AAM Module

class aam.AAMPoints(normalized_flattened_points_list=None, points_list=None, actual_shape=())

Object to store AAM points / landmarks. Tries to keep the scaling of these points transparent.

calculate_bounding_box()

Calculate bounding box in the scaled points list The empasis on on scaled because the convexHull does not support small values, the normalized_flattened_points_list does not work.

Use get_scaled_points first, with a shape that is needed. The shape should be the dimensions of the out image, example (480, 640), ie., (height, width)

get_bounding_box()

Get the bounding box around the points.

Returns:

OpenCV rectangle:

x, y, w, h

get_scaled_points(width_height_dimensions, rescale=False)

Scale the normalized flattened points list to a scale given by ‘shape’. The x and y values should be scaled to the width and height of the image.

Args:

shape(tuple): (height, width) rescal(boolean): flag if we should rescale or not because if we already scaled, we are not going to do it again by default.

Returns:

ndarray scaled to ‘shape’ width and height.

aam.build_shape_feature_vectors(files, get_points, flattened=False)

Gets the aam points from the files and appends them seperately to one array.

Args:

files (list): list files get_points(function): function that gets the points/landmarks given a list of files.

Returns:

list. List of feature vectors

aam.build_texture_feature_vectors(files, get_image_with_points, mean_points, triangles)

Args:

files (list): list files get_image_with_points (function): That can return the image together with the location. mean_points(AAMPoints): AAMPoints object

Returns:

list: list of feature vectors

aam.get_mean(vector)

Construct a mean from a matrix of x,y values

Args:

points(numpy array) that follows the following structure:

Returns:

mean_values (numpy array)

Example:

Input observations:

0. [[x_0_0, y_0_0], ... , [x_0_m, y_0_m]],

1. [[x_1_0, y_1_0], ... , [x_1_m, y_1_m]],

2. [[x_2_0, y_2_0], ... , [x_2_m, y_2_m]],

3. [[x_3_0, y_3_0], ... , [x_3_m, y_3_m]]

   .... .... .....

14. [[x_4_0, y_4_0], ... , [x_n_m, y_n_m]]

This vector containts the mean values of the corresponding column, like so:

0. [[x_0_0, y_0_0], ... , [x_0_k, y_0_k]],

1. [[x_1_0, y_1_0], ... , [x_1_k, y_1_k]],

2. [[x_2_0, y_2_0], ... , [x_2_k, y_2_k]],

3. [[x_3_0, y_3_0], ... , [x_3_k, y_3_k]]

   .... .... .....

14. [[x_4_0, y_4_0], ... , [x_n_k, y_n_k]]

mean. [[x_mean_0, y_mean_0], ... [x_mean_n, y_mean_n]]

aam.get_pixel_values(image, points)

deprecated

aam.get_triangles(x_vector, y_vector)

Perform triangulation between two 2d vectors

Args:

x_vector(ndarray): list of x locations y_vector(ndarray): list of y locations

aam.sample_from_triangles(src, points2d_src, points2d_dst, triangles, dst)

Get pixels from within the triangles [[p1, p2, p3]_0, .. [p1, p2, p3]_n].

Args:

src(ndarray, dtype=uint8): input image

points2d_src(ndarray, dtype=np.int32): shape array [[x, y], ... [x, y]]

points2d_dst(ndarray, dtype=np.int32): shape array [[x, y], ... [x, y]]

triangles(ndarray, ndim=3, dtype=np.int32): shape array [[p1, p2, p3]_0, .. [p1, p2, p3]_n].