mlxtend version: 0.23.4

EyepadAlign

EyepadAlign(verbose=0)

Class to align/transform face images to facial landmarks, based on eye alignment.

1. A scaling factor is computed based on distance between the
left and right eye, such that the transformed face image will
have the same eye distance as a reference face image.

2. A transformation is performed based on the eyes' center point.
to align the face based on the reference eye location.

3. Finally, the transformed image is padded with zeros to match
the desired final image size.

Parameters

  • verbose : int (default=0)

    Verbose level to display the progress bar and log messages. Setting verbose=1 will print a progress bar upon calling fit_directory.

Attributes

  • target_landmarks_ : target landmarks to transform new face images to.

    Depending on the chosen fit parameters, it can be either (1) assigned to pre-fit shapes, (2) computed from a single face image (3) computed as the mean of face landmarks from all face images in a file directory of face images.

  • eye_distance_ : the distance between left and right eyes

    in the target landmarks.

  • target_height_ : the height of the transformed output image.

  • target_width_ : the width of the transformed output image.

Returns

  • self : object

Examples

For usage examples, please see https://rasbt.github.io/mlxtend/user_guide/image/eyepad_align/

Methods

fit_directory(target_img_dir, target_height, target_width, file_extension='.jpg', pre_check=True)

Calculates the average landmarks for all face images in a directory which will then be set as the target landmark set.

Arguments

  • target_img_dir : str

    Directory containing the images

  • target_height : int

    Expected image height of the images in the directory

  • target_width : int

    Expected image width of the images in the directory

    file_extension str (default='.jpg'): File extension of the image files.

    pre_check Bool (default=True): Checks that each image has the dimensions specificed via target_height and target_width on the whole directory first to identify potential issues that are recommended to be fixed before proceeding. Raises a warning for each image if dimensions differ from the ones specified and expected.

Returns

  • self : object

fit_image(target_image)

Derives facial landmarks from a target image.

Arguments

  • target_image : uint8 numpy.array, shape=[width, height, channels]

    NumPy array representation of the image data.

Returns

  • self : object

fit_values(target_landmarks, target_width, target_height)

Used for determining the eye location from pre-defined landmark arrays, eliminating the need for re-computing the average landmarks on a target image or image directory.

Arguments

  • target_landmarks : np.array, shape=(height, width)

    NumPy array containing the locations of the facial landmarks as determined by mlxtend.image.extract_face_landmarks

  • target_height : int

    image height

  • target_width : int

    image width

Returns

  • self : object

transform(img)

transforms a single face image (img) to the target landmarks based on the location of the eyes by scaling, translation and cropping (if needed):

(1) Scaling the image so that the distance of the two eyes
in the given image (img) matches the distance of the
two eyes in the target landmarks.

(2) Translation is performed based on the middle point
between the two eyes.

Arguments

  • img : np.array, shape=(height, width, channels)

    Input image to be transformed.

Returns

  • self : object

extract_face_landmarks

extract_face_landmarks(img, return_dtype=)

Function to extract face landmarks.

Note that this function requires an installation of
the Python version of the library "dlib": https://dlib.net

Parameters

  • img : array, shape = [h, w, ?]

    Numpy array of a face image or imageio.core.util.Array. E.g., img = imageio.core.util.Array(ary)

    Supported shapes are - 3D tensors with 1 or more color channels, for example, RGB: [h, w, 3] - 2D tensors without color channel, for example, Grayscale: [h, w]

    return_dtype: the return data-type of the array, default: np.int32.

Returns

  • landmarks : numpy.ndarray, shape = [68, 2]

    A numpy array, where each row contains a landmark/point x-y coordinates. Return None if no face is detected by Dlib.

Examples

For usage examples, please see https://rasbt.github.io/mlxtend/user_guide/image/extract_face_landmarks/