OpenCv-dlib人臉識别

概況：

使用OpenCv,dlib人臉識别庫進行人臉實時識别

程式說明：

get_face.py：對人臉進行拍照，并将人臉圖檔儲存。

save_csv.py：讀取儲存的人臉圖檔，提取人臉的128D特征值存入csv檔案。

face_detect.py：打開攝像頭進行人臉的實時識别。

程式示例：

get_face.py

import cv2

# 打開攝像頭，0代表内置攝像頭，1代表外置攝像頭
camera = cv2.VideoCapture(0)

while True:
    ret, frame = camera.read()

    cv2.imshow('', frame)

    # 按q鍵拍照儲存圖檔并退出
    if cv2.waitKey(1) == ord('q'):
        cv2.imwrite('人臉.png', frame)
        break

# 釋放攝像頭資源
camera.release()
cv2.destroyAllWindows()
           

save_csv.py

import csv
import dlib
from skimage import io
import cv2

# Dlib 正向人臉檢測器
detector = dlib.get_frontal_face_detector()

# Dlib 人臉預測器
predictor = dlib.shape_predictor('data/data_dlib/shape_predictor_68_face_landmarks.dat')

# Dlib 人臉識别模型
facerec = dlib.face_recognition_model_v1('data/data_dlib/dlib_face_recognition_resnet_model_v1.dat')


# 傳回單張圖像的 128D 特征
def feature(img):
    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    faces = detector(img_gray, 1)
    if len(faces) != 0:
        shape = predictor(img_gray, faces[0])
        descriptor = facerec.compute_face_descriptor(img_gray, shape)
    else:
        descriptor = 0
    return descriptor


# 将照片特征提取出來, 寫入 CSV
def write_csv(face_path, csv_path):
    image = io.imread(face_path)
    feature_128d = feature(image)
    with open('data.csv', 'w') as csvfile:
        writer = csv.writer(csvfile)
        writer.writerow(feature_128d)


if __name__ == '__main__':
    write_csv('人臉.png', 'data.csv')
           

face_detect.py

import cv2
import dlib
import pandas as pd
import numpy as np

# Dlib 正向人臉檢測器
detector = dlib.get_frontal_face_detector()

# Dlib 人臉預測器
predictor = dlib.shape_predictor('data/data_dlib/shape_predictor_68_face_landmarks.dat')

# Dlib 人臉識别模型
facerec = dlib.face_recognition_model_v1('data/data_dlib/dlib_face_recognition_resnet_model_v1.dat')


# 計算兩個128D向量間的歐式距離
def distance(feature_1, feature_2):
    feature_1 = np.array(feature_1)
    feature_2 = np.array(feature_2)
    dist = np.linalg.norm(feature_1 - feature_2)
    if dist > 0.4:
        return False
    else:
        return True


# 處理存放所有人臉特征的 csv
csv_rd = pd.read_csv('data.csv', header=None)
# 用來存放所有錄入人臉特征的數組
known_arr = []
# 讀取已知人臉資料
for i in range(csv_rd.shape[0]):
    someone_arr = []
    for j in range(0, len(csv_rd.ix[i, :])):
        someone_arr.append(csv_rd.ix[i, :][j])
    known_arr.append(someone_arr)

camera = cv2.VideoCapture(0)

while True:
    ret, frame = camera.read()

    # 取灰階
    img_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    # 人臉數 faces
    faces = detector(img_gray, 0)
    # 存儲目前攝像頭中捕獲到的所有人臉的名字
    namelist = []

    # 按下 q 鍵退出
    if cv2.waitKey(1) == ord('q'):
        break
    else:
        # 檢測到人臉
        if len(faces) != 0:
            feature_arr = []
            # 擷取目前捕獲到的圖像的所有人臉的特征，存儲到 feature_arr
            for i in range(len(faces)):
                shape = predictor(img_gray, faces[i])
                feature_arr.append(facerec.compute_face_descriptor(img_gray, shape))
            # 周遊捕獲到的圖像中所有的人臉
            for k in range(len(faces)):
                # 先預設所有人不認識，是 unknown
                namelist.append('unknown')
                # 對于某張人臉，周遊所有存儲的人臉特征
                for i in range(len(known_arr)):
                    # 将某張人臉與存儲的所有人臉資料進行比對
                    compare = distance(feature_arr[k], known_arr[i])
                    # 找到了相似臉
                    if compare == True:
                        if i == 0:
                            namelist[k] = 'wei'
                # 繪制矩形框
                for kk, d in enumerate(faces):
                    cv2.rectangle(frame, (d.left(), d.top()), (d.right(), d.bottom()), (0, 255, 0), 2)
            # 在人臉框下面寫人臉名字
            for i in range(len(faces)):
                cv2.putText(frame, namelist[i], (faces[i].left(), faces[i].top()), 0, 1.5, (0, 255, 0), 2)
    cv2.imshow('', frame)
# 釋放攝像頭
camera.release()
# 删除建立的視窗
cv2.destroyAllWindows()