SimCam AI Camera.

The first on-device AI Security Camera for smart home.

The SimCam uses AI for facial recognition, pet monitoring, and more via location training. The SimCam has a 5 megapixel image sensor with night vision for still images and 1080 HD videos. The IP65 waterproof rated indoor/outdoor, camera can rotate 360 degrees while tracking objects.

With the open SDK of SimCam, you can customize the settings to meet your needs.

The work flow of SimCam in developer mode is shown in the following figure.

As shown in the figure, SimCam consists of two embedded boards, one is HI3516 and another is Movidius. HI3516 is used for video capture and video play. Movidius is used for deep learning computing. Two embedded boards communicate by spi.

There are some documents for developers inside docs folder including :

• Quick start guide of SimCam in developer mode;
• Toolchain installation and usage guide;
• SimCam API guide;
• How to train object detection model

However, here is some guide for really really quick starters 🏃 Let's get started😄

• Installation
• Run_detection_demo
• Face_recognition_demo

Installation

1. Get the SDK. We will call the directory that you cloned SIMCAM SDK into $SIMCAM_SDK

git clone https://github.com/simshineaicamera/SIMCAM_SDK.git
cd SIMCAM_SDK

Here is SDK folder tree:

SIMCAM_SDK
├── docs
├── examples
├── img
├── libs
├── src
├── tools
└── train

Note* if you want to run only detection demo, you can skip installation process and jump to Run_detection_demo. However, for face recognition demo and future developments you need to install SIMCAM SDK tools.

HI3516 toolchain installation

2. Enter $SIMCAM_SDK/tools/arm_toolchain folder, and execute below commands:

cd $SIMCAM_SDK/tools/arm_toolchain
sudo chmod +x cross.v300.install
sudo ./cross.v300.install

3. If you have 64-bit OS, you need to install 32-bit OS compiler compatibility packages:

sudo apt-get install lib32z1
sudo apt-get install lib32stdc++6-4.8-dbg

4. Add toolchain path to environment variable

vi ~/.bashrc
export PATH="/opt/hisi-linux/x86-arm/arm-hisiv300-linux/target/bin:$PATH"

5. Reload .bashrc file

source ~/.bashrc

6. Execute below command to check toolchain version:

arm-hisiv300-linux-gcc  -v

If you can see “gcc version 4.8.3 20131202 (prerelease) (Hisilicon_v300)” at the end of the version description, Congratulations!, the toolchain installation is finished successfully.

Movidius toolchain installation

7. Enter $SIMCAM_SDK/tools/mv_toolchain/model_conversion/ folder and execute install-ncsdk.sh script

cd $SIMCAM_SDK/tools/mv_toolchain/model_conversion/
sudo ./install-ncsdk.sh

This script will install Movidius model conversion toolkit, and also caffe-ssd cpu version on your system. Execute below commands to see Movidius moldel conversion toolkit.

mvNCCompile -h
mvNCCompile v02.00, Copyright @ Movidius Ltd 2016

usage: mvNCCompile [-h] [-w WEIGHTS] [-in INPUTNODE] [-on OUTPUTNODE]
                   [-o OUTFILE] [-s NSHAVES] [-is INPUTSIZE INPUTSIZE]
                   network

mvNCCompile.py converts Caffe or Tensorflow networks to graph files that can
be used by the Movidius Neural Compute Platform API

positional arguments:
  network               Network file (.prototxt, .meta, .pb, .protobuf)

optional arguments:
  -h, --help            show this help message and exit
  -w WEIGHTS            Weights file (override default same name of .protobuf)
  -in INPUTNODE         Input node name
  -on OUTPUTNODE        Output node name
  -o OUTFILE            Generated graph file (default graph)
  -s NSHAVES            Number of shaves (default 1)
  -is INPUTSIZE INPUTSIZE
                        Input size for networks that do not provide an input
                        shape, width and height expected

If you can see above result, congratulations you have finished installation, it was really easy, right?!

Install opencv

Note* If you have already installed opencv>=3.4.3 versions you can skip this step.

8. Enter $SIMCAM_SDK/tools/ folder and run install_opencv.sh script :

cd $SIMCAM_SDK/tools/
sudo ./install_opencv.sh

Above command will install opencv 3.4.3 version on your system. Hurray, we have finished installation process completely! We can move on more interesting parts.

Run_detection_demo

1. Prepare sd card and your SIMCAM AI camera, if you still don't have the camera , buy one from here.
2. Copy following files into your sd card, files are located inside the $SIMCAM_SDK\src folder:

├── config.txt
├── Detect_Server_Process
├── emotion
├── gender
├── person_face
├── rootfs
├── rtl_hostapd_2G.conf

and also $SIMCAM_SDK\examples\Detect_Demo executable file.

3. Open config.txt file to be familiar with it, it is in json format, here is written a brief introduction about config file and parameters inside it.

{
"wifi_mode":"AP_MODE",  # In AP_MODE,the camera creates LAN hotspot, in STA_MODE,  connects to the internet.
"if_send":0, # switch to 1 if you want to send detection results to your server.
"server_IP":"118.190.201.26", # you can set your server's IP address.
"port":8001, # your server's port
"model":[{  
    "model_have":1,    # model switch parameter, 0 or 1.   
    "input_width":300, #  input width
    "input_height":300,# input height
    "shave_num":6,     # number of shaves when you convert the detection graph
    "input_color":1,   # input color 1 for RGB, 0 for Gray
    "mean0":127.5,     # image processing parameters
    "mean1":127.5,     # image processing parameters
    "mean2":127.5,     # image processing parameters
    "std":127.5,       # image processing parameters
    "label":3,        # this parameter in use for classification models,
    "conf_thresh":0.5  # confidence threshold
},
{
    "model_have":1,
    "input_width":64,
    "input_height":64,
    "shave_num":1,
    "input_color":0,
    "mean0":127.5,
    "mean1":0,
    "mean2":0,
    "std":127.5,
    "label":3,  # face class id in our person_face detection model.
    "conf_thresh":0.5
},
{
    "model_have":1,
    "input_width":64,
    "input_height":64,
    "shave_num":2,
    "input_color":0,
    "mean0":127.5,
    "mean1":127.5,
    "mean2":127.5,
    "std":127.5,
    "label":3,
    "conf_thresh":0.5
}]
}

SIMCAM camera can run 3 deep learning models simultaneously, first one is detection model, another two are classification models.

4. Insert sd card to the camera and power on it. In default config.txt, SIMCAM camera runs in AP_MODE, and creates LAN hotspot with default ssid=REVO_DPL, password=87654321, and IP = 192.168.0.1.

5. Connect your PC or laptop to the REVO_DPL wifi. Open your terminal and run following commands:

telnet 192.168.0.1

6. Default user login is root and password is blank.

7. Enter /mnt/DCIM/ folder and run Detect_Demo executable file:

cd /mnt/DCIM/
./Detect_Demo

8. And that's it, you are running SIMCAM camera with person and face detection,and also gender and emotion classification models. You can see results on the terminal like below:

shmid=0
ntpd: bad address 'us.ntp.org.cn'
send app Detect_Server_Process size :862620
sleep 3s for 2450 boot up
fd : 4,ret : 0
ssd cfg size:136
person_face size:1378032
gender size:124760
emotion size:124760
No detection, spi loop>>>>>>>>>>>>>>>>>>>>>>>>
No detection, spi loop>>>>>>>>>>>>>>>>>>>>>>>>
DetectionModelResult: class: 1, x1: 0.002197, y1: 0.007080, x2: 0.660156, y2: 0.994141
DetectionModelResult: class: 1, x1: 0.011475, y1: 0.007813, x2: 0.713867, y2: 0.995117
DetectionModelResult: class: 3, x1: 0.011475, y1: 0.007813, x2: 0.713867, y2: 0.995117
FirstClassificationModelResult, class0: 0.354736
FirstClassificationModelResult, class1: 0.645020
SecondClassificationModelResult, class0: 0.051086
SecondClassificationModelResult, class1: 0.508789
......

If you want to see real time results with bounding boxes, you can install VLC media player on your machine, and open network streams at this address: rtsp://192.168.0.1 . Another option is Kalay app for mobile phones, both for Android and ios. But please make sure your smart phone is connected REVO_DPL wifi.

Face_recognition_demo

I hope you have already successfully run our detection demo and you have already become close friend with SIMCAM camera. Next step is to be familiar with face recognition demo.

1. Prepare face images of people in the folder with their names, those who you want to identify by using SIMCAM camera, at least one picture for one person. Here is an example:
2. Copy your folders into: $SIMCAM_SDK/examples/face_recognition/extract_face_features/face_images

cp -r BruceLee/ $SIMCAM_SDK/examples/face_recognition/extract_face_features/face_images

3. Open terminal inside $SIMCAM_SDK/examples/face_recognition/extract_face_features, and execute following command:

./main face_images/

This command will extract face features of each person and save them into faces.db sqlite database.

4. We consider, you have already copied files inside $SIMCAM_SDK\src folder into your sd card,and then copy another necessary files into your sd card:

cp $SIMCAM_SDK/examples/face_recognition/extract_face_features/faces.db $sdcardpath # face features database
cp $SIMCAM_SDK/examples/face_recognition/Demo # demo executable for face recognition
cp $SIMCAM_SDK/examples/models/lcnn/lcnn # face feature extractor model,

5. Open config.txt file and do following changes (marked as changing line):

{
"wifi_mode":"AP_MODE",  
"if_send":0,
"server_IP":"118.190.201.26",
"port":8001,
"model":[{  
    "model_have":1,    
    "input_width":300,
    "input_height":300,
    "shave_num":6,     
    "input_color":1,   
    "mean0":127.5,     
    "mean1":127.5,     
    "mean2":127.5,     
    "std":127.5,       
    "label":3,        
    "conf_thresh":0.5
},
{
    "model_have":1,
    "input_width":128,  # changing line, changed 64->128
    "input_height":128, # changing line  changed 64->128
    "shave_num":2,       # changing line changed 1->2
    "input_color":0,
    "mean0":0,           # changing line changed 127.5->0
    "mean1":0,
    "mean2":0,
    "std":256,  #  changing line changed 127.5->256
    "label":3,  
    "conf_thresh":0.5
},
{
    "model_have":0,     # changing line changed 1->0
    "input_width":64,
    "input_height":64,
    "shave_num":2,
    "input_color":0,
    "mean0":127.5,
    "mean1":127.5,
    "mean2":127.5,
    "std":127.5,
    "label":3,
    "conf_thresh":0.5
}]
}

You can find model parameters information for each model in the model folder.

6. Connect the camera through telnet and execute Demo. You will get results similar to this:

/mnt/DCIM ./Demo
shmid=0
send app Detect_Server_Process size :862620
sleep 3s for 2450 boot up
fd : 4,ret : 0
ssd cfg size:136
person_face size:1378032
lcnn size:2786192
No detection, spi loop>>>>>>>>>>>>>>>>>>>>>>>>
DetectionModelResult: class: 1, x1: 0.051270, y1: 0.007813, x2: 0.909180, y2: 0.971680
DetectionModelResult: class: 3, x1: 0.051270, y1: 0.007813, x2: 0.909180, y2: 0.971680
Recognized as : BruceLee
......

Developers can train their own object detection models using Caffe deep learning framework and neural network architecture provided by SimCam team.

Instruction can be found in this document. However, SIMACAM team has provided several robust detection models, such as baby climb detection model, gesture detection model, person car face detection model, pet magic detection models. Here is some interesting results of detection for some models:
Pet magic detection model:

Baby climb detection model:

Using SIMCAM camera and its SDK you can develop your own amazing AI applications. SIMCAM team glad to see you on this repo and wish you good luck on your AI journey!!!

Support

If you need any help, please post us an issue on Github Issues or send us an email sdk@simcam.ai. You are welcome to contact us for your suggestions!