face，Pool

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在使用Python进行系统管理时，特别是同时操作多个文件目录或者远程控制多台主机，并行操作可以节约大量的时间。如果操作的对象数目不大时，还可以直接使用Process类动态的生成多个进程

，十几个还好，但是如果上百个甚至更多，那手动去限制进程数量就显得特别的繁琐，此时进程池就派上用场了。 

Pool类可以提供指定数量的进程供用户调用，当有新的请求提交到Pool中时，如果池还没有满，就会创建一个新的进程来执行请求。如果池满，请求就会告知先等待，直到池中有进程结束，才会创建新的进程来执行这些请求。

import os
import face_recognition
from multiprocessing.dummy import Pool

pool = Pool(8)  # 启动进程池


def helloface(known_face_encodings, known_face_names, unknown_pic):
    """
    单个图片识别
    :param known_face_encodings: single known img's encodings
    :param known_face_names: all names, >> list
    :param unknown_pic:  single unknown img's path
    :return: face result >>name
    """
    name = "Unknown"
    unknown_image = face_recognition.load_image_file(unknown_pic)
    face_locations = face_recognition.face_locations(unknown_image)  # box size?
    face_encodings = face_recognition.face_encodings(unknown_image, face_locations)
    for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
        matches = face_recognition.compare_faces(known_face_encodings, face_encoding, tolerance=0.39)
        if True in matches:
            first_match_index = matches.index(True)
            name = known_face_names[first_match_index]
        else:
            name = "Unknown"
        return name


def faceRecog(imglist, known_face_names, unknownpath):
    """
    :param imglist: [list known img address]
    :param known_face_names: all known names
    :param unknownpath: single unknownpath
    :return:  The name of the identification And index in names
    """
　　 ## pool.map(func, iter) 
    known_face_encodings = pool.map(mutl_reg, imglist)  # 返回已知图片encodings, list
    name = "Unknown"
    first_match_index = "notMatch"
    unknown_image = face_recognition.load_image_file(unknownpath)
    face_locations = face_recognition.face_locations(unknown_image)
    face_encodings = face_recognition.face_encodings(unknown_image, face_locations)
    for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
        matches = face_recognition.compare_faces(known_face_encodings, face_encoding, tolerance=0.39)
        # print(matches, "--------------------------")
        if True in matches:
            first_match_index = matches.index(True)
            name = known_face_names[first_match_index]
            return name, first_match_index
        else:
            return name, first_match_index


def mutl_reg(imglist):
    """
    :param imglist: known img paths, >> list
    :return: known img's encodings, >> list
    """
    obama_image = face_recognition.load_image_file(imglist)
    known_face_encodings = face_recognition.face_encodings(obama_image)[0]
    return known_face_encodings


def get_all_path(path='./known/'):
    """
    获取指定路径下的文件的url， note: path下最好没有其他的目录
    :param path: 指定路径
    :return: 改路径下的地址
    """
    all_files = os.walk(path)
    files_dir = []
    for i, v, files in all_files:
        for file in files:
            files_dir.append(os.path.join(path, file))
    return files_dir


def faceRecogUnknownList(known_img_list, names, unknownpaths):
    """  """
    result = []
    for unknownpath in unknownpaths:
        name, index = faceRecog(known_img_list, names, unknownpath)
        # if index != 'notMatch':
        result.append({name: unknownpath, 'index': index})
        result.append(name)
        print('It is [%s] to identify the  [%s] through a face' % (name, unknownpath))

    return result


if __name__ == '__main__':

    # known_face_pic = face_recognition.load_image_file('./known/Comi.jpg')
    # known_face_encodings = face_recognition.face_encodings(known_face_pic)
    # name = 'Comi'
    # unknown_pic_path = './unknown/unknown1.jpg'
    # name = helloface(known_face_encodings, [name,], unknown_pic_path)
    # print(name)

    known_img_list = get_all_path('./known/')
    names = ['Obama', 'Comi', 'Bidden']
    unknownpaths = get_all_path('./unknown/')

    result = faceRecogUnknownList(known_img_list, names, unknownpaths)
    print(result)