https://github.com/qengineering/age-gender-opencv-raspberry-pi-4
Age and Gender estimation on a Raspberry Pi 4 with OpenCV
https://github.com/qengineering/age-gender-opencv-raspberry-pi-4
age-estimation age-gender-estimation deep-learning face-detection gender-classification gender-estimation gender-recognition opencv opencv-dnn raspberry-pi-4 raspberry-pi-64-os
Last synced: 3 months ago
JSON representation
Age and Gender estimation on a Raspberry Pi 4 with OpenCV
- Host: GitHub
- URL: https://github.com/qengineering/age-gender-opencv-raspberry-pi-4
- Owner: Qengineering
- License: bsd-3-clause
- Created: 2021-04-26T10:21:53.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2022-03-22T12:29:30.000Z (over 3 years ago)
- Last Synced: 2025-01-26T03:45:48.192Z (8 months ago)
- Topics: age-estimation, age-gender-estimation, deep-learning, face-detection, gender-classification, gender-estimation, gender-recognition, opencv, opencv-dnn, raspberry-pi-4, raspberry-pi-64-os
- Language: C++
- Homepage: https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html
- Size: 1.88 MB
- Stars: 4
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Age Gender Raspberry Pi 4
## Age and gender estimation with the OpenCV framework.
[](https://opensource.org/licenses/BSD-3-Clause)
Paper: https://talhassner.github.io/home/publication/2015_CVPR
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)
------------
## Benchmark.
RPi 4 64-OS 1950 MHz
FPS = 1/(0.2 * Faces + 0.157)
------------
## Dependencies.
To run the application, you have to:
- A raspberry Pi 4 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/Age-Gender-OpenCV-Raspberry-Pi-4/archive/refs/heads/main.zip
$ unzip -j master.zip
Remove master.zip, LICENSE and README.md as they are no longer needed.
$ rm master.zip
$ rm LICENSE
$ rm README.md
Your *MyDir* folder must now look like this:
sample1.jpg
sample3.jpg
AgeGender.cpb
AgeGender.cpp
opencv_face_detector.pbtxt
opencv_face_detector_uint8.pb
gender_deploy.prototxt
age_deploy.prototxt
Do **not forget** to download the caffe models!
------------
## Running the app.
Download [age_deploy.caffemodel](https://drive.google.com/file/d/1pNDFo7WBcf4fo5DefGEbM01TJP8_z5H5/view?usp=sharing)
Download [gender_deploy.caffemodel](https://drive.google.com/file/d/1X8_2hTEUGculDA9gt_pIyTV31CNew8_b/view?usp=sharing)
To run the application load the project file YoloV5.cbp in Code::Blocks.
Next, follow the instructions at [Hands-On](https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html#HandsOn).
Many thanks to [GilLevi](https://github.com/GilLevi/AgeGenderDeepLearning)
TensorFlow implementation [dpressel](https://github.com/dpressel/rude-carnie)