DeepCamera

Open-Source AI Camera. Empower any camera/CCTV with state-of-the-art AI, including facial recognition, person recognition(RE-ID) car detection, fall detection and more

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Quick Overview

DeepCamera is an open-source AI-powered surveillance system that uses deep learning for real-time object detection and face recognition. It's designed to work with various camera types and can be deployed on edge devices, making it suitable for home security, retail analytics, and smart city applications.

Pros

Supports multiple camera types (IP cameras, USB cameras, etc.)
Edge computing capabilities for reduced latency and improved privacy
Customizable and extensible architecture
Open-source, allowing for community contributions and modifications

Cons

Requires some technical knowledge to set up and configure
May have higher hardware requirements compared to traditional surveillance systems
Privacy concerns related to facial recognition technology
Limited documentation for advanced customization

Code Examples

# Initialize DeepCamera
from deep_camera import DeepCamera

camera = DeepCamera(camera_url="rtsp://your_camera_ip:554/stream")

# Perform object detection
results = camera.detect_objects(frame)
for obj in results:
    print(f"Detected {obj.label} with confidence {obj.confidence}")

# Face recognition
face_results = camera.recognize_faces(frame)
for face in face_results:
    if face.known:
        print(f"Recognized {face.name}")
    else:
        print("Unknown face detected")

Getting Started

Clone the repository:

git clone https://github.com/SharpAI/DeepCamera.git
cd DeepCamera

Install dependencies:
```
pip install -r requirements.txt
```
Configure your camera settings in config.yaml
Run the main application:
```
python main.py
```

For more detailed instructions and advanced configuration options, refer to the project's documentation.

Competitor Comparisons

opencv

78,537

Open Source Computer Vision Library

Pros of OpenCV

Extensive library with a wide range of computer vision algorithms and functions
Large and active community, providing support and continuous development
Well-documented with numerous tutorials and examples

Cons of OpenCV

Steeper learning curve for beginners due to its comprehensive nature
Can be resource-intensive for some applications, especially on mobile devices
Requires manual implementation of deep learning models for advanced tasks

Code Comparison

OpenCV:

import cv2

img = cv2.imread('image.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)

DeepCamera:

from deepcamera import DeepCamera

camera = DeepCamera()
result = camera.detect_faces('image.jpg')

OpenCV provides lower-level access to image processing functions, while DeepCamera offers a higher-level API for specific tasks like face detection. OpenCV requires more manual setup and configuration, whereas DeepCamera abstracts some of these complexities for easier use in certain scenarios.

models

77,006

Models and examples built with TensorFlow

Pros of TensorFlow Models

Extensive collection of pre-trained models and implementations
Well-documented and maintained by Google's TensorFlow team
Supports a wide range of machine learning tasks and applications

Cons of TensorFlow Models

Can be complex for beginners due to its broad scope
May require more computational resources for some models
Less focused on specific camera-based applications

Code Comparison

DeepCamera:

from deepcamera import DeepCamera

camera = DeepCamera()
result = camera.detect_objects(image)

TensorFlow Models:

import tensorflow as tf
from object_detection.utils import label_map_util

detection_graph = tf.Graph()
with detection_graph.as_default():
    od_graph_def = tf.GraphDef()
    # Load frozen inference graph

Summary

DeepCamera is more specialized for camera-based AI applications, offering a simpler API for quick implementation. TensorFlow Models provides a broader range of machine learning models and tools, but may require more setup and configuration for specific tasks. DeepCamera might be preferable for projects focused on computer vision, while TensorFlow Models is better suited for diverse machine learning applications and research.

yolov5

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YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite

Pros of YOLOv5

Highly optimized and efficient object detection model
Extensive documentation and community support
Easier to integrate into various applications

Cons of YOLOv5

Limited to object detection tasks
Requires more manual configuration for specific use cases

Code Comparison

YOLOv5:

from ultralytics import YOLO

# Load a pretrained model
model = YOLO('yolov5s.pt')

# Perform inference
results = model('image.jpg')

DeepCamera:

from deepcamera import DeepCamera

# Initialize DeepCamera
dc = DeepCamera()

# Perform detection
results = dc.detect('image.jpg')

Key Differences

YOLOv5 focuses on object detection, while DeepCamera offers a broader range of computer vision capabilities
DeepCamera is designed for edge computing and IoT devices, whereas YOLOv5 is more versatile in its deployment options
YOLOv5 has a larger community and more frequent updates, while DeepCamera may offer more specialized features for specific use cases

Use Case Considerations

Choose YOLOv5 for general object detection tasks with high performance requirements
Opt for DeepCamera when working with edge devices or need a more comprehensive computer vision solution

face_recognition

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The world's simplest facial recognition api for Python and the command line

Pros of face_recognition

Simpler to use and integrate, with a more straightforward API
Better documentation and examples for quick implementation
Wider community support and more frequent updates

Cons of face_recognition

Less focused on real-time video processing and camera integration
Limited to face recognition tasks, while DeepCamera offers broader functionality
May require more manual setup for advanced use cases

Code Comparison

face_recognition:

import face_recognition

image = face_recognition.load_image_file("image.jpg")
face_locations = face_recognition.face_locations(image)
face_encodings = face_recognition.face_encodings(image, face_locations)

DeepCamera:

from deepcamera import DeepCamera

dc = DeepCamera()
dc.start_camera()
faces = dc.detect_faces()
dc.recognize_faces(faces)

The code snippets demonstrate the difference in approach. face_recognition focuses on processing static images, while DeepCamera is designed for real-time video processing with built-in camera integration.

face_recognition is more suitable for developers who need a quick and easy solution for face recognition tasks on static images. DeepCamera, on the other hand, is better suited for projects requiring real-time video processing and broader AI capabilities beyond just face recognition.

dlib

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A toolkit for making real world machine learning and data analysis applications in C++

Pros of dlib

More comprehensive and versatile library for machine learning and computer vision
Longer development history and larger community support
Extensive documentation and examples available

Cons of dlib

Steeper learning curve due to its broad scope
Potentially heavier resource usage for simple tasks
Less focused on specific camera-based applications

Code Comparison

DeepCamera:

from deepcamera import FaceDetector

detector = FaceDetector()
faces = detector.detect(image)

dlib:

import dlib

detector = dlib.get_frontal_face_detector()
faces = detector(image, 1)

Key Differences

DeepCamera is specifically designed for camera-based AI applications, while dlib is a more general-purpose machine learning and computer vision library.
dlib offers a wider range of algorithms and tools, making it suitable for various tasks beyond just camera-related applications.
DeepCamera may be easier to set up and use for specific camera-based AI tasks, while dlib provides more flexibility but requires more expertise to utilize effectively.
dlib has a larger community and more extensive documentation, which can be beneficial for troubleshooting and learning.
DeepCamera might be more optimized for real-time camera processing, while dlib's performance may vary depending on the specific use case.

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README

DeepCamera

Empower camera with SOTA AI
ML pipeline for AI camera/CCTV
Easy to use Edge AI development

DeepCamera empowers your traditional surveillance cameras and CCTV/NVR with machine learning technologies. It provides open source facial recognition based intrusion detection, fall detection and parking lot monitoring with the inference engine on your local device.

SharpAI-hub is the cloud hosting for AI applications which help you deploy AI applications with your CCTV camera on your edge device in minutes.

Features

Empower any camera with the state of the art AI

facial recognition
person recognition(RE-ID)
parking lot management
fall detection
more comming

ML pipeline for AI camera/CCTV development

feature clustering with vector database Milvus
labelling with Labelstudio

Easy to use Edge AI development environment

AI frameworks in docker
desktop in docker with web vnc client, so you don't need even install vnc client

Application 1: Self-supervised person recognition(REID) for intruder detection

SharpAI yolov7_reid is an open source python application leverages AI technologies to detect intruder with traditional surveillance camera. Source code is here It leverages Yolov7 as person detector, FastReID for person feature extraction, Milvus the local vector database for self-supervised learning to identity unseen person, Labelstudio to host image locally and for further usage such as label data and train your own classifier. It also integrates with Home-Assistant to empower smart home with AI technology. In Simple terms yolov7_reid is a person detector.

Machine learning technologies
- Yolov7 Tiny, pretrained from COCO dataset
- FastReID ResNet50
- Vector Database Milvus for self-supervised learning
Supported Devices
- Nvidia Jetson
  - Nano (ReComputer j1010)
  - Xavier AGX
- Single Board Computer (SBC)
  - Raspberry Pi 4GB
  - Raspberry Pi 8GB
- Intel X64
  - MacOS
  - Windows
  - Ubuntu
- MCU Camera
  - ESP32 CAM
  - ESP32-S3-Eye
- Tested Cameras/CCTV/NVR
  - RTSP Camera (Lorex/Amrest/DoorBell)
  - Blink Camera
  - IMOU Camera
  - Google Nest (Indoor/Outdoor)

Installation Guide

pip3 install sharpai-hub
sharpai-cli yolov7_reid start

Prerequisites

1. Docker (Latest version)
2. Python (v3.6 to v3.10 will work fine)

Step-by-step guide

NOTE: Before executing any of commands mentioned below please start Docker. This guide is to install the sharpai and run the yolov7_reid service but can also be used to start other services.

Install SharpAI-Hub by running the following command in a Command Prompt and Terminal. Remeber this as Command Prompt 1. This will be needed in further steps:
```
pip3 install sharpai-hub
```
Now run the following command:
```
sharpai-cli yolov7_reid start
```

NOTE: If in a Windows system after running command mentioned in Step 2 if you get error: 'sharpai-cli' is not recognized as an internal or external command, operable program or batch file. Then it means environment variable is not set for Python on your system. More on this at the end of page in FAQ section.

If you are using Windows and get error in step 2 you can also use following command line to start yolov7_reid

python3 -m sharpai_hub.cli yolov7_reid start

python -m sharpai_hub.cli yolov7_reid start

Go to directory C:\Users and open the folder with name of current user. Here look for a folder .sharpai . In .sharpai folder you will see a folder yolov7_reid. Open it and start a new Command Prompt here. Remember this as Command Prompt 2
In Command Prompt 2 run the below command:

docker compose up

NOTE: DO NOT TERMINATE THIS COMMAND. Let it complete. After running the above command it will take roughly 15-20 minutes or even more time to complete depending upon your system specifications and internet speed. After 5-10 minutes of running the command in the images tab of Docker will images will start to appear. If the command ran successful then there must be seven images in images tab plus one container named as yolov7_reid in the container tab.

Go to folder yolov7_reid mentioned in step 4. In this folder there will be file .env. Delete it. Now close the Command Prompt 1. Open and new Command prompt and run the following command again. We will call this as Command Prompt 3.

sharpai-cli yolov7_reid start

python3 -m sharpai_hub.cli yolov7_reid start

python -m sharpai_hub.cli yolov7_reid start

Running command in Step 6 will open a Signup/Signin page in the browser and in Command Prompt it will ask for the Labelstudio Token. After Signing up in you will be taken to your account. At the top right corrent you will see a small cirle with your account initials. Click on it and after that click on Account Setting. Here at the right side of page you will see a Access token. Copy the token and paste it carefully in the command prompt 3.
Add Camera to Home-Assistant, you can use "Generic Camera" to add camera with RTSP url
In this step, we will obtain the camera entity ID of your cameras. After adding your camera to home-Assistant, go to the Overview tab. Here all your cameras will be listed. Click on the video stream of a camera, after which a small popup will open. At the top right of the popup, click the gear icon to open the settings page. A new popup will open with a few editable properties. Here look for Entity ID, which is in the format camera.IP_ADDRESS_OF_CAMERA, copy/note this entity ID (these entity ids will be required later). If you have multiple cameras, we will need each cameras Entity ID. Note all these camera entity IDs.
Run following two commands to open and edit the configuration.yaml of Home-Assistant:

docker exec -ti home-assistant /bin/bash

vi configuration.yaml

NOTE FOR WINDOWS SYSTEM USERS: These commands wont work with windows Systems. For Windows system, please open Docker (the instance of Docker, which is already running from the start) and in the container tab, open the yolov7_reid. Here look for the home-assistant container. Hover your mouse cursor on the home-assistant container, and a few options will appear. Click on cli. An inbuilt console will start on the same page. If the typing cursor keeps blinking and nothing shows up on the inbuilt console, then click on Open in External Terminal, which is just above the blinking cursor. After clicking it, a new command prompt will open. To check everything is working as expected, run the command ls and see if the commands list the files and folders in the config folder.

Now run a command vi configuration.yaml. This command will open your configuration file of the home-assistant in the Vi editor. Vi Editor is a bit tricky if you are unfamiliar with using it. You will now have to enter into Insert mode to add the integration code mentioned in Step 9 to the configuration file. Press the I key to enter Insert mode and go end of the file using the down arrow key. Next, press the right mouse (while the mouse cursor is inside the command prompt window) while in the command prompt. This will paste the integration code that you had copied earlier. After making changes to the config file, press the escape key, type the following :wq (yes with colon) and press enter key. You will be back taken to /config #. This command :wq means you want to write changes to the config file and quit (I told you Vi is a bit tricky for beginners). You can now close the command prompt.

Add the below code to the end of configuration.yaml file.

Here, replace camera.<camera_entity_id> with the camera entity ID we obtained in Step 9. If you have multiple cameras then keep adding the entity_id under images_processing.

stream:
  ll_hls: true
  part_duration: 0.75
  segment_duration: 6

image_processing:
  - platform: sharpai
    source:
      - entity_id: camera.<camera_entity_id>
    scan_interval: 1

If you have multiple cameras then after changing the 'entity_id' the code will become similar to this:

stream:
  ll_hls: true
  part_duration: 0.75
  segment_duration: 6

image_processing:
  - platform: sharpai
    source:
      - entity_id: camera.192_168_29_44
      - entity_id: camera.192_168_29_45
      - entity_id: camera.192_168_29_46
      - entity_id: camera.192_168_29_47
    scan_interval: 1

At home-assistant homepage http://localhost:8123 select Developer Tools. Look for and click Check Configuration under Configuration Validation. If everything went well then it must show "Configuration Valid'. Click Restart.Now go to the container tab of docker, click three vertical dots under Actions and press restart. Open the Overview tab of home-assitant. If you see Image Processing beside your cameras and below it Sharp IP_ADDRESS_OF_YOUR_CAMERA, then congrats. Everything is working as expected.

NOTE: Till further steps are added you can use demo video in the beginning tutorial for further help.

Important Links

The yolov7 detector is running in docker, you can access the docker desktop with http://localhost:8000
Home-Assistant is hosted at http://localhost:8123
Labelstudio is hosted at http://localhost:8080

Application 2: Facial Recognition based intruder detection with local deployment

We received feedback from community, local deployment is needed. With local deepcamera deployment, all information/images will be saved locally.
sharpai-cli local_deepcamera start

Application 3: DeepCamera Facial Recognition with cloud for free

Register account on SharpAI website
Login on device: sharpai-cli login
Register device: sharpai-cli device register
Start DeepCamera: sharpai-cli deepcamera start

Application 4: Laptop Screen Monitor for kids/teens safe

SharpAI Screen monitor captures screen extract screen image features(embeddings) with AI model, save unseen features(embeddings) into AI vector database Milvus, raw images are saved to Labelstudio for labelling and model training, all information/images will be only saved locally.

sharpai-cli screen_monitor start

Access streaming screen: http://localhost:8000

Access labelstudio: http://localhost:8080

Application 5: Person Detector

sharpai-cli yolov7_person_detector start

SharpAI-Hub AI Applications

SharpAI community is continually working on bringing state-of-the-art computer vision application to your device.

sharpai-cli <application name> start

Application	SharpAI CLI Name	OS/Device
Intruder detection with Person shape	yolov7_reid	Jetson Nano/AGX /Windows/Linux/MacOS
Person Detector	yolov7_person_detector	Jetson Nano/AGX /Windows/Linux/MacOS
Laptop Screen Monitor	screen_monitor	Windows/Linux/MacOS
Facial Recognition Intruder Detection	deepcamera	Jetson Nano
Local Facial Recognition Intruder Detection	local_deepcamera	Windows/Linux/MacOS
Parking Lot monitor	yoloparking	Jetson AGX
Fall Detection	falldetection	Jetson AGX

Tested Devices

Edge AI Devices / Workstation

Jetson Nano (ReComputer j1010)
Jetson Xavier AGX
MacOS 12.4
Windows 11
Ubuntu 20.04

Tested Camera:

DaHua / Lorex / AMCREST: URL Path: /cam/realmonitor?channel=1&subtype=0 Port: 554
Ip Camera Lite on IOS: URL Path: /live Port: 8554
Nest Camera indoor/outdoor by Home-Assistant integration

Support

If you are using a camera but have no idea about the RTSP URL, please join SharpAI community for help.
SharpAI provides commercial support to companies which want to deploy AI Camera application to real world.

Click to join sharpai slack channel

DeepCamera Architecture

architecture

DeepCamera Feature List

Commercial Version

Provide real time pipeline on edge device
E2E pipeline to support model customization
Cluster on the edge
Port to specific edge device/chipset
Voice application (ASR/KWS) end to end pipeline
ReID model
Behavior analysis model
Transformer model
Contrastive learning
Click to join sharpai slack channel for commercial support

FAQ

How to install python3

How to install pip3

How to install Docker-compose on Jetson Nano

sudo apt-get install -y libhdf5-dev python3 python3-pip
pip3 install -U pip
sudo pip3 install docker-compose==1.27.4

How to create token for Telegram Bot(DOC W.I.P)

Create Telegram Bot through @BotFather
Set Telegram Token in Configure File
Send message to the new bot you created

Contributions

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Quick Overview

Pros

Cons

Code Examples

Getting Started

Competitor Comparisons

Pros of OpenCV

Cons of OpenCV

Code Comparison

Pros of TensorFlow Models

Cons of TensorFlow Models

Code Comparison

Summary

Pros of YOLOv5

Cons of YOLOv5

Code Comparison

Key Differences

Use Case Considerations

Pros of face_recognition

Cons of face_recognition

Code Comparison

Pros of dlib

Cons of dlib

Code Comparison

Key Differences

Convert designs to code with AI

README

DeepCamera

Empower camera with SOTA AI ML pipeline for AI camera/CCTV Easy to use Edge AI development

Features

Empower any camera with the state of the art AI

ML pipeline for AI camera/CCTV development

Easy to use Edge AI development environment

Application 1: Self-supervised person recognition(REID) for intruder detection

Installation Guide

Prerequisites

Step-by-step guide

Important Links

Application 2: Facial Recognition based intruder detection with local deployment

Application 3: DeepCamera Facial Recognition with cloud for free

Application 4: Laptop Screen Monitor for kids/teens safe

Access streaming screen: http://localhost:8000

Access labelstudio: http://localhost:8080

Application 5: Person Detector

SharpAI-Hub AI Applications

Tested Devices

Edge AI Devices / Workstation

Tested Camera:

Support

DeepCamera Architecture

Commercial Version

FAQ

How to install Docker-compose on Jetson Nano

How to create token for Telegram Bot(DOC W.I.P)

Top Related Projects

Convert designs to code with AI

Empower camera with SOTA AI
ML pipeline for AI camera/CCTV
Easy to use Edge AI development