https://github.com/shhossain/facedb
A package designed for efficient face recognition across extensive photo collections, optimized for large-scale processing.
https://github.com/shhossain/facedb
face-detection face-recognition large-scale
Last synced: about 1 year ago
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A package designed for efficient face recognition across extensive photo collections, optimized for large-scale processing.
- Host: GitHub
- URL: https://github.com/shhossain/facedb
- Owner: shhossain
- License: mit
- Created: 2023-09-02T11:58:23.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-09-14T15:27:35.000Z (almost 2 years ago)
- Last Synced: 2025-04-16T01:12:24.795Z (about 1 year ago)
- Topics: face-detection, face-recognition, large-scale
- Language: Python
- Homepage:
- Size: 355 KB
- Stars: 63
- Watchers: 2
- Forks: 14
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
# FaceDB - A Face Recognition Database
FaceDB is a Python library that provides an easy-to-use interface for face recognition and face database management. It allows you to perform face recognition tasks, such as face matching and face searching, and manage a database of faces efficiently. FaceDB supports two popular face recognition frameworks: DeepFace and face_recognition.
## Links
[Pypi](https://pypi.org/project/facedb/)
[Github](https://github.com/shhossain/facedb)
## Installation
FaceDB can be installed using pip:
```bash
pip install facedb
```
You can use face_recognition or DeepFace for face recognition. If you want to use DeepFace, you need to install the following dependencies:
for face_recognition:
```bash
pip install face_recognition
```
for DeepFace:
```bash
pip install deepface
```
## Simple Usage
This will create a chromadb database in the current directory.
```python
# Import the FaceDB library
from facedb import FaceDB
# Create a FaceDB instance
db = FaceDB(
path="facedata",
)
# Add a new face to the database
face_id = db.add("John Doe", img="john_doe.jpg")
# Recognize a face
result = db.recognize(img="new_face.jpg")
# Check if the recognized face is similar to the one in the database
if result and result["id"] == face_id:
print("Recognized as John Doe")
else:
print("Unknown face")
```
## Advanced Usage
You need to install pinecone first to use pinecone as the database backend.
```bash
pip install pinecone
```
```python
import os
os.environ["PINECONE_API_KEY"] = "YOUR_API_KEY"
db = FaceDB(
path="facedata",
metric='euclidean',
database_backend='pinecone',
index_name='faces',
embedding_dim=128,
module='face_recognition',
)
# This will create a pinecone index with name 'faces' in your environment if it doesn't exist
# add multiple faces
from glob import glob
from pathlib import Path
files = glob("faces/*.jpg") # Suppose you have a folder with imgs with names as filenames
imgs = []
names = []
for file in files:
imgs.append(file)
names.append(Path(file).name)
ids, failed_indexes = db.add_many(
imgs=imgs,
names=names,
)
unknown_face = "unknown_face.jpg"
result = db.recognize(img=unknown_face, include=['name'])
if result:
print(f"Recognized as {result['name']}")
else:
print("Unknown face")
# Include img in the result
result = db.recognize(img=unknown_face, include=['img'])
if result:
result.show_img()
# # Use can also use show_img() for multiple results
results = db.all(include='name')
results.show_img() # make sure you have matplotlib installed
# or
img = result['img'] # cv2 image (numpy array)
# Include embedding in the result
result = db.recognize(img=unknown_face, include=['embedding'])
if result:
print(result['embedding'])
# Search for similar faces
results = db.search(img=unknown_face, top_k=5, include=['name'])[0]
for result in results:
print(f"Found {result['name']} with distance {result['distance']}")
# or search for multiple faces
multi_results = db.search(img=[img1, img2], top_k=5, include=['name'])
for results in multi_results:
for result in results:
print(f"Found {result['name']} with distance {result['distance']}")
# get all faces
faces = db.get_all(include=['name', 'img'])
# Update a face
db.update(face_id, name="John Doe", img="john_doe.jpg", metadata1="metadata1", metadata2="metadata2")
# Delete a face
db.delete(face_id)
# Count the number of faces in the database
count = db.count()
# Get pandas dataframe of all faces
df = db.all().df
```
## Simple Querying
```python
# First add some faces to the database
db.add("Nelson Mandela", img="mandela.jpg", profession="Politician", country="South Africa")
db.add("Barack Obama", img="obama.jpg", profession="Politician", country="USA")
db.add("Einstein", img="einstein.jpg", profession="Scientist", country="Germany")
# Query the database by name
results = db.query(name="Nelson Mandela")
# Query the database by profession
results = db.query(profession="Politician")
```
## If you don't have an API key
You can follow the official pinecone tutorial : https://docs.pinecone.io/docs/new-api
It's easy to use and to understand, don't worry.
## Advanced Querying
You can use following operators in queries:
- $eq - Equal to (number, string, boolean)
- $ne - Not equal to (number, string, boolean)
- $gt - Greater than (number)
- $lt - Less than (number)
- $in - In array (string or number)
- $regex - Regex match (string)
```python
results = db.query(
profession={"$eq": "Politician"},
country={"$in": ["USA", "South Africa"]},
)
# or write in a single json
results = db.query(
where={
"profession": {"$eq": "Politician"},
"country": {"$in": ["USA", "South Africa"]},
}
)
# you can use show_img(), df, query to further filter the results
results.show_img()
results.df
results.query(name="Nelson Mandela")
```