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 os
import copy
import argparse
import facenet
import align.detect_face
from flask import Flask
from flask import request
import json
import uuid
import base64
import cv2
import dlib
import sqllite
from concurrent.futures import ThreadPoolExecutor
import time
import threading
import sqlite3
#图片对比
def main(image):
print("对比图片：")
emb = getEmb(image)
return imageExist(emb)
#图片检测
def test(image, flag):
# cv2读取图像
img = cv2.imread(image)
# 取灰度
img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# 人脸数rects
rects = detector(img_gray, 0) #原来0
print('人脸数', len(rects))
landmarks = np.matrix([[p.x, p.y] for p in predictor(img, rects[0]).parts()])
dic = dict()
for idx, point in enumerate(landmarks):
# 68点的坐标
pos = (point[0, 0], point[0, 1])
dic[idx] = pos
# 利用cv2.circle给每个特征点画一个圈，共68个
# cv2.circle(img, pos, 1, color=(0, 255, 0))
# # 利用cv2.putText输出1-68
# font = cv2.FONT_HERSHEY_SIMPLEX
# cv2.putText(img, str(idx + 1), pos, font, 0.8, (0, 0, 255), 1, cv2.LINE_AA)
# cv2.namedWindow("img", 2)
# cv2.imshow("img", img)
# cv2.waitKey(0)
print("38", dic[37][0], dic[37][1])
print("42", dic[41][0], dic[41][1])
print("39", dic[38][0], dic[38][1])
print("41", dic[40][0], dic[40][1])
right1 = cal_len(dic[37][0], dic[37][1], dic[41][0], dic[41][1])
right2 = cal_len(dic[38][0], dic[38][1], dic[40][0], dic[40][1])
right = (right1+right2)*3
print("44", dic[43][0], dic[43][1])
print("48", dic[47][0], dic[47][1])
print("45", dic[44][0], dic[44][1])
print("47", dic[46][0], dic[46][1])
left1 = cal_len(dic[43][0], dic[43][1], dic[47][0], dic[47][1])
left2 = cal_len(dic[44][0], dic[44][1], dic[46][0], dic[46][1])
left = (left1+left2)*3
print("62", dic[61][0], dic[61][1])
print("68", dic[67][0], dic[67][1])
print("63", dic[62][0], dic[62][1])
print("67", dic[66][0], dic[66][1])
print("64", dic[63][0], dic[63][1])
print("66", dic[65][0], dic[65][1])
mouth1 = cal_len(dic[61][0], dic[61][1], dic[67][0], dic[67][1])
mouth2 = cal_len(dic[62][0], dic[62][1], dic[66][0], dic[66][1])
mouth3 = cal_len(dic[63][0], dic[63][1], dic[65][0], dic[65][1])
mouth = mouth1+mouth2+mouth3
print("37", dic[36][0], dic[36][1])
print("46", dic[45][0], dic[45][1])
print("28", dic[27][0], dic[27][1])
right_eye = cal_len(dic[36][0], dic[36][1], dic[27][0], dic[27][1])
left_eye = cal_len(dic[45][0], dic[45][1], dic[27][0], dic[27][1])
eye_eye = cal_len(dic[45][0], dic[45][1], dic[36][0], dic[36][1])
print("49", dic[48][0], dic[48][1])
print("55", dic[54][0], dic[54][1])
mouth_w = cal_len(dic[48][0], dic[48][1], dic[54][0], dic[54][1])
print("28", dic[27][0], dic[27][1])
print("34", dic[33][0], dic[33][1])
head = cal_len(dic[27][0], dic[27][1], dic[33][0], dic[33][1])
print("right_eye", right_eye) #右眼到中间间距
print("left_eye", left_eye) #左眼到中间间距
print("eye_eye", eye_eye) #两眼间距
print("eye_len_right", right) #眨右眼
print("eye_len_left", left) #眨左眼
print("mouth_len", mouth) #张嘴
print("mouth_w", mouth_w) #微笑
print("head", head) #点头
lens = {
"right_eye": right_eye,
"left_eye": left_eye,
"eye_eye": eye_eye,
"eye_len_right": right,
"eye_len_left": left,
"mouth_len": mouth,
"mouth_w": mouth_w,
"head": head
}
return lens
# #拍照检测
# def cutTest(image):
# # cv2读取图像
# img = cv2.imread(image)
# # 取灰度
# img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# # 人脸数rects
# rects = detector(img_gray, 0)
# landmarks = np.matrix([[p.x, p.y] for p in predictor(img, rects[0]).parts()])
# dic = dict()
# for idx, point in enumerate(landmarks):
# # 68点的坐标
# pos = (point[0, 0], point[0, 1])
# dic[idx] = pos
#
# eye_max = cal_len(dic[36][0], dic[36][1], dic[45][0], dic[45][1])
# eye_min = cal_len(dic[39][0], dic[39][1], dic[42][0], dic[42][1])
#
# param1 = eye_max/eye_min
#
# eye_nose = cal_len((dic[39][0]+dic[42][0])/2, (dic[39][1]+dic[42][1])/2,dic[33][0], dic[33][1])
# nose_low = cal_len(dic[33][0], dic[33][1], dic[8][0], dic[8][1])
#
# param2 = eye_nose/nose_low
#
# side1 = cal_len(dic[0][0], dic[0][1], dic[16][0], dic[16][1])
# side2 = cal_len(dic[2][0], dic[2][1], dic[14][0], dic[14][1])
# side3 = cal_len(dic[4][0], dic[4][1], dic[12][0], dic[12][1])
#
# param3 = side1/side2
# param4 = side2/side3
#
# nose = cal_len(dic[31][0], dic[31][1], dic[35][0], dic[35][1])
# mouth = cal_len(dic[48][0], dic[48][1], dic[54][0], dic[54][1])
#
# param5 = mouth/nose
#
# param6 = eye_max/mouth
# param7 = side2/mouth
#
# nose_mouth = cal_len(dic[33][0], dic[33][1], (dic[48][0]+dic[54][0])/2, (dic[48][1]+dic[54][1])/2)
# param8 = nose_low/nose_mouth
#
# a = cal_dif(500, param1, param2, param3, param4, param5, param6, param7, param8)
# param = round(np.log10(a), 3)
# return param
#拍照检测
def cutTest(image, name, codeid):
param = '-1'
emb = getEmb(image)
# print(emb)
if emb != '-1':
param = imageExist(emb)
if param != '-1':
return param
imagels = str(list(emb[0, :]))
#print(imagels)
uu = str(uuid.uuid1())
name = name
codeid = codeid
sqllite.saveImage(uu, imagels, name, codeid)
param = uu
return param
def imageExist(emb):
flag = '-1'
sql = 'select id, image from image'
conn = sqlite3.connect('test.db', check_same_thread=False)
try:
cursor = conn.cursor()
imbs = cursor.execute(sql)
a = list()
b = list()
e = list()
for imb in imbs:
a.append(emb)
b.append(str2array(imb[1]))
e.append(imb[0])
# t = threading.Thread(target=cal_vstack, args=(a, emb, b, imb, e))
# t.start()
# t.join()
if len(b) > 0:
m = np.array(a)
if m.shape[0] != 1:
c = np.squeeze(m)
else:
c = np.array(a).reshape(1, 512)
d = np.array(b)
s = np.sqrt(np.sum(np.square(np.subtract(c, d)), axis=1))
dic = dict()
for i, core in enumerate(s):
dic[e[i]] = core
dd = sorted(dic.items(), key=lambda x:x[1], reverse=False)
for ii, kk in dd:
if kk < 0.8:
flag = ii
break
finally:
conn.close()
return flag
def cal_docaltt(index, imbs, emb, lben):
i = index + 200
if i < lben:
result = cal_docalt(imbs, emb, index, i)
if result and result != '-1':
return result
else:
result = cal_docalt(imbs, emb, index, lben)
if result and result != -1:
return result
def cal_docalt(imbs, emb, m, n):
with ThreadPoolExecutor(8) as executor:
for imb in imbs[m:n]:
future = executor.submit(cal_compare, emb, imb)
a = future.result()
if a and a.__len__() > 0:
return a
def cal_vstack(a, emb, b, imb, e):
a.append(emb)
b.append(str2array(imb[1]))
e.append(imb[0])
def cal_compare(emb, imb):
em = str2array(imb.image)
score = np.sqrt(np.sum(np.square(np.subtract(emb[0, :], em))))
#print("对比结果", score)
if score < 0.8:
#print("返回id", imb.id)
return imb.id
def getEmb(image):
minsize = 20 # minimum size of face
threshold = [0.6, 0.7, 0.7] # three steps's threshold
factor = 0.709 # scale factor
# margin = 44
margin = 0
image_size = 160
print('Creating networks and loading parameters')
img = misc.imread(os.path.expanduser(image), mode='RGB')
bounding_boxes, _ = align.detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)
# print('bounding_boxes', bounding_boxes)
# print('faces', bounding_boxes.shape[0])
img_size = np.asarray(img.shape)[0:2]
img_list = list()
if len(bounding_boxes) >= 1:
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.append(prewhitened)
images = np.stack(img_list)
feed_dict = {images_placeholder: images, phase_train_placeholder: False}
emb = sess.run(embeddings, feed_dict=feed_dict)
return emb
return '-1'
def str2array(s):
s = s.replace('[', '')
s = s.replace(']', '')
ss = s.split(',')
ls = list()
for i in ss:
ls.append(i)
k = list(map(float, ls))
na = np.array(k)
return na
# 获取一张图片中的多个人脸
def getFaces(image):
minsize = 20 # minimum size of face
threshold = [0.6, 0.7, 0.7] # three steps's threshold
factor = 0.709 # scale factor
# margin = 44
margin = 0
image_size = 160
print('Creating networks and loading parameters')
img = misc.imread(os.path.expanduser(image), mode='RGB')
bounding_boxes, _ = align.detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)
faces = bounding_boxes.shape[0]
if faces > 0:
dets = np.squeeze(bounding_boxes[:, 0:4])
img_size = np.asarray(img.shape)[0:2]
flagls = list()
for i in range(faces):
if faces == 1:
det = dets
else:
det = dets[i, :]
# print('det', det)
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)
# path = 'E:/pictests/person/'+str(uuid.uuid1())+'.jpg'
# misc.imsave(path, aligned)
img_list = list()
img_list.append(prewhitened)
images = np.stack(img_list)
feed_dict = {images_placeholder: images, phase_train_placeholder: False}
emb = sess.run(embeddings, feed_dict=feed_dict)
id = imageExistFaces(emb, flagls)
if id != '-1':
immm = sqllite.queryImageById(id)
print("emb", id, immm.name, immm.codeid)
flagls.append(id)
als = not_in_list(flagls)
if als and len(als) > 0 :
for imb in als:
print('缺勤人', imb[2], imb[3])
return '1'
def not_in_list(flagls):
str1 = 'id not in ('
for ind, s in enumerate(flagls):
if ind == len(flagls) - 1:
str1 = str1 + '"' + s + '"'
else:
str1 = str1 + '"' + s + '", '
str1 = str1 + ')'
if len(str1) > 0:
sql = 'select id, image, name, codeid from image where ' + str1
conn = sqlite3.connect('test.db', check_same_thread=False)
try:
cursor = conn.cursor()
imbs = cursor.execute(sql)
als = list()
for imb in imbs:
als.append(imb)
return als
finally:
conn.close()
def imageExistFaces(emb, flagls):
flag = '-1'
str1 = 'id not in ('
for ind, s in enumerate(flagls):
if ind == len(flagls)-1:
str1 = str1 + '"' + s + '"'
else:
str1 = str1 + '"' + s + '", '
str1 = str1 + ')'
if len(str1) > 0:
sql = 'select id, image from image where '+str1
else:
sql = 'select id, image from image'
# print('sql', sql)
conn = sqlite3.connect('test.db', check_same_thread=False)
try:
cursor = conn.cursor()
imbs = cursor.execute(sql)
a = list()
b = list()
e = list()
for imb in imbs:
a.append(emb)
b.append(str2array(imb[1]))
e.append(imb[0])
if len(b) > 0:
m = np.array(a)
if m.shape[0] != 1:
c = np.squeeze(m)
else:
c = np.array(a).reshape(1, 512)
d = np.array(b)
s = np.sqrt(np.sum(np.square(np.subtract(c, d)), axis=1))
dic = dict()
for i, core in enumerate(s):
dic[e[i]] = core
dd = sorted(dic.items(), key=lambda x:x[1], reverse=False)
for ii, kk in dd:
if kk < 0.8:
flag = ii
break
finally:
conn.close()
return flag
# 拍照检测
# def cutTest(image):
# 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')
# img = misc.imread(os.path.expanduser(image), mode='RGB')
# bounding_boxes, points = align.detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)
#
# eye1_w = points[0]
# eye2_w = points[1]
# nose_w = points[2]
# mouth1_w = points[3]
# mouth2_w = points[4]
# eye1_h = points[5]
# eye2_h = points[6]
# nose_h = points[7]
# mouth1_h = points[8]
# mouth2_h = points[9]
#
# eye_len = cal_len(eye1_w, eye1_h, eye2_w, eye2_h)
# mouth_len = cal_len(mouth1_w, mouth1_h, mouth2_w, mouth2_h)
# left_eye_nose_len = cal_len(eye1_w, eye1_h, nose_w, nose_h)
# right_eye_nose_len = cal_len(eye2_w, eye2_h, nose_w, nose_h)
# left_mouth_nose_len = cal_len(mouth1_w, mouth1_h, nose_w, nose_h)
# right_mouth_nose_len = cal_len(mouth2_w, mouth2_h, nose_w, nose_h)
# eye_nose_len = cal_len((eye1_w+eye2_w)/2, (eye1_h+eye2_h)/2, nose_w, nose_h)
# mouth_nose_len = cal_len((mouth1_w+mouth2_w)/2, (mouth1_h+mouth2_h)/2, nose_w, nose_h)
#
# print("眼睛距离", eye_len)
# print("嘴间距离", mouth_len)
# print("左眼鼻距离", left_eye_nose_len)
# print("右眼鼻距离", right_eye_nose_len)
# print("左嘴鼻距离", left_mouth_nose_len)
# print("右嘴鼻距离", right_mouth_nose_len)
#
# param1 = round(eye_len/mouth_len, 3)
# param2 = round((right_eye_nose_len+left_eye_nose_len)/(right_mouth_nose_len+left_mouth_nose_len), 3)
# param3 = round(eye_nose_len/mouth_nose_len, 3)
#
# a = cal_dif(300, param1, param2, param3)
# param = round(np.log10(a), 3)
# return param
def cal_len(x1, y1, x2, y2):
eye_em = np.array([[x1, y1], [x2, y2]])
eye_len = np.sqrt(np.sum(np.square(np.subtract(eye_em[0, :], eye_em[1, :]))))
return eye_len
def opt(image, minsize, threshold, factor, margin, image_size, img_list, image_paths):
img = misc.imread(os.path.expanduser(image), mode='RGB')
img_size = np.asarray(img.shape)[0:2]
bounding_boxes, _ = align.detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)
if len(bounding_boxes) >= 1:
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.append(prewhitened)
else:
image_paths.remove(image)
print("can't detect face, remove ", image)
def load_and_align_data(image_paths, image_size, margin):
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')
tmp_image_paths = copy.copy(image_paths)
img_list = []
with ThreadPoolExecutor(8) as executor:
for image in tmp_image_paths:
executor.submit(opt, image, minsize, threshold, factor, margin, image_size, img_list, image_paths)
# t = threading.Thread(target=opt, args=(image, minsize, threshold, factor, margin, image_size, img_list
# , image_paths))
# t.start()
# t.join()
images = np.stack(img_list)
return images
def parse_arguments(argv):
parser = argparse.ArgumentParser()
parser.add_argument('image_files', type=str, 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=1.0)
return parser.parse_args(argv)
def parse_arguments2(argv):
parser = argparse.ArgumentParser()
parser.add_argument('model', type=str,
help='Could be either a directory containing the meta_file and ckpt_file or a model protobuf (.pb) file')
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=1.0)
return parser.parse_args(argv)
app = Flask(__name__)
def resize2(filepath):
img = cv2.imread(filepath)
img2 = cv2.resize(img, (160, 160), interpolation=cv2.INTER_AREA)
cv2.imwrite(filepath, img2)
def writeImage(imPath, im):
with open(imPath, "wb") as f:
f.write(im)
#图片对比接口
@app.route("/compare", methods=["POST"])
def compare():
print("进入compare")
#获取图片
dic = json.loads(request.get_data())
image1 = dic["image1"]
# image2 = dic["image2"]
im1 = base64.b64decode(image1)
# im2 = base64.b64decode(image2)
im1path = "C:/Users/hbw/facenet/src/img/"+str(uuid.uuid1())+".png"
# im2path = "C:/Users/hbw/facenet/src/img/"+str(uuid.uuid1())+".png"
#写入并处理图片
writeImage(im1path, im1)
# writeImage(im2path, im2)
resize2(im1path)
# resize2(im2path)
#调用对比方法
# imls = list()
# imls.append(im1path)
# imls.append(im2path)
t = dict()
t['data'] = main(im1path)
#删除图片
os.remove(im1path)
# os.remove(im2path)
#返回结果
return json.dumps(t, ensure_ascii=False)
#图片检测接口
@app.route("/detect", methods=["post"])
def detect():
#获取图片
dic = json.loads(request.get_data())
image1 = dic["image1"]
im1 = base64.b64decode(image1)
im1path = "C:/Users/hbw/facenet/src/img/"+str(uuid.uuid1())+".png"
#写入图片
writeImage(im1path, im1)
# resize2(im1path)
#调用检测方法
# flag = dic["flag"]
t = dict()
t["data"] = str(test(im1path, "1"))
#删除图片
os.remove(im1path)
#返回结果
return json.dumps(t, ensure_ascii=False)
#拍照功能
@app.route("/cut", methods=["post"])
def cut():
# 获取图片
dic = json.loads(request.get_data())
image1 = dic["image1"]
name = dic["name"]
codeid = dic["codeid"]
im1 = base64.b64decode(image1)
im1path = "C:/Users/hbw/facenet/src/img/" + str(uuid.uuid1()) + ".png"
# 写入图片
writeImage(im1path, im1)
resize2(im1path)
# 调用检测方法
# imls = list()
# imls.append(im1path)
param = cutTest(im1path, name, codeid)
print("param", param)
t = dict()
t["data"] = param
# 删除图片
os.remove(im1path)
# 返回结果
return json.dumps(t, ensure_ascii=False)
@app.route("/deleteall", methods=["post"])
def deleteAll():
sqllite.removeImageAll()
t = dict()
t['data'] = '1'
return json.dumps(t, ensure_ascii=False)
@app.route('/absenttest', methods=["post"])
def absent():
# 获取图片
dic = json.loads(request.get_data())
image1 = dic["image1"]
im1 = base64.b64decode(image1)
im1path = "C:/Users/hbw/facenet/src/img/" + str(uuid.uuid1()) + ".png"
# 写入并处理图片
writeImage(im1path, im1)
t = dict()
t['data'] = getFaces(im1path)
# 删除图片
os.remove(im1path)
# 返回结果
return json.dumps(t, ensure_ascii=False)
if __name__ == '__main__':
#设置使用GPU
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.7 # 占用GPU90%的显存
config.gpu_options.allow_growth = True
session = tf.Session(config=config)
#设置模型并运行
models = list()
models.append('C:/Users/hbw/facenet/src/20180402-114759/20180402-114759.pb')
args = parse_arguments2(models)
with tf.Graph().as_default():
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.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)
# with tf.Session() as sess:
# Load the model
facenet.load_model(args.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")
#人脸检测dblib加载
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('E:/dev/facerecognize/blib/shape_predictor_68_face_landmarks.dat')
app.run(host='192.168.2.179', port=8000, debug=True, threaded=True)
人脸识别及对比2-多张人脸识别
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转载自blog.csdn.net/a13662080711/article/details/81334899
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