76 lines
2.2 KiB
Python
76 lines
2.2 KiB
Python
# Sebastian Raschka 2014-2020
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# contributor: Vahid Mirjalili
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# mlxtend Machine Learning Library Extensions
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#
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# A counter class for printing the progress of an iterator.
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# Author: Sebastian Raschka <sebastianraschka.com>
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#
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# License: BSD 3 clause
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import dlib
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import numpy as np
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import os
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import warnings
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from .utils import check_exists, download_url, extract_file
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predictor_path = '~/mlxtend_data/shape_predictor_68_face_landmarks.dat'
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predictor_url = ("http://dlib.net/files/"
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"shape_predictor_68_face_landmarks.dat.bz2")
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if not check_exists(predictor_path):
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download_url(predictor_url, save_path='~/mlxtend_data/')
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extract_file('~/mlxtend_data/shape_predictor_68_face_landmarks.dat.bz2')
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detector = dlib.get_frontal_face_detector()
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predictor = dlib.shape_predictor(os.path.expanduser(predictor_path))
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def extract_face_landmarks(img, return_dtype=np.int32):
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"""Function to extract face landmarks.
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Note that this function requires an installation of
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the Python version of the library "dlib": http://dlib.net
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Parameters
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----------
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img : array, shape = [h, w, ?]
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Numpy array of a face image or
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imageio.core.util.Array. E.g.,
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img = imageio.core.util.Array(ary)
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Supported shapes are
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- 3D tensors with 1
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or more color channels, for example,
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RGB: [h, w, 3]
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- 2D tensors without color channel, for example,
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Grayscale: [h, w]
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return_dtype: the return data-type of the array,
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default: np.int32.
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Returns
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----------
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landmarks : numpy.ndarray, shape = [68, 2]
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A numpy array, where each row contains a landmark/point x-y coordinates.
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Return None if no face is detected by Dlib.
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Examples
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----------
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For usage examples, please see
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http://rasbt.github.io/mlxtend/user_guide/image/extract_face_landmarks/
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"""
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faces = detector(img, 1) # detecting faces
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if not faces:
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warnings.warn('No face detected.')
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return None
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shape = predictor(img, faces[0])
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landmarks = np.zeros(shape=(68, 2))
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for i in range(68):
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p = shape.part(i)
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landmarks[i, :] = np.array([p.x, p.y])
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return landmarks.astype(return_dtype)
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