From 2aaa918ef511c391bc4cfe72a70e972e5f17fcd0 Mon Sep 17 00:00:00 2001 From: pfzpyac3f <344492480@qq.com> Date: Sat, 17 Dec 2022 09:37:24 +0800 Subject: [PATCH] ADD file via upload --- api.py | 226 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 226 insertions(+) create mode 100644 api.py diff --git a/api.py b/api.py new file mode 100644 index 0000000..80a3f7f --- /dev/null +++ b/api.py @@ -0,0 +1,226 @@ +# -*- 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 face_encodings: 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 images: 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 images: 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, model="small"): + """ + 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) + :param model: Optional - which model to use. "large" or "small" (default) which only returns 5 points but is faster. + :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) + 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)