前端也可以这样零基础入门Pinecone五

假如你和我一样在准备24年的春招，在前端全栈外，再准备一些AI的内容是非常有必要的。24年是AI红利年，AIGC+各种岗位大厂机会会多些，同意的请点赞。也欢迎朋友们加我微信shunwuyu, 一起交流。

前言

上篇文章我们使用混合向量开发了时尚品的文搜图功能，本文我们来看Pinecone在面部识别领域的使用。

安装依赖

# DeepFace 是一个基于 Python 的开源人脸识别库，它封装了深度学习模型来实现人脸识别、人脸验证、人脸检测和人脸属性分析等功能
!pip install deepface

引入依赖

from deepface import DeepFace
# 向量数据库
from pinecone import Pinecone, ServerlessSpec
# Scikit-learn是基于Python的开源机器学习库
# PCA 成份分析
from sklearn.decomposition import PCA
# 
from sklearn.manifold import TSNE
# 命令行工具
from tqdm import tqdm
# 上下文管理器
import contextlib
# 匹配格式
import glob
# 可视化库
import matplotlib.pyplot as plt
# python中的科学计算库
import numpy as np
import os
# 数据分析
import pandas as pd
# 时间标准库
import time

准备数据集

我们这里的数据集主要是图片集，使用wget把它下载下来,主要是面部图片。

下载并解压数据

# wget 下载命令
# -q 安静模式
# --show-progress 显示下载进度条
# -o 重命名为family_photos.zip  
# 资源来自dropbox
!wget -q --show-progress -O family_photos.zip https://www.dropbox.com/scl/fi/yg0f2ynbzzd2q4nsweti5/family_photos.zip?rlkey=00oeuiii3jgapz2b1bfj0vzys&dl=0%22
# 解压缩
!unzip -q family_photos.zip

解压后图片文件夹是family,里面包含child,dad,woman三种图片

封装显示图片函数

# matplotlib 是可视化库  取名为plt
import matplotlib.pyplot as plt
# 定义一个显示图片的函数
def show_img(f):
  # 使用imread方法将文件读入内存
  img = plt.imread(f)
  # 新建一个图表 大小为宽4inch  高3inch
  plt.figure(figsize=(4,3))
  将图片显示出来
  plt.imshow(img)

show_img('family/dad/P06260_face5.jpg')

这不是美国那位吉吉国王吗？就是他和建国同志在任的时候，我国发展非常迅猛... 期待明年建国同志胜出。

再看一张

show_img('family/child/P04414_face1.jpg')

child? 这85年的吧....

实例化Pinecone

INDEX_NAME = 'dl-ai'
pinecone = Pinecone(api_key='你自己的key')

使用DeepFace生成嵌入

# 但若使用 Facenet 模型进行特征提取，则意味着在调用此函数时，它会根据 Facenet 的架构和训练参数来处理输入图像，并返回相应的人脸特征向量
MODEL="Facenet"
def generate_vectors():
  # 后缀为vec的文件
  VECTOR_FILE = "./vectors.vec"
  # 当出现文件不存在错误时，不中止程序运行，移除文件
  # contextlib 表示python运行上下文
  with contextlib.suppress(FileNotFoundError):
    os.remove(VECTOR_FILE)
  # 以写的方式打开文件 with 会在操作完后关闭文件
  with open(VECTOR_FILE, "w") as f:
    # 遍历每个子文件夹
    for person in ["mom", "dad", "child"]:
      # 使用glob匹配文件夹下的所有文件
      files = glob.glob(f'family/{person}/*')
      # 遍历这些文件
      for file in tqdm(files):
        # try except 
        try:
          # 提取人脸特征
          # img_path 图片  model_name指定模型  
          # enforce_detection在提取特征前进行人脸检测
          embedding = DeepFace.represent(img_path=file, model_name=MODEL, enforce_detection=False)[0]['embedding']
          # 向文件写入embedding 某个类型：文件名：embedding
          f.write(f'{person}:{os.path.basename(file)}:{embedding}\n')
        except (ValueError, UnboundLocalError, AttributeError) as e:
          print(e)

generate_vectors()

这样我们就完成了头像图片的向量化， 这次是保存在了本地文件中。使用!head -10 vectors.vec命令查看内容：

mom:P11968_face0.jpg:[-0.968216061592102, 0.445657879114151, -0.3362976908683777, -1.7201595306396484, -2.2699625492095947, 0.09954167902469635, 1.3653056621551514, 1.2067477703094482, 0.717734694480896, 0.15687556564807892, 1.3817498683929443, -1.1955968141555786, -0.09449852257966995, -1.1754977703094482, 0.580062210559845, 0.6901472806930542, 0.24539628624916077, -0.3208266496658325, -0.12135984003543854, -2.2810440063476562, -0.7281702756881714, -1.360960602760315, 0.04452701658010483, 1.062891960144043, 0.752934992313385, -0.06543054431676865, 1.2959623336791992, 0.0018401220440864563, -0.9969148635864258, 0.9450318217277527, -0.5798383951187134, -0.8587795495986938, 0.8826146721839905, 0.7304753065109253, 1.8957456350326538, -0.7780979871749878, -0.8198385834693909, 2.914519786834717, 0.6683512330055237, 0.7456151247024536, -1.2877490520477295, -1.9466755390167236, -1.9843796491622925, -0.4559321403503418, 0.7317990660667419, -0.7632724046707153, 0.7336459755897522, 0.33599770069122314, 1.6119154691696167, -2.933316946029663, -0.9780626893043518, 0.6531170606613159, -0.7011042237281799, 0.7603591084480286, -1.05730402469635, 1.9409853219985962, 0.7820557355880737, 1.0014525651931763, -1.3079169988632202, -0.5261071920394897, -0.5466775894165039, 2.2767152786254883, -1.9704135656356812, -0.07503129541873932, 2.7618868350982666, 0.16430547833442688, -0.7275070548057556, 0.7848039865493774, -0.22396039962768555, 1.4048340320587158, 1.9987218379974365, -0.07592078298330307, 0.2544087767601013, -0.8740766048431396, -0.04062746837735176, -1.2916429042816162, -0.6586376428604126, -0.0978061854839325, -1.0827528238296509, 0.2725602984428406, 0.9461637735366821, -1.8358964920043945, 0.9150750041007996, -0.9315770864486694, 1.0054022073745728, 1.333577275276184, 0.6469479203224182, -0.7008591294288635, -2.018449068069458, -0.3792751133441925, 1.1759753227233887, -1.714511752128601, 0.4413492977619171, -1.2517683506011963, -0.46000176668167114, 2.300297737121582, 0.06113551929593086, 1.3942131996154785, 0.2523031532764435, 1.0765972137451172, -1.5842845439910889, 1.158530592918396, 1.401320457458496, 1.6537895202636719, -0.4092678427696228, 2.0831165313720703, -0.5654659867286682, -0.0054601505398750305, 0.7226055860519409, 0.41471683979034424, -1.4260722398757935, -1.465698003768921, -1.611175298690796, -1.009490728378296, -1.5150566101074219, 0.3955858051776886, 0.2544900178909302, 0.19256356358528137, 0.5462937355041504, -0.9514122605323792, 1.7075181007385254, 1.5241360664367676, -0.47267794609069824, -0.13427861034870148, -0.24972860515117645, 0.3868950307369232, -0.9492344260215759, -0.12300881743431091] mom:P12008_face0.jpg:[-0.5887061357498169, 0.5304117202758789, 0.15951555967330933, -0.7273534536361694, 0.5044214725494385, 0.3508467674255371, 0.8140199780464172, 2.5588343143463135, -0.02461611106991768, -0.016419153660535812, 1.0168629884719849, -2.4186062812805176, -2.042652130126953, 1.2099231481552124, 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-1.3055018186569214, -0.6944471597671509, 0.33265402913093567, -0.802715539932251, 0.4412311017513275, 0.9702012538909912, -0.3406965136528015, 2.3435862064361572, -0.3081841766834259, -0.04755358397960663, -0.6835049390792847, -0.42298680543899536, 0.31368690729141235, -1.1777665615081787, -0.08537508547306061, 0.6878381371498108, 0.45548215508461] mom:P11998_face0.jpg:[-0.412310391664505, -0.49975332617759705, 0.5541101694107056, -2.0296390056610107, -1.29654860496521, -0.925977885723114, -0.6339708566665649, 0.3960404098033905, -0.5907497406005859, -0.6090978384017944, 0.19209453463554382, 0.42242810130119324, 0.5719138383865356, 0.14031006395816803, 1.2219016551971436, 0.5340520739555359, 3.018969774246216, -1.5609216690063477, -0.3702901005744934, -1.0981788635253906, 0.15212541818618774, -1.68440580368042, -0.8833028078079224, -0.08193070441484451, 0.5595159530639648, 0.08149875700473785, 0.6767213344573975, 0.6471714973449707, -0.9967234134674072, -0.18320824205875397, 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-1.2215116024017334, 0.8878392577171326] mom:P06319_face0.jpg:[-0.6978793144226074, 0.2951180040836334, -0.07888594269752502, -1.6831132173538208, 0.6121690273284912, 0.26322001218795776, -0.23936022818088531, 2.282067060470581, 0.649990439414978, -1.0031386613845825, 1.2148176431655884, 0.18154875934123993, -0.23976898193359375, -0.15431073307991028, -0.06951509416103363, 0.5285994410514832, 0.10464830696582794, -0.8303992748260498, -0.3329645097255707, -1.2330975532531738, -0.6142452955245972, -0.906767725944519, -0.36039087176322937, 0.3742542266845703, -0.3867936432361603, -0.5678381323814392, 0.5982503890991211, -0.32306286692619324, -0.7716593742370605, 0.24828223884105682, -0.16026702523231506, -1.6291357278823853, 0.30355319380760193, 0.6977019309997559, -0.25949403643608093, 0.2595826983451843, 1.387563705444336, 0.44351378083229065, -0.6232916116714478, -0.20366737246513367, 0.24274009466171265, 0.2544693648815155, 0.11326797306537628, 0.4013108015060425, -0.6009215116500854, 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绘制图像数据

# 因为DeepFace的向量是高维的，所以要做一些转化工作 
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
def gen_tsne_df(person, perplexity):
    # 准备向量数组
    vectors =[]
    # 以读的方式打开vectors文件
    with open('./vectors.vec', 'r') as f:
      # 逐行读取 并匹配进度
      for line in tqdm(f):
        # 将本行切割成 类别，文件名，向量
        p, orig_img, v = line.split(':')
        # 如果类型一致 比如都是child
        if person == p:
            # 添加向量
            vectors.append(eval(v))
    # 实例化 降维 只保留8个部份
    pca = PCA(n_components=8)
    # 非线性降维 第一个参数2 二维空间 
    # perplexity 困惑度 
    # n_iter 找到降维，最多迭代次数
    # metric='euclidean' 欧几里得计算
    # 更新低维空间坐标的速度
    tsne = TSNE(2, perplexity=perplexity, random_state = 0, n_iter=1000,
        verbose=0, metric='euclidean', learning_rate=75)
    print(f'transform {len(vectors)} vectors')
    
    pca_transform = pca.fit_transform(vectors)
    embeddings2d = tsne.fit_transform(pca_transform)
    # 返回pd 数据结构 
    return pd.DataFrame({'x':embeddings2d[:,0], 'y':embeddings2d[:,1]})

封装tsne绘制函数

def plot_tsne(perplexity, model):
    #新建一个包含多个子区域的图， 8，5 是尺寸
    # ax 代表坐标轴对象
    (_, ax) = plt.subplots(figsize=(8,5))
    #plt.style.use('seaborn-whitegrid')
    # 画grid
    plt.grid(color='#EAEAEB', linewidth=0.5)
    ax.spines['top'].set_color(None)
    ax.spines['right'].set_color(None)
    ax.spines['left'].set_color('#2B2F30')
    ax.spines['bottom'].set_color('#2B2F30')
    #不同的类型用不同的颜色 
    colormap = {'dad':'#ee8933', 'child':'#4fad5b', 'mom':'#4c93db'}

    for person in colormap:
        embeddingsdf = gen_tsne_df(person, perplexity)
        # 绘制散点图
        ax.scatter(embeddingsdf.x, embeddingsdf.y, alpha=.5, 
                   label=person, color=colormap[person])
    plt.title(f'Scatter plot of faces using {model}', fontsize=16, fontweight='bold', pad=20)
    plt.suptitle(f't-SNE [perplexity={perplexity}]', y=0.92, fontsize=13)
    plt.legend(loc='best', frameon=True)
    plt.show()

# 44的熵
plot_tsne(44, 'facenet')

• 创建index

from pinecone import Pinecone, ServerlessSpec

INDEX_NAME='dl-ai'
pinecone = Pinecone(api_key='515c9a29-ebf3-4b0b-ab55-e67e50cf31cc')
if INDEX_NAME in [index.name for index in pinecone.list_indexes()]:
  pinecone.delete_index(INDEX_NAME)
pinecone.create_index(name=INDEX_NAME, dimension=128, metric='cosine',
  spec=ServerlessSpec(cloud='aws', region='us-west-2'))

index = pinecone.Index(INDEX_NAME)

• 将向量存入数据库

def store_vectors():
  # 以读的方式打开文件
  with open("vectors.vec", "r") as f:
    # 遍历每一行
    for line in tqdm(f):
        # 以:切割开 类型 文件名  向量
        person, file, vec = line.split(':')
        # upsert 存入 
        index.upsert([(f'{person}-{file}', eval(vec), {"person":person, "file":file})])
store_vectors()
index.describe_index_stats()

• 执行查询

child_base = 'family/child/P06310_face1.jpg'
show_img(child_base)

这哥们发量有点感人啊。

输入也是一个头像，生成嵌入。

# represent 方法负责生成嵌入
embedding = DeepFace.represent(img_path=child_base, model_name=MODEL)[0]['embedding']
print(embedding)

# 这里的query方法，多了一个filter查询 
query_response = index.query(
      top_k=3,
      vector = embedding,
      # 分类 等于  dad
      filter={
        "person": {"$eq": "dad"}
      },
      include_metadata=True
)

photo = query_response['matches'][0]['metadata']['file']
show_img(f'family/dad/{photo}')

至此，我们就完成了相似脸型的查询

总结

• 面部数据集family_photos
• 面部识别模型 DeepFace
• 降维函数gen_tsne_df
• query方法的filter 参数

参考资料

• DLAI - Learning Platform Beta (deeplearning.ai)