假设你和我一样在预备24年的春招，在前端全栈外，再预备一些AI的内容对错常有必要的。24年是AI盈利年，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, 一同沟通。
前语
上篇文章咱们运用混合向量开发了时尚品的文搜图功用，本文咱们来看Pinecone在面部辨认范畴的运用。
安装依靠
# DeepFace是一个依据 Python 的开源人脸辨认库，它封装了深度学习模型来实现人脸辨认、人脸验证、人脸检测和人脸属性剖析等功用
!pip install deepface
引进依靠
from deepface import DeepFace
# 向量数据库
from pinecone import Pinecone, ServerlessSpec
# Scikit-learn是依据Python的开源机器学习库
# PCA 成份剖析
from sklearn.decomposition import PCA
#
from sklearn.manifold import TSNE
# 指令行东西
from tqdm import tqdm
# 上下文管理器
import contextlib
# 匹配格局
import glob
# 可视化库
import matplotlib.pyplot as plt
# python中的科学核算库
import numpy as np
import os
# 数据剖析
import pandas as pd
# 时间规范库
import time
预备数据集
咱们这儿的数据集主要是图片集，运用wget把它下载下来,主要是面部图片。
下载并解压数据
# wget 下载指令
# -q 安静形式
# --show-progress 显现下载进展条
# -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,里边包含child,dad,woman三种图片
封装显现图片函数
# matplotlib 是可视化库 取名为plt
import matplotlib.pyplot as plt
# 界说一个显现图片的函数
def show_img(f):
# 运用imread办法将文件读入内存
img = plt.imread(f)
# 新建一个图表 巨细为宽4inch 高3inch
plt.figure(figsize=(4,3))
将图片显现出来
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，我国开展非常迅猛… 等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')
运用DeepFace生成嵌入
# 但若运用 Facenet 模型进行特征提取，则意味着在调用此函数时，它会依据 Facenet 的架构和训练参数来处理输入图画，并回来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"
# 当呈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)
# 以写的办法翻开文件 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"]:
# 运用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()
这样咱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。运用!head -10 vectors.vec指令查看内容：
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, 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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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制作图画数据
# 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):
# 预备向量数组
vectors =[]
# 以读的办法翻开vectors文件
with open('./vectors.vec', 'r') as f:
# 逐行读取 并匹配进展
for line in tqdm(f):
# 将本行切割成 类别，文件名，向量
p, orig_img, v = line.split(':')
# 如果类型共同 比方都是child
if person == p:
# 增加向量
vectors.append(eval(v))
# 实例化 降维 只保存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' 欧几里得核算
# 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)
# 回来pd 数据结构
return pd.DataFrame({'x':embeddings2d[:,0], 'y':embeddings2d[:,1]})
封装tsne制作函数
def plot_tsne(perplexity, model):
#新建一个包含多个子区域的图， 8，5 是尺度
# ax 代表坐标轴目标
(_, 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色彩
colormap = {'dad':'#ee8933', 'child':'#4fad5b', 'mom':'#4c93db'}
for person in colormap:
embeddingsdf = gen_tsne_df(person, perplexity)
# 制作散点图
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():
# 以读的办法翻开文件
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)
输入也是一个头像，生成嵌入。
# represent 办法担任生成嵌入
embedding = DeepFace.represent(img_path=child_base, model_name=MODEL)[0]['embedding']
print(embedding)
# 这儿的query办法，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，咱们就完成了类似脸型的查询
总结
- 面部数据集family_photos
- 面部辨认模型 DeepFace
- 降维函数gen_tsne_df
- query办法的filter 参数
