本地搜图系列-1：人脸搜图-基于Python的本地文件夹人脸图片查找 上传人脸查找对应人物的所有本地图片
·
启动py程序后，在浏览器访问，然后上传对应人物脸部照片，就可以在本地文件夹搜索查找同一个人的其他照片了。（代码在结尾处）
将网上找到的图片上传，也可以在本地找到对应人物照片：搜索时注意调整相似度即可，相似度建议设置到0.2以上，避免丢失准确率。
程序使用FaceAnalysis提取人物脸部特征并保存到图片文件夹根目录的SQLite数据库中，减少每次提取特征消耗时间，文件夹内添加或删除图片后，再次点击创建/更新索引即可；（本地图片库以开源图片库为例，避免肖像权风险）
程序启动后，在浏览器访问http://127.0.0.1:5000即可，访问后将本地需要查找的路径粘贴到2的位置，然后创建索引，首次创建会消耗比较多的时间，索引创建完成后，就可以上传需要查找的人物脸部照片进行搜索了
可以设置不同的相似度进行搜索；
以下为代部分（千问及百度提供技术支持）：默认文件夹路径，默认相似度，最多查找图片数量，修改代码中对应的以下变量值即可；
TARGET_FOLDER = "D:\study\AI\imgeDeal\commonPic_many2one"
DEFAULT_SIMILARITY_THRESHOLD = 0.8 # ✅ 降低阈值，更容易匹配
DEFAULT_RESULTS_COUNT = 5
#以图找人，人脸识别，SQLite创建索引
# pose_match_web.py
import os
import sys
import cv2
import sqlite3
import numpy as np
from pathlib import Path
import logging
from flask import Flask, render_template_string, send_file,request, jsonify, send_from_directory #, url_for
from werkzeug.utils import secure_filename
from urllib.parse import quote, unquote
import traceback
import base64
from insightface.app import FaceAnalysis # 使用 InsightFace（ArcFace 实现）
# 初始化模型环境
# 获取 python.exe 所在目录 → D:\study\AI\imgeDeal\python-3.10.11-embed-amd64
PYTHON_DIR = os.path.dirname(os.path.abspath(sys.executable))
print("...python环境路径：",PYTHON_DIR)
# models 路径（models 与 python.exe 同级）
MODEL_DIR = PYTHON_DIR
print("...模型路径:", MODEL_DIR)
# 初始化人脸引擎（自动下载模型，仅首次需联网）
#模型说明：name='antelopev2',name='buffalo_l' antelopev2:早期，更快，点少；buffalo_l：新版2023，稍慢，点多
face_app = FaceAnalysis(name='buffalo_l',root=MODEL_DIR, providers=['CPUExecutionProvider'])
face_app.prepare(ctx_id=0, det_size=(640, 640))
print("✅ 模型加载成功！路径:", MODEL_DIR)
# -----------------------------
# 配置
# -----------------------------
INDEX_DB = 'imperial_faces.db' #索引库文件
UPLOAD_FOLDER = 'uploads'
TARGET_FOLDER = "D:\study\AI\imgeDeal\commonPic_many2one"
DEFAULT_SIMILARITY_THRESHOLD = 0.8 # ✅ 降低阈值，更容易匹配
DEFAULT_RESULTS_COUNT = 5
os.makedirs(UPLOAD_FOLDER, exist_ok=True)
# -----------------------------
# 初始化 Flask
# Flask App
# -----------------------------
web_app = Flask(__name__)
web_app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
# 初始化索引库
def init_database(db_folder):
"""处理 SQLite 数据库文件夹路径"""
if not isinstance(db_folder, Path):
db_folder = Path(db_folder)
# 确保文件夹存在
db_folder.mkdir(parents=True, exist_ok=True)
# 构建数据库文件的完整路径
db_path = db_folder / INDEX_DB
"""初始化 SQLite 数据库"""
conn = sqlite3.connect(db_path)
cursor = conn.cursor()
cursor.execute('''
CREATE TABLE IF NOT EXISTS faces_index (
id INTEGER PRIMARY KEY,
filename TEXT UNIQUE NOT NULL,
file_path TEXT NOT NULL,
file_size INTEGER NOT NULL,
file_mtime REAL NOT NULL,
age TEXT NOT NULL,
gender TEXT NOT NULL,
face_bbox BLOB NOT NULL, -- 存储人脸特征向量
embedding BLOB NOT NULL
)
''')
# 创建索引，加速查询
cursor.execute('CREATE INDEX IF NOT EXISTS idx_filename ON faces_index(filename)')
cursor.execute('CREATE INDEX IF NOT EXISTS idx_mtime ON faces_index(file_mtime)')
conn.commit()
conn.close()
print("✅ 索引数据库初始化完成")
SUPPORTED_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.bmp', '.webp', '.tiff', '.tif'}
# -----------------------------
# 构建索引 → 保存在目标文件夹下
# -----------------------------
def build_or_update_index(image_folder = UPLOAD_FOLDER):
"""构建或更新 SQLite 索引"""
image_folder = Path(image_folder)
if not image_folder.exists():
raise FileNotFoundError(f"图片文件夹不存在: {image_folder}")
image_files = [f for f in image_folder.iterdir()
if f.is_file() and f.suffix.lower() in SUPPORTED_EXTENSIONS]
print("更新索引，目标文件夹：",image_folder,"文件数：",len(image_files))
# 构建数据库文件的完整路径
index_path = image_folder / INDEX_DB
conn = sqlite3.connect(index_path)
cursor = conn.cursor()
processed = 0
reused = 0
for img_path in image_files:
stat = img_path.stat()
file_size = stat.st_size
file_mtime = stat.st_mtime
filename = img_path.name
# 检查是否已存在且未修改
cursor.execute(
"SELECT file_mtime, embedding FROM faces_index WHERE filename = ?",
(filename,)
)
row = cursor.fetchone()
if row and abs(row[0] - file_mtime) < 1: # 时间差小于1秒视为未修改
reused += 1
continue # 跳过，复用旧特征
# 否则重新提取特征
try:
faces = extract_sift_faces(img_path)
for face in faces:
bbox = face.bbox.astype(np.float32).tobytes()
embedding = face.embedding.astype(np.float32).tobytes()
# 插入或替换
cursor.execute('''
INSERT OR REPLACE INTO faces_index
(filename, file_path, file_size, file_mtime, age,gender,face_bbox,embedding)
VALUES (?, ?, ?, ?, ?,?,?,?)
''', (filename, str(img_path), file_size, file_mtime, face.age,face.gender,bbox,embedding))
processed += 1
print(f"✅ 处理成功，计数{processed}，文件{img_path}")
except Exception as e:
print(f"❌ 处理失败，计数{processed}，文件{img_path}: {e}")
traceback.print_exc()#打印追踪信息
#break
continue # 出错也跳过，不要中断整个索引过程
conn.commit()
conn.close()
print(f"✅ 索引更新完成 | 新增/更新: {processed}, 复用: {reused}, 总计: {processed + reused}")
return processed,reused,processed + reused,index_path
#人脸提取
def extract_sift_faces(image_path):
img = cv2.imread(image_path)
faces = face_app.get(img)
return faces
#获取上传图片的人脸特征
def get_query_faces(filename):
if not filename:
return jsonify({'error': '缺少查询图片'})
query_path = os.path.join(UPLOAD_FOLDER, filename)
if not os.path.exists(query_path):
return jsonify({'error': f'查询图片不存在: {query_path}'})
faces = extract_sift_faces(query_path)
if len(faces) < 1:
raise ValueError("未检测到人脸")
return faces
def get_max_face(query_faces):
return max(query_faces, key=lambda x: (x.bbox[2]-x.bbox[0])*(x.bbox[3]-x.bbox[1]))
def load_index_db():
# 获取目标文件夹（默认 images）
folder = request.args.get('folder', '').strip()
image_folder = Path(folder)
if not image_folder.is_absolute():
image_folder = Path.cwd() / image_folder
index_path = image_folder / INDEX_DB
if not index_path.exists():
return jsonify({'error': f'索引文件不存在: {index_path}'})
return load_index_from_sqlite(index_path)
def load_index_from_sqlite(db_path):
try:
conn = sqlite3.connect(str(db_path))
cursor = conn.cursor()
cursor.execute("SELECT filename, file_path, file_size, file_mtime, age,gender,face_bbox,embedding FROM faces_index")
rows = cursor.fetchall()
conn.close()
return rows
except Exception as e:
raise RuntimeError(f"读取 SQLite 索引失败: {e}")
# HTML 模板（内嵌）
HTML_TEMPLATE = '''
<!DOCTYPE html>
<html lang="zh">
<head>
<meta charset="UTF-8">
<title>图片查找服务</title>
<style>
body { font-family: Arial, sans-serif; margin: 20px;}
.all {display: flex; gap: 20px; /* 现代浏览器支持的间距属性 */ }
.resultsAll {display: flex; gap: 20px; /* 现代浏览器支持的间距属性 */ align-items: flex-start;}
.upload, .query, .index { margin: 20px 0; padding: 15px;width: 400px; border: 1px solid #ddd; border-radius: 5px; }
.results { display: flex; flex-wrap: wrap; gap: 10px; margin-top: 20px; }
.result-item { text-align: center; max-width: 150px; }
.result-item img { width: 100%; height: auto; }
input[type="text"], input[type="number"] { width: 200px; padding: 5px; margin: 5px 0; }
button { padding: 8px 16px; margin: 5px; }
/* 图片缩放功能 */
.gallery { display: flex; flex-wrap: wrap; gap: 15px; justify-content: center; }
.gallery img { width: 200px; height: 200px; object-fit: cover; border: 3px solid #ddd; border-radius: 8px; cursor: pointer; transition: transform 0.2s, box-shadow 0.2s; }
.gallery img:hover { transform: scale(1.05); box-shadow: 0 4px 10px rgba(0,0,0,0.2); }
.modal { display: none; position: fixed; top: 0; left: 0; width: 100%; height: 100%; background-color: rgba(0,0,0,0.9); justify-content: center; align-items: center; z-index: 1000; animation: fadeIn 0.3s ease-in-out; overflow: hidden; }
.modal img { max-width: 90vw; max-height: 90vh; border: 4px solid white; border-radius: 6px; box-shadow: 0 0 20px rgba(255,255,255,0.1); animation: zoomIn 0.3s ease-out; transition: transform 0.2s; cursor: grab; }
.modal img:active { cursor: grabbing; }
@keyframes fadeIn { from { opacity: 0; } to { opacity: 1; } }
@keyframes zoomIn { from { transform: scale(0.8); opacity: 0; } to { transform: scale(1); opacity: 1; } }
</style>
</head>
<body>
<h1>📷 图片查找服务</h1>
<div class = "all">
<div class="upload">
<h2>上传查询图片</h2>
<form method="post" enctype="multipart/form-data" action="/upload" onsubmit="handleUpload(event)">
<input onchange="handleUpload(event)" type="file" name="file" accept="image/*" required>
<button type="submit">上传</button>
</form>
<p><strong>当前图片:</strong> <span id="currentFile">无</span></p>
</div>
<div class="index">
<h2>创建/更新索引</h2>
<form method="post" action="/create_index" onsubmit="updateIndex(event)">
<label>目标文件夹路径（如：images, yoga_poses 等）:</label><br>
<input type="text" id = "folder1" name="folder" value="images" style="width: 350px;"><br>
<button type="submit">🔍 创建/更新索引</button>
</form>
<p><strong>状态:</strong> <span id="indexStatus">未创建</span></p>
</div>
<div class="query">
<h2>搜图</h2>
<label>相似度阈值（0.0~1.0，默认 0.6）:</label><br>
<input type="number" id="similarity" step="0.01" min="0" max="1" value=""><br>
<label>显示数量（默认 5）:</label><br>
<input type="number" id="count" min="1" value=""><br>
<label>关注部位:</label><br>
<div class="controls" style="display:none">
<button id="selectAll">全选/取消</button>
<label><input type="checkbox" id="left_hand" checked> 左手</label>
<label><input type="checkbox" id="right_hand" checked> 右手</label>
<label><input type="checkbox" id="left_leg" checked> 左腿</label>
<label><input type="checkbox" id="right_leg" checked> 右腿</label>
</div>
<button onclick="findByFace()">🔎 人脸搜图</button>
<!--
<button onclick="findSimilar()">🔎 以图搜图</button>
<button onclick="findPose()">🔎 姿势搜图</button>
-->
</div>
</div>
<div class = "resultsAll">
<h2>上传图片</h2>
<div id="uploadSource" class="results"></div>
<h2>匹配结果</h2>
<div id="results" class="results"></div>
</div>
<div class="modal" id="modal" onclick="closeModal(event)">
<img id="modal-img" />
</div>
<script>
document.addEventListener('DOMContentLoaded', function () {
// DOM加载完成后执行的代码 而不需要等待样式表、图片和子框架的加载
fetch('/getArgs', {
method: 'POST'
})
.then(response => response.json())
.then(data => {
document.getElementById('similarity').value = data.similarity;
document.getElementById('count').value = data.count;
document.getElementsByName('folder')[0].value = data.targetFolder;
});
});
let currentFile = null;
function handleUpload(event) {
const fileInput = event.target;
const file = fileInput.files[0];
if (!file) {
return;
}
document.getElementById('currentFile').textContent = file.name;
// 创建FormData对象并添加文件
const formData = new FormData();
formData.append('file', file);
fetch('/upload', {
method: 'POST',
body: formData
})
.then(response => response.json())
.then(data => {
if (data.filename) {
currentFile = data.filename;
document.getElementById('currentFile').textContent = currentFile;
const uploadedUrl = data.uploaded_img_url;
const names = "up_"+data.filename;
document.getElementById('uploadSource').innerHTML =
`<div class="result-item">
<img src="${uploadedUrl}" class="result-item" onclick="zoomImg(event)" ><br>
<p>性别：${data.gender} 年龄：${data.age}</p>
<img id = "img_${names}" src="" onclick = zoomImg(event) >
</div>`
document.getElementById('results').innerHTML = '';
} else {
alert('上传失败: ' + (data.error || '未知错误'));
}
});
}
function updateIndex(event) {
document.getElementById('indexStatus').textContent = '更新中...';
event.preventDefault();
const formData = new FormData(event.target);
fetch('/create_index', {
method: 'POST',
body: formData
})
.then(response => response.json())
.then(data => {
if (data.error) {
document.getElementById('indexStatus').textContent = '错误: ' + data.error;
} else {
document.getElementById('indexStatus').textContent =
`✅ 索引已更新，新增 ${data.processed}，
更新 ${data.reused}，共计${data.allcount}：张图片，索引路径${data.index_path}`;
}
});
}
function findByFace() {
if (!currentFile) {
alert('请先上传一张图片！');
return;
}
document.getElementById('results').innerHTML = '🔍 匹配中...';
const similarity = document.getElementById('similarity').value;
const count = document.getElementById('count').value;
const selected = getSelected();
//const folder1 = document.getElementById('folder1').value;
const folder = document.getElementsByName('folder')[0].value;
//alert(folder)
fetch('/find_similar_by_face?filename=' + encodeURIComponent(currentFile) +
'&similarity=' + similarity + '&count=' + count + '&folder=' + folder + '&selected=' + selected.join(','))
.then(response => response.json())
.then(data => {
const resultsDiv = document.getElementById('results');
resultsDiv.innerHTML = '';
if (data.error) {
resultsDiv.innerHTML = '<p style="color:red;">' + data.error + '</p>';
return;
}
if (data.length === 0) {
resultsDiv.innerHTML = '<p>未找到相似姿势</p>';
return;
}
data.forEach(item => {
resultsDiv.innerHTML += `
<div class="result-item">
<img src="${item.img}" alt="match" onclick=zoomImg(event)>
<p>名称：${item.image_name}</p>
<p>相似度: ${item.similarity.toFixed(3)}</p>
</div>`;
});
});
}
function findPose() {
if (!currentFile) {
alert('请先上传一张图片！');
return;
}
document.getElementById('results').innerHTML = '🔍 匹配中...';
const similarity = document.getElementById('similarity').value;
const count = document.getElementById('count').value;
const selected = getSelected();
//const folder1 = document.getElementById('folder1').value;
const folder = document.getElementsByName('folder')[0].value;
//alert(folder)
fetch('/find_similar?filename=' + encodeURIComponent(currentFile) +
'&similarity=' + similarity + '&count=' + count + '&folder=' + folder + '&selected=' + selected.join(','))
.then(response => response.json())
.then(data => {
const resultsDiv = document.getElementById('results');
resultsDiv.innerHTML = '';
if (data.error) {
resultsDiv.innerHTML = '<p style="color:red;">' + data.error + '</p>';
return;
}
if (data.length === 0) {
resultsDiv.innerHTML = '<p>未找到相似姿势</p>';
return;
}
data.forEach(item => {
resultsDiv.innerHTML += `
<div class="result-item">
<img src="${item.img}" alt="match" onclick="showImage('${item.image_name}','${item.image_name}','null')">
<p>名称：${item.image_name}</p>
<p>相似度: ${item.similarity.toFixed(3)}</p>
<p>展示：<span id = "${item.image_name}"/></p>
<img id = "img_${item.image_name}" src="" onclick = zoomImg(event) >
</div>`;
});
});
}
function showImage(name,names,folder) {
if (folder == "null"){
folder = document.getElementsByName('folder')[0].value;
}
types = name.split('.').pop();
const selected = getSelected();
const spanEle = document.getElementById(names);
const imgEle = document.getElementById('img_'+names);
spanEle.innerHTML = '';
imgEle.src = '';
const url = '/show_image?folder=' + folder + '&name=' + name + '&types=' + types + '&selected=' + selected.join(',');
fetch(url, {
method: 'POST',
headers: {
'Content-Type': 'web_application/x-www-form-urlencoded',
},
})
.then(response => response.json())
.then(data => {
if (data.error) {
spanEle.innerHTML = 'data.error';
return;
}
spanEle.innerHTML = '√';
//imgEle.src=`"data:image/${data.type};base64,${data.str}"`
const blobUrl = base64ToBlobUrl(`image/${data.type}`, data.str);
imgEle.onload = () => {
URL.revokeObjectURL(blobUrl); // 自动释放
resolve(imgEle);
};
imgEle.onerror = () => {
URL.revokeObjectURL(url); // 错误时也要释放
reject(new Error('Load failed'));
};
imgEle.src = blobUrl;
//alert(imgEle.src)
});
}
function base64ToBlobUrl(mimeType,base64Str) {
// 将 base64 转换为二进制数据
const binaryString = atob(base64Str);
const bytes = new Uint8Array(binaryString.length);
for (let i = 0; i < binaryString.length; i++) {
bytes[i] = binaryString.charCodeAt(i);
}
// 创建 Blob 和 URL
const blob = new Blob([bytes], { type: mimeType });
return URL.createObjectURL(blob);
}
document.getElementById('selectAll').addEventListener('click', function() {
const checkboxes = document.querySelectorAll('.controls input[type="checkbox"]');
const currentStatus = Array.from(checkboxes).some(cb => !cb.checked);
checkboxes.forEach(cb => cb.checked = currentStatus);
});
function getSelected() {
const selected = [];
if (document.getElementById('left_hand').checked) selected.push('0');
if (document.getElementById('right_hand').checked) selected.push('1');
if (document.getElementById('left_leg').checked) selected.push('2');
if (document.getElementById('right_leg').checked) selected.push('3');
if (selected.length === 0) {
//alert("请至少选择一个部位");
}
return selected;
}
//图片缩放函数------start
const gallery = document.getElementById('gallery');
const modal = document.getElementById('modal');
const modalImg = document.getElementById('modal-img');
let scale = 1;
let isDragging = false;
let startPos = { x: 0, y: 0 };
let currentTranslate = { x: 0, y: 0 };
// 打开模态框
function zoomImg(e) {
if (e.target.tagName === 'IMG') {
modalImg.src = e.target.src;
modal.style.display = 'flex';
document.body.style.overflow = 'hidden';
scale = 1;
currentTranslate = { x: 0, y: 0 };
modalImg.style.transform = `scale(${scale}) translate(0px, 0px)`;
}
}
// 关闭模态框（直接绑定在HTML上）
function closeModal(e) {
if (e.target === modal || e.target === modalImg) {
modal.style.display = 'none';
document.body.style.overflow = 'auto';
// 重置状态
isDragging = false;
scale = 1;
currentTranslate = { x: 0, y: 0 };
}
}
// 滚轮缩放
modalImg.addEventListener('wheel', function(e) {
e.preventDefault();
const delta = e.deltaY > 0 ? -0.1 : 0.1;
scale = Math.min(Math.max(scale + delta, 0.5), 5); // 支持放大到5倍
web_applyTransform();
});
// 鼠标按下：开始拖拽
modalImg.addEventListener('mousedown', function(e) {
if (scale <= 1) return; // 仅在放大时允许拖拽
isDragging = true;
startPos = { x: e.clientX - currentTranslate.x, y: e.clientY - currentTranslate.y };
modalImg.style.cursor = 'grabbing';
});
// 鼠标移动：实时更新位置
document.addEventListener('mousemove', function(e) {
if (!isDragging) return;
currentTranslate = {
x: e.clientX - startPos.x,
y: e.clientY - startPos.y
};
web_applyTransform();
});
// 鼠标松开：结束拖拽
document.addEventListener('mouseup', function() {
isDragging = false;
modalImg.style.cursor = 'grab';
});
// 应用缩放和平移变换
function web_applyTransform() {
modalImg.style.transform = `scale(${scale}) translate(${-currentTranslate.x}px, ${-currentTranslate.y}px)`;
}
//图片缩放函数------end
</script>
</body>
</html>
'''
@web_app.route('/')
def index():
return render_template_string(HTML_TEMPLATE)
@web_app.route('/upload', methods=['POST'])
def upload():
try:
if 'file' not in request.files:
return jsonify({'error': '未选择文件'})
file = request.files['file']
if file.filename == '':
return jsonify({'error': '文件名为空'})
filename = secure_filename(file.filename)
upload_path = os.path.join(web_app.config['UPLOAD_FOLDER'], filename)
file.save(upload_path)
# 返回上传图的 URL
uploaded_img_url = f"/uploads/{filename}"
# 提取上传图片人脸特征
query_faces = get_query_faces(filename)
# 取最大人脸（或可遍历所有人脸）
query_face = get_max_face(query_faces)
#query_face = query_faces[0]
print("上传检测：上传图片人物：性别:", query_face.gender,"年龄:", query_face.age,"3D 姿态:", query_face.pose)
return jsonify({
'filename': filename,
'uploaded_img_url': uploaded_img_url,
"upload_path":web_app.config['UPLOAD_FOLDER'],
'gender': "男" if str(query_face.gender) == "1" else "女",
'age': int(query_face.age)
})
except Exception as e:
traceback.print_exc()#打印追踪信息
return jsonify({'error': str(e)})
@web_app.route('/getArgs', methods=['POST'])
def getArgs():
return jsonify({
'similarity': DEFAULT_SIMILARITY_THRESHOLD,
'count': DEFAULT_RESULTS_COUNT,
'targetFolder': TARGET_FOLDER,
})
@web_app.route('/uploads/<filename>')
def uploaded_file(filename):
return send_from_directory(UPLOAD_FOLDER, filename)
@web_app.route('/create_index', methods=['POST'])
def create_index():
folder = request.form.get('folder', '').strip()
if not folder:
return jsonify({'error': '请填写图片文件夹路径'})
try:
print(f"=====目标路径文件夹{folder}")
image_folder = Path(folder)
if not image_folder.is_absolute():
image_folder = Path.cwd() / image_folder
# 1. 初始化数据库
init_database(folder)
# 2. 构建或更新索引
#build_or_update_index(str(image_folder))
processed, reused,allcount,index_path = build_or_update_index(str(folder))
return jsonify({
'status': 'success',
'processed': processed,
'reused': reused,
'allcount': allcount,
'index_path': str(index_path)
})
except Exception as e:
traceback.print_exc()#打印追踪信息
return jsonify({'error': str(e)})
@web_app.route('/find_similar_by_face')
def find_similar():
print("-----开始匹配")
filename = request.args.get('filename')
try:
similarity_threshold = float(request.args.get('similarity', 0.1))
results_count = int(request.args.get('count', 5))
except:
return jsonify({'error': '参数错误'})
# 提取上传图片人脸特征
query_faces = get_query_faces(filename)
try:
# ✅ 使用 SQLite 加载索引
index_data = load_index_db()
except Exception as e:
return jsonify({'error': f'加载索引失败: {str(e)}'})
matches = search_similar_images(query_faces,index_data,similarity_threshold,results_count)
print(f"🎯 匹配完成，匹配数: {len(matches)}")
return jsonify(matches)
# -----------------------------
# 相似度匹配
# -----------------------------
def search_similar_images(query_faces, index_data,similarity_threshold, top_k=5):
print(f"🔍 检测到 {len(query_faces)} 张人脸")
if query_faces is None or len(query_faces) == 0:
return []
# 取最大人脸（或可遍历所有人脸）
query_face = get_max_face(query_faces)
query_emb = query_face.embedding
print("匹配检测：上传图片人物：性别:", query_face.gender,"年龄:", query_face.age,"3D 姿态:", query_face.pose)
# 创建 FLANN 匹配器
FLANN_INDEX_KDTREE = 1
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50)
flann = cv2.FlannBasedMatcher(index_params, search_params)
results = []
num = 0;
for row in index_data:
num = num + 1
print("-----比对数:",num)
img_name = row[0]
img_path = row[1]
db_emb = np.frombuffer(row[7], dtype=np.float32)
# 余弦相似度
sim = np.dot(query_emb, db_emb) / (np.linalg.norm(query_emb) * np.linalg.norm(db_emb))
if sim >= similarity_threshold:
print("✅✅✅✅✅✅✅✅命中")
# 使用 URL 编码路径，防止特殊字符
img_url = f"/image_proxy/{quote(str(img_path))}"
results.append({
"image_name": img_path,
"img": img_url,
"similarity": float(sim) # 转为 Python float，便于 JSON 序列化
})
# 按 similarity 降序排序
results.sort(key=lambda x: x["similarity"], reverse=True)
return results[:top_k]
@web_app.route('/image_proxy/<path:encoded_path>')
def image_proxy(encoded_path):
try:
img_path = unquote(encoded_path)
img_path = Path(img_path)
if not img_path.exists() or not img_path.is_file():
return 'Image not found', 404
# 自动判断 MIME 类型
mimetype = 'image/jpeg'
ext = img_path.suffix.lower()
if ext in ['.png']: mimetype = 'image/png'
elif ext in ['.gif']: mimetype = 'image/gif'
elif ext in ['.webp']: mimetype = 'image/webp'
elif ext in ['.bmp']: mimetype = 'image/bmp'
return send_file(str(img_path), mimetype=mimetype)
except Exception as e:
print(f"Error serving image: {e}")
return 'Error loading image', 500
if __name__ == '__main__':
os.makedirs(UPLOAD_FOLDER, exist_ok=True)
print("✅ 服务启动中...")
print(f"📁 上传目录: {os.path.abspath(UPLOAD_FOLDER)}")
print("🌐 访问 http://127.0.0.1:5000")
web_app.run(debug=False, host='127.0.0.1', port=5000)更多推荐
5
0- 0
已为社区贡献3条内容
所有评论(0)