python3.6+opencv+face_recognition+knn分类器实现人脸识别

# -*- coding: utf-8 -*-
# !/usr/bin/env python
# @Time    : 2019/1/10 15:50
# @Author  : xhh
# @Desc    : 利用knn分类器来进行人脸识别
# @File    : face_recognition_knn1.py
# @Software: PyCharm

import math
from sklearn import neighbors
import os
import os.path
import pickle
from PIL import Image, ImageDraw
import face_recognition
from face_recognition.face_recognition_cli import image_files_in_folder

ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'}


def train(train_dir, model_save_path=None, n_neighbors=None, knn_algo='ball_tree', verbose=False):
    """
    Structure:
        <train_dir>/
        ├── <person1>/
        │   ├── <somename1>.jpeg
        │   ├── <somename2>.jpeg
        │   ├── ...
        ├── <person2>/
        │   ├── <somename1>.jpeg
        │   └── <somename2>.jpeg
        └── ...
    :param train_dir: 训练集地址
    :param model_save_path: 模型所保存的地址
    :param n_neighbors: 在训练时如果没有指定权重，则自动选择权重
    :param knn_algo:
    :param verbose:
    :return: knn_clf返回训练后的模型
    """
    X = []
    y = []
    # 循环遍历训练集中的每一个人
    for class_dir in os.listdir(train_dir):
        if not os.path.isdir(os.path.join(train_dir, class_dir)):
            continue
        # 循环遍历当前训练集中的每个人
        for img_path in image_files_in_folder(os.path.join(train_dir, class_dir)):
            image = face_recognition.load_image_file(img_path)
            face_bounding_boxes = face_recognition.face_locations(image)
            if len(face_bounding_boxes) != 1:
                # 如果该训练集中没有人或者有很多人，则跳过该图像
                if verbose:
                    print("Image {} not suitable for training: {}".format(img_path,
                                                                          "Didn't find a face" if len(
                                                                              face_bounding_boxes) < 1 else "Found more than one face"))
            else:
                # 将图片中的人脸的编码加入到训练集中
                X.append(face_recognition.face_encodings(image, known_face_locations=face_bounding_boxes)[0])
                y.append(class_dir)
    # 确定KNN分类器中的权重
    if n_neighbors is None:
        n_neighbors = int(round(math.sqrt(len(X))))
        if verbose:
            print("Chose n_neighbors automatically:", n_neighbors)
    # 建立并训练KNN训练集
    knn_clf = neighbors.KNeighborsClassifier(n_neighbors=n_neighbors, algorithm=knn_algo, weights='distance')
    knn_clf.fit(X, y)
    # 保存KNN分类器
    if model_save_path is not None:
        with open(model_save_path, 'wb') as f:
            pickle.dump(knn_clf, f)
    return knn_clf


def predict(X_img_path, knn_clf=None, model_path=None, distance_threshold=0.6):
    """
    :param X_img_path: 测试集的图片地址
    :param knn_clf: 训练好的模型
    :param model_path: 模型地址
    :param distance_threshold: 给出当前测试图片中的人脸向量与模型中的距离
    :return:
    """
    if not os.path.isfile(X_img_path) or os.path.splitext(X_img_path)[1][1:] not in ALLOWED_EXTENSIONS:
        raise Exception("Invalid image path: {}".format(X_img_path))
    if knn_clf is None and model_path is None:
        raise Exception("Must supply knn classifier either thourgh knn_clf or model_path")

    # 加载KNN模型
    if knn_clf is None:
        with open(model_path, 'rb') as f:
            knn_clf = pickle.load(f)

    # 加载图片文件夹以及人脸
    X_img = face_recognition.load_image_file(X_img_path)
    X_face_locations = face_recognition.face_locations(X_img)

    # 如过图片中没有人脸，则返回空的结果集
    if len(X_face_locations) == 0:
        return []
    # 找出测试集中的人脸编码
    faces_encodings = face_recognition.face_encodings(X_img, known_face_locations=X_face_locations)
    # 利用KNN模型找出测试集中最匹配的人脸
    closest_distances = knn_clf.kneighbors(faces_encodings, n_neighbors=1)
    are_matches = []
    for i in range(len(X_face_locations)):
        are_matches.append(closest_distances[0][i][0] <= distance_threshold)
    # 预测类并删除不在阈值范围内的分类
    return [(pred, loc) if rec else ("unknown", loc) for pred, loc, rec in
            zip(knn_clf.predict(faces_encodings), X_face_locations, are_matches)]


def show_prediction_labels_on_image(img_path, predictions):
    """
    在图片给出标签并展示人脸
    :param img_path: path to image to be recognized
    :param predictions: results of the predict function
    :return:
    """
    pil_image = Image.open(img_path).convert("RGB")
    draw = ImageDraw.Draw(pil_image)
    for name, (top, right, bottom, left) in predictions:
        # 将人脸框出来利用pillow进行标注
        draw.rectangle(((left, top), (right, bottom)), outline=(0, 0, 255))
        name = name.encode("UTF-8")
        # 在人脸下面进行标配注
        text_width, text_height = draw.textsize(name)
        draw.rectangle(((left, bottom - text_height - 10), (right, bottom)), fill=(0, 0, 255), outline=(0, 0, 255))
        draw.text((left + 6, bottom - text_height - 5), name, fill=(255, 255, 255, 255))
    # 根据文档删除标注
    del draw
    # 显示图片结果
    pil_image.show()


if __name__ == "__main__":
    # 1:训练KNN分类器，并保存
    # Once the model is trained and saved, you can skip this step next time.
    # print("Training KNN classifier...")
    # classifier = train("knn_examples/train1", model_save_path="model/trained_knn_model.clf", n_neighbors=2)
    # print("Training complete!")

    # STEP 2: 利用分类器，来预测该图片是否为已知的
    for image_file in os.listdir("knn_examples/test"):
        full_file_path = os.path.join("knn_examples/test", image_file)
        print("Looking for faces in {}".format(image_file))
        # Find all people in the image using a trained classifier model
        # Note: You can pass in either a classifier file name or a classifier model instance
        predictions = predict(full_file_path, model_path="model/trained_knn_model.clf")
        # 在控制台打印结果
        for name, (top, right, bottom, left) in predictions:
            print("- Found {} at ({}, {})".format(name, left, top))
        # 在结果集中进行显示
        show_prediction_labels_on_image(os.path.join("knn_examples/test", image_file), predictions)