# HEAT_MAP_NN-AMG8833_NNOM
Mainly using AMG8833, RT-Thread and NNOM libs to run heat-map recognition and setup neural network on STM32F4
(1). Introduction:
This project is my first attempt to do a neural network recognizing heat-map on stm32f4.
Main references are:
1. NNOM : https://github.com/majianjia/nnom
2. RE-Thread : https://www.rt-thread.org/document/site/tutorial/quick-start/introduction/introduction/
(2). instructions:
1. AMG883-Lenet directory is using python3 and keras to train and test amg8833 heat-map data collected by recieve.py.
Trainning is in amg8833_lenet-5.py. Some function is in utils.py.
2. stm32f407-NNOM-AMG8833-lenet.rar file includes mainly stm32 code. Also needs some libs like CMSIS-NN(version>1.8) and Rt- Thread(version>3.0) and CMSIS(version>5.2).
3. This is the first version, later I'd like to upload and modify some files.
(3). Results:
I trained 3 types of gestures recognition : None--0, one finger--1, two finger--2, it's about 70-80% accuracy in test, and FPS is 10.
Any questions please put issues or connect : 997398715(QQ)
没有合适的资源?快使用搜索试试~ 我知道了~
Mainly using AMG8833, RT-Thread and NNOM libs to run heat-map r
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py:5个
h:4个
pyc:3个
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2024-10-02
20:15:32
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# HEAT_MAP_NN-AMG8833_NNOM Mainly using AMG8833, RT-Thread and NNOM libs to run heat-map recognition and setup neural network on STM32F4 (1). Introduction: This project is my first attempt to do a neural network recognizing heat-map on stm32f4. Main references are: 1. NNOM : https://github.com/majianjia/nnom 2. RE-Thread : https://www.rt-thread.org/document/site/tutorial/quick-start/introduction/introduction/ (2). instructions: 1. AMG883-Lenet directory is using python3 and keras to train
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HEAT_MAP_NN-AMG8833_NNOM-master.zip (20个子文件)
HEAT_MAP_NN-AMG8833_NNOM-master
stm32f407-NNOM-AMG8833-lenet.rar 17.69MB
result_img
2.jpg 2.78MB
1.jpg 2.8MB
0.jpg 2.32MB
README.md 1KB
AMG8833-Lenet
mcu
image.h 9KB
weights.h 270KB
main.c 2KB
model
utils.py 2KB
image.h 9KB
fully_connected_opt_weight_generation.py 6KB
recieve.py 1KB
amg8833_lenet-5.py 5KB
saved_models
AMG8833_trained_model.h5 825KB
weights.h 270KB
evaluation.txt 132B
__pycache__
nnom_utils.cpython-36.pyc 25KB
fully_connected_opt_weight_generation.cpython-36.pyc 4KB
utils.cpython-36.pyc 1KB
nnom_utils.py 41KB
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