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@ageitgey @JKCooper2 @Sukrit-Mehta @Napat @zxu7 @Vermeille
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# -*- coding: utf-8 -*-
import PIL.Image
import dlib
import numpy as np
from PIL import ImageFile
try:
import face_recognition_models
except Exception:
print("Please install `face_recognition_models` with this command before using `face_recognition`:\n")
print("pip install git+https://github.com/ageitgey/face_recognition_models")
quit()
ImageFile.LOAD_TRUNCATED_IMAGES = True
face_detector = dlib.get_frontal_face_detector()
predictor_68_point_model = face_recognition_models.pose_predictor_model_location()
pose_predictor_68_point = dlib.shape_predictor(predictor_68_point_model)
predictor_5_point_model = face_recognition_models.pose_predictor_five_point_model_location()
pose_predictor_5_point = dlib.shape_predictor(predictor_5_point_model)
cnn_face_detection_model = face_recognition_models.cnn_face_detector_model_location()
cnn_face_detector = dlib.cnn_face_detection_model_v1(cnn_face_detection_model)
face_recognition_model = face_recognition_models.face_recognition_model_location()
face_encoder = dlib.face_recognition_model_v1(face_recognition_model)
def _rect_to_css(rect):
"""
Convert a dlib 'rect' object to a plain tuple in (top, right, bottom, left) order
:param rect: a dlib 'rect' object
:return: a plain tuple representation of the rect in (top, right, bottom, left) order
"""
return rect.top(), rect.right(), rect.bottom(), rect.left()
def _css_to_rect(css):
"""
Convert a tuple in (top, right, bottom, left) order to a dlib `rect` object
:param css: plain tuple representation of the rect in (top, right, bottom, left) order
:return: a dlib `rect` object
"""
return dlib.rectangle(css[3], css[0], css[1], css[2])
def _trim_css_to_bounds(css, image_shape):
"""
Make sure a tuple in (top, right, bottom, left) order is within the bounds of the image.
:param css: plain tuple representation of the rect in (top, right, bottom, left) order
:param image_shape: numpy shape of the image array
:return: a trimmed plain tuple representation of the rect in (top, right, bottom, left) order
"""
return max(css[0], 0), min(css[1], image_shape[1]), min(css[2], image_shape[0]), max(css[3], 0)
def face_distance(face_encodings, face_to_compare):
"""
Given a list of face encodings, compare them to a known face encoding and get a euclidean distance
for each comparison face. The distance tells you how similar the faces are.
:param faces: List of face encodings to compare
:param face_to_compare: A face encoding to compare against
:return: A numpy ndarray with the distance for each face in the same order as the 'faces' array
"""
if len(face_encodings) == 0:
return np.empty((0))
return np.linalg.norm(face_encodings - face_to_compare, axis=1)
def load_image_file(file, mode='RGB'):
"""
Loads an image file (.jpg, .png, etc) into a numpy array
:param file: image file name or file object to load
:param mode: format to convert the image to. Only 'RGB' (8-bit RGB, 3 channels) and 'L' (black and white) are supported.
:return: image contents as numpy array
"""
im = PIL.Image.open(file)
if mode:
im = im.convert(mode)
return np.array(im)
def _raw_face_locations(img, number_of_times_to_upsample=1, model="hog"):
"""
Returns an array of bounding boxes of human faces in a image
:param img: An image (as a numpy array)
:param number_of_times_to_upsample: How many times to upsample the image looking for faces. Higher numbers find smaller faces.
:param model: Which face detection model to use. "hog" is less accurate but faster on CPUs. "cnn" is a more accurate
deep-learning model which is GPU/CUDA accelerated (if available). The default is "hog".
:return: A list of dlib 'rect' objects of found face locations
"""
if model == "cnn":
return cnn_face_detector(img, number_of_times_to_upsample)
else:
return face_detector(img, number_of_times_to_upsample)
def face_locations(img, number_of_times_to_upsample=1, model="hog"):
"""
Returns an array of bounding boxes of human faces in a image
:param img: An image (as a numpy array)
:param number_of_times_to_upsample: How many times to upsample the image looking for faces. Higher numbers find smaller faces.
:param model: Which face detection model to use. "hog" is less accurate but faster on CPUs. "cnn" is a more accurate
deep-learning model which is GPU/CUDA accelerated (if available). The default is "hog".
:return: A list of tuples of found face locations in css (top, right, bottom, left) order
"""
if model == "cnn":
return [_trim_css_to_bounds(_rect_to_css(face.rect), img.shape) for face in _raw_face_locations(img, number_of_times_to_upsample, "cnn")]
else:
return [_trim_css_to_bounds(_rect_to_css(face), img.shape) for face in _raw_face_locations(img, number_of_times_to_upsample, model)]
def _raw_face_locations_batched(images, number_of_times_to_upsample=1, batch_size=128):
"""
Returns an 2d array of dlib rects of human faces in a image using the cnn face detector
:param img: A list of images (each as a numpy array)
:param number_of_times_to_upsample: How many times to upsample the image looking for faces. Higher numbers find smaller faces.
:return: A list of dlib 'rect' objects of found face locations
"""
return cnn_face_detector(images, number_of_times_to_upsample, batch_size=batch_size)
def batch_face_locations(images, number_of_times_to_upsample=1, batch_size=128):
"""
Returns an 2d array of bounding boxes of human faces in a image using the cnn face detector
If you are using a GPU, this can give you much faster results since the GPU
can process batches of images at once. If you aren't using a GPU, you don't need this function.
:param img: A list of images (each as a numpy array)
:param number_of_times_to_upsample: How many times to upsample the image looking for faces. Higher numbers find smaller faces.
:param batch_size: How many images to include in each GPU processing batch.
:return: A list of tuples of found face locations in css (top, right, bottom, left) order
"""
def convert_cnn_detections_to_css(detections):
return [_trim_css_to_bounds(_rect_to_css(face.rect), images[0].shape) for face in detections]
raw_detections_batched = _raw_face_locations_batched(images, number_of_times_to_upsample, batch_size)
return list(map(convert_cnn_detections_to_css, raw_detections_batched))
def _raw_face_landmarks(face_image, face_locations=None, model="large"):
if face_locations is None:
face_locations = _raw_face_locations(face_image)
else:
face_locations = [_css_to_rect(face_location) for face_location in face_locations]
pose_predictor = pose_predictor_68_point
if model == "small":
pose_predictor = pose_predictor_5_point
return [pose_predictor(face_image, face_location) for face_location in face_locations]
def face_landmarks(face_image, face_locations=None, model="large"):
"""
Given an image, returns a dict of face feature locations (eyes, nose, etc) for each face in the image
:param face_image: image to search
:param face_locations: Optionally provide a list of face locations to check.
:param model: Optional - which model to use. "large" (default) or "small" which only returns 5 points but is faster.
:return: A list of dicts of face feature locations (eyes, nose, etc)
"""
landmarks = _raw_face_landmarks(face_image, face_locations, model)
landmarks_as_tuples = [[(p.x, p.y) for p in landmark.parts()] for landmark in landmarks]
# For a definition of each point index, see https://cdn-images-1.medium.com/max/1600/1*AbEg31EgkbXSQehuNJBlWg.png
if model == 'large':
return [{
"chin": points[0:17],
"left_eyebrow": points[17:22],
"right_eyebrow": points[22:27],
"nose_bridge": points[27:31],
"nose_tip": points[31:36],
"left_eye": points[36:42],
"right_eye": points[42:48],
"top_lip": points[48:55] + [points[64]] + [points[63]] + [points[62]] + [points[61]] + [points[60]],
"bottom_lip": points[54:60] + [points[48]] + [points[60]] + [points[67]] + [points[66]] + [points[65]] + [points[64]]
} for points in landmarks_as_tuples]
elif model == 'small':
return [{
"nose_tip": [points[4]],
"left_eye": points[2:4],
"right_eye": points[0:2],
} for points in landmarks_as_tuples]
else:
raise ValueError("Invalid landmarks model type. Supported models are ['small', 'large'].")
def face_encodings(face_image, known_face_locations=None, num_jitters=1):
"""
Given an image, return the 128-dimension face encoding for each face in the image.
:param face_image: The image that contains one or more faces
:param known_face_locations: Optional - the bounding boxes of each face if you already know them.
:param num_jitters: How many times to re-sample the face when calculating encoding. Higher is more accurate, but slower (i.e. 100 is 100x slower)
:return: A list of 128-dimensional face encodings (one for each face in the image)
"""
raw_landmarks = _raw_face_landmarks(face_image, known_face_locations, model="small")
return [np.array(face_encoder.compute_face_descriptor(face_image, raw_landmark_set, num_jitters)) for raw_landmark_set in raw_landmarks]
def compare_faces(known_face_encodings, face_encoding_to_check, tolerance=0.6):
"""
Compare a list of face encodings against a candidate encoding to see if they match.
:param known_face_encodings: A list of known face encodings
:param face_encoding_to_check: A single face encoding to compare against the list
:param tolerance: How much distance between faces to consider it a match. Lower is more strict. 0.6 is typical best performance.
:return: A list of True/False values indicating which known_face_encodings match the face encoding to check
"""
return list(face_distance(known_face_encodings, face_encoding_to_check) <= tolerance)
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