Python3 人脸识别 源码

Posted 2022-01-27 流星蝴蝶没有剑

Python3 人脸识别

face_recognition 源码

人脸图片进行训练，识别摄像头人脸

• 代码和人脸库在同一级
  

• 训练库中以人名命名文件夹
  

• 每个人可以多张图片，这里的名字无所谓
  

Ubuntu 20 安装人脸识别库

# 先：
sudo su root

apt-get install -y git

apt-get install -y cmake 

apt-get install -y python3-pip 

apt-get install libboost-all-dev

git clone https://github.com/davisking/dlib.git 
cd dlib 
mkdir build 
cd build 
cmake .. -DDLIB_USE_CUDA=0 -DUSE_AVX_INSTRUCTIONS=1 
cmake --build . 
cd .. 

# 可能会报错
# 第一种：
python3 setup.py install --yes USE_AVX_INSTRUCTIONS --no DLIB_USE_CUDA
# 第二种
python3 setup.py install --set USE_AVX_INSTRUCTIONS=1 --set DLIB_USE_CUDA=1


# 修改 ~/.pip/pip.conf (没有就创建一个)
[global]
index-url = https://pypi.tuna.tsinghua.edu.cn/simple


pip3 install face_recognition

pip install scikit-build

sudo apt-get install build-essential
sudo apt-get install cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev
sudo apt-get install python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev

# 上面有报错的执行
sudo add-apt-repository "deb http://security.ubuntu.com/ubuntu xenial-security main"
sudo apt update
sudo apt install libjasper1 libjasper-dev


python3 -m pip install --upgrade pip


pip3 install opencv-python

pip3 install scikit-learn



# npm
sudo apt-get install npm
npm config set registry https://registry.npm.taobao.org

人脸识别源码：

"""
$ sudo apt-get install face_recognition
$ pip3 install scikit-learn
$ pip3 install numpy
$ pip3 install opencv-contrib-python
"""

import cv2
import math
from sklearn import neighbors
import os
import os.path
import pickle
from numba import cuda
from PIL import Image, ImageDraw
import face_recognition
from face_recognition.face_recognition_cli import image_files_in_folder
import numpy as np

ALLOWED_EXTENSIONS = 'png', 'jpg', 'jpeg', 'JPG'

# cuda.select_device(1)
# @cuda.jit
# def gpu():
#     pass

# 训练(训练的目录, 训练库保存的位置, )
def train(train_dir, model_save_path=None, n_neighbors=None, knn_algo='ball_tree', verbose=False):
    X = []
    y = []
    for class_dir in os.listdir(train_dir):
        if not os.path.isdir(os.path.join(train_dir, class_dir)):
            continue
        print("开始训练用户：", class_dir, "...", end="")
        images = []
        for img_path in image_files_in_folder(os.path.join(train_dir, class_dir)):
            image = face_recognition.load_image_file(img_path)
            images.append(image)

        S = face_recognition.batch_face_locations(images, number_of_times_to_upsample=1, batch_size=len(images))
        for face_bounding_boxes in S:
            if len(face_bounding_boxes) != 1:
                if verbose:
                    print("图片中人脸太多或者不存在人脸，不适合进行训练")
            else:
                X.append(face_recognition.face_encodings(image, known_face_locations=face_bounding_boxes)[0])
                y.append(class_dir)
        print("训练结束")

    if n_neighbors is None:
        n_neighbors = int(round(math.sqrt(len(X))))
        if verbose:
            print("Chose n_neighbors automatically:", n_neighbors)

    knn_clf = neighbors.KNeighborsClassifier(n_neighbors=n_neighbors, algorithm=knn_algo, weights='distance')
    knn_clf.fit(X, y)

    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_frame, knn_clf=None, model_path=None, distance_threshold=0.5):
    if knn_clf is None and model_path is None:
        raise Exception("Must supply knn classifier either thourgh knn_clf or model_path")
    if knn_clf is None:
        with open(model_path, 'rb') as f:
            knn_clf = pickle.load(f)

    X_face_locations = face_recognition.face_locations(X_frame)
    if len(X_face_locations) != 1:
        return []
    faces_encodings = face_recognition.face_encodings(X_frame, known_face_locations=X_face_locations)
    closest_distances = knn_clf.kneighbors(faces_encodings, n_neighbors=1)
    are_matches = [closest_distances[0][i][0] <= distance_threshold for i in range(len(X_face_locations))]
    return [(pred, loc) if rec else ("", loc) for pred, loc, rec in
            zip(knn_clf.predict(faces_encodings), X_face_locations, are_matches)]
    """
    X_face_locations = face_recognition.face_locations(X_frame)
    S = face_recognition.batch_face_locations([X_frame], number_of_times_to_upsample=1, batch_size=128)
    for face_bounding_boxes in S:
        print(face_bounding_boxes)

        if len(face_bounding_boxes) != 1:
            return []
        faces_encodings = face_recognition.face_encodings(X_frame, known_face_locations=face_bounding_boxes)
        # 匹配人脸
        closest_distances = knn_clf.kneighbors(faces_encodings, n_neighbors=1)
        are_matches = [closest_distances[0][i][0] <= distance_threshold for i in range(len(X_face_locations))]
        return [(pred, loc) if rec else ("", loc) for pred, loc, rec in
                zip(knn_clf.predict(faces_encodings), X_face_locations, are_matches)]
    return []
    """

def show_prediction_labels_on_image(frame, predictions):
    pil_image = Image.fromarray(frame)
    draw = ImageDraw.Draw(pil_image)
    for name, (top, right, bottom, left) in predictions:
        # enlarge the predictions for the full sized image.
        top *= 2
        right *= 2
        bottom *= 2
        left *= 2
        # Draw a box around the face using the Pillow module
        if name == "":
            rgbFace = (0, 0, 255)
        else:
            rgbFace = (118, 238, 0)
        draw.rectangle(((left, top), (right, bottom)), outline=rgbFace)
        name = name.encode("UTF-8")
        text_width, text_height = draw.textsize(name)
        draw.rectangle(((left, bottom - text_height - 10), (right, bottom)), fill=rgbFace, outline=rgbFace)
        draw.text((left + 6, bottom - text_height - 5), name, fill=(255, 255, 255, 255))
    del draw
    opencvimage = np.array(pil_image)
    return opencvimage


def set_gpus(gpu_index):
    if type(gpu_index) == list:
        gpu_index = ','.join(str(_) for _ in gpu_index)
    if type(gpu_index) == int:
        gpu_index = str(gpu_index)
    os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
    os.environ["CUDA_VISIBLE_DEVICES"] = gpu_index


if __name__ == "__main__":
    set_gpus(0)
    train_flag = 1 
    # 1表示训练、0表示不训练【训练一次之后建议改为0，提高效率，更图片或者添加人脸后需要重新训练】
    if train_flag:
        print("Training KNN classifier...")
        classifier = train("./train", model_save_path="trained_knn_model.clf", n_neighbors=2)
        print("Training complete!")
    print('Setting cameras up...')
    
	# 摄像头读取：https://blog.csdn.net/qq_44009311/article/details/121799698
    cap = cv2.VideoCapture(0)
    while 1 > 0:
        ret, frame = cap.read()
        if ret:
            img = cv2.resize(frame, (0, 0), fx=0.5, fy=0.5)
            predictions = predict(img, model_path="trained_knn_model.clf")
            frame = show_prediction_labels_on_image(frame, predictions)
            cv2.imshow('camera', frame)
            if ord('q') == cv2.waitKey(10):
                cap.release()
                cv2.destroyAllWindows()
                exit(0)