1. face_locations(img, number_of_times_to_upsample=1, model='hog')
函数作用：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".
函数返回:return: A list of tuples of found face locations in css (top, right, bottom, left) order
2.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
3. 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
4.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)