python 人脸识别

"""Performs face alignment and calculates L2 distance between the embeddings of images."""

# MIT License
# 
# Copyright (c) 2016 David Sandberg
# 
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# 
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# 
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from scipy import misc
import tensorflow as tf
import numpy as np
import sys
import os
import argparse
import facenet
import align.detect_face

def main():
    model = "../models/20170216-091149"
    image_files = ['compare_images/index10.png', 'compare_images/index73.png']
    image_size = 160
    margin = 44
    gpu_memory_fraction = 0.5

    images = load_and_align_data(image_files, image_size, margin, gpu_memory_fraction)
    with tf.Graph().as_default():

        with tf.Session() as sess:
      
            # Load the model
            facenet.load_model(model)

            # Get input and output tensors
            images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
            embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
            phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")

            # Run forward pass to calculate embeddings
            feed_dict = { images_placeholder: images, phase_train_placeholder:False }
            emb = sess.run(embeddings, feed_dict=feed_dict)
            
            nrof_images = len(image_files)

            print('Images:')
            for i in range(nrof_images):
                print('%1d: %s' % (i, image_files[i]))
            print('')
            
            # Print distance matrix
            print('Distance matrix')
            print('    ', end='')
            for i in range(nrof_images):
                print('    %1d     ' % i, end='')
            print('')
            for i in range(nrof_images):
                print('%1d  ' % i, end='')
                for j in range(nrof_images):
                    dist = np.sqrt(np.sum(np.square(np.subtract(emb[i,:], emb[j,:]))))
                    print('  %1.4f  ' % dist, end='')
                print('')
            
            
def load_and_align_data(image_paths, image_size, margin, gpu_memory_fraction):

    minsize = 20 # minimum size of face
    threshold = [ 0.6, 0.7, 0.7 ]  # three steps's threshold
    factor = 0.709 # scale factor
    
    print('Creating networks and loading parameters')
    with tf.Graph().as_default():
        gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction)
        sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
        with sess.as_default():
            pnet, rnet, onet = align.detect_face.create_mtcnn(sess, None)
  
    nrof_samples = len(image_paths)
    img_list = [None] * nrof_samples
    for i in range(nrof_samples):
        img = misc.imread(os.path.expanduser(image_paths[i]))
        img_size = np.asarray(img.shape)[0:2]
        bounding_boxes, _ = align.detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)
        det = np.squeeze(bounding_boxes[0,0:4])
        bb = np.zeros(4, dtype=np.int32)
        bb[0] = np.maximum(det[0]-margin/2, 0)
        bb[1] = np.maximum(det[1]-margin/2, 0)
        bb[2] = np.minimum(det[2]+margin/2, img_size[1])
        bb[3] = np.minimum(det[3]+margin/2, img_size[0])
        cropped = img[bb[1]:bb[3],bb[0]:bb[2],:]
        aligned = misc.imresize(cropped, (image_size, image_size), interp='bilinear')
        prewhitened = facenet.prewhiten(aligned)
        img_list[i] = prewhitened
    images = np.stack(img_list)
    return images

# def parse_arguments(argv):
#     parser = argparse.ArgumentParser()
#
#     parser.add_argument('model', type=str, default="./models/20170216-091149",
#         help='Could be either a directory containing the meta_file and ckpt_file or a model protobuf (.pb) file')
#     parser.add_argument('image_files', type=str, default="src/compare_images/index10.png src/compare_images/index73.png "
#         , nargs='+', help='Images to compare')
#     parser.add_argument('--image_size', type=int,
#         help='Image size (height, width) in pixels.', default=160)
#     parser.add_argument('--margin', type=int,
#         help='Margin for the crop around the bounding box (height, width) in pixels.', default=44)
#     parser.add_argument('--gpu_memory_fraction', type=float,
#         help='Upper bound on the amount of GPU memory that will be used by the process.', default=0.5)
#     return parser.parse_args(argv)

if __name__ == '__main__':
    main()

"""Validate a face recognizer on the "Labeled Faces in the Wild" dataset (http://vis-www.cs.umass.edu/lfw/).
Embeddings are calculated using the pairs from http://vis-www.cs.umass.edu/lfw/pairs.txt and the ROC curve
is calculated and plotted. Both the model metagraph and the model parameters need to exist
in the same directory, and the metagraph should have the extension '.meta'.
"""
# MIT License
#
# Copyright (c) 2016 David Sandberg
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf
import numpy as np
import argparse
import facenet
import lfw
import os
import sys
import math
from sklearn import metrics
from scipy.optimize import brentq
from scipy import interpolate
import numpy
from PIL import Image,ImageDraw
import cv2

from scipy import misc
import argparse
import align.detect_face

def detetet_face_init():
    cap = cv2.VideoCapture(0)
    print(cap.isOpened())
    classifier=cv2.CascadeClassifier("./xml/haarcascade_frontalface_alt.xml")
    count=0
    return cap,classifier,count

def detect_face_clear():
    cap.release()
    cv2.destroyAllWindows()

def load_and_align_data(image_paths, image_size, margin, gpu_memory_fraction):

    minsize = 20 # minimum size of face
    threshold = [ 0.6, 0.7, 0.7 ]  # three steps's threshold
    factor = 0.709 # scale factor
    
    print('Creating networks and loading parameters')
    with tf.Graph().as_default():
        gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction)
        sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
        with sess.as_default():
            pnet, rnet, onet = align.detect_face.create_mtcnn(sess, None)
  
    nrof_samples = len(image_paths)
    img_list = [None] * nrof_samples
    for i in range(nrof_samples):
        img = misc.imread(os.path.expanduser(image_paths[i]))
        img_size = np.asarray(img.shape)[0:2]
        bounding_boxes, _ = align.detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)
        det = np.squeeze(bounding_boxes[0,0:4])
        bb = np.zeros(4, dtype=np.int32)
        bb[0] = np.maximum(det[0]-margin/2, 0)
        bb[1] = np.maximum(det[1]-margin/2, 0)
        bb[2] = np.minimum(det[2]+margin/2, img_size[1])
        bb[3] = np.minimum(det[3]+margin/2, img_size[0])
        cropped = img[bb[1]:bb[3],bb[0]:bb[2],:]
        aligned = misc.imresize(cropped, (image_size, image_size), interp='bilinear')
        prewhitened = facenet.prewhiten(aligned)
        img_list[i] = prewhitened
    images = np.stack(img_list)
    return images

def compare_facevec(facevec1, facevec2):
    dist = np.sqrt(np.sum(np.square(np.subtract(facevec1, facevec2))))
    #print('  %1.4f  ' % dist, end='')
    return dist

def face_recognition_using_facenet():
    cap,classifier,count = detetet_face_init()
    model = "../models/20170216-091149"
    image_files = ['compare_images/index10.png', 'compare_images/index73.png']
    image_size = 160
    margin = 44
    gpu_memory_fraction = 0.5
    with tf.Graph().as_default():
        with tf.Session() as sess:
            # Load the model
            facenet.load_model(model)

            # Get input and output tensors
            images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
            embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
            phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")

            #image_size = images_placeholder.get_shape()[1]  # For some reason this doesn't work for frozen graphs
            image_size = 160
            embedding_size = embeddings.get_shape()[1]
            index = 0
            th = 0.7
            face_recognition_tag = True
            color = (0,255,0)
            exist_face_vec = []
            face_detect_vec = []
            while count > -1:
                ret,img = cap.read()
                faceRects = classifier.detectMultiScale(img, 1.2, 2, cv2.CASCADE_SCALE_IMAGE,(20,20))
                if len(faceRects)>0:
                    for faceRect in faceRects:
                            x, y, w, h = faceRect
                            #cv2.rectangle(img, (int(x), int(y)), (int(x)+int(w), int(y)+int(h)), (0,255,0), 2,0)
                            #print "save faceimg"
                            face_win = img[int(y):int(y) + int(h), int(x):int(x) + int(w)]
                            face_detect = cv2.resize(face_win,(image_size,image_size),interpolation=cv2.INTER_CUBIC)
                            #cv2.imwrite('faceimg/index' + str(index) + '.bmp', face_win)
                            # Run forward pass to calculate embeddings
                            #print('Runnning forward pass on face detect')
                            nrof_samples = 1
                            img_list = [None] * nrof_samples
                            prewhitened = facenet.prewhiten(face_detect)
                            img_list[0] = prewhitened
                            images = np.stack(img_list)
                            if index == 10:
                                feed_dict = {images_placeholder:images, phase_train_placeholder:False }
                                exist_face_vec = sess.run(embeddings, feed_dict=feed_dict)
                            elif index > 10  and index % 10 == 0:
                                feed_dict = {images_placeholder:images, phase_train_placeholder:False }
                                face_detect_vec = sess.run(embeddings, feed_dict=feed_dict) 
                                cp = compare_facevec(face_detect_vec, exist_face_vec)
                                print("index ", index, " dist ", cp)
                                if cp < th:
                                    print(True)
                                    face_recognition_tag = True
                                else:
                                    print(False)
                                    face_recognition_tag = False
                            index +=1
                            # if face_recognition_tag == True:
                            #     cv2.rectangle(img, (int(x), int(y)), (int(x)+int(w), int(y)+int(h)), (255,0,0), 2,0)
                            # else:
                            #     cv2.rectangle(img, (int(x), int(y)), (int(x)+int(w), int(y)+int(h)), (0,255,0), 2,0)
                            cv2.rectangle(img, (int(x), int(y)), (int(x)+int(w), int(y)+int(h)), (0,255,0), 2,0)
                            
                            
                cv2.imshow('video',img)
                key=cv2.waitKey(1)
                if key==ord('q'):
                    break
    detect_face_clear(cap)

if __name__ == '__main__':
    face_recognition_using_facenet()