Raspberry pi Smart cam with Intel Neural Compute Stick

Raspberry pi Smart cam with Intel Neural Compute Stick

Intel Neural Compute Stick + OpenVINO Version


System Diagram

System Requirements

Hardware

·     Raspberry Pi Board (4B , 3B+)
·     Intel Neural Compute Stick ( V1 or V2 )

·     Pi Camera ( V1 or V2 ) or USB cam

·     SD Card 32GB

·     5V DC. 2A Power Supply

Software

·     OS Raspbien 10 ( Buster )

·     Python 3.7.3

·     OpenVINO Toolkit 2019.R3

·     OpenCV 4.0.0

·     Flask 1.0.2

·     LINE Notify 0.1.4

Detect up to 12 FPS. on Pi camara V2

Setting

Report  ( Save to database )




LINE Notify

Raspberry pi Smart Cam AI kit. ( Include Full Source Code in SD card )

1.       Raspberry pi 4B ( 1 GB ) + Case
2.       SDcard 32 GB. ( Software installed )
3.       5V DC. 2A Power Supply

Option

• USB Webcam Model Logitech C270 
• Intel Neural Compute Stick2 

If You want Raspberry pi AI Kit. Please contact email amphancm@gmail.com 

 

UPDATE  ( 2020 APR )

Select time to Line Notification

Reference

Install OpenVINO™ toolkit for Raspbian* OS

https://docs.openvinotoolkit.org/latest/_docs_install_guides_installing_openvino_raspbian.html

Object Detection SSD Python* Demo, Async API performance showcase
https://docs.openvinotoolkit.org/latest/_demos_python_demos_object_detection_demo_ssd_async_README.html

Raspberry pi Smart cam AI kit with Pi Cam

Raspberry pi Smart cam AI kit with Pi Cam or USB cam

TensorFlow-lite Version then no need intel Neural Compute Stick yet.

System Diagram

System Requirements

Hardware

·     Raspberry Pi Board (4B , 3B+)

·     Pi Camera ( V1 or V2 ) or USB cam

·     SD Card 32GB

·     5V DC. 2A Power Supply

Software

·     OS Raspbien 10 ( Buster )

·     Python 3.7.3

·     Tensorflow lite 1.14.0

·     OpenCV 4.0.0

Machine Learning Model : MobileNet SSD V2 Quantised

Main Page ( Home )

Report Page

Setting Page

Set WiFi

Source Code ( Python )

https://github.com/EdjeElectronics/TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi

Raspberry pi Smart Cam AI kit. ( Include Full Source Code in SD card )

1.       Raspberry pi 4B ( 1 GB ) + Case
2.       SDcard 32 GB. ( Software installed )
3.       5V DC. 2A Power Supply

Option

• USB Webcam Model Logitech C270 
• Intel Neural Compute Stick2 

If You want Raspberry pi AI Kit. Please contact email amphancm@gmail.com 

Reference

Install Tensorflow on Raspberry pi

https://www.tensorflow.org/install/source_rpi

Tensorflow Model Zoo

https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md

How to Run TensorFlow Lite Object Detection

https://github.com/EdjeElectronics/TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi/blob/master/Raspberry_Pi_Guide.md

Raspberry pi TensorFlow-lite Object detection

Raspberry pi TensorFlow-lite Object detection

How to use TensorFlow Lite object detection models on the Raspberry Pi.



TensorFlow Lite is an optimized framework for deploying lightweight deep learning models on resource-constrained edge devices. TensorFlow Lite models have faster inference time and require less processing power, so they can be used to obtain faster performance in realtime applications.


  

System Requirements

Hardware

·     Raspberry Pi Board (4B , 3B+)

·     IP Camera, USB Camera or Pi Camera

·     SD Card 32GB

·     5V DC. 2A Power Supply

Software

·     OS Raspbien 10 ( buster )

·     Python 3.7.2

·     Tensorflow lite 1.14.0

·     OpenCV 4.0.0

Machine Learning Model : MobileNet SSD V2 Quantised

 

Source Code
https://github.com/EdjeElectronics/TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi


Reference

Install Tensorflow on Raspberry pi
https://www.tensorflow.org/install/source_rpi

Tensorflow Model Zoo
https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md

How to Run TensorFlow Lite Object Detection

https://github.com/EdjeElectronics/TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi/blob/master/Raspberry_Pi_Guide.md

People tracking and counting with Raspberry pi 4 and Intel neural compute stick

People tracking and counting with Raspberry pi 4 and Intel neural compute stick

or Pedestrian Tracker

Hardware

▪ Raspberry pi 4B ( 1GB )
▪ Intel Neural Compute Stick 2
▪ 32GB SD card

Software

▪ Raspbien 10 ( buster )

▪ OpenVINO toolkit 2019 R1

▪ OpenCV 4.0.0

▪ Code C/C++

▪ Model : Person-detection-retail-0013

▪ Model : Person-reidentification-0031

▪ Video MP4 768 x 432 12 fps

 

What is Intel Neural Compute Stick and OpenVINO?

https://raspberrypi4u.blogspot.com/2019/04/raspberry-pi-openvino-intel-movidius.html

Pedestrian Tracker C++ Demo

This demo showcases Pedestrian Tracking scenario: it reads frames from an input video sequence, detects pedestrians in the frames, and builds trajectories of movement of the pedestrians in a frame-by-frame manner. You can use a set of the following pre-trained models with the demo:

• person-detection-retail-0013, which is the primary detection network for finding pedestrians
• person-reidentification-retail-0031, which is the network that is executed on top of the results from inference of the first network and makes reidentification of the pedestrians

Source Code

https://docs.openvinotoolkit.org/latest/_demos_pedestrian_tracker_demo_README.html

Reference

Install the Intel® Distribution of OpenVINO™ Toolkit for Raspbian* OS

https://software.intel.com/en-us/articles/OpenVINO-Install-RaspberryPI

Pretrained Models

https://software.intel.com/en-us/openvino-toolkit/documentation/pretrained-models

Inference Engine Samples 

http://docs.openvinotoolkit.org/latest/_docs_IE_DG_Samples_Overview.html

OpenVINO, OpenCV, and Movidius NCS on the Raspberry Pi

https://www.pyimagesearch.com/2019/04/08/openvino-opencv-and-movidius-ncs-on-the-raspberry-pi/