https://github.com/qengineering/mobilenet_ssd_opencv_tensorflow
https://github.com/qengineering/mobilenet_ssd_opencv_tensorflow
bare-raspberry-pi cpp dnn mobilenet-ssd opencv raspberry-pi ssd tensorflow testopencv-tensorflow
Last synced: 6 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/qengineering/mobilenet_ssd_opencv_tensorflow
- Owner: Qengineering
- License: bsd-3-clause
- Created: 2020-01-17T14:39:38.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2021-12-27T09:56:34.000Z (almost 4 years ago)
- Last Synced: 2025-03-27T09:23:20.382Z (6 months ago)
- Topics: bare-raspberry-pi, cpp, dnn, mobilenet-ssd, opencv, raspberry-pi, ssd, tensorflow, testopencv-tensorflow
- Language: C++
- Size: 97.7 KB
- Stars: 14
- Watchers: 2
- Forks: 13
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# MobileNetV1/V2_SSD for the DNN modul of OpenCV
## A example of OpenCV dnn framework working on a bare Raspberry Pi with TensorFlow models.
[](https://opensource.org/licenses/BSD-3-Clause)
Paper: https://arxiv.org/abs/1611.10012
Special made for a bare Raspberry Pi 4 see [Q-engineering deep learning examples](https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html)
------------
Training set: COCO
Size: 300x300
Frame rate V1 : 3.19 FPS (RPi 4)
Frame rate V1_0.75: 4.98 FPS (RPi 4)
Frame rate V2 : 2.02 FPS (RPi 4)
Frame rate V2 Lite: 3.86 FPS (RPi 4)
------------
## Dependencies.
To run the application, you have to:
- A raspberry Pi 4 with a 32 or 64-bit operating system. It can be the Raspberry 64-bit OS, or Ubuntu 18.04 / 20.04. [Install 64-bit OS](https://qengineering.eu/install-raspberry-64-os.html)
- OpenCV 64 bit installed. [Install OpenCV 4.5](https://qengineering.eu/install-opencv-4.5-on-raspberry-64-os.html)
- Code::Blocks installed. (```$ sudo apt-get install codeblocks```)
------------
## Installing the app.
To extract and run the network in Code::Blocks
$ mkdir *MyDir*
$ cd *MyDir*
$ wget https://github.com/Qengineering/MobileNet_SSD_OpenCV_TensorFlow/archive/refs/heads/master.zip
$ unzip -j master.zip
Remove master.zip and README.md as they are no longer needed.
$ rm master.zip
$ rm README.md
Your *MyDir* folder must now look like this:
Traffic.jpg
COCO_labels.txt
frozen_inference_graph_V1.pb (download this file from: https://drive.google.com/open?id=1sDn1guYV6oj-AeYuC-riGRh4kv9XBTMz )
frozen_inference_graph_V2.pb (download this file from: https://drive.google.com/open?id=1EU6tVcDNLNwv-pbJUXL7wYUchFHxr5fw )
ssd_mobilenet_v1_coco_2017_11_17.pbtxt
ssd_mobilenet_v2_coco_2018_03_29.pbtxt
TestOpenCV_TensorFlow.cpb
MobileNetV1.cpp (can be use for V2 version also)
------------
## Running the app.
To run the application load the project file TestOpenCV_TensorFlow.cbp in Code::Blocks. More info or
if you want to connect a camera to the app, follow the instructions at [Hands-On](https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html#HandsOn).
------------
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