.draw_results_real_size(folder=r"C:\myimageresults\2", color=(255, 255, 0))
.get_shapely()
)
shapelydata = cbackend.shapelydata
print(shapelydata)