calculate_dection_face.py

import cv2

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

def main(args):
    dection()
    
def dection():
    # 将目标图片文件夹下的图片地址append进list,传入load_and_align_data(),对图片进行切割（因为其图片参数为list）
    # 这里的位置改为test_img文件夹的绝对路径
    img_dir='/home/wind/facenet-master/src/test_img/'
    img_path_set=[]
    for file in os.listdir(img_dir):
        single_img=os.path.join(img_dir,file)
        print(single_img)
        print('loading...... :',file)
        img_path_set.append(single_img)

    images = load_and_align_data(img_path_set, 160, 44, 1.0)

    
    # 改为emb_img文件夹的绝对路径  
    emb_dir='/home/wind/facenet-master/src/emb_img'
    
    if(os.path.exists(emb_dir)==False):
        os.mkdir(emb_dir)

    count=0
    for file in os.listdir(img_dir):
        misc.imsave(os.path.join(emb_dir,file),images[count])
        count=count+1

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)
  
    tmp_image_paths=copy.copy(image_paths)
    img_list = []
    for image in tmp_image_paths:
        print(image)
        img = misc.imread(os.path.expanduser(image), mode='RGB')
        # img = misc.imread(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:
          image_paths.remove(image)
          print("can't detect face, remove ", image)
          continue
        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],:]

        # 根据cropped位置对原图resize，并对新得的aligned进行白化预处理
        aligned = misc.imresize(cropped, (image_size, image_size), interp='bilinear')
        prewhitened = facenet.prewhiten(aligned)
        img_list.append(prewhitened)
    images = np.stack(img_list)
    return images



def parse_arguments(argv):
    parser=argparse.ArgumentParser()
    parser.add_argument('--img_size',type=int,default=160)

    return parser.parse_args(argv)


if __name__=='__main__':
    main(parse_arguments(sys.argv[1:]))