BoW.py File Reference

Classes
class	CfgBoW
	Configuration setting specifier for ColorBoWMatcher class. More...
class	ColorBoWMatcher

Namespaces
	puzzle.piece.BoW
	Bag-of-Words style color matching system using OpenCV K-Means.
	puzzle.pieces.BoW

Functions
np.ndarray	build_vocabulary (list[RGBMatrix] groups, int n_words=20, *int max_iter=100, float epsilon=1.0, int attempts=5, int random_seed=42)
list[Histogram]	encode_all (list[RGBMatrix] groups, np.ndarray centroids, *bool normalize=True)
Histogram	encode_histogram (RGBMatrix group, np.ndarray centroids, *bool normalize=True)
float	histogram_distance (Histogram h1, Histogram h2, DistanceMetric metric="chi2")

Variables
int	best = top[1] if top[0]["distance"] < 1e-6 else top[0]
	DistanceMetric = Literal["chi2", "intersection", "hellinger", "l2", "cosine"]
	Literal type enumerating supported histogram distance metrics. More...
	groups
	hist_img = matcher.histogram_image(group_idx=0)
	Histogram = np.ndarray
	A (n_words,) float32 numpy ndarray representing an L1-normalized BoW histogram. More...
	labels
	matcher = ColorBoWMatcher(n_words=20, metric="chi2")
	metric
	n_groups
	n_pixels_per_group
	n_true_colors
dictionary	nonzero = {f"w{i}": f"{v:.3f}" for i, v in enumerate(hist) if v > 0}
	query_group = groups[0]
	results = matcher.query(query_group)
	RGBMatrix = np.ndarray
	A (3, N) numpy ndarray of dtype uint8 or float32 holding RGB pixel data. More...
	rng = np.random.default_rng(42)
	top = matcher.query(query_group, top_k=2)
	vocab_img = matcher.vocabulary_as_image(swatch_size=50)