Parameters

width : int

Width of the detection window.

height : int

Height of the detection window.

feature_type : str

The type of feature to consider:

• 'type-2-x': 2 rectangles varying along the x axis;

• 'type-2-y': 2 rectangles varying along the y axis;

• 'type-3-x': 3 rectangles varying along the x axis;

• 'type-3-y': 3 rectangles varying along the y axis;

• 'type-4': 4 rectangles varying along x and y axis.

Returns

feature_coord : (n_features, n_rectangles, 2, 2), ndarray of list of tuple coord

Coordinates of the rectangles for each feature.

feature_type : (n_features,), ndarray of str

The corresponding type for each feature.

Compute the coordinates of Haar-like features.

Examples

See :

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them

---

File: None#None
type: <class 'builtin_function_or_method'>
Commit: