Face Recognition in Python

使用 OpenCv 將影片導入影片，而影片是由很多張照片構成，搭配人臉辨識模組使用就可以在一段影片中找以存放在資料庫的人臉。

主要以此兩篇文章作為學習目標，並做出一定程度的修改。

Face Recognition 人臉辨識 Python 教學. 人臉辨識在 Computer Vision… | by 李謦伊 | 謦伊的閱讀筆記 | Medium
使用深度學習進行人臉辨識: Triplet loss, Large margin loss(ArcFace) | by 張銘 | Taiwan AI Academy | Medium

主要區別:

結合兩者，添加相關知識、資料，並做出更詳細的註解(docstring and comment)
使用 Tkinter 做 GUI
優化檔案結構、模組化
除了辨識圖片外，也可以辨識影片中的人臉

Demo: Face Recognition in Python - YouTube

Tree Structure

.
├── Python_Final_109403019_Face_Reconition.pptx # 15頁簡報
├── README.md   # 專案完整README文件
├── assets      # 靜態資源
│   ├── README  # README會用到的圖片
│   ├── images  # 專案運行可選擇使用的圖片
│   └── videos  # 專案運行可選擇使用的影片
├── database    # 資料庫
├── model       # 因檔案過大，若要執行程式碼需下載，請參考下方章節 -> 下載辨識模型
├── requirements.txt   # 環境安裝所需模組
├── src                # 專案程式碼
│   ├── __init__.py
│   ├── database.py
│   ├── face_alignment.py
│   ├── face_comparison.py
│   ├── face_detection.py
│   ├── face_recognition.py
│   ├── feature_extraction.py
│   ├── gui.py
│   ├── main.py        # 主程式
│   └── utils.py
└── tree.txt       # 此資料夾內之檔案樹狀結構

環境及模組

Python: 3.9
IDE: Pycharm
GUI: Tkinter
Face detection package: RetinaFace
Computer Vision Library: OpenCV
Database: sqlite3
Package Management: pipenv

環境安裝

由於部分套件的依賴套件較為老舊，所以不是所有平台都支援，可能需要使用 Conda 或 Venv 等虛擬環境。目前 Windows11 沒問題，不過 M2 Macbook Pro 嘗試起來好像不支援。

$ pip install -r requirements.txt

或使用 pipenv

安裝 pipenv
```
$ pip install pipenv
```
在專案目錄中創建一個新的虛擬環境，並使用 Python 3.9

本機系統中必須有 Python 3.9，pipenv 才能找到並使用
```
$ pipenv shell --python 3.9
```
這會創建一個新的虛擬環境，並且將終端機/命令行界面設置到這個虛擬環境中。
用 pipenv 安裝 requirements.txt 文件中的所有套件
```
pipenv install -r requirements.txt
```

下載辨識模型

因檔案較大，請自行下載並放入 ./model 資料夾中~也可以選擇其他模型

arcface_r100_v1

Face Verification? Face Recognition?

Computer Vision 是一門研究機器如何「看」的科學

人臉驗證與人臉辨識的差別又為何呢?

Face Verification 人臉驗證

確定一張臉是否為某人
1 : 1 matching problem
應用

Face Recognition 人臉辨識

從擁有 k 個人物的 DataBase 確定一張臉的身分
1 : k
應用

Face Recognition 主要三部分

人臉偵測 Face Detection
人臉對齊 Face Alignment
特徵表徵 Feature Representation

五大流程

Face Detection
Face Alignment
Feature extraction
Create Database
Face Recognition

Step 1. Face Detection

偵測人臉並取得座標值

使用 RetinaFace 進行人臉辨識

也可用 MTCNN，不過 RetinaFace 的辨識表現略為優異。
參考 → RetinaFace: Single-stage Dense Face Localisation in the Wild

CODE

import cv2
from retinaface import RetinaFace

# init with normal accuracy option
detector = RetinaFace(quality='normal')

# 讀取圖檔，因為 opencv 預設是 bgr 因此要轉成 rgb
img_path = 'database/Elon Musk/elon_musk_1.jpg'
img_bgr = cv2.imread(img_path, cv2.IMREAD_COLOR)
img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)

# 取得人臉及特徵的座標值 landmarks
detections = detector.predict(img_rgb)
print(detections)
print(len(detections))

# 將取得的座標值畫上記號及框框
img_result = detector.draw(img_rgb, detections)

# 轉回 cv2 可讀的 gbr
img = cv2.cvtColor(img_result, cv2.COLOR_RGB2BGR)

# show using cv2
cv2.imshow('img', img)
if cv2.waitKey(0) == ord('q'):  # press q to quit
    print('exit')

cv2.destroyWindow('img')

output

[{'x1': 238, 'y1': 107, 'x2': 396, 'y2': 311, 'left_eye': (285, 191), 'right_eye': (358, 191),
  'nose': (326, 224), 'left_lip': (295, 265), 'right_lip': (352, 265)}]

模組化以方便後續調用

# in src/face_detection.py

def face_detect(img_path, detector):
    img_bgr = cv2.imread(img_path, cv2.IMREAD_COLOR)
    img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
    detections = detector.predict(img_rgb)

    return img_rgb, detections


# 這個是辨識影片為求方便會用到的
def face_detect_bgr(img_bgr, detector):
    img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
    detections = detector.predict(img_rgb)

    return img_rgb, detections

若出現錯誤可能是因為無法導入 shapely.geometry 模塊，因此需下載 Shapely.package 再執行以下指令導入

pip install <your Shapely package path>

Step 2. Face Alignment

將人臉對齊，也就是將傾斜的人臉轉至端正的角度。

要將人臉對齊就得先定義對齊的座標 (標準臉的座標)。參考 → Inference Demo for ArcFace models

接著用 skimage 套件 transform.SimilarityTransform() 得到要變換的矩陣，利用仿射變換進行對齊。參考 → SKimage - transform.SimilarityTransform 相似變換及其人臉對齊的應用

CODE

import numpy as np
import cv2
from retinaface import RetinaFace
from skimage import transform as trans

detector = RetinaFace(quality='normal')  # init with normal accuracy option
img_path = 'database/Elon Musk/elon_musk_1.jpg'
img_rgb, detections = face_detect(img_path)
img_result = detector.draw(img_rgb, detections)
img = cv2.cvtColor(img_result, cv2.COLOR_RGB2BGR)

# 標準臉的 landmark points。許多網站都以此二維陣列作為標準臉關鍵點
src = np.array([
    [30.2946, 51.6963],
    [65.5318, 51.5014],
    [48.0252, 71.7366],
    [33.5493, 92.3655],
    [62.7299, 92.2041]], dtype=np.float32)

# 取得臉部特徵點座標 face_landmark points
face_landmarks = []
for i, face_info in enumerate(detections):
    face_landmarks = [face_info['left_eye'], face_info['right_eye'], face_info['nose'], face_info['left_lip'],
                      face_info['right_lip']]

# 將取得的 face_landmarks 作轉置 => 跟 src 矩陣一樣
dst = np.array(face_landmarks, dtype=np.float32).reshape(5, 2)

# 把 face_landmarks 跟 標準臉的 landmark points對齊
tform = trans.SimilarityTransform()  # 轉換矩陣 transformation matrix
tform.estimate(dst, src)  # 從一組對應點估計變換矩陣。 return True, if model estimation succeeds.
M = tform.params[0:2, :]  # 要用的轉換矩陣

# 仿射變換 affine transformation (原點變動，圖形及比例不變)
# cv2.warpAffine(輸入圖檔, 轉換矩陣, 輸出圖像大小, 邊界填充值)
aligned_rgb = cv2.warpAffine(img_rgb, M, (112, 112), borderValue=0)
aligned_bgr = cv2.cvtColor(aligned_rgb, cv2.COLOR_RGB2BGR)  # 轉回 bgr 才能用 opencv show 出來

# show using cv2
cv2.imshow('img', img)
cv2.imshow('aligned_bgr', aligned_bgr)
if cv2.waitKey(0) == ord('q'):  # press q to quit
    print('exit')

cv2.destroyWindow('aligned')
cv2.destroyWindow('img')

對齊 landmark points 後的 Elon Musk

模組化以方便後續調用

# in src/face_alignment.py

import cv2
import numpy as np
from skimage import transform as trans


def face_align(img_rgb, landmarks):
    src = np.array([
        [30.2946, 51.6963],
        [65.5318, 51.5014],
        [48.0252, 71.7366],
        [33.5493, 92.3655],
        [62.7299, 92.2041]], dtype=np.float32)

    dst = np.array(landmarks, dtype=np.float32).reshape(5, 2)

    tform = trans.SimilarityTransform()
    tform.estimate(dst, src)

    M = tform.params[0:2, :]

    aligned = cv2.warpAffine(img_rgb, M, (112, 112), borderValue=0)

    return aligned

Step 3. Feature extraction

提取人臉特徵 (landmark points)

使用 onnx ArcFace model 進行提取。以下兩種都可用

InsightFace-REST 模型 arcface_r100_v1 → 下載
onnx 官方模型 → 下載

這邊選擇前者，因為若使用 onnx 官方模型需要進行更新，而更新過程非常耗時(我自己的環境相差快五六分鐘)，且更新後模型準確度較差。參考 → onnx/models#156

需進行 特徵標準化 Features Normalization

為甚麼要特徵標準化 ? Ans : 提升預測準確度。更詳細前往 → link

CODE

import onnxruntime as ort
from sklearn.preprocessing import normalize
import numpy as np
import cv2
from retinaface import RetinaFace
from skimage import transform as trans

detector = RetinaFace(quality='normal')  # init with normal accuracy option
img_path = 'database/Elon Musk/elon_musk_1.jpg'
img_rgb, detections = face_detect(img_path)

onnx_path = 'model/arcface_r100_v1.onnx'
EP_list = ['CPUExecutionProvider']

# Create inference session
sess = ort.InferenceSession(onnx_path, providers=EP_list)

# 取得臉部位置 positions 及特徵點座標 landmark points
face_positions = []
face_landmarks = []
for i, face_info in enumerate(detections):
    face_positions = [face_info['x1'], face_info['y1'], face_info['x2'], face_info['y2']]
    face_landmarks = [face_info['left_eye'], face_info['right_eye'], face_info['nose'], face_info['left_lip'],
                      face_info['right_lip']]

# 取得對齊後的圖片並作轉置
aligned = face_align(img_rgb, face_landmarks)
t_aligned = np.transpose(aligned, (2, 0, 1))

# 將轉置後的人臉轉換 dtype 為 float32，並擴充矩陣維度，因為後續函式需要二維矩陣
inputs = t_aligned.astype(np.float32)
input_blob = np.expand_dims(inputs, axis=0)

# get the outputs metadata and inputs metadata
first_input_name = sess.get_inputs()[0].name
first_output_name = sess.get_outputs()[0].name

# inference run using image_data as the input to the model
# pass a tuple rather than a single numpy ndarray.
prediction = sess.run([first_output_name], {first_input_name: input_blob})[0]

# 進行正規化並且轉成一維陣列
final_embedding = normalize(prediction).flatten()

模組化以方便後續調用

# in src/feature_extraction.py

import numpy as np
from face_alignment import face_align
from sklearn.preprocessing import normalize


def feature_extract(img_rgb, detections, sess):
    positions = []
    landmarks = []
    embeddings = np.zeros((len(detections), 512))
    for i, face_info in enumerate(detections):
        face_position = [face_info['x1'], face_info['y1'], face_info['x2'], face_info['y2']]
        face_landmarks = [face_info['left_eye'], face_info['right_eye'],
                          face_info['nose'], face_info['left_lip'], face_info['right_lip']]

        positions.append(face_position)
        landmarks.append(face_landmarks)

        aligned = face_align(img_rgb, face_landmarks)
        t_aligned = np.transpose(aligned, (2, 0, 1))

        inputs = t_aligned.astype(np.float32)
        input_blob = np.expand_dims(inputs, axis=0)

        first_input_name = sess.get_inputs()[0].name
        first_output_name = sess.get_outputs()[0].name

        prediction = sess.run([first_output_name], {first_input_name: input_blob})[0]
        final_embedding = normalize(prediction).flatten()

        embeddings[i] = final_embedding

    return positions, landmarks, embeddings

補充 : Deep Learning Training vs. Inference - Official NVDIA Blog

After training is completed, the networks are deployed into the field for “inference” — classifying data to “infer” a result

Step 4. Create Database

創建資料庫並放入照片以供我們後續進行比對

使用 sqlite3 及管理工具 DB Browser for sqlite 參考 → SQLite-Python、DB Browser 簡單介紹

sqlite3 中無法直接用 blob 數據存取 numpy 數組因此需要 adapter 及 converter 參考 → Python insert numpy array into sqlite3 database - Stack Overflow

CODE

import sqlite3
import io
import os
import numpy as np


def adapt_array(arr):
    out = io.BytesIO()
    np.save(out, arr)
    out.seek(0)
    return sqlite3.Binary(out.read())


def convert_array(text):
    out = io.BytesIO(text)
    out.seek(0)
    return np.load(out)


# 將 file_path 底下的資料全部存進 file_data (list)
def load_file(file_path):
    file_data = {}
    for person_name in os.listdir(file_path):
        person_file = os.path.join(file_path, person_name)

        total_pictures = []
        for picture in os.listdir(person_file):
            picture_path = os.path.join(person_file, picture)
            total_pictures.append(picture_path)

        file_data[person_name] = total_pictures

    return file_data


# converts np.array to TEXT when inserting
sqlite3.register_adapter(np.ndarray, adapt_array)

# converts TEXT to np.array when selecting
sqlite3.register_converter("ARRAY", convert_array)

# 連接到 SQLite 數據庫。 若文件不存在則會自動創建
conn_db = sqlite3.connect('database/database.db')

# 創建表
conn_db.execute('CREATE TABLE face_info \
            (ID INT PRIMARY KEY NOT NULL, \
            NAME TEXT NOT NULL, \
            Embedding ARRAY NOT NULL)')

# 將 database 載入數據庫
file_path = 'database'
if os.path.exists(file_path):
    file_data = load_file(file_path)

    for i, person_name in enumerate(file_data.keys()):
        picture_path = file_data[person_name]
        sum_embeddings = np.zeros([1, 512])

        # 將所有同對象的圖片的臉部特徵值加總
        for j, picture in enumerate(picture_path):
            img_rgb, detections = face_detect(picture)
            position, landmarks, embeddings = feature_extraction(img_rgb, detections)
            sum_embeddings += embeddings

        final_embedding = sum_embeddings / len(picture_path)  # 平均值
        adapt_embedding = adapt_array(final_embedding)

        # 插入值
        conn_db.execute("INSERT INTO face_info (ID, NAME, Embeddings) VALUES (?, ?, ?)",
                        (i, person_name, adapt_embedding))
    conn_db.commit()
    conn_db.close()

模組化以方便後續調用

# in src/database.py

import sqlite3
import os
import numpy as np
import io
from face_detection import face_detect
from feature_extraction import feature_extract


def adapt_array(arr):
    out = io.BytesIO()
    np.save(out, arr)
    out.seek(0)

    return sqlite3.Binary(out.read())


def convert_array(text):

    out = io.BytesIO(text)
    out.seek(0)
    return np.load(out)


def load_file(file_path):
    file_data = {}
    for person_name in os.listdir(file_path):
        person_dir = os.path.join(file_path, person_name)

        person_pictures = []
        for picture in os.listdir(person_dir):
            picture_path = os.path.join(person_dir, picture)
            person_pictures.append(picture_path)

        file_data[person_name] = person_pictures

    return file_data


def create_db(db_path, file_path, detector, sess):
    if os.path.exists(file_path):
        conn_db = sqlite3.connect(db_path)
        conn_db.execute("CREATE TABLE face_info \
                            (ID INT PRIMARY KEY NOT NULL, \
                             NAME TEXT NOT NULL, \
                            Embeddings ARRAY NOT NULL)")
        file_data = load_file(file_path)
        for i, person_name in enumerate(file_data.keys()):
            picture_path = file_data[person_name]
            sum_embeddings = np.zeros([1, 512])
            for j, picture in enumerate(picture_path):
                img_rgb, detections = face_detect(picture, detector)
                position, landmarks, embeddings = feature_extract(img_rgb, detections, sess)
                sum_embeddings += embeddings

            final_embedding = sum_embeddings / len(picture_path)
            adapt_embedding = adapt_array(final_embedding)

            conn_db.execute("INSERT INTO face_info (ID, NAME, Embeddings) VALUES (?, ?, ?)",
                            (i, person_name, adapt_embedding))
        conn_db.commit()
        conn_db.close()
    else:
        print('database path does not exist')

Step 5. Face Recognition

將輸入的照片與資料庫中的照片進行比對

使用 L2-Norm 計算之間 最佳的距離 (distance)，可視為兩張人臉之 差異程度。可以參考 → 理解 L1，L2 範數在機器學習中應用

給定 threshold，若 distance > threshold ⇒ 不同人臉，反之則視為同一張臉

比對照片找出最相似的人並判斷差異是否低於門檻

import numpy as np
import sqlite3
import io
import os

# 連接資料庫並取得內部所有資料
conn_db = sqlite3.connect('database/database.db')
cursor = conn_db.cursor()
db_data = cursor.fetchall()

# 跟 database 中的數據做比較
total_distances = []
total_names = []
for data in db_data:
    total_names.append(db_data[1])
    db_embeddings = convert_array(data[2])
    distance = round(np.linalg.norm(db_embeddings - embeddings), 2)
    total_distances.append(distance)

# 所有人比對的結果
total_resul = dict(zip(total_names, total_distances))

# 找到距離最小者，也就是最像的人臉
idx_min = np.argmin(total_distances)

# 最小距離者的名字與距離
name, distance = total_names[idx_min], total_distances[idx_min]

# set threshold
threshold = 1

# 差異是否低於門檻
if distance < threshold:
    print('Found!', name, distance, total_result)
else:
    print('Unknown person', name, distance, total_result)

做成 function 以便調用

def compare_face(embeddings, threshold):
    conn_db = sqlite3.connect('database.db')
    cursor = conn_db.cursor()
    db_data = cursor.fetchall()

    total_distances = []
    total_names = []
    for data in db_data:
        total_names.append(data[1])
        db_embeddings = convert_array(db_data[2])
        distance = round(np.linalg.norm(db_embeddings - embeddings), 2)
        total_distances.append(distance)

    total_result = dict(zip(total_names, total_distances))
    idx_min = np.argmin(total_distances)

    name, distance = total_names[idx_min], total_distances[idx_min]

    if distance > threshold:
        name = 'Unknown person'

    return name, distance, total_result

Face Recognition in Picture - DEMO

total_result: {'Elon Musk': 1.49, 'Mark Zuckerberg': 0.6, 'Steve Jobs': 1.38}
total_result: {'Elon Musk': 0.73, 'Mark Zuckerberg': 1.48, 'Steve Jobs': 1.39}
total_result: {'Elon Musk': 1.33, 'Mark Zuckerberg': 1.43, 'Steve Jobs': 1.28}
total_result: {'Elon Musk': 1.49, 'Mark Zuckerberg': 1.49, 'Steve Jobs': 0.95}

參考資料、網頁

主要參考
1. Face Recognition 人臉辨識 Python 教學. 人臉辨識在 Computer Vision… | by 李謦伊 | 謦伊的閱讀筆記 | Medium
2. 使用深度學習進行人臉辨識: Triplet loss, Large margin loss(ArcFace) | by 張銘 | Taiwan AI Academy | Medium
Face Detection
1. retinaface : retinaface · PyPI
2. RetinaFace paper : 1905.00641.pdf (arxiv.org)
3. ArcFace 論文詳解 : 人臉識別：《Arcface》論文詳解，對損失層的改進有較為詳細的講解人臉識別：《Arcface》論文詳解 - IT 閱讀 (itread01.com)
Face Alignment
Face Extraction
1. Deep Learning Training? Inference? : What’s the Difference Between Deep Learning Training and Inference? - The Official NVIDIA Blog
2. Normalization : sklearn.preprocessing 之資料預處理 | 程式前沿 (codertw.com)
3. sklearn.preprocessing.normalize API : sklearn.preprocessing.normalize — scikit-learn 1.0.2 documentation
4. 為甚麼要特徵標準化 : 为什么要特征标准化 (深度学习)? Why need the feature normalization (deep learning)? - YouTube
Create Database - using SQLite
Face Recongition
1. 理解 L1，L2 範數在機器學習中應用 - 台部落 (twblogs.net)
OpenCV
1. 【python】opencv 2 小時初學者教學｜影像辨識｜影像處理｜人臉辨識｜電腦視覺 - YouTube
onnx
1. models/arcface_inference.ipynb at master · onnx/models (github.com)
2. ONNX models | Microsoft Docs
其他

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README.md

README.md

Face Recognition in Python

目錄

Tree Structure

環境及模組

環境安裝

下載辨識模型

Face Verification? Face Recognition?

Face Verification 人臉驗證

Face Recognition 人臉辨識

Face Recognition 主要三部分

五大流程

Step 1. Face Detection

Step 2. Face Alignment

Step 3. Feature extraction

Step 4. Create Database

Step 5. Face Recognition

Face Recognition in Picture - DEMO

參考資料、網頁

Files

README.md

Latest commit

History

README.md

File metadata and controls

Face Recognition in Python

目錄

Tree Structure

環境及模組

環境安裝

下載辨識模型

Face Verification? Face Recognition?

Face Verification 人臉驗證

Face Recognition 人臉辨識

Face Recognition 主要三部分

五大流程

Step 1. Face Detection

Step 2. Face Alignment

Step 3. Feature extraction

Step 4. Create Database

Step 5. Face Recognition

Face Recognition in Picture - DEMO

參考資料、網頁